INSTITUTE OF PHYSICS PUBLISHING MEASUREMENT SCIENCE AND TECHNOLOGY

Meas. Sci. Technol. 16 (2005) R37–R46 doi:10.1088/0957-0233/16/4/R02

REVIEW ARTICLE

Sensor communication technologytowards ambient intelligenceJ Delsing and P Lindgren

Luleå University of Technology, EISLAB, 971 87 Luleå, Sweden

E-mail: jerker.delsing@ltu.se

Received 16 September 2003, in final form 6 December 2004Published 24 February 2005Online at stacks.iop.org/MST/16/R37

AbstractThis paper is a review of the fascinating development of sensors and thecommunication of sensor data. A brief historical introduction is given,followed by a discussion on architectures for sensor networks. Further,realistic specifications on sensor devices suitable for ambient intelligenceand ubiquitous computing are given. Based on these specifications, thestatus and current frontline development are discussed. In total, it is shownthat future technology for ambient intelligence based on sensor and actuatordevices using standardized Internet communication is within the range ofpossibilities within five years.

Keywords: sensors, communication of sensor data, industry, industrial,medical, sport

(Some figures in this article are in colour only in the electronic version)

1. Introduction

In the ancient past sensors were mainly read manually and datadistributed from mouth to mouth, as rumours. In early times ofindustrialization, sensor data started to be communicated usingpneumatic means. The large change of paradigm was withthe introduction of sensors with an electrical signal output.In the beginning, the electrical output was used to drive ananalogue output device. Later on, with the introduction ofdigital electronics and computers, we started to have sensordata in digital form. Computers also allowed mass storageand advanced manipulation of data.

This review will try to give a brief historical overview onthe development of electrical connected and networked sensorsand measuring devices. Then the concept of connected andnetworked sensors will be discussed. Finally, the perspectivesof Internet networked sensors as part of an ambient intelligentsurrounding will be considered in detail.

2. Connected sensors: an historical overview

Today devices such as sensors and measuring instrumentsare widely used to provide information to people and

to enable system operation. These devices have thecapability to communicate data by either a display or someelectronic data communication means. For a long time mostsensor communication schemes deployed were point-to-pointcommunication such as RS-232 [43, 114] or current loop. Itcan be noted that the RS-232 standard was published in theearly 1960s.

In the 1980s, instrumentation communication suchas the IEEE-488 standard [59] was developed. Latercame the Fieldbus standard targeting sensors, actuators andinstrumentation in industrial environments with tight real-time requirements [51, 57]. Other approaches were the ASistandard [3] targeting sensor communication and the IEEE1451 standard [27, 60] targeting the conversion of low levelcommunications such as RS-232 and current loops to a busstructure. However, all these approaches make data availableonly to a few predetermined users.

Based on these types of standards we now see theevolution of virtual measuring environments. By adoptingInternet technology, sensors and actuators thus becomeavailable to users worldwide.

These ideas of Internet connected devices and sensors,coined ubiquitous computing, were pioneered within the

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Figure 1. Large server architecture for connecting small devices tothe Internet.

computer and computer communication community by MarcWeiser [124–127].

In the measurement and sensor community, we findexamples of virtual measuring environments. Most often thesevirtual environments make use of a large server approach toprovide Internet access, as illustrated in figure 1. Internetaccess is often implemented by connecting sensors, actuatorsor other devices to a base unit (large server) on which a gatewayapplication is running that enables the device(s) to be visibleon the Internet. Some examples of large server architecturesin the literature are [73, 7, 103, 45]. Current applications ofsuch architectures are for example the measuring of medicalquantities [39, 89, 67].

The large server architecture suffers from the needto develop specific applications that are capable ofcommunicating with the devices connected, e.g. theMobihealth BANware [67]. Such a base unit is requiredto have the hardware communication capabilities that enablethe device communication. To make sensors mobile in thelarge server architecture, it is possible to use suitable radio oroptical communication. However, the access (radio or optical)infrastructure has to be built for the particular case. This willcertainly hinder a rapid development of large server-basedsensor network applications.

