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AbstractIoT devices are becoming a part of the mainstream electronics culture and people are adopting smart devices into their homes faster than ever. IoT devices are becoming a part of the mainstream electronics culture and people are adopting smart devices into their homes faster than ever. By 2021, it is estimated that there will be up to 27 billion connected devices to the internet. IoT devices will be a huge part of how we interact with basic everyday objects.

The IoT is viewed as billions of smart connected things (a sort of global neural network in the cloud computing world) that will encompass every aspect of our lives, and its foundation is the intelligence that embedded processing provides. The IoT is comprised of smart machines, smart homes, smart cities, smart bridges, etc. interacting and communicating with other machines, objects, environments and infrastructures. As a result, huge volumes of data are being generated, and that data is being processed into useful actions that can command and control things to make our lives much easier and safer and to reduce our impact on the environment. The creativity of this new era is boundless, with amazing potential to improve our lives.

A radical evolution of the current Internet into a Network of interconnected objects that not only harvests information from the environment (sensing) and interacts with the physical world (actuation/ command/control), but also uses existing Internet standards to provide services for information transfer, analytics, applications, and communications. Fueled by the prevalence of devices enabled by open wireless technology such as Bluetooth, radio frequency identification (RFID), Wi-Fi, and telephonic data services as well as embedded sensor and actuator nodes, IoT has stepped out of its infancy and is on the verge of transforming the current static Internet into a fully integrated Future Internet.

Smart connectivity with existing networks and context-aware computation using network resources is an indispensable part of IoT. With the growing presence of Wi-Fi, ZigBee, Z-waves and 5G wireless Internet access, the evolution towards ubiquitous information and communication networks is already evident. However, for the Internet of Things vision to successfully emerge, the computing paradigm will need to go beyond traditional mobile computing scenarios that use smart phones and portables and evolve into connecting everyday existing objects and embedding intelligence into our environment. For technology to disappear from the consciousness of the user, the Internet of Things demands: a shared understanding of the situation of its users and their appliances, software architectures and pervasive communication networks to process and convey the contextual information to where it is relevant, and the analytics tools in the Internet of Things that aim for autonomous and smart behavior. With these three fundamental grounds in place, smart connectivity and context-aware computation can be accomplished.

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Table of ContentsAbstract............................................................................................................................................i

CHARPTER ONE: Consumer and infrastructure....................................................................1

1.1 THE PAST AND PRESENT IOT AND ITS TECHNOLOGIES....................................1

1.1.1 The History of IoT...........................................................................................................1

1.1.2 Connecting Devices in New Ways within the IoT..........................................................3

1.2 The Definition and meaning of IoT...................................................................................3

1.3 The technologies that enabled to the growth of IoT..............................................................4

1.4 How IoT Ecosystem worked between 2008-2014.................................................................5

1.4.1 Sensors used for IoT devices...........................................................................................5

1.4.1.1 Types of sensors used in IoT devices.......................................................................7

1.4.1.2 Factors that led to rapid adoption within the IoT field.............................................7

1.4.1.3 Challenges sensors faced and potential solutions.....................................................9

1.4.2 Network Technology that were used in IoT..................................................................10

1.4.2.2 Factors that enabled network technologies.............................................................11

1.4.2.2.1 Classic Bluetooth and Bluetooth Low Energy.................................................12

1.4.2.2.2 Ethernet and Low Power Wi-Fi.......................................................................12

1.4.2.2.3 Worldwide Interoperability for Microwave Access (WiMAX) version 1 andWiMAX version 2...........................................................................................................13

1.4.2.3 Factors that led to adoption of the IoT networks technologies...........................14

1.4.2.4 Challenges that faced network technologies and their potential solutions.........15

1.4.3 Standards technologies in IoT.......................................................................................16

1.4.3.1 Technologies that enabled standards in IoT...........................................................17

1.4.3.2 Factors that enabled great adoption within the IoT................................................19

1.4.3.3 Challenges facing Standards and their potential solutions.....................................20

1.4.4 Augmented behavior technology of IoT.......................................................................21

1.4.4.1 Enabling technologies in augmented behavior.......................................................22

1.4.4.2 Factors enabling the adoption within the IoT field.................................................24

1.4.4.3 Challenges that faced augmented behavior and their potential solutions...............26

1.5 THE IOT TECHNOLOGY ARCHITECTURE..................................................................29

CHARPTER TWO........................................................................................................................35

2.0 The Present and Future of IoT.............................................................................................35

2.1 Standard IoT Protocols........................................................................................................35

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2.1.1 Bluetooth.......................................................................................................................35

2.1.1.1 Bluetooth Versions.................................................................................................36

2.1.2 Wi-Fi.............................................................................................................................38

2.1.3 ZigBee...........................................................................................................................39

2.1.4 MQTT IoT.....................................................................................................................40

2.1.5 CoAP.............................................................................................................................40

2.1.6 NFC...............................................................................................................................41

2.2 CELLULAR NETWORK IN IOT.......................................................................................41

2.2.1 5G network in IoT.........................................................................................................41

2.2.1.1 5G and MNOs.........................................................................................................42

2.3 IPV6 for IOT........................................................................................................................45

2.3.1 IPv6 advantages for IoT................................................................................................46

2.3.2 IPv6 Features.................................................................................................................47

2.4 Summary..............................................................................................................................49

Bibliography..................................................................................................................................50

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List of TablesTable 1 Technologies enabling IoT...............................................................................................................4Table 2 Broad network classes with representative examples by connection types....................................12Table 3 Machine feature..............................................................................................................................27Table 4 Bluetooth Versions.........................................................................................................................37

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List of FiguresFigure 1 some of the components which have been made smart and how they interconnect to each other. 2Figure 2 the Interconnection of IoT components, E.g. Mobile Phone connecting to a smart fan.................3Figure 3 How IoT Ecosystem worked 10 years ago......................................................................................5Figure 4 Example of temperature Sensor used 2010.....................................................................................6Figure 5. Sensors prices on the decline over the last 27 years......................................................................8Figure 6 Computing speed continuously increasing after every three years.................................................8Figure 7 Network Interconnection in IoT....................................................................................................11Figure 8 Data rates in last 35 years..............................................................................................................14Figure 9 Typical data aggregation process observing the data aggregation standard.................................18Figure 10 Representative examples of M2M applications..........................................................................21Figure 11 Representative examples of M2H application............................................................................22Figure 12 Average selling price of industrial robots...................................................................................24Figure 13 Unit sales of industrial robots globally in the last 19 years........................................................24Figure 14 Machine enchantment hierarchy Source:....................................................................................28Figure 15 An illustration of the relationship-building process between a machine and its user.................28Figure 16 The IoT business view.................................................................................................................30Figure 17 The IoT functional view..............................................................................................................31Figure 18 The IoT usage view.....................................................................................................................32Figure 19 The IoT implementation view.....................................................................................................33Figure 20 The IoT specifications view........................................................................................................34Figure 21 How Bluetooth is predicated to work in the future.....................................................................38Figure 22 The Future of WiFi Interconnection............................................................................................39Figure 23 The interconnection of Components on ZigBee.........................................................................39Figure 24 The Interconnection of components on CoAP............................................................................40Figure 25 NFC.............................................................................................................................................41Figure 26 AES cryptography.......................................................................................................................43Figure 27 Encryption Process in IOT..........................................................................................................44Figure 28 Adoption of IPV6.......................................................................................................................46

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List of abbreviationIoT -Internet of Things

GPS -Global positioning Satellite

DAPRA -Defense Advanced Research Project Agency

APRNET- Advanced Research Projects Agency Network

IPV6 -Internet protocol version 6

MEMS -Micro Electromechanical systems

M2M -Machine to machine

RFID- Radio Frequency Identification

RADAR- Radio Detection and Ranging

HTTP - Hypertext Transfer Protocol.

WiMAX - Worldwide Interoperability for Microwave Access

BLTE-Bluetooth Low Energy

ETSI - European Telecommunications Standards Institute

IEEE - Institute of Electrical and Electronics Engineers

ISO-International Organization for Standardization

FIPP-Fair Information Practice Principles

ETL - Extraction, Transformation and Loading

HDFS- Hadoop Distributed File System

SQL- structured query language

NoSQL -Not only structured query language

BR -Basic Rates

ED- Enhanced Data Rates

HS-High Speed

LE -Low Energy

NFC - Near Field Communication

MMTC -Machine to Machine type communication

AES- Advanced encryption Standard.

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TLS -Transport Layer Secure

SSL- Secure Sockets Layer

MQTT-Message Queuing Telemetry Transport

IETF-Internet Engineering Task Force

IPV4- Internet Protocol version 4

NAT-Network Address Translation

DTLS-Datagram Transport Layer Security

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CHARPTER ONE: Consumer and infrastructure1.1 THE PAST AND PRESENT IOT AND ITS TECHNOLOGIES

1.1.1 The History of IoT

The Internet, itself a significant component of the IoT, started out as part of DARPA (Defense Advanced Research Projects Agency) in 1962, and evolved into ARPANET in 1969. In the 1980s, commercial service providers began supporting public use of ARPANET, allowing it to evolve into our modern Internet. Global Positioning Satellites (GPS) became a reality in early 1993, with the Department of Defense providing a stable, highly functional system of 24 satellites. This was quickly followed by privately owned, commercial satellites being placed in orbit.

One additional and important component in developing a functional IoT was IPV6 remarkably intelligent decision to increase address space (Number of connected devices).

Realizing the ConceptThe Internet of Things, as a concept, wasn’t officially named until 1999. One of the first examples of an Internet of Things is from the early 1980s, and was a Coca Cola machine, located at the Carnegie Melon University. Local programmers would connect by Internet to the refrigerated appliance, and check to see if there was a drink available, and if it was cold, before making the trip.

