Eindrapport Sensor and Actuator Networks in the Netherlands...Iris Theunisse 0.2 concept 03-12-2015 Chapter 1 and 2 v1 added Iris Theunisse 0.3 concept 15-12-2015 Review Chapter 1-2,

Faculteit der Economische

Wetenschappen en Bedrijfskunde

BIG DATA

Sensor and Actuator Networks in the Netherlands

Document Information

Title Sensor and Actuator Networks in the Netherlands

Project code 2790683

Project owner Rijkswaterstaat

CIV

Postbus 5023

2600 GA Delft

Project executor Vrije Universiteit Amsterdam

FEWEB-RE / SPINlab

De Boelelaan 1105

1081HV Amsterdam

Document file name Big Data Externe (sensor) databronnen.pdf

Appendix A - Overview Sensor networks.pdf

All rights reserved. No part of this publication may be reproduced in any form, by print or photo print, microfilm or any other means, without written permission by the author

page 1/41

STATUS Definitief

VERSION 1.0

AUTHORS drs. E van der Zee

ir. Iris Theunisse

prof. dr. H.J. Scholten

dr. Jasper Dekkers

dr. ir. John Steenbruggen

DATE 19.12.2015

VERSION

HISTORY

VERSION STATUS DATE DESCRIPTION AUTHOR(S)

0.1 concept 13-11-2015 Definition table of contents Erik van der Zee Iris Theunisse

0.2

concept 03-12-2015 Chapter 1 and 2 v1 added Iris Theunisse

0.3 concept 15-12-2015 Review Chapter 1-2, List of References added, Chapter 4 v1 added

Jasper Dekkers

0.4 concept 17-12-2015 Chapter 3 v1 added

Iris Theunisse

0.5 concept 21-12-2015 Chapter 5 v1 added

Iris Theunisse

1.0 final 22-12-2015 Chapter 4 v1 added, Chapter 6 v1 added, Appendix A added, Report reviewed

Erik van der Zee Jasper Dekkers


page 2/41

Table of Contents

Chapter 1 Introduction .......................................................................................................................... 3

1.1 Context .......................................................................................................................................... 3

1.2 Problem statement ........................................................................................................................ 3

1.3 Goals and Scope ........................................................................................................................... 4

1.4 Research Questions ...................................................................................................................... 4

1.5 Report set-up ................................................................................................................................. 4

Chapter 2 Theoretical Framework ....................................................................................................... 5

2.1 Introduction .................................................................................................................................... 5

2.2 Big Data ......................................................................................................................................... 5

2.4 Smart Cities ................................................................................................................................... 7

2.4.1 OODA loop .............................................................................................................................. 9

2.4.2 Central versus decentral ......................................................................................................... 9

Chapter 3 Taxonomy of sensors and actuators .............................................................................. 10

3.1 Introduction .................................................................................................................................. 10

3.2 Taxonomic structure .................................................................................................................... 10

3.3 Sensors ....................................................................................................................................... 11

3.3.1 Human Sensors .................................................................................................................... 12

3.3.2 Physical Sensors .................................................................................................................. 13

3.4 Actuators ..................................................................................................................................... 14

3.4.1 Human Actuators .................................................................................................................. 14

3.4.2 Physical Actuators ................................................................................................................ 14

Chapter 4 Sensor and Actuator Networks in the Netherlands ....................................................... 16

4.1 Introduction .................................................................................................................................. 16

4.2 International networks ................................................................................................................. 16

4.3 National networks ........................................................................................................................ 18

4.4 Regional networks ....................................................................................................................... 20

4.5 Local networks and ad-hoc initiatives .......................................................................................... 21

4.5.1 Ad-hoc initiatives ................................................................................................................... 24

4.6 Publish – Find – Bind for sensor data APIs (Sensor Catalogs) .................................................. 25

4.7 Sensor Data Broker Platforms ..................................................................................................... 25