Massively distributed systems are a well-establishedresearch area in the realm of computer engineering andcomputer communication. Research results naturally applyto sensor networking and have now become a research fieldof their own. Examples of interesting papers overviewingrequirements and problems related to communication in sensornetworks are [95, 109, 44]. Here we also find examples ofsensor networking requiring fewer computational resources,e.g. the time triggered architecture investigated in the carindustry ([71, 119]). The, so far, most challenging and groundbreaking ideas on small device networking are the electronicdust project from Berkeley [68, 123, 40] which had its roots inthe LWIM project at UCLA [12, 4, 86]. However, in all thesecases the sensor networks utilize non-standard or proprietaryprotocols. Substantial work has been done on the networkingproblem related to massively distributed sensors, e.g. [47, 13].Power consumption issues are critical in the design of smallnetworked sensor devices. There are a large number of paperstargeting this field, e.g. [77, 105]. Other interesting work isthat on modular computers/networks using LEGO buildingblocks, see for example [80, 96].

Many commercial actors projected that most, if not all,sensors and measuring devices will be given the capabilityto act on their own as a node in the public Internet [2].

Figure 2. Minimal Internet server architecture for ad hocconnection of small devices like sensors connected to the Internet.

Today we see M2M (machine to machine) as an early startof this scenario. More developed scenarios for distributedsensors connected to the Internet are already given in positiondocuments and research at different places, examples are [85]and [84].

This is achieved by embedding the Internetcommunication capabilities into the device itself, figure 2.Recent achievements in the direction of embedded internetsystems (EIS) can be found in [33, 101, 34, 36]. White papersfrom market research institutes predict similar developments,particularly in the M2M business [2]. The EIS architectureprovides a new means to distribute data to users worldwide.In turn this may lead to new applications and ways of doingbusiness. Of course this will also give rise to new problems;technical, legal as well as society oriented.

In the following, we will discuss requirements for movingtoday’s state-of-the-art solutions to a level that will enableambient technology.

3. Technology needs for ambient intelligence

Based on a large number of different consumer and industrialrequirements, we propose the following major specificationfor future ambient intelligent devices in table 1:

Table 1. EIS specification.

Power lifetime >5 yearsMinimal size <100 mm3 (4.6 mm a side)Low production cost <2 USDWireless Internet network connectionFunctional and physical robust implementationSensor system, data and communication securityAutomatic system integration of distributed sensors

Based on today’s existing technology, we can identifywhere technology development is needed to meet the abovetargets.

3.1. Power lifetime

If power supply is based on today’s battery technology, wewill have a maximum of approximately 1.2 mWh available.This is based on L-ion battery technology that has a currentdensity of about 1 A h cm−3 at 1.5 V [110, 116] and a targetlifetime of 5 years in a volume of 100 mm3. We then get acontinuous average current draw of about 2.3 µA. This shouldbe compared with state-of-the-art current leakage. For actualdevices leakage current comes to the order of 10–100 nA.An example of this is the micro-controller MSP430 by Texas

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Table 2. Sensing energy cost estimations for different sensors.

Excitation Byte perSensor current (µA) data point

Temperature PT-100 0.1 2Ultrasound PZ-27 16 mm diameter 0.1 [65] 3

Instrument [117] which has a power down current of 100 nA.Thus, the leakage for a complete EIS device is in the order of1–10% of the available current.

For EIS systems of today we can make an energyestimation as follows. Note that the estimates are generaland that large differences due to application can be claimed.The energy cost of sensing is difficult to estimate since it isvery dependent on sensor type and the application. In general,these types of estimations can be divided into estimations ofenergy cost for different parts of the EIS device. We havechosen the following partitioning:

• sensor,• amplification,• A/D conversion,• digital signal processing,• communication cost per byte.

Some examples of estimated energy cost for different sensorsare found in table 2.