By the year 2013, the Internet of Things had evolved into to a system using multiple technologies, ranging from the Internet to wireless communication and from micro-electromechanical systems (MEMS) to embedded systems. The traditional fields of automation (including the automation of buildings and homes), wireless sensor networks, GPS, control systems, and others, all support the IoT.

Simply stated, the Internet of Things consists of any device with an on and off switch connected to the Internet. This includes almost anything you can think of, ranging from cellphones to building maintenance to the jet engine of an airplane. Medical devices, such as a heart monitor implant or a biochip transponder in a farm animal, can transfer data over a network and are members the IoT. If it has an off and on switch, then it can, theoretically, be part of the system. The IoT consists of a gigantic network of internet connected “things” and devices. Ring, a doorbell that links to your smart phone, provides an excellent example of a recent addition to the

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Internet of Things. Ring signals you when the doorbell is pressed, and lets you see who it is and to speak with them.

Kevin Ashton, the Executive Director of Auto-ID Labs at MIT, was the first to describe the Internet of Things, while making a presentation for Procter & Gamble.

Figure 1 some of the components which have been made smart and how they interconnect to each other.

It was Ashton that helped the world, or at least those from Procter and Gamble, realize that developing technology at the time could actually provide a measure of connection with everything else, given the right vision.

LG Electronics then put out a refrigerator known as the Internet Digital DIOS back in the year 2000 which was connected to internet.

The Internet Digital DIOS refrigerator kept track of the kinds of food items that were stored in it as well as their respective quantities by scanning their Radio Frequency identification tags.

Unfortunately for LG, the Internet Digital DIOS refrigerator didn’t sell well because most people at the time thought it was too expensive for their needs.

Nevertheless, the Internet Digital DIOS would eventually pave the way for the potential of everyday household objects and handheld devices to become connected to the Internet.

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1.1.2 Connecting Devices in New Ways within the IoTWhen thinking of the IoT, consider the idea; any device capable and can be interconnected with other devices that support interconnection capability, the IoT provides a nearly endless supply of opportunities to the interconnected devices and components. In terms of creativity and intelligence, this field is wide open, with an infinite number of ways to interconnect the devices. The IoT offers both opportunities and potential security problems. At present, the Internet of Things is best viewed with an open mind, for purposes of creativity, and a defensive posture for purposes of privacy and security. This because the internet of things uses the internet and standards of security have been put on the internet to promote security and privacy E.g. Anti-Ransomware, Anti-Spam etc.

1.2 The Definition and meaning of IoTThe internet of Things (IoT) refers to the ever-growing network of physical objects that have an internet protocol (IP) address for internet connectivity, that have a sensor, can retrive and process data and the communication that occurs between objects/devices connected and that which are internet-enabled. E.g. Fridge connected to a Wi-Fi and can sense temperature changes and can switch on/off depending on the changes in temperature, and can communicate with a smart mobile phone to show the updates of temperature changes.

Figure 2 the Interconnection of IoT components, E.g. Mobile Phone connecting to a smart fan

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1.3 The technologies that enabled to the growth of IoT

Technology Definition Examples

Sensors Two types i.e. Normal and Pressure, level Aquatic, Temperature,Smart Sensor. Normal sensor is infrared, Proximity sensors etc. Proximitya device that generates an Sensor used to detect the presence of nearbyelectronic signal from a physical objects without any physical contact. Examplecondition or event. Smart proximity sensor in smart mobile phone.Sensor is a device that takes Pressure sensor used for controlling andinput from the physical monitoring of many applications andenvironment and uses built-in components. E.g. variable like fluid/gas, watercompute resources to perform flow etc.predefined functions upondetection of specific input andthen process data before passingit on.

Networks A mechanism that enable smart Wireless networking technologies can delivermachines to communicate to bandwidths of 10 megabits per second (Mbps)each other under some protocols. to 1 gigabit per second (Gbps) with near

ubiquitous coverage. Example retrieving of afile from one smart machine to another smartmachine at speed of 10Mbps.

Standards Commonly accepted rules which Technical standards enable processing of dataare put in place when an action and allow for interoperability of aggregatedhas to be taken. data sets. In the near future, we could see

mandates from industry consortia and/orstandards bodies related to technical andregulatory IoT standards.

Augmented Intelligence Analytical tools that improve the Petabyte-sized (101 bytes, or 10 terabytes)ability to describe, predict, and databases can now be searched and analyzed,exploit relationships among even when populated with unstructured (e.g.phenomenon, action or event. text, image, video or sound) data sets.

Software that learns might substitute forhuman analysis and judgment in a fewsituations. In short artificial intelligent robots,data boats, etc.

Augmented behavior Technologies and techniques Machine-to-machine interfaces are removingthat improve compliance with reliably fallible human intervention intoprescribed action otherwise optimized processes. Insights into

human cognitive biases are makingprescriptions for action based on augmentedintelligence more effective and reliable.

Table 1 Technologies enabling IoT

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1.4 How IoT Ecosystem worked between 2008-2014An IoT internet system consists of web enabled smart devices that use embedded sensors or processors, internet capability and communication hardware that collects, sends and acts on the data it acquires from its environment of operation.

Figure 3 How IoT Ecosystem worked 10 years ago

1.4.1 Sensors used for IoT devicesMost objects/things ranging from automobiles (vehicles) to Zambonis (ice resurface), the human body included, have long operated with their location, position, and functional state unknown or even unknowable. The strategic significance of the IoT is born of the ever advancing ability to break that constraint, and to create information, without human observation, in all manner of circumstances that were previously invisible. What allows us to create information from action is the use of sensors, a generic term intended to capture the concept of a sensing system which include sensors, microcontrollers, modem chips, power sources, and any other related devices.

A sensor converts a non-electrical input into an electrical signal that can be sent to an electronic circuit. Can be also be defined as an electronic device that produces electrical, optical, or digital data derived from a physical condition or event. Data produced from sensors is then electronically transformed, by another device, into information (output) that is useful in decision making done by intelligent devices or individuals (people).

The technological complement to a sensor is an actuator, a device that converts an electrical signal into action, often by converting the signal to nonelectrical energy, such as motion. A simple example of an actuator is an electric motor that converts electrical energy into mechanical energy. Sensors and actuators belong to the broader category of transducers: A sensor converts

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energy of different forms into electrical energy; a transducer is a device that converts one form of energy (electrical or not) into another (electrical or not). For example, a loudspeaker is a transducer because it converts an electrical signal into a magnetic field and subsequently into sound waves.

Different sensors capture different types of information. Accelerometers measure linear acceleration, detecting whether an object is moving and in which direction, while proximity sensor used to detect the presence of nearby objects without any physical contact. By combining multiple sensors, each serving different purposes, it is possible to build complex value loops that exploit many different types of information. For example:

Canary: A home security system that comes with a combination of temperature, motion, light and humidity sensors. Computer vision algorithms analyze patterns in behaviors of people and pets, while machine learning algorithms improve the accuracy of security alerts over time.

Thing see: A do-it-yourself IoT device that individuals can use to combine sensors such as accelerometers, proximity, and magnetometers with other sensors that measure temperature, humidity, pressure, and light in order to collect personally interesting data.

Figure 4 Example of temperature Sensor used 2010

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1.4.1.1 Types of sensors used in IoT devicesSensors are often categorized based on their power sources: active versus passive. Active sensors emit energy of their own and then sense the response of the environment to that energy. Radio Detection and Ranging (RADAR) is an example of active sensing: A RADAR unit emits an electromagnetic signal that bounces off a physical object and is sensed by the RADAR system. Passive sensors simply receive energy (in whatever form) that is produced external to the sensing device. A standard camera is embedded with a passive sensor - it receives signals in the form of light and captures them on a storage device.

Passive sensors require less energy, but active sensors can be used in a wider range of environmental conditions. For example, RADAR provides day and night imaging capacity undeterred by clouds and vegetation, while cameras require light provided by an external source.

Of course, the choice of a specific sensor is primarily a function of the signal to be measured (e.g. position versus motion sensors). There are however, several generic factors that determine the suitability of a sensor for a specific application. These include:

Accuracy: A measure of how precisely a sensor reports the signal. E.g. when the pressure content of a fluid/gas is 60% and then the sensor reports 60%.3 is more accurate than the one that reports the same content as 60.9%.

Repeatability: A sensor’s performance in consistently reporting the same response when subjected to the same input under constant environmental conditions. Range- band of input signals within which a sensor can perform accurately. Input signals beyond the range lead to inaccurate output signals and potential damage to sensors. E.g. a sensor that can be exposed very high temperatures is more preferable than the one which cannot be exposed to the same temperature.

Noise: The fluctuations in the output signal resulting from the sensor or the external environment. E.g. A sensor which senses fast to changes in temperature is more important than one which sense very slow in change of temperatures.

Resolution: The smallest incremental change in the input signal that the sensor requires to sense and report a change in the output signal.

Selectivity: The sensor’s ability to selectively sense and report a signal. E.g. selectivesensor that sense only the presence of oxygen despite the presence of other gases.

1.4.1.2 Factors that led to rapid adoption within the IoT field

There are three primary factors driving the implementation and deployment of sensor technology. E.g. price, capability, and size. As sensors get less expensive, smarter and smaller,

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they can be used in a wider range of applications and can generate a wider range of data at a lower cost.

Cheaper sensors: The price of sensors has consistently fallen over the past several years, and these price declines are expected to continue into the future. E.g. the average cost of an accelerometer stands at around $1.4 - $1.5 as per 2014, compared to $22-$24 in 1992. Sensors vary widely in price, but many are now cheap enough to support broad business and IT applications.