Chapter 5 Standardization initiatives ................................................................................................ 27

5.1 Introduction .................................................................................................................................. 27

5.2 International ................................................................................................................................. 27

5.2.1 Open Geospatial Consortium ............................................................................................... 27

5.2.2 ISO Standards ...................................................................................................................... 29

5.2.3 W3C Standards..................................................................................................................... 29

5.3 National ....................................................................................................................................... 32

Chapter 6 Conclusions and Recommendations .............................................................................. 33

6.1 I-Strategy ..................................................................................................................................... 33 6.2 Conclusions ................................................................................................................................. 34

6.2.1 Overall conclusions ............................................................................................................... 34

6.2.2 Conclusions related to the research questions ..................................................................... 34

6.3 Recommendations ....................................................................................................................... 35

References ........................................................................................................................................... 36

Appendix A - Overview of sensor networks ..................................................................................... 40


page 3/41

Chapter 1 Introduction

1.1 Context The world is facing challenges in all three dimensions of sustainable development (economic, social, and environmental) (van der Zee & Scholten, 2014) due to increased complexity and urbanization of our society. Urban planning has become extremely important as it is predicted that the population will grow and the population living in urban areas is projected to rise (United Nations 2004; United Nations 2012). Therefore, changes in the way urban development is designed and managed are needed. The Dutch environment is experiencing increased stress due to this worldwide population growth and increased complexity. The stress for receiving and retaining a high level of prosperity and welfare demands for well-designed city and landscape development of (cities within) the Netherlands. Therefore, evident scientific knowledge of both the present situation and the impact of any intervention is needed and can be gained by analyzing the current situation and developing scenarios of possible futures. Spatial data can be used as the basis for analysis and predictions. The amount of spatial data will increase exponentially due to the on-going development and decrease in costs of data collection technologies that enable us to easily collect data from different sources. Although, this is not a guarantee for improved urban development as large datasets, so-called “Big Data”, are useless and inaccessible without proper processing, management and presentation. Currently, the Dutch governmental organization Rijkswaterstaat (RWS), responsible for the control and development of infrastructures and sustainable environment, wants to increase their insight into the possibilities that Big Data offers to enhance and enrich processes and collaborations. RWS also wants to expand their knowledge of data-processing and -management techniques to transform Big Data into useful and accessible high quality ‘business intelligence’ models that can be used for ‘evidence-based’ and ‘data-driven’ decision-making. An important aspect of the improvement of urban development on which RWS wants to further develop vision and enlarge knowledge, is the use of sensor and actuator networks to make cities within the Netherlands more sustainable and efficient. The term “Smart City” is used to indicate cities that function in a sustainable and intelligent way, by integrating all infrastructures and services into a cohesive whole and using intelligent devices for monitoring and control, to ensure sustainability and efficiency (Giffinger, 2007). These intelligent devices are sensors and actuators, which come in an enormous variety of types.

1.2 Problem statement The changing patterns of our society and the political pressure of flexibility and decreased costs, demands for new approaches, knowledge, attitude and behavior for Rijkswaterstaat. In their business plan, aspects concerning these changes are prescribed. The aim of Rijkswaterstaat is to work as one organization closely together with other organizations and citizens to improve results and develop policy. Accessible data and information form the basis, therefore existing information facilities should be improved by making them more efficient, faster and less expensive. To provide the ability for evidence-based and data-driven decision-making by policy makers and politicians, a shift from ‘data-poor’ studies to ‘data-rich’ policy is needed. Although the amount of data is increasing, it is not necessarily leading to better information or new insights. A lack of knowledge and awareness of the full possibilities of sensor and actuator networks is holding back the expansion of the vision RWS has on the use of different data sources including sensor data and crowd-sourced data such as social media feeds to make cities more sustainable and efficient and improve collaboration with external (government-) organizations. To make the change from data to insight and to create successful information services, a focus, ambition and investment in data use is necessary.