Analogue signal processing of the sensor signal is mostoften needed. Usually that will require an amplifier and levelor time detection. For frequencies up to 10 kHz currentconsumptions of 200–400 nA are possible [46, 52]. For higherfrequencies up to 100 MHz current consumptions of 100 µAto a few mA are possible [1, 22].

For amplitude and level detection an A/D converter isnecessary. Assuming 12 bits and around 25 kHz sampling,100–200 µA is required [108, 50]. If we increase the signalfrequency to a number of MHz requiring sampling frequenciesof 50–100 MHz, the power consumption goes up to 15–50 mA[20, 14].

Time measurement is cheaper and for moderatefrequencies (<100 kHz), most often found in sensing, suchtime measurement even with very high single shot resolutionrequires about 1 µA or less [24–26, 79].

Based on today’s most advanced commercial digital microcontrollers, the power usage is in the ball park of 250 µA/

MIPS [117]. Based on currently known implementations,it is possible to compute a data point from a measurementand encapsulate within the ‘TCP/IP’ protocol and appropriatetransport layer protocol for 5 µW/byte [81].

Based on today’s most efficient radio technology, weestimate the energy cost of one byte transmission to the orderof 10 nWs/byte/m [106, 107, 9]. The other side of the coin isthe communication overhead in the form of protocol headersneeded to fulfil the communication. In the case of TCP/IPprotocol, the transmission of one data point of 2 bytes requiresanother 48 bytes of communication protocol header (IPv4).Thus, in total 50 bytes will be sent per data point. Furtheroverheads may be introduced by other communication layers.

The total energy cost will be shown by an example. Herewe have estimated the total energy cost for a standard caseof temperature sensing using an EIS device applying state-of-the-art technology. For the temperature sensing, we use

Table 3. Energy budget for a temperature sensing EIS system permeasured and transmitted data point.

Current consumptionTotal energy per data point (µA)

Sensing 10Analogue signal processing 101Computing 10Communication 625

Total 746

a PT100 sensor and a standard measurements solution. Thesensor has a resistance of 100 �. We use a drive current of10 µA that gives a 1 mV output signal with a variationof 10 µV ◦C−1. Thus, to obtain a temperature resolutionof 0.1 ◦C we have to measure the signal with 10 bits resolution.To enable this an amplification of 100 times is needed. State-of-the-art amplifier technology will consume less than 1 µAand a 10 bit A/D converter sampling at maximum 100 Hzwill consume another 100 µA. The current cost of the digitalelectronics needed for processing the measured data is of theorder of 10 µA. In total this gives a current consumption of120 µA for a temperature measurement.

Assuming the sensor is in thermal equilibrium the timeto make one measurement is about 10 µs. Using a 1.5 Vvoltage gives us a power consumption of about 180 µW toproduce a 2 byte data point. The communication of this datapoint encapsulated in a TCP/IP (UDP mode) package overa 1 m radio link will send 50 bytes at an energy cost of10 nW s/byte. Assuming a communication bandwidth of500 kbit s−1 gives us a current consumption of about 100 µA.Assuming the bandwidth is the time limiting factor indicatesa sampling rate of about 1.2 kHz. These sampling rates areoften required, for example in the process industry.

In summary, this gives us the energy budget for themeasurement and communication of one temperature datapoint over a distance of 1 m as shown in table 3.

The data of some 746 µA from table 3 are clearly veryfar from meeting the power usage target given in table 1.It is obvious that the communication uses the vast majorityof power. Thus, the only short way to reduce powerusage substantially is by reducing the communication andminimizing the communication overhead and introducing non-continuous, i.e. low sample rate, measurement combined withefficient electronics power down control. Minimization ofpower usage for data collection on a mobile sensor networknode is described in detail in [82].