Figure 5. Sensors prices on the decline over the last 27 years

Smarter sensors: is a part of a larger system that comprises of a microprocessors, modem chips, power sources and any other related devices. Over the last two decades, microprocessors computational power has improved by doubling after every three years.

Figure 6 Computing speed continuously increasing after every three years

Smaller sensors: There has been a rapid growth in the use of smaller sensors that can be embedded in smartphones and wearables. Micro electro mechanical systems (MEMS) sensors – is small devices that combine digital electronics and mechanical components have the potential to drive wider IoT applications. The average number of sensors on a smartphone has increased from three

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(accelerometer, proximity, ambient light) in 2003 to at least ten (including advanced sensors such as fingerprint and gesture-based sensors) in 2015.

1.4.1.3 Challenges sensors faced and potential solutions

1. Power consumption: Sensors are powered either through in-line connections or batteries. In-line power sources are constant but may be impractical or expensive in many instances. Batteries may represent a convenient alternative, but battery life, charging, and replacement, especially in remote areas, may represent significant issues.

There are two dimensions to power:Efficiency: Silicon technologies have advanced promoting efficiency, some sensors can now stay live on batteries for over 16 years, thus reducing battery replacement cost and efforts. However, improved efficiency is counter balanced by the power needed for increased numbers of sensors. Hence, systems’ overall power consumption often does not decrease or may, in fact, increase. This is an underlying challenge as both energy and financial resources are finite (they can come to any end can change to another form which is not like the previous form).Source: While sensors often depend on batteries, energy harvesting of alternative energy sources such as solar energy, wind energy etc. may provide some alternatives, at a minimum cost providing support during the battery changing life time (Things to do with recharging the battery in order to make the sensor active). However, energy harvesters that are currently available are expensive, and companies are hesitant to make that investment (installation plus maintenance costs) given the unreliability associated with the supply of alternative power.

2. Security of sensors: Executives considering IoT deployments often cite security as a key concern. Tackling the problem at the source may be a logical approach. Complex cryptographic algorithms might ensure data integrity, though sensors’ relatively low pro-cessing power, the low memory available to them, and concerns about power consump-tion may all limit the ability to provide this security. Companies need to be mindful of the constraints involved as they plan their IoT deployments. Some security measures which have been put in place are, installing of a software that detects invalid input data on the sensor in case a security alert is channeled into the system. E.g. Sensor made to detect motion cannot be compromised to detect light. In case it is compromised to detect light then it alerts the user of insecurity issues.

3. Interoperability: Most of the sensor systems currently in operation are proprietary and are designed for specific applications. This leads to interoperability issues in heterogeneous sensor systems related to communication, exchange, storage and security of data, and scalability. Communication protocols are required to facilitate communication between heterogeneous sensor systems. Due to various limitations such

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as low processing power, memory capacity, and power availability at the sensor level, lightweight communication protocols are preferable. Constrained Application Protocol (CoAP) is an open-source protocol that transfers data packets in a format that is lighter than that of other protocols such as Hypertext Transfer Protocol (HTTP), a protocol familiar to many, as it appears in most web addresses. While CoAP is well suited for energy-constrained sensor systems, it does not come with in-built security features, and additional protocols are needed to secure intercommunications between sensor systems.

1.4.2 Network Technology that were used in IoT

An overview

Information that sensors create rarely attains its maximum value at the time and place of creation. The signals from sensors often must be communicated to other locations for aggregation and analysis. This typically involves transmitting data over a network.

Sensors and other devices are connected to networks using various networking devices such as hubs, gateways, routers, network bridges, and switches, depending on the application. For example, laptops, tablets, mobile phones, and other devices are often connected to a network, such as Wi-Fi, using a networking device (in this case, a Wi-Fi router). The first step in the process of transferring data from one machine to another via a network is to uniquely identify each of the machines. The IoT requires a unique name for each of the “things” on the network. Network protocols are a set of rules that define how computers identify each other. Broadly, network protocols can be proprietary or open. Proprietary network protocols allow identification and authorization to machines with specific hardware and software, making customization easier and allowing manufacturers to differentiate their offerings. Open protocols allow interoperability across heterogeneous devices, thus improving scalability. Internet Protocol (IP) is an open protocol that provides unique addresses to various Internet-connected devices; currently, there are two versions of IP: IP version 4 (IPv4) and IP version 6 (IPv6). IP was used to address com-puters before it began to be used to address other devices. About 4 billion IPv4 addresses out of its capacity of 6 billion addresses have already been used. IPv6 has superior scalability with approximately 3.4x1038 unique addresses compared to the 6 billion addresses supported by IPv4. Since the number of devices connected to the Internet is estimated to be 26 billion as of 2015 and projected to grow to 50 billion or more by 2020, the adoption of IPv6 has served as a key enabler of the IoT.

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Figure 7 Network Interconnection in IoT

1.4.2.2 Factors that enabled network technologiesNetwork technologies are classified broadly as wired or wireless. With the continuous movement of users and devices, wireless networks provide convenience through almost continuous connectivity, while wired connections are still useful for relatively more reliable, secured, and high-volume network routes.

The choice of a network technology depends largely on the geographical range to be covered. When data have to be transferred over short distances (E.g. inside a House), devices can use wireless personal area network (PAN) technologies such as Bluetooth and ZigBee as well as wired connections through technologies such as Universal Serial Bus (USB). When data have to be transferred over a relatively bigger area such as an offices and across nearby buildings, devices can use local area network (LAN) technologies. E.g. of wired LAN technologies include Ethernet and fiber optics. Wireless LAN networks include technologies such as Wi-Fi. When data are to be transferred over a wider area beyond buildings and cities, an internetwork called wide area network (WAN) is set up by connecting a number of local area networks through routers. The Internet is an example of a WAN. Data transfer rates and energy requirements are two key considerations when selecting a network technology for a given application. Technologies such as 4G (LTE, LTE-A) and 5G are favorable for IoT applications, given their high data transfer rates. Technologies such as Bluetooth Low Energy and Low Power Wi-Fi are well suited for energy-constrained devices.

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Personal area network (PAN) Local area Wide areanetwork (LAN) network(WAN)

Wired USB Ethernet, Fiber Not available for

connections Optics now

Wireless Bluetooth, ZigBee, CoAP, Wi-Fi, WiMAX WiMAX,

connections Wi-Fi, Z-wave weightless, cellulartechnologies suchas 2G, 3G, 4G(LTE, LITE-A)and 5G which is tocome.

Table 2 Broad network classes with representative examples by connection types

Below is a brief explanation of wireless network technologies that could be used for IoT applications. E.g. bandwidth rates, recent advances, and their limitations.

1.4.2.2.1 Classic Bluetooth and Bluetooth Low EnergyIt was introduced in 1999, Bluetooth technology is a wireless technology known for its ability to transfer data over short distances in personal area networks. Bluetooth Low Energy (BLTE) is a recent addition to the Bluetooth technology and consumes about half the power of a Bluetooth classic and standard device unlike the original version of Bluetooth. The energy efficiency of BLTE is attributable to the shorter scanning time needed for BLTE devices to detect other devices: 0.4 to 1.0 milliseconds (ms) compared to 22.5 ms for Bluetooth Classic and Standard. In addition, the efficient transfer of data during the transmitting and receiving states enables BLTE to deliver higher energy efficiency compared to Bluetooth Classic. Higher energy efficiency comes at the cost of lower data rates: BLTE supports 270 kilobits per second (Kbps) while Bluetooth Classic supports up to 2.5 Mbps. Existing penetration, coupled with low device costs, positions BLTE as a technology well suited for IoT applications. BLTE is compatible with only the relatively newer dual-mode Bluetooth devices (called dual mode because they support BLTE as well as Bluetooth Classic).

1.4.2.2.2 Ethernet and Low Power Wi-FiAlthough Ethernet has been in use since between 1970s- 1980s, Wi-Fi is a more recent wireless technology that is widely popular and known for its high-speed data transfer rates in personal area network and local area networks.

Wi-Fi devices keep latency or delays in the transmission of data by remaining active even when no data are being transmitted. Such Wi-Fi connections are often set up with a dedicated power line or batteries that need to be charged after some time. Higher-cost lower-power Wi-Fi devices sleep when not transmitting data and need just 10 milliseconds to wake up when called upon. Low Power Wi-Fi with batteries can be used for remote sensing and control applications.

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1.4.2.2.3 Worldwide Interoperability for Microwave Access (WiMAX) version 1 and WiMAX

version 2

It was introduced in 2000. Worldwide Interoperability for Microwave Access version 1 was developed by the European Telecommunications Standards Institute (ETSI) in cooperation with IEEE. Worldwide Interoperability for Microwave Access version 2 is the latest technology in the WiMAX family. Worldwide Interoperability for Microwave Access version 2 offers maximum data speed of 1 Gbps. WiMAX version one offered 100 Mbps.

In addition to higher data speeds, WiMAX version 2 has better backward compatibility than WiMAX version 1. WiMAX version 2 network operators can provide seamless service by using 3G, 2G or Long Term Evolution (LTE) and LTE-A networks when required.

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1.4.2.3 Factors that led to adoption of the IoT networks technologies

Networks are able to transfer data at higher speeds, at lower costs and with lower energy requirements than ever before. Also, with the introduction of Internet protocol version 6, the number of connected devices is rising rapidly. As a result, bodies involved are seeing an increasingly diverse composition of connected devices, from laptops and smartphones to home appliances, vehicles, traffic signals, and wind turbines. Such diversity in the nature of connected devices is making a wider-scale adoption of an extensive range of network technologies.