page 4/41

1.3 Goals and Scope The general goal of this report is to make the first step in the enlargement of a vision on how to use sensor data sources by showing different possibilities. Giving an overview of existing sensor types to retrieve real data, the specifications of these sensors and current sensor and actuator networks on different levels that are in use nowadays, attempts to the creation of this vision. The sub-goals to achieve the general goal are as follows:

- Developing a theoretical framework to better understand the context and concepts; - Explaining types of sensors and actuators and their possibilities and potentials; - Providing an overview of existing sensor and actuator networks in the Netherlands to explain

the current state; - Giving useful recommendations.

The following items limit the scope:

- Only a selection of sensor and actuator network relevant for the enlargement of a vision will be discussed;

- Only a selection of sensor and actuator networks that are on national, regional and local scales within the Netherlands will be discussed.

1.4 Research Questions The use of sensors and actuators is promising in theory. However, in practice challenges remain for the effective use. This report will identify the possibilities to improve vision of using sensor data with respect to current available possibilities. Therefore, the main research question to be answered in this report is as follows:

How can sensor and actuator networks be used to increase the implementation of the Smart City concept in The Netherlands?

To answer the main research question, sub-questions that need to be answered in order to create a comprehensive overview of sensor and actuator network possibilities are as follows:

1. What is Big Data and the Internet of Things (IoT)? 2. How can sensor & actuator types be categorized? 3. Which sensor & actuator networks are currently available in the Netherlands? 4. Which standardization initiates of sensor networks are developed?

1.5 Report set-up The introduction of this report is described in this chapter by explaining the context, problem statement, goals and scope and formulated research questions. The second chapter of this report comprehends a detailed and elaborated theoretical framework in which concepts including ‘big data’, ‘the internet of things’ and ‘smart city’ are treated from a broad perspective to gain knowledge and get a better understanding of the context. Chapter 3 firstly includes a taxonomy of sensors and actuators including sensor types, specifications and applications and secondly a taxonomy of crowdsourcing possibilities including definition of new concepts such as people as sensors, collecting sensing and citizen science. In chapter 4 examples of existing sensor and actuator networks on national, regional and local scale are given. Applications and specifications of each of these networks are described. The role of Sensor Data Brokers is explained in the same chapter. Chapter 5 includes an overview of standardization initiates of sensor networks. Conclusions and recommendations are given in Chapter 6, the last chapter of this report.


page 5/41

Chapter 2 Theoretical Framework

2.1 Introduction This reports aims at providing insight in and creating vision on how to use sensor data sources and actuators. This chapter explores the definitions and context of sensor and actuator networks in the Netherlands to gain useful solid background information which is the first step in developing insight and vision and is needed to better understand the following chapters.