Techniques reducing the protocol overhead are alreadyavailable from voice over IP developments [31, 30]. Thiswill reduce the number of bytes required in protocol overheadfrom 48 bytes to 1 byte. Thus for a payload of 2 bytea reduction from 50 to 3 bytes is obtained, giving a totaldata reduction with a factor of more than 16. The datareduction potentially reduces the power dissipation of datatransmission accordingly, however radio mode managementalso plays an important role as pointed out in [106]. Toreduce power dissipation, it is crucial to shut down radiooperation whenever neither transmitting nor receiving data[128]. One commercially available approach in this directionis the Bluetooth ‘Sniff mode’ protocol, which controls radio

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receive, transmit and power down modes using synchronizedtime-slot communication [9].

Another way of reducing overhead and thus powerusage is to communicate data batch-wise making the headerinformation even less influential. Such a mode of operationwill prevent most usage as a real-time sensing device.

The option of reducing the sampling frequency will giveus the following situation. The sampling frequency period ofta of

ta = tt It − tpIp

Ia

(1)

where tt is the total device lifetime, It is the total allowedcontinuous current consumption allowing for the requestedpower lifetime, tp is the power down time, Ip is the powerdown current consumption and Ia is the active device currentconsumption. Further assuming that

tt = ta + tp (2)

the active time ta , equation (1), thus becomes

ta = tt ∗ (It − Ip)

Ia − Ip

. (3)

Assuming leakage currents of 0.1 µA and active currentconsumption of 750 µA and current supply accordingto specification 1, gives an active time ta available formeasurement of about 0.3% of the total time tt . For manymeasurements it will be possible to obtain a data point inmilliseconds. Provided that power up and power down timescan be of the same order of time, it indicates possible samplerates of a few Hertz for a device that can live for 5 years onon-board power supply. Such sampling rates may well beappropriate in many applications.

To get EIS devices that can also meet the real-timerequirements of kHz sampling rate and still meet the otherbasic requirements, see table 1, major achievements have tobe made in Si technology, digital and analogue electronicsdesign, wireless communication, low power sensors, lowpower software design and system architecture for low power.From the literature [64], it should be possible to halve thedigital signal processing power costs every 18 months butthe improvements on the analogue side are much slower. Themajor bottleneck of radio communication will improve butalso at a slower pace. In summary, we expect it to be possibleto meet all major EIS specifications, table 1, within some yearsfrom now.

3.2. Functionally and physically robust implementation

The implementation problem can be divided into three majorareas:

• physically robust implementation, environmentallyendurable,

• functionally robust, e.g. software robust,• system and network response time.

3.2.1. Physically robust. Building a sensor device thatis physically robust so it can withstand harsh industrialenvironments is a very complicated task. At a global levelit can be divided into three major problem areas:

• robust electronics,• robust device encapsulation,• robust sensor elements.

Figure 3. Environmentally and EM robust encapsulation of EISdevice with ports for sensor interface and wireless datacommunication.

The building of robust electronics is today a fairly welldeveloped area. A good overview of the field is found in forexample [49].

For robust encapsulation of the complete system we haveto consider both environmentally robust and EM robust incombination. This needs to be paired with providing thecontact between the sensor element and real world on whicha measurement will be taken. In addition, the communicationchannel needed for networking will need to be made availablethrough the encapsulation, cf figure 3.

A number of well-established technologies are around tomeet each of these requirements. An interesting developmentwhich allows combining the above requirements is based onlaminated material and is called ProofCap [74].

Robust sensor elements can be the most challenging partsince here the active part most often has to be in direct contactwith the environment. This most often drives cost and make anumber of technologies just promising.

3.2.2. Functionally robust. For robust functionality ofembedded devices we have both hardware- and software-oriented problems. The hardware robustness very much drawson physical robustness as discussed above. The softwarerobustness has an entirely different background. Here robustsoftware design methodology is the key element. Muchwork has been done in this area over the years and will notbe commented on here at any length. Recently Java-basedsoftware design has gained a large market share. Java wasinitially designed with programmers’ skills in mind, underthe assumption that programmers are not smart and educatedenough [41, 37]. Put simply, the language needs to be simpleenough that programmers get it right. However, for embeddedsystems Java is cumbersome for two major reasons:

• Java consumes too much memory (Java micro editionvirtual machine from SUN is about 120 kBytes) and isslow on small devices [115].