Data rates: In the last 30-40 years, data rates have increased from 250 bps to 1 Gbps, facilitating seamless transfer of heavy data files (see figure 8 for various cellular-tech-nology generations). The transition from the first cellular generation to the second changed the way communication messages were sent from analog signals to digital signals. The transition from the second to the third generation marked a leap in capability, enabling users to share multimedia content over high-speed connections.

Figure 8 Data rates in last 35 years

Internet Protocol version 6 adoption: Given IPv6’s massive address space, new devices are typically IPv6-based, while companies are upgrading the existing devices from IPv4 to IPv6. In 2015, according to Cisco, the number of IPv6-capable websites increased by 33% over the previous year. By 2018, 50% of all fixed and mobile device connections are expected to be IPv6-based, compared to 16% in 2013.

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1.4.2.4 Challenges that faced network technologies and their potential solutions

Interconnections: Metcalfe Law states that the value of a network is proportional To the square of the number of compatibly communicating devices. There is limited value in connecting the devices to the Internet. Companies can create enhanced value by connecting devices to the network and to each other. Different network technologies require gateways to connect with each other. This adds cost and complexity, which can often make security management more difficult.

Network penetration: There is limited penetration of high-bandwidth technologies such as LTE and LTE-A, while 5G technology has yet to arrive, 3G was the top cellular network in the most parts of the world in 2013, while 4G is the current top it accounts for only 5% of the world’s total mobile connections. LTE and LTE-A penetration as a percentage of connections is 63% in India, 36% in USA, and 30 % in the China, but its penetration in the developing world stands at just 1.5%. In emerging markets, network operators are treading the slow-and-steady path to LTE infrastructure, given the accompanying high costs and their focus on fully reaping the returns on the investments in 3G technology that they made in the last three to five years.

Security: With a growing number of sensor systems being connected to the network, there is an increasing need for effective authentication and access control. The Internet Protocol Security (IPsec) suite provides a certain level of secured IP connection between devices. However, there are outstanding risks associated with the security of one or more devices being compromised and the impact of such breaches on connected devices. Maintaining data integrity while remaining energy efficient stands as an enduring challenge.

Power: Devices connected to a network consume power and some consume a lot of power due their speed when connected to the internet and providing a continuous power source is a pressing concern for the IoT. Depending on the application, a combination of techniques such as power aware routing/ratio and sleep scheduling protocols can help improve power management in networks. Power aware routing/routing protocols determine the routing decision based on the most energy efficient route for transmitting data packets, sleep scheduling protocols define how devices can sleep and remain inactive for better energy efficiency without impacting the output.

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1.4.3 Standards technologies in IoTData collected by sensors in different locations are aggregated so that meaningful conclusions can be made. Aggregation increases the value of data by increasing, E.g. the scale, scope, and frequency of data available for analysis. Aggregation is achieved through the use of various standards depending on the IoT application at hand. According to the International Organization for Standardization (ISO), a standard is a document that provides requirements, specifications, guidelines or characteristics that can be used consistently to ensure that materials, products, processes and services are fit for their purpose.

Two broad types of standards relevant for the aggregation process are technology standards (including network protocols, communication protocols, and data-aggregation standards) and regulatory standards (related to security and privacy of data, among other issues).

The second type of standards relates to regulatory standards that play an important role in shaping the IoT landscape. There is a need for clear regulations related to the collection, handling, ownership, use, and sale of the data. Within the context of expanding IoT applications, it is worthwhile to consider the US Federal Trade Commission’s privacy and security recommendations dubbed the Fair Information Practice Principles (FIPPs). This standards are described by the following principles;

Choice and notice principle: The principle of choice and notice states that entities that collect data should give users the option to choose what they reveal and notify users when their personal information is being recorded. This may not be required for IoT applications that aggregate information, de-linked to any specific individual.

Purpose specification and use limitation principle: This principle states that entities collecting data must clearly state the purpose to the authority that permits the collection of those data. The use of data must be limited to the purpose specified, although this might hinder creative uses of collected data sets in various IoT applications.

Data minimization principle: The principle of data minimization suggests that a company can collect only the data required for a specific purpose and delete that data after the intended use. This necessarily restricts the scope of analysis that can result from slicing and dicing the IoT data.

Security and accountability Principle: This principle states that entities that collect and store data are accountable and must deploy security systems to avoid any unauthorized access, modification, deletion, or use of the data.

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1.4.3.1 Technologies that enabled standards in IoTTechnology standards comprised of three elements; network protocols, communication protocols, and data aggregation standards. Network protocols define how machines identify each other, while communication protocols provide a set of rules or a common language for devices to communicate. Once the devices are communicating to each other and sharing data, aggregation standards help to aggregate and process the data so that those data become usable.

Network protocols: Network protocols refer to a set of rules by which machines identify and authorize each other to communicate. Interoperability issues result from multiple network protocols in existence. In recent years, companies in the IoT value chain have begun working together to help align multiple network protocols. E.g. is the AllJoyn standard established by Qualcomm in early 2014 that allows devices to discover, connect, and communicate directly with other AllJoyn-enabled prod such as Wi-Fi, Ethernet, and possibly Bluetooth, z-wave and ZigBee.

Communication protocols: Once devices are connected to a network and then they identify each other, communication protocols (a set of rules) provide a common language for devices to communicate. Various communication protocols are used for device-to-device communication; broadly, they vary in the format in which data packets are transferred. There have been ongoing efforts to identify protocols better suited to IoT applications. Toward that end, as earlier said, the advantages and limitations of the Constrained Application Protocol, a communication protocol lighter than other popular protocols such as HTTP, FTP etc.

Data aggregation standards: Data collected from multiple devices come in different formats and at different sampling rates i.e. the frequency at which data are collected. One set of data-aggregation tools is Extraction, Transformation, Loading in short stated as (ETL) tools do aggregate, process, and store data in a format that can be used for analytics applications. Extraction is preferably to as acquiring data from multiple sources and multiple formats and then validating to ensure that only data that meet a criterion are included. Transformation are therefore, activities such as splitting, merging, sorting, and transforming the data into a desired formats, E.g. names can be split into first and last names, while addresses can be merged into city and state format. And then Loading refers to the process of loading the data into a database that can be used for analytics applications.

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Figure 9 Typical data aggregation process observing the data aggregation standard

Traditional ETL tools aggregate and store the data in relational databases (SQL), in which data are organized by establishing relationships based on a unique identifier. It is easy to enter, store, and query structured data in relational databases using structured query language (SQL). The American National Standards Institute standardized SQL as the querying language for relational databases in 1986. SQL provides users a medium to communicate with databases and perform tasks such as data modification and retrieval. As the standard, SQL aids aggregation not just in centralized databases (all data stored in a single location) but also in distributed databases (data stored on several computers with concurrent data modifications).

With recent advances in easy and cost-effective availability of large volumes of data, there is a question about the adequacy of traditional ETL tools that can typically handle data in terabytes (1 terabyte = 1012 bytes). Big-data ETL tools developed in recent years can handle a much higher volume of data, such as in petabytes (1 petabyte = 1000 terabytes or 1015 bytes). In addition to handling large volume, big-data tools are also considered to be better suited to handle the variety of incoming data, structured as well as unstructured. Structured data are typically stored in spreadsheets, while unstructured data are collected in the form of images, videos, web pages, emails, blog entries, documents, etc.

With recent advances in easy and cost-effective availability of large volumes of data, there is a question about the adequacy of traditional ETL tools that can typically handle data in terabytes (1 terabyte = 1012 bytes). Big-data ETL tools developed in recent years can handle a much higher volume of data, such as in petabytes (1 petabyte = 1000 terabytes or 1015 bytes). In addition to handling large volume, big-data tools are also considered to be better suited to handle the variety of incoming data, structured as well as unstructured. Structured data are typically stored in spreadsheets, while unstructured data are collected in the form of images, videos, web pages, emails, blog entries, documents, etc.

Apache Hadoop is a big-data tool useful especially for unstructured data. Based on the Java programming language, Hadoop, developed by the Apache Software Foundation, is an open-source tool useful for processing large data sets. Hadoop enables parallel processing of large data across clusters of computers wherein each computer offers local aggregation and storage.

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Hadoop comprises two major components: Map Reduce and Hadoop Distributed File System (HDFS). While Map Reduce enables aggregation and parallel processing of large data sets, HDFS is a file-based storage system and a type of Not only SQL (NoSQL) database. Compared to relational databases, NoSQL databases represent a wider variety of databases that can store unstructured data. Data processed and stored on Hadoop systems can be queried through Hadoop application program interfaces (APIs) that offer an easy user interface to query the data stored on HDFS for analytics applications.

Depending on the type of data and processing, different tools could be used. While Map Reduce works on parallel processing, Spark, another big-data tool, works on both parallel processing and in-memory processing. Considering storage databases, HDFS is a file-based database that stores batch data such as quarterly and yearly company financial data, while HBase and Cassandra are event-based storage databases that are useful for storing streaming (or real-time) data such as stock-performance data.

1.4.3.2 Factors that enabled great adoption within the IoT

The IoT landscape is in a nascent stage, and existing technology standards serve specific solutions and stakeholder requirements. There are many efforts under way to develop standards that can be adopted more widely. Primarily, there are two types of developments: vendors (across the IoT value chain) coming together to an agreement, and standards bodies (E.g. IEEE or ETSI) working to develop a standard that vendors follow. Time will tell which one of these two options will prevail. Ultimately, it might be difficult to have one universal standard or one ring to rule them all either at the network or communication protocol level or at the data-aggregation level.