2.2 Big Data The amount of data in our world is growing exponentially (van der Zee & Scholten, 2014) and also the extent of individual datasets is increasing due to the on-going development of data acquisition technologies among which GPS, cameras, social media and sensors. The growth of data is not only caused by more data streams, but also by entirely new types of data. Countless digital sensors are placed worldwide in, for example, industrial equipment, to measure all kinds of data including location, movement, temperature, humidity, air quality and other phenomena in the world (Lohr, 2012). Over the last years, due to the exponential grow, the term “Big Data” has been introduced to identify large or complex datasets (Manyika, 2011). Big data became a booming topic of interest, not only in the scientific community but also in the enterprise world (Preuveneers, 2014) and brought on many discussions in, for example, the field of information technologies, governmental organizations and policy makers as it enables organizations to perform new types of analysis, such as analysis on human behavior. Analyzing large datasets and discovering relationships across structured datasets is a significant opportunity to boost innovation, production, and competition (Preuveneers, 2014). The impact of the so-called “data revolution” and innovative aspects is broad and in governmental context often related to the development of the “Open Data” policy (van Loenen & Verdonk, 2012), based on a policy decision made by the EU from economical potential perspective. The new possibilities coming along with rise of Big Data will become a key basis for advanced trends in technology that open doors for new approaches to understand the world (Lohr, 2012) and to gain new insights in phenomena. Nowadays this insight can be used to make data-driven decisions as future scenarios can be developed by analyzing this data. Often, the challenge of using Big Data for analysis and forecasting is no longer the data acquisition and implementation but rather the finding of smart solutions for using or reusing data and combining parts of datasets as ingredients for innovations and new services, i.e. data management and modelling. It is predicted that the Big Data trend will persists for years to come and therefore data management and modelling challenges will remain important, demanding innovating efforts from data-science and geo-science. The added value of Big Data can only be realized if the data can be managed and modelled correctly and when there is the ability for data-analysts and data-scientists to generate meaningful data from unstructured Big Data. An example of Big Data as added value is the creation of ‘Business Intelligence’, which is often referred to as the techniques, technologies, systems, practices, methodologies, and applications that analyze critical business data to help an enterprise better understand its business and market and make timely business decisions (Chen, Chiang & Storey, 2012). Different methods and techniques can be used to adjust data from Business Intelligence to complete accessible and meaningful information with which complex problems can be solved. The advantages of Big Data are therefore not linearly correlated to the amount of data but exponentially increase when the accessibility of this data reaches a certain level.


page 6/41

2.3 The Internet of Things

Figure 2.1 “Will the Internet of Things please shut up for an instant!” Source: Volkskrant, 7 Nov. 2015 Another interesting cause of the rise of Big Data is the increased volume and detail of information captured by enterprises and the rise of multimedia and social media (Manyika, 2011). This in combination with the increased use of the Internet and emerging technologies such as near-field communications, real-time localization, and embedded sensors, transform everyday objects into smart objects (Figure 2.2) capable of understanding and reacting to their environment.

Figure 2.2 Example of an everyday object turned into a smart object Such smart objects enable new computing applications and are at the base of the vision of a global infrastructure of networked physical objects known today as the Internet of Things (IoT) (Preuveneers, 2014). IoT therefore refers to the networked connection of everyday objects, which are often equipped with ubiquitous intelligence (Xia, 2012) and as network availability and speed are improving at a steady rate and computers become smaller, more energy efficient and lower priced internet connected devices with sensors are deployed on ever larger scales in our environment, leading to this IoT (van der Zee & Scholten, 2014). The vision of an Internet of Things built from smart objects raises several important research questions in terms of system architecture, design and development, and human involvement (Preuveneers, 2014). New technology developments continue to penetrate countries in all regions of the world, as more and more people and objects are getting connected to the Internet with estimates ranging from 16 to 50 billion Internet connected devices by 2020 (ITU, 2012). The hardest challenge for large-scale, context-aware data intensive applications and services is to make this valuable information of ever growing data streams originating from everyday devices transparent and available and to extract hidden but relevant and meaningful information and human behavioral patterns from the data (Preuveneers, 2014). Only then the data can be used at a much higher frequency to substantially improve the decision-making and prediction capabilities of the applications and services.


page 7/41

Figure 2.3 Overview of the Internet of Things, source: Beecham Research Data collected by the above-mentioned devices such as sensors or by human using applications running on devices consists of device state properties. Big Data has to be geo-referenced to be able to spatially analyze the data from internet connected devices for real-time spatial decision making (van der Zee & Scholten, 2014) and to discover the spatial context of smart objects capturing the data. Geography can be considered an important binding principle in the Internet of Things as all physical objects and the devices producing sensor data can have a position, dimension, and orientation in space and time, and spatial relationships exist between them (Huisman & de By, 2009). Once the spatial context is known, a smart object can efficiently interact with other smart objects in its vicinity (van der Zee & Scholten, 2014). By applying spatial relationships, functions, and models to the spatial characteristics of smart object and the sensor data, the flows and behavior of objects and people in the cities can be more efficiently monitored.