• Java has clear limitations for real-time embeddedapplications; garbage collection is one issue [48].

Another fairly new approach initially supported byDARPA is Timber (TIMe as a basis for embedded real-timesystems), a novel programming language under development.It offers an interesting alternative to traditional RTOS-basedembedded software design methodologies. The semantics of

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Timber allows the reactive, real-time system behaviour tobe unambiguously captured. Ongoing work is investigatinghow specifications in Timber can be translated into executableobject code or hardware descriptions meeting the design goals[8, 70, 99, 66, 18, 56]. Another feature of Timber is thatpower consumption can be minimized as the system operatesin a purely reactive manner [100]. In a system where allpending reactions are carried out, all parts of the system exceptthose detecting incoming actions, may be completely powereddown.

3.2.3. Response time. The response time of a networkedsensor depends on a few major issues:

• physical sensor response time,• sensor system responders time,• network latency.

The physical sensor response time is governed by thesensor physics involved, see major sensor books like [92].The sensor system response time includes the time for sensorsystem electronics to make the appropriate signal processingand packaging of the data into the communication protocol.This time is most often negligible compared to the sensorphysics time involved. The total time of sensor physics andsensor system is predictable and fairly constant with mostvariables.

Regarding the network latency, this is dependent on thenetwork structure and protocols chosen as well as on thenetwork load. We do find systems with constant or maximumlatency such as Fieldbus and CAN bus [57, 63]. And forautomotive application, an interesting approach is the timetriggered architecture [119]. A number of new and non-standard protocols specifically targeting sensor networks canalso be found in the literature. Here both power aspects andbandwidth aspects are of particular interest, see for example[118, 112, 21, 23, 54, 76].

When using Internet protocols, it is well-known that thelatency is non-constant and non-predictable. The only real-time support available is the RTP protocol [111]. RTP providessupport for time stamping of the package sent. At the receivingend, the data can be time sorted and a quasi-constant latencycan be obtained. The most well-known application for RTP isvoice over IP. A general problem here is the synchronizationof device clocks over the network. Mechanisms such as NTP[88] are provided but do not provide the millisecond accuracythat most often is requested in control applications. However,GPS technology can be used to give accurate absolute time.Thus, if a GPS-sensor is part of a confined sensor network,more accurate real-time performance will be possible.

3.3. Miniaturization of distributed sensors

The general problem is how small a EIS device can we makeand still get the required functionality as stated in table 1.

The most spectacular work is the electronics dust project atBerkeley [68, 69, 40], claiming a device size of about 2 mm3.This shows that today’s frontier technology will be able tobuild devices small enough to still allow for a large enoughpower supply within the specified 100 mm3.

To the authors knowledge, implementations targeting theEIS specifications are in the order of 4 cm3 with an on-board

power lifetime of a few weeks of operation. Here we have adesign which is 40 times bigger than specification and with apower lifetime of about 2% of the specification.

3.3.1. Cost of devices. In general, the cost of electronicdevices has a direct correlation to size and production volume[64]. Reducing the size and cost of the physical sensor willbe the most challenging part of the device. For sensors basedon MEMS technology and upcoming nanotechnology, the costwill probably follow the same trends as the electronics. Usingother types of sensor technology will introduce production andmaterial technology challenges to meet the cost targets.

3.4. Sensor reliability, communication and data security

The security issue for an EIS device comes at three levels:

• sensor reliability and susceptibility to misuse, handlingerrors, installation errors, installation effects, fraud, etc,

• sensor communication channel reliability and security,• sensor data security and encryption.

The reliability of the communication channel and itssecurity is extensively covered in the telecom researchcommunity and is not further commented on here.