In terms of network and communication protocols, a few large players have at hand a meaningful opportunity to drive the standards that IoT players will follow for years to come. As an example, Qualcomm—with other companies such as Sony, Bosch, and Cisco—has developed the All Seen Alliance that provides the AllJoyn platform. On similar lines, through the Open Interconnect Consortium, Intel launched the open-source IoTivity platform that facilitates device-to-device connectivity. IoTivity offers its members a free license of the code, while All Seen does not. However, All Seen-compliant devices are already available, while devices compliant with IoTivity are expected to be available by the second half of 2023 both platforms are comparable but not interoperable, just as iOS and android are.

Concurrently, various standards bodies are also working to develop standards (for network and communication protocols) that apply to their geographical boundaries and could extend well beyond to facilitate worldwide IoT communications. As an example, the ETSI, which primarily has a focus on Europe, is working to develop an end-to-end architecture called the oneM2M platform that could be used worldwide. IEEE, another standards body, is making progress with the IEEE P2413 working group and is coordinating with standards bodies such as ETSI and ISO to develop a global standard by 2016.

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In terms of data aggregation, relational databases and SQL are considered to be the standards for storing and querying structured data. However, we do not yet have a widely used standard for handling unstructured data, even though various big-data tools are available. I discuss this challenge below.

1.4.3.3 Challenges facing Standards and their potential solutions

For effective aggregation and use of the data for analysis, there is a need for technical standards to handle unstructured data and legal and regulatory standards to maintain data integrity. There are gaps in people skills to leverage the newer big data tools, while security remains a major concern, given the fact that all the data are aggregated and processed at this stage of the Information Value Loop.

Standard for handling unstructured data: Structured data are stored in relational databases and queried through SQL. Unstructured data are stored in different types of NoSQL databases without a standard querying approach. Hence, new databases created from unstructured data cannot be handled and used by legacy database-management systems that companies typically use, thus restricting their adoption.

Security and privacy issues: There is a need for clear guidelines on the retention, use, and security of the data as well as metadata, the data that describe other data. As discussed earlier, there is a trade-off between the level of security and the memory and bandwidth requirements

Regulatory standards for data markets: Data brokers are companies that sell data collected from various sources. Even though data appear to be the currency of the IoT, there is lack of transparency about who gets access to data and how those data are used to develop products or services and sold to advertisers and third parties. A very small fraction of the data collected online is sold online, while a larger share is sold through offline transactions between providers and users.

Technical skills to leverage newer aggregation tools: Companies that are keen on leveraging big-data tools often face a shortage of talent to plan, execute, and maintain systems. There is an uptrend in the number of engineers being trained to use newer tools such as Spark and Map Reduce, but this is far fewer than the number of engineers trained in traditional languages such as SQL.

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1.4.4 Augmented behavior technology of IoTIn its simplest sense, the concept of augmented behavior is the doing of some action that is the result of all the preceding stages of the value loop from sensing to analysis of data. Augmented behavior, the last phase in the loop, restarts the loop because action leads to creation of data, when configured to do so.

There is a thin line between augmented intelligence and augmented behavior. For our purpose, augmented intelligence drives informed action, while augmented behavior is an observable action in the real world.

Augmented behavior finds expression in at least three ways practical matters. They are as follows:

Machine-to-machine (M2M) interfaces: M2M interfaces refer to the set of technologies that enable machines to communicate with each other and drive action. In common vernacular, M2M is often used interchangeably with the IoT. For our purposes, though, the IoT is a broader concept that includes machine-to-machine and machine-to-human (M2H) interfaces, as well as support systems that facilitate the management of information in a way that creates value.

Figure 10 Representative examples of M2M applications

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Machine-to-human interfaces- Based on the data collected and algorithmic calculations, machines have the potential to convey suggestive actions to individuals who then exercise their discretion to take or not to take the recommended action. With human interaction, the IoT discussion shifts into a slightly different direction, toward behavioral sciences, which is distinct from the data science that encapsulates the preceding four stages focused on creating, communicating, aggregating, and analyzing the data to derive meaningful insights.

Figure 11 Representative examples of M2H application

Organizational entities: Organizations include individuals and machines and thus involve the benefits as well as the challenges of both M2M and M2H interfaces. Managing augmented behavior in organizational entities requires changes in people’s behaviors and organizational processes. Business managers could focus on process redesign based on how information creates value in different ways.

1.4.4.1 Enabling technologies in augmented behaviorThe enabling technologies for both M2M and M2H interfaces prompt a consideration of the evolution in the role of machines from simple automation that involves repetitive tasks requiring strength and speed in structured environments to sophisticated applications that require situational awareness and complex decision making in unstructured environments. The shift toward sophisticated automation requires machines to evolve in two ways: improvements in the

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machine’s cognitive abilities (E.g. decision making and judgment) and the machine’s execution or actuation abilities (E.g. higher precision along with strength and speed). With respect to robots specifically, below is an overview of how machines have ascended this evolutionary path:

Between 1940–1970

In the late 1940s, a few non-programmable robots were developed. These robots could not be reprogrammed to adjust to changing situations and, as such, merely served as mechanical arms for heavy, repetitive tasks in manufacturing industries. In 1954, George Devol developed one of the first programmable robots, and in the early 1960s, an increasing number of companies started using programmable robots for industrial automation applications such as warehouse management and machining.

Between 1970–2000

This period witnessed key developments related to the evolution of adaptive robots. As the name suggests, adaptive robots embedded with sensors and sophisticated actuation systems can adapt to a changing environment and can perform tasks with higher precision and complexity compared to earlier robots. During this period, robotic machines that could adapt to varying situations were used to identify objects and autonomously take action in applications such as space vehicles, unmanned aerial vehicles and submarines.

Between 2000–2010

The development of an open-source robot operating system (developed by the Open Source Robotics Foundation) in 2006 was an important driver enabling the development and testing of various robotic technologies. As robots’ intelligence and precision of execution improved, they increasingly started working with human beings on critical tasks such as medical surgeries. Following the US Defense Advanced Research Project Agency’s competition for developing autonomous military vehicles in 2004, many automakers made a headway into military and civilian autonomous vehicles. Even though the underlying technology is available, legal and social challenges related to the use of autonomous vehicles are yet to be resolved.

From 2010 and onward

With the availability of big data, cloud-based memory and computing, new cognitive technologies, and machine learning, robots in general seem to be getting better at decision making and are gradually approaching autonomy in many actions. We are witnessing the development of machines that have anthropomorphic features and possess human-like skills such as visual perception and speech recognition. Machines are automating intelligence work such as writing news articles and doing legal research tasks that could be done only by humans earlier.

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1.4.4.2 Factors enabling the adoption within the IoT field

Improved functionality at lower prices is driving higher penetration of industrial robots and increasing the adoption of surgical robots, personal-service robots, and so on. For situations where a user needs to take the action, machines are increasingly being developed with basic behavioral-science principles in mind. This allows machines to influence human behaviors in effective ways.

Lower machine prices: The decreasing prices of underlying technologies such as sensors, network connections, data processing and computing tools, and cloud-based storage are leading to lower prices of robots. Figures 12 and 13 below show a decline in the average selling price of industrial robots alongside increasing unit sales.

Figure 12 Average selling price of industrial robots

Note: The prices shown above do not include the cost of peripheral devices required and installation and maintenance charges. Industrial robots available at different price points can perform a wide range of activities. Robots available at the above price points can perform welding, loading, and painting, among other activities.

Figure 13 Unit sales of industrial robots globally in the last 19 years

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Improved machine functionality: There is a thin line between augmented intelligence and augmented behavior. An underlying driver for the shift in the use of robots from mundane to sophisticated tasks is the development of elaborate algorithms that focus on the quality of fine decision making in live environments, and not simply a binary yes/no decision.

Robots made force-fitted decisions based on programmed algorithms, irrespective of the situation and information availability. However, recent advances in robotic control architecture prompt the machine to ask for more information if there is an information insufficiency before taking a decision. E.g. a robot offers a pill to the patient and the patient refuses; instead of repeating the same instruction, the robot could analyze the patient’s behavior patterns based on his personal data stored on the cloud and try to deduce the reason for refusal. The robot may also deduce from the environment that the patient has a fever and inform the doctor. One of the many techniques under development enables users to train robots by rewarding them in cases where they have made the right decision by telling them so and asking them to continue to do the same kind of positive reinforcement.

Machines “influencing” human actions through behavioral-science rationale: Literature suggests that creating a new human behavior is challenging. Creating a new human behavior that endures is even more challenging. Nudge techniques attempts to influence people’s behaviors involve the design of choices that prompt them to move from intention to action. E.g. placing a fruit at eye level is a nudge technique, while banning junk food is not, according to Richard Thaler and Cass Sun stein. Choice designs could be built by consciously choosing the options that should be presented to an individual and the manner in which the options are presented. E.g. menus sometimes start with expensive items followed by relatively less expensive ones. This makes the customer feel that she is making a judicious choice by ordering any of the latter items, since her reference price is much higher. Furthermore, a health-conscious restaurateur could place the relatively healthful food items at relatively lower prices and, in so doing, nudge the customer to order them. In an analogous fashion, IoT devices can nudge human behaviors by establishing a feedback loop. A school in Berlin was trying to find a solution to speeding drivers who were undeterred even by police ticketing. The school authorities experimented with a creative signboard that compared two data points: your speed (speed of a passing car measured by a radar sensor) against the speed limit of 30 miles/hour. Even though the radar signboard offered drivers no new information as the dashboard display readily provides driver speed the signboards nudged them into reducing their speed by an average of 20%, bringing their speed within the permissible limit or sometimes even lower.