2.4 Smart Cities As explained in the previous sections, Big Data can be managed, modelled and analyzed in order to provide more efficient monitoring of objects and people in the urban environment. This aspect is of huge importance due to the state of flux and exhibit complex dynamicity of cities in which nowadays half of the World’s population live or work. For instance, in the European Union an enormous rise of the share of citizens living in cities took place, from 50 percent in 1950 to more than 75 percent in 2010. Predicted is that this will increase to 85 percent within the next 40 years (Caragliu, Del Bo & Nijkamp, 2011). Over the past years, not only the size of urban areas has substantially increased, but also urban density. The urbanization is due to the employment and to cultural and social life within the cities creating vivacity as an advantage of living in a city. At the same time, cities encapsulate disadvantages as a result of the high density such as decreased air quality, having the worst effects of social and environmental degradation depending on the urban design and influencing the urban environment and livability. The problems concerning these disadvantages have usually been solved by creativity, human capital, cooperation among stakeholders, and bright scientific ideas i.e. “smart” solutions. (Caragliu, Del Bo & Nijkamp, 2011).


page 8/41

Figure 2.4 Continuous loop of sensing, analysis and acting (source: Van der Zee & Scholten, 2014)

According to GSMA (2013), “a smart city is a city that makes extensive use of information and communication technologies, including mobile networks, to improve the quality of life of its citizens in a sustainable way. It combines and shares disparate datasets captured by intelligently-connected infrastructure, people and things, to generate new insight and provide ubiquitous services that enable citizens to access information about city services, more around easily, improve the efficiency of city operations and enhance security”. Van der Zee & Scholten (2014) added to that: “Smart cities can support changes by using information technology and (spatial) Big Data to monitor, steer and optimize processes in our environment in real-time”. All infrastructures and services of a city must be integrated into a cohesive whole by using intelligent devices for monitoring and control, to ensure sustainability and efficiency (Giffinger et al., 2007). According to Caragliu, Del Bo & Nijkamp (2011), the label “smart city” should point to clever solutions allowing modern cities to thrive to qualitative and quantitative improvements in productivity. The smart city concept is extremely important in a world where population numbers are constantly rising, significantly driving the consumption of resources causing resource shortages and climate change (Hancke, Silva & Hancke, 2012). It covers a wide range of needs among which energy and water management, public safety, education and transportation. Qualitative evidence on the correlations between the dimensions of smart cities and a measure of wealth is found by Caragliu, Del Bo & Nijkamp (2011).

Although smart city sounds as a promising concept, cities have to set up their networks first to become an intelligently managed space that maximizes the requirements of the users while minimizing resources (Crowley et al., 2012). Deploying urban sensor networks, while costly, is a realistic goal and some cities and countries are already investing heavily in smart energy grids, traffic monitoring sensors, weather stations, and parking sensors to help manage the city (see Chapter 4 for some examples). These networks are physical worlds that are interwoven with sensors, actuators, displays and computational elements, embedded seamlessly into everyday objects that are connected to the internet or network through WiFi, 3G or other protocols usually with a display and now commonly with touch or voice activated controls (Weiser, Gold & Brown, 1999). Therefore, smart cities require smart infrastructure with advanced sensing capabilities (Hancke, Silva & Hancke, 2012). The networks of smart cities are networks in which big sensor data and actuators are key requirement. As explained above, geo-referencing can retrieve context-awareness and characteristics that can be used for the effective deployment of smart objects within smart cities. By applying spatial relationships, functions, and models to the spatial characteristics of smart objects and sensor data, the flows and behavior of objects and people in smart cities can be more efficiently monitored and steered (van der Zee & Scholten, 2014). Contextual sensing may be seen as a first link in a value creation chain towards a more holistic process understanding, specifically for smart city development (Sagl, Resch & Blaschke, 2015). Current technological trends and, in particular, wearable computing


page 9/41

(Swan, 2012), foster the development of smart citizens and their potential to capture contextual information. This means that smart citizens and their smart objects are likely to become key contributors to the development of smarter cities. The geographic phenomena of interest need to be put into context in order to better understand the phenomena and the possible underlying processes (Sagl, 2012). A key challenge for smart cities is therefore to take into account spatiotemporal contexts, particularly with regard to how people interact with a smart city, and how people respond to diverse urban situations (Sagl, Resch & Blaschke, 2015).