The two technologies where we find establishedinfrastructure are Bluetooth and IEEE 802.11 [9, 58]. None ofthe technologies will enable us to meet the sensor networknode power specifications put forward in table 1. Forpower consumption, Bluetooth is clearly better than IEEE802.11. Some technology comparison with application tosensor networks can be found in [36].

Upcoming technology is the Zigbee standard put forwardby Philips [61]. Here power consumption is clearly better thanfor both Bluetooth and 802.11. Further, the bandwidth is moreappropriate for sensor communication.

3.4.1. Sensor reliability and security. The capability of asensor to produce reliable data results will be dependent onnot only the sensor technology itself but also often more onthe way it is used and handled. Regarding sensor technologywe direct the reader to standard work such as [92, 38, 19].

The use and handling cases for reliability and security ofthe sensors can be divided into the following categories:

• misuse,• handling and installation errors,• installation effects.

These types of errors are not very well documented inthe literature and are often difficult to correct since people aremuch involved. Some interesting work here has been doneby some gas distribution companies [87]. In general handlingerrors are more common than we like and expect [120, 97].

Fraud problems are found when the measured data areused for invoicing and for fiscal reasons. Examples of workcovering these types of errors are [93, 98].

Sensor installation effects are a common problem in theindustry. A lot of work has been published, see for example[83, 55].

Some work on the diagnosis and automatic detection ofthese types of errors has been done. Interesting work in

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the flow metering area are meter self-diagnostics of staticinstallation effects [16], dynamic installation effects [5, 121]and temperature effects [17].

The introduction of the EIS concept enables interestingpossibilities in combination with automated technologies thatcan self-diagnose misuse, handling errors and installationeffects. Interesting work on automatic sensor self-diagnosiscan be found in [15, 6]. Thus, a sensor can diagnose a problemand communicate it to the appropriate receiver.

When moving to the paradigm of sensor networks andambient intelligence, sensors can start using their neighboursto either enhance the sensor data quality or for sensordiagnosis. We now see emerging ideas in sensor fusion basedself-diagnostics in the literature [32].

3.4.2. Sensor data security and encryption. The level ofdata security needed is very application-dependent rangingfrom no data security to bank level security. The no datasecurity is for obvious reasons easy. Bank security and dataencryption are also possible using already established securitytechnology. The major problem although is to implement therequired security and encryption using the minimal resourcesavailable.

3.5. Automatic system integration of distributed sensors

The problem of automatic system integration of distributedsensors can be divided into two fields:

• a low level communication problem—ad hoc networking,• automatic integration of application and distributed

sensors.

They will be discussed below.

3.5.1. Ad hoc networking. In the paradigm taken that everydevice will have an IP-number, the general problem of EISdevice networking concerns the lowest layers in the OSI-model[62]. The major problems are:

• how to establish a network connection,• how to distribute IP numbers,• how to ensure addressability in IPv4.

Much work on ad hoc sensor networks has beendone especially in the network and communication researchcommunity. A lot of this is targeting new radio protocols[105] and network protocols [44, 13] aiming at communicationefficiency and low power communication. Most of thesetechnologies are not possible to use practically due to thelack of a widespread available infrastructure.

One of the available infrastructures for ad hoc networkingis Bluetooth technology. The Bluetooth standard providescapabilities for ad hoc networking using a pico-net approach.To enable immediate usage of sensor networks, architecturesbased on well-established communication technologies andstandard protocols are needed. Such interesting architecturesprovide a full small server solution, based on Bluetoothand GSM/GPRS technology [36]. Multihop solutions usingBluetooth are found in [75].

The distribution of IP-numbers is a major effort in theInternet community. For the case of a small server approach

based on EIS devices, the EIS server needs an IP-number thatcan be recognized by DNS servers and thus make it an availableresource on the Internet. IPv6 [94] will solve the problem.The drawback with IPv6 is the introduction of more protocoloverhead which will consume power in the communicationchannel at the EIS device. Another more temporary drawbackis lack of infrastructure support for IPv6 devices in the currentInternet.