In addition to influencing the choices of individuals on a stand-alone basis, IoT devices can also drive adoption by using social or peer pressure to achieve a desired result. E.g. when a fitness device suggests to an individual that it’s time for a workout, the recommendation may go unheard. However, if the device shows how the user is doing vis-à-vis his peers (lagging

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or outperforming), the user is potentially more likely to act. The manufacturer of an electronic soap dispenser fitted its product with a computer chip that records the frequency with which health care professionals in different hospital wards wash their hands; it then compares these results with World Health Organization standards and conveys the comparisons back to the wards at a group level. Such a process of aggregation and comparison effectively makes personal hygiene a team sport: Each worker, in turn, is effectively nudged into a greater awareness of his hygienic habits, as he knows that he is a part of a team effort.

Other examples abound showing how the IoT can influence human behavior to achieve normative outcomes. The larger point, though, is that the IoT may augment human behavior as much as it augments mechanical behavior. And the interplay between the IoT and human choice will likely only evolve and become more prominent in the years ahead.

1.4.4.3 Challenges that faced augmented behavior and their potential solutions

There are challenges related to machines’ judgment in unstructured situations and the security of the information informing such judgments. Interoperability is an additional issue when heterogeneous machines must work in tandem in an M2M setup. Beyond the issues related to machine behavior, managing human behaviors in the cases of M2H interfaces and organizational entities present their own challenges.

Machines’ actions in unpredictable situations: Machines are typically considered to bemore reliable than human beings in structured environments that can be simulated in programming models; however, in the real world, most situations we encounter are unstructured. In such cases, machines cannot possibly be relied upon completely; I.e. the control should fall back to human beings.

Information security and privacy: There is a looming risk of compromise to the machine’s security. An example of a privacy concern relates to an appliance manufacturer that offers televisions with voice-recognition systems. The voice-recognition feature collects information related to not only voice commands but also any other audio information it can sense. This raises concerns about the manner in which the audio information collected by the software will be used both in benign and personally invasive ways. A benign use could include, for E.g., the personalization of television advertising. A personally invasive use might be the unauthorized sale of such information.

Machine interoperability: Performance of M2M interfaces is impacted by interoperability challenges resulting from heterogeneous brands, hardware, software, and network connections. There is a need for a convergence of standards, as discussed earlier.

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That machines perform as desired in a particular context is a matter of getting the technology right, which is currently in an evolving stage. Perfecting human behavior is another matter entirely.

Mean-reverting human behaviors: One of the main challenges in M2H interfaces is that, although users have the smart devices, they minimally follow the suggested course of action and, eventually, the devices end up serving as shelf ware. David Rose states that machines cater to human drives; he cites six such drives: omniscience (the need to know all), telepathy (human-to-human connections), safekeeping (protection from harm), immortality (longer life), teleportation (hassle-free travel), and expression (the desire to create and express). Machines serve human drives through one or more of their features. These features also determine the machine’s position in the enchantment hierarchy. The enduring association between the machine and its user is a function of the machine’s position in the enchantment hierarchy, starting from the most basic level of connection and going all the way up to storyification.

Glanceability Ability of devices to convey informationat a glance-easy reading andcomprehension

Gesturability Ability of machines to understandhuman gestures and take action

Affordability Ability to improve functionality andincrease customization due to betteraffordability

Wearability Ease of wearing and even ingestingdevices

Indestructibility Flexibility to use the device in any waysince it is strong and sturdy

Usability Ability of the machine to embed one ormore usable features

Loveability Ability of the device to look loveableand behave in amicable ways throughthe use of physical features

Table 3 Machine feature

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Figure 14 Machine enchantment hierarchy Source:

Figure 15 An illustration of the relationship-building process between a machine and its user

Inertia to technology-driven decision making in organizational entities: Decision makers have decades of experience running successful businesses wherein they have primarily relied on professional judgment. They typically encounter resistance to newer developments such as predictive and prescriptive analytics. Executives are skeptical of the accuracy and efficacy of conclusions drawn out of statistical analyses, since they view augmented intelligence tools as “black boxes” in which they do not understand how the outcome has been churned. To manage these augmented behavior changes in organizations, decision makers could give the new technologies a fair chance to contribute in their decision-making process by setting aside their biases. At the same time, data scientists and developers could focus on two objectives: continuously improving the statistical tools and the algorithms to bring the machine’s decision-making ability closer to reality, and making it easier for business users to comprehend the results through means such as easy-to-use visualization tools. In the current state of affairs,

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augmented behavior has the potential to grow, with an increasing number of successful use cases over time.

1.5 THE IOT TECHNOLOGY ARCHITECTUREThe Information Value Loop can serve as the cornerstone of an organization’s approach to IoT solution development for potential use cases. To transform ideas and concepts discussed earlier into the concrete building blocks of a solution, I posit an end-to-end IoT technology architecture to guide IoT solution development. This architecture links strategy decisions to implementation activities. It can serve as a playbook for establishing the vision for an IoT solution and for converting that vision into tangible reality. The Information Value Loop informs and is present in each phase of this development, whereby ideas are made progressively more specific, and tactical decisions remain consistent with the overall strategic goals. The process of turning ideas into IoT solutions. The architecture for guiding the development and deployment of IoT systems consists of the following views:

1. Business. This view defines the vision for an IoT system and covers aspects such as return on investment, value proposition, customer satisfaction, and maintenance costs.

2. Functional. The linchpin of the reference architecture, this view spans modules that cater to high-level information flow through the system. It contains the functional layers for data creation, processing, and presentation.

3. Usage. This view shows how the reference model realizes key capabilities desired in a usage scenario. It may include the detailed use case description, user journey, and requirements.

4. Implementation. A technical representation of usage scenario deployment, this view incorporates the technologies and system components required to implement the functions prescribed by the usage and functional viewpoints.

5. Specifications. Finally, this view captures the complete IoT stack to be deployed. It includes detailed technical specifications for the build-out of the solution, and translates these blueprints into the individual components needed to design, build, and implement the components and interconnections shown in the functional and implementation views.

In the business view, the Information Value Loop stages are utilized to examine the flow of information which guides strategic decisions for the use case at hand. These decisions further help define the overall IoT strategy. An example of how value can be realized using IoT in health monitoring;

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Figure 16 The IoT business view

Prior to the IoT, the patient could wear a heart monitor, but the monitor’s data would usually be communicated to the external world using written records that had to be carried each time. This represented a blockage at the “communicate” step (see arrow “A”).

With the introduction of the IoT, data can now be communicated between a patient and the physician using network connections. However, there is still a bottleneck associated with the ability of the smart systems to interface with existing electronic health record (EHR) systems in order to aggregate data. Alleviating this bottleneck is key to IoT applications in the health care industry.

In the “Meet Isabel’ scenario of figure 16, the bottleneck associated with data aggregation and use can be addressed by the “integration” layer wherein standards for sensor management, data

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transfer, storage, and aggregation come together in an integral fashion. In the earlier part of this report, we discuss “standards” as they relate to specific technologies that fall under the integration layer described further in the functional view.

The functional view categorizes the components of an IoT system across the five value loop stages and five functional layers—sensors, network, integration, augmented intelligence, and augmented behavior. It serves as a guide to the functional considerations and technology choices of an IoT solution. (see figure 16)

Sensors create the data that are sent downstream to subsequent layers of the architecture. Network is the connectivity layer that communicates data from the sensors and connects them to the Internet. The integration layer manages the sensor and network elements, and aggregates data from various sources for analysis. The augmented intelligence layer processes data into actionable insights. Finally, augmented behavior encapsulates the actions or changes in human or machine behavior resulting from these insights. The augmented behavior layer includes an edge computing sub-layer defined by local analysis (near the source of data) and action without the need for human intervention. Aligned with these layers and the value loop stages are standards for sensor management and data management and use, as well as security considerations including end-point protection, network security, intrusion prevention, and privacy and data protection.

The usage view sets up the technical solution by describing the user’s journey through all the steps of the use case being implemented. This view would include the key actors, that may be users and/or machines, and the activities involved. The usage view also describes the use case from the point of view of user needs and system capabilities.

Figure 17 The IoT functional view

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Figure 17 illustrates a typical IoT use in a brick-and-mortar store. The implementation view delves deeper into specific technology choices and the vendor solutions that are used to deploy those choices. It leverages the high-level component view from the functional architecture to frame the specific system implementation.

The IoT reference architecture describes parameters and benchmark criteria that can be used to identify the best mix of product solutions for an IoT implementation across different layers.

Figure 18 The IoT usage view

Figure 18 shows a representative implementation view for the retail use case example described earlier. The specifications view captures the final translation of the various viewpoints described above as part of the IoT Reference Architecture into ground-level deployment. It crystalizes the functional requirements and specific technology choices identified earlier into detailed specification definitions that describe how all the selected components must be linked to work together.

A sample specifications view is shown in figure 19. Together, all the myriad viewpoints that comprise the Deloitte IoT Reference Architecture form an end-to-end blueprint for realizing an IoT system from strategy through implementation.

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Figure 19 The IoT implementation view

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Figure 20 The IoT specifications view

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CHARPTER TWO2.0 The Present and Future of IoT

IoT devices are becoming a part of the mainstream electronics culture and people are adopting smart devices into their homes faster than ever. IoT devices are becoming a part of the mainstream electronics culture and people are adopting smart devices into their homes faster than ever. By 2021, it is estimated that there will be up to 27 billion connected devices to the internet. IoT devices will be a huge part of how we interact with basic everyday objects.

In one year IoT field have shifted from 4 million IoT devices connected to the internet to 3 billion. The future is happening now, and these devices are getting smarter every day through machine learning and artificial intelligence. To prove that IoT is taking off rapidly, Target opened up a store in Texas that exclusively sells IoT devices. There is big money in the IoT space currently, and it will only continue to grow as technology improves.