2.4.1 OODA loop1 The smart city concepts consist of sensor and actuator networks. The OODA loop (Figure 2.5) is used to better explain the flow of smart cities. Once the positions and other spatial properties of objects and events are clear, real-time spatial analysis can be performed, and based on this analysis, decisions can be made. This process is described by the OODA loop (Boyd, 1987).

Figure 2.5 OODA loop, source: Boyd, 1987 In a smart city, large numbers of smart objects are connected to the Internet. Actuators can use these smart objects either to monitor their environment through their sensors or to act on the environment. To use the capabilities of smart objects efficiently, a continuous process of orchestration and choreography of smart objects is needed. The Observe-Orient-Decide-Act (OODA) model captures what happens between the onset of a stimulus and the onset of a reaction to that stimulus. In all the phases of the OODA loop, spatial concepts and technology can be integrated and used to improve the efficiency of processes in a smart city. Based on location, appropriate sensor and sensing ranges can be selected or activated. Spatial decisions can be made in real-time based on spatial characteristics of observations, and by applying spatial algorithms to position objects and events. Furthermore, based on detected spatial patterns or exceeded spatial thresholds, appropriate actuators and actuating ranges can be selected or activated spatially (van der Zee & Scholten, 2014).

2.4.2 Central versus decentral The process of spatial orchestration and choreography of smart objects has to be managed either centrally or decentralized depending on the smartness of the object. When smart objects become autonomous intelligent agents, they can operate independently and make certain (spatial) decisions autonomously (client-side), based on their spatial capabilities (e.g. calculating the shortest route). Additionally, agents can acquire (pull) additional contextual spatial information or receive (push) instructions from central systems. Intelligent systems can also retrieve contextual spatial information directly from other smart objects nearby. Depending on the intelligence of a smart object, their events are either simple measurement values or the outcome of a complex internal analysis, performed client-side by the smart object, signaling a problem or an impending problem, an opportunity, a threshold, or a deviation (van der Zee & Scholten, 2014).

1 Some sections of this report are taken from the book chapter by Van der Zee & Scholten, 2014. Because of the

length of these quotes, they are not put between quotes, but they are referenced at every use.


page 10/41

Chapter 3 Taxonomy of sensors and actuators

3.1 Introduction

Nowadays, the Dutch environment is being more and more monitored and managed according to the Smart City concepts as explained in the previous chapter. The current energy crisis requires significant energy reduction in all areas (Han et al., 2014) that can be achieved by making objects or a complete city smart. Therefore each day new devices are connected to the internet. It is expected that in a few years our lives become more dependent on these connected objects (Francisco & Arsenio, 2014). New technologies that partially emerged with the introduction of the internet of things where not only internet standards such as Web 2.0 but also embedded devices such as sensors and actuators (Moin, 2014). These technologies have been introduced and implemented to strive towards the smart city concept. Sensors interact with the environment, measure for example temperatures, humidity or light, perform some computation (centralized or decentralized) and finally take one or more actions by using actuators in the environment (Moin, 2014). In order for this to work, specific software that provides services to applications for integrated operation within networked resources is required.

Sensors Actuators

Figure 3.1 Sensors and actuators In this chapter, a classification overview and description, or taxonomy, of sensors and actuators is given including specifications and applications. This overview helps to enlarge insight in and knowledge about existing and expected possibilities in order to better predict future developments and