Using IP-numbers based on IPv4, we need a solutionwhere DNS addressability is provided. One such solution isbased on fixed IPv4 IP number given by an Internet provider.Such services are now offered by many IP-providers. Thelimitation is the number of addresses available. Anothersolution is based on IPv4 [104] and DNS [90, 91] and NAT[42, 113] distribution of C-class IP-numbers. Interestingdevelopments using NAT distribution of C-class IP-numbersin a Bluetooth sensor network have been made [35]. Thisarchitecture has also been demonstrated in real applicationssuch as Vasaloppet, the world’s largest and longest crosscountry ski-race [53].

3.5.2. Automatic integration of application and distributedsensors. The general problem of integrating a distributedsensing or actuating device into a governing application canbe stated as:

• Ad hoc integration of a networked EIS device into agoverning application without re-configuration of eitherEIS device or governing application.

The ideal solution is a technology that will automaticallydetect an EIS device and its capabilities and be able toautomatically integrate the sensor or actuator capabilities intothe governing application.

It is possible to divide the approaches proposed so far tothis problem into the following categories:

• data and commanding format standardization,• network oriented,• agent oriented.

Standardization of data and commanding formats havebeen around for many years. One of the first was IEEE-488 [59]. Other more modern attempts in this directionare CORBA [28] and DCOM [29]. The old IEEE-488is simple and fairly easy to implement in a resource andpower limited device. Both CORBA and DCOM are fairlyresource demanding since they have been developed underthe assumption that standard computers, i.e. at least 32bit processing power with many megabyte of memory, areavailable. This will not be the situation with most networkedsensors in the future.

The network approach is taken primarily by the Java–Jinicommunity [41, 122]. This is the most promising availabletechnology for automatic system integration of distributedsensors. However, we have a very major drawback in theneed for a Java virtual machine and a large number of classesto be able to run Jini implementations. These requirementscall for too many resources compared to what is expected inambient intelligent devices. The Java–Jini solution to thisso far is the Java 2 Micro Edition [115]. Although thisis a minimal implementation of Java it is far too resource

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demanding to enable the full usage of Java–Jini concepts insmall EIS devices capable of many years of operation withon-board power supply.

Agent technology is based on agents surfing the netand discovering servers and the services and capabilitiesoffered by the server [11, 78, 10]. The technology is mostlyapplied to large scale systems for business applications. Sincethis approach places very little demand on the networkedsensor computing and memory resources, it holds interestingpromises for development of networked sensors [72, 102].

4. Conclusion

This review of the technology suitable for ambient intelligenceclearly shows that feasible technology should becomeavailable in the near future. We project that very small, lessthan 0.1 cm3, self-sustained devices with a power lifetimeof 5 years, based on standardized Internet communicationtechnology, will be possible within a few years. More andmore intelligence will be distributed to the sensors in thefuture, creating not only local intelligence but also localcluster intelligence. Although not capable of real-time datasampling and communication, as required in the processindustry, devices will be seen that sample data at a few 10 Hzwhich is feasible for a very large number of applications.

The integration of ambient sensor data into applicationsis not straightforward. Standards and in particular standardsfeasible for ambient intelligence from the computing powerand energy consumption point of view are lacking.

When such standards become widely used, we can expectboth improved sensor accuracy and reliability by using self-diagnosis technology. Further deployment of sensor fusionideas will enhance data quality and usability.

The major bottlenecks in the development towards amassive deployment of ambient intelligence is probably:

• providing an infrastructure to enable sensor networkaccess,

• the energy consumption of the radio communication,• lack of standardized ways for integration of ambient

intelligent sensors into applications.

It is felt that society is not yet ready for a massivecommercial roll-out and use of ambient intelligence. Theuse of the technology will most likely face resistance due tocultural and generation attitudes. Further legal, integrity andother reasons related to social and society issues certainly willinfluence the time when ambient intelligence will be widelydeployed.

Acknowledgments

The authors are very grateful to all colleagues of EISLAB formany interesting discussions and their valuable help in writingthis review.

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