The more data that IoT devices collect, the smarter they will become. Cities will transform into smart cities through the use of IoT connected devices. Think of smart traffic lights that collect data on traffic, and use that data to sync lights to peak traffic times.

This improves cities overall efficiency and saves the government money since everything can be remotely managed. Smart homes, thermostats, lighting systems and coffee makers will all collect data on your habits and patterns of usage. All this data will be collected to help facilitate machine learning.

2.1 Standard IoT Protocols2.1.1 Bluetooth

Bluetooth is the most common wireless communication technology available for mobile devices. It has progressively improved over the years with newer iterations, being able to support more features and profiles. However, it's easy to get confused in the terminology for the various components of Bluetooth.

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2.1.1.1 Bluetooth VersionsBluetooth Versions Optional Features Bluetooth Version description

Basic Enhanced High Low Slotrate(BR) Data Speed Energy Availability

Rate (EDR) (HS) (LE) Masking(SAM)

Bluetooth 1.x Yes No No No No The basic Bluetooth ratewith noadditional/optionalprofiles or codecs. Thisversion of Bluetooth isobsolete and was rarelyimplemented on mobiledevices due to its limitedspeed of 1mbs anddifficulty pairing.

Bluetooth 2.x Yes Yes No No No The most popularvariant of Bluetooth,especially in the earlierdays when phones werenot as advanced. Itsupports enhanced datarates (EDR) up to 3Mbps, and the V2.1variant significantlysimplified the pairingprocedure making it amore practical forcommercial use.

Bluetooth 3.x Yes Yes Yes No No Bluetooth 3.0 improveson the speed limitationsof Bluetooth 2.1, withthe optional High-Speedfeature (HS), whichallows the Bluetoothmodule to transmit overan adjacent radio(802.11). However,Bluetooth 3.0 consumesa lot more power thanBluetooth 2.x.

Bluetooth 4.x Yes Yes Yes Yes No Bluetooth 4.0 has thehigh-speed capability ofBluetooth 3.0 but also

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comes with a LowEnergy feature to collectdata from the sensors oflow rate devices. Thisfeature allows theBluetooth module toreduce powerconsumption withconnected devices likewearable smartwatches,heart monitors, mobilephones and smartheadphones.

Bluetooth 5.x Yes Yes Yes Yes Yes The most recent iterationof Bluetooth, bettersuited for the Internet ofThings (IoT). It'sspeculated to have twicethe bandwidth ofBluetooth 4.2 LE and 4xthe range. It also has anew feature called SlotAvailability Masking(SAM) which can detectand prevent interferenceon neighboring bandsfor a more efficient useof broadcastingchannels. However, wehave yet to test theBluetooth 5.0'scapabilities forourselves.

Table 4 Bluetooth Versions

One of the most broadly used wireless technologies of short-range is Bluetooth. You can quickly get Bluetooth apps that offer you wearable technology for pairing up with the smart gadgets. The recently introduced Bluetooth protocol among the IoT protocols is BLE or Bluetooth Low-Energy protocol. It will afford the range of conventional Bluetooth in combined with lower

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power consumption supremacy.

Figure 21 How Bluetooth is predicated to work in the future

Remember that BLE is not designed for transferring large files and will go perfectly with the small portions of data. This is the reason for Bluetooth leading the internet of things protocols of this century. The newly invented Bluetooth Core Specification 4.2 adds up one innovative Internet Protocol Support Profile. It permits Bluetooth Smart Sensor to get access on the internet straight via 6LoAPAN.

2.1.2 Wi-FiFor IoT integration, Wi-Fi is a favored choice according to many electronic designers. It is because of the infrastructure it bears. It has quick data transfer rates along with the aptitude to control a large quantity of data.

The widespread Wi-Fi standard 802.11 presents you the ability to transfer hundreds of megabits in only one second. The only own drawback of this IoT protocol is it can consume excessive power for some of the IoT Application. It ranges approximately 50 m, and along with working on internet protocol standards, it includes IoT Cloud infrastructure access. The frequencies are 2.4GHz and 5GHz bands.

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Figure 22 The Future of WiFi Interconnection

2.1.3 ZigBeeJust like Bluetooth, there is a vast user base of ZigBee. Among the internet of things protocols, ZigBee is designed more for the industrials and less for the consumers. It usually operates at a frequency of 2.4GHz. This is ideal for the industrial sites where data is generally transferred over small rates amongst home or a building.

ZigBee and the popular ZigBee Remote Control are popular as famed IoT Security Protocols for supplying secure, low-power, scalable solutions along with high node counts. The ZigBee 3.0 has taken the protocol to a single standard. It made it handier.

Figure 23 The interconnection of Components on ZigBee

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2.1.4 MQTT IoTMQTT IoT is a message protocol and full form is Message Queue Telemetry Transport. It developed in 1999 by Arlen Nipper (Arcom) and Andy Stanford-Clark (IBM.) This is mostly used for monitoring from a remote area in IoT. The principal task that MQTT does is obtaining data from so many electrical devices. It also conveys them to the IT communications or infrastructure. A hub-and-spoke architecture is fundamentally ordinary for MQTT IoT Protocol. It works on top of the TCP for supplying reliable yet simple streams of data.

This MQTT protocol is made of three core components or mechanisms: Subscriber, Publisher, and Broker. The work of the publisher is generating data and transmitting the data to the subscriber with the help of the broker. Ensuring security is the job of the broker. It does it by checking and rechecking the authorization of the subscribers and the publishers.

This protocol is a preferred option for all devices that are IoT based, and these are also capable of providing enough information-routing functions to the cheap, low-memory power-consuming and small devices with the help of low and vulnerable bandwidth based network.

2.1.5 CoAPThe CoAP or Constrained Application Protocol, an internet productivity and utility protocol, is mainly developed for the restricted smart gadgets. The design of CoAP is for using it among the devices that have an identical restricted community. It includes general nodes and devices on the internet and different restrained networks and devices that are joined on the internet.

Figure 24 The Interconnection of components on CoAP

IoT systems based on the HTTP protocols can go tremendously with CoAP IoT Network Protocols. It uses the protocol-UDP for implementation of lightweight data. Just like the HTTP, it also uses the restful architecture. It is also used inside the mobiles and the other social communities that are basic programs. CoAP helps in getting rid of ambiguity through HTTP get, put up, delete and placed strategies.

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2.1.6 NFCNFC from the IoT Protocols takes the benefit of safe two-way communication linking. The NFC IoT Communication Protocols are applicable for the smartphones.

The NFC or Near Field Communication allows the clients to connect to the electronic devices, to use digital contents and to do the contactless payment transaction. The essential work of NFC is to expand the contactless card technology. It works within 4cm (between devices) by enabling the devices for sharing information.

Figure 25 NFC

2.2 CELLULAR NETWORK IN IOT2.2.1 5G network in IoT

Even though the specifications of 5G remain unknown, the next generation of mobile technology is predicted to greatly benefit IoT innovation.

5G is expected to provide:

Faster speeds - Nokia and Huawei predicts 5G internet network speeds as high as 10Gbps

Lowered latency Network support for massive increases in data traffic Expansion of cell sites

Enabling completely new applications while also benefitting many IoT applications, a 5G platform will impact many industries including automotive, entertainment, agriculture, manufacturing and IT.

Consider these few examples of the necessity of 5G progress:

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Machine-to-machine communications, if stalled by connectivity issues, can lead to revenue lost due to production line slowdowns

A connected car traveling at 75 m/hr would travel over 10 feet further before applying the brakes if the system was experiencing a 100-millisecond delay

Augmented and virtual reality both rely on speed and low latency as they demand immediate interactivity

Connectivity predictions

Machina Research forecasts IoT will account for one-quarter of the global 41 million 5G connections in 2023. Approximately 3/4 of these will be in the auto industry via embedded vehicle connections.

Yet a wide range of applications will benefit from 5G’s ultra-fast networks and real-time responsiveness, such as:

Massive Machine Type Communications (mMTC) such as solar-powered streetlights or other innovations to help citywide infrastructure

Device-to-device public safety communications that don’t need active cellular

coverage Real-time operations employing robotics to link surgeons with remote sites

The report also predicts 5G deployment will remain highly concentrated in Japan, Korea, Europe, China and North America.

The advent of 5G, then, will also help operators extend their opportunities in new markets.

2.2.1.1 5G and MNOsMachine-to-machine and IoT innovation also demand shifts in overall architecture, such as a move to cognitive networks. Telco’s will need on-demand, agile and programmable infrastructure, predicted Hossein Moiin, EVP and CTO, Nokia Networks.

5G will also take systems beyond radio to be cloud-optimized and able to use big data analytics and artificial intelligence. At the same time, this evolution will require an eagle eye on security, looking at the entire chain to be sure all elements are secured.

An earlier Machina study predicted enterprises deploying multi-country solutions would switch off the earlier generations by 2021 (when full commercialization and global ratification of standards is expected).

Although not fully operational for four more years, test projects are already underway. Get ready. 5G is poised to make the Internet of Things much more efficient, it’s up to network providers to make sure connectivity is effective and secure.

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How do we keep these devices secure connected on internet?

With the billions of IoT devices connected to the open internet, how do we ensure these devices are secure?

Encryption: AES vs. TLS/SSL

Encryption solves a very complicated problem. When people think about encryption, many will turn to TLS/SSL, however, these protocols don’t cut it for encryption and processing. The reason these protocols aren’t optimal is they are point-to-point solutions and not end-to-end solutions. When data has to go through many different points on the chain, you are going to have to account for different security protocols and devices. This requires a security solution like AES since it provides end-to-end security, and encrypts the message all the way through. Only devices with the encryption keys can decrypt the encrypted data as it’s sent and received.

Figure 26 AES cryptography

AES also allows you to wrap the message body with AES and leave all the actionable data in TLS. Actionable data, for instance, would be temperature information that you are trying to read. In addition, we need to prevent all inbound ports from being open at all costs since this can leave your IoT devices open to vulnerabilities and DDOS attacks. Devices should only make outbound connections, so that way the door is closed to accessing applications and services behind those open ports. The connection outward can be left open so the device can listen in with a secure tunnel back from the network.

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Figure 27 Encryption Process in IOT

Publish-Subscribe Paradigm

A great solution too many of these problems is the publish-subscribe paradigm. Sending info through MQTT, Web sockets, or Streaming HTTP allows connections to be secure, however, on a large scale, many of these protocols can have issues.

That’s where Pub Nub comes in since we keep a globally secure network running and support secure message delivery among devices. Through the publish-subscribe model, the publisher is given a write token and the subscriber a read token. Each token can be revoked at any time and tokens can also have an expiry. In addition, tokens can be set to work with only certain DataStream (in this case channel names), that way you can control what is going in and out of your network.

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2.3 IPV6 for IOTIPv6 is good for IoT and IoT is good for IPv6. There are several arguments and features that demonstrate that IPv6 is actually a key communication enabler for the future Internet of Things:· Adoption is just a matter of time. The Internet Protocol is a must and a requirement for any Internet connectivity.

Internet Protocol version 6 (IPv6) is the most recent version of the Internet Protocol (IP), the communications protocol that provides an identification and location system for computers on networks and routes traffic across the Internet. IPv6 was developed by the Internet Engineering Task Force (IETF) to deal with the long-anticipated problem of IPv4 address exhaustion. IPv6 is intended to replace IPv4. In December 1998, IPv6 became a Draft Standard for the IETF, who subsequently ratified it as an Internet Standard on 14 July 2017.

Devices on the Internet are assigned a unique IP address for identification and location definition. With the rapid growth of the Internet after commercialization in the 1990s, it became evident that far more addresses would be needed to connect devices than the IPv4 address space had available. By 1998, the Internet Engineering Task Force (IETF) had formalized the successor protocol. IPv6 uses a 128-bit address, theoretically allowing 2128, or approximately 3.4×1038 addresses. The actual number is slightly smaller, as multiple ranges are reserved for special use or completely excluded from use. The total number of possible IPv6 addresses is more than 7.9×1028 times as many as IPv4, which uses 32-bit addresses and provides approximately 4.3 billion addresses. The two protocols are not designed to be interoperable, complicating the transition to IPv6. However, several IPv6 transition mechanisms have been devised to permit communication between IPv4 and IPv6 hosts.

IPv6 provides other technical benefits in addition to a larger addressing space. In particular, it permits hierarchical address allocation methods that facilitate route aggregation across the Internet, and thus limit the expansion of routing tables. The use of multicast addressing is expanded and simplified, and provides additional optimization for the delivery of services. Device mobility, security, and configuration aspects have been considered in the design of the protocol.

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IPv6 adoption

Figure 28 Adoption of IPV6

2.3.1 IPv6 advantages for IoTIPv6 is good for IoT and IoT is good for IPv6. There are several arguments and features that demonstrate that IPv6 is actually a key communication enabler for the future Internet of Things:

Adoption is just a matter of time

The Internet Protocol is a must and a requirement for any Internet connectivity. It is the addressing scheme for any data transfer on the web. The limited address capacity of its predecessor, IPv4, has made the transition to IPv6 unavoidable. Google’s figures are revealing an IPv6 adoption rate following an exponential curve, doubling every 6 months.

Scalability

IPv6 offers a highly scalable address scheme. The present scheme of Internet Governance provides at most 2 x 1019 unique, globally routable, addresses. This is many orders of magnitude more than the 2 x 109 that is possible with IPv4 and the 1013 that is the largest estimate of IoT devices that will be used this century. It is quite sufficient to address the needs of any present and future communicating device still allowing it to have many addresses.

Solving the NAT barrier

Due to the limits of the IPv4 address space, the current Internet had to adopt a stopgap solution to face its unplanned expansion: the Network Address Translation (NAT). It enables several users and devices to share the same public IP address. This solution is working but with two main trades-off:

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The NAT users are borrowing and sharing IP addresses with others. While this technique allows single stakeholders to mount large applications, it becomes completely unmanageable if the same end-points are to be used by many different stakeholders; this would occur in an IoT deployment where the same sensors are to be used by multiple, independent, stakeholders. Secondly the mechanism cannot be used to access specific end-points from the Internet.

Multi-Stakeholder Support

IPv6 provides for end devices to have multiple addresses and an even more distributed routing mechanism than the IPv4 Internet. This allows different stakeholders to assign IoT end-device addresses that are consistent with their own application and network practices. Thus multiple stakeholders can deploy their own applications, sharing a common sensor/actuation infrastructure, without impacting the technical operation or governance of the Internet.

2.3.2 IPv6 FeaturesMany features have been built into the basic IPv6 specifications that are very useful both for the operation and the deployment of IoT. Besides the features already mentioned, these include multicast, any cast, mobility support, and auto-configuration and address scope. Over the last decade, many new higher level protocols have been developed that are both useful for IoT and are well-suited to devices with constrained resources. Examples are 6LowPAN (wireless nets), COAP (transport with web services) and DTLS (secured datagrams). Indeed there is a whole REST environment targeted at constrained devices.

Tiny operating systems and network stacks

IPv6 application to the Internet of Things has been researched for many years. The research community has developed several operating systems like TinyOS and Contiki that are relatively small and support the above protocol suites and environments. While the main IPv6 is very rich in possible features, these reduced environments have often restricted carefully the features available in order to meet IoT needs while reducing the size of the underlying system and leaving more space for applications. For example a basic Contiki system takes less than 20KByte, and even one supporting a full IPv6 stack and the other high-level protocols including DTLS can probably fit into 70 Kbyte

Increased hardware support

The operating system and network stack (with security) could be made much more compact by providing more hardware support in the chipset (or a co-processor). However such initiatives would detract from the efficient porting of the system to other chipsets. It would be desirable to make such upgrades for large deployments in commercial environments

Mapping of physical systems onto IPv6 address and Privacy extension

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We have shown it is possible to map many features of the physical IoT devices onto IPv6 addresses. This can ease large-scale deployments – though at the cost of revealing to anyone interested architectural features of the IoT devices because of the transparency of the Domain Name Service entries.

In contrast, IPv6 provides for privacy by automatically randomizing the suffix of the IPv6 address to hide the MAC address or any serial number used as identifier when connecting to the Internet. This feature is made available on all operating systems automatically.

Of course these two techniques have contradictory aims and effects; their relevance are determined by the needs of the IoT application.

Use of Identifiers and improved functionality

We have shown that by the use of Identifiers in conjunction with IPv6, one can take advantages of IPv6 features without their drawbacks. For example, with systems like Handle the structure can mirror the topology of a deployment, while the security features of the identifier system precludes unauthorized access to this information. At the same time IPv6 addresses can be attributes of the Handle Identifiers, but use the privacy enhancements at the same time.

Enabling the extension of the Internet to the web of things

Thanks to its large address space, IPv6 enables the extension of the Internet to any device and service. Experiments have demonstrated the successful use of IPv6 addresses to large-scale deployments of sensors in smart buildings, smart cities and even with cattle. Moreover, the CoAP protocol enables the constrained devices to behave as web services easily accessible and fully compliant with REST architecture.

Mobility

IPv6 provides strong features and solutions to support mobility of end-nodes, as well as mobility of the routing nodes of the network. The project has also achieved some interesting results on including Mobile IP in the Contiki stack.

Address auto-configuration

IPv6 provides an address self-configuration mechanism (Stateless mechanism). The nodes can define their addresses in very autonomous manner. This enables drastic reduction of IoT configuration effort and deployment cost. With an Identifier-based system like Handle, this technique can be combined with automated procedures to derive authentication tokens from the device, and have access control features added.

Fully Internet compliant Gateways

IPv6 Gateways can be fully Internet compliant. In other words, it is possible to build a proprietary network of smart things or to interconnect one’s own smart things with the rest of the World via a gateway that is fully compliant with IP requirements towards the Internet.

Standardization

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Some of the IoT6 developments like GLowBALIP and the Identifier system would benefit hugely if their attributes were standardized in this context much more rigidly for IoT. EC initiatives should support directly such standardization – possibly in a Support Action.

Dissemination

Much detailed dissemination has been achieved in IoT6. However applicability to new applications by new entities would require even more dissemination. Again this activity could be included in a further Support Action.

2.4 SummaryThe Internet of Things definition: Sensors, actuators or silicon embedded in physical objects are linked through wired and wireless networks.There are a number of similar concepts e.g. Machine to machine communication, artificial intelligence but Internet of Things is by far the most popular term to describe this phenomenon.The term Internet of Things was invented in 1999, initially to promote RFID technology The popularity of the term IoT did not accelerate until 2010/2011 and reached massmarket in early 2014.M2M or the Industrial internet are not opposing concepts to the Internet of Things. Rather, they are sub-segments.

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BibliographyBatalla, C. X. (June 2016). Internet of Things (IoT) in 5G Mobile Technologies. Zurich: Springer, Cham.

Dirk Slama, F. P. ( 2015). Enterprise IoT.

JOHNSON, B. (June, 2014). How the Internet of Things Works.

Kranz, M. (2 November 2016). Building the Internet of Things: Implement New Business Models, Disrupt Competitors, Transform Your Industry.

Waher, P. (January 27, 2015). Learning Internet of Things.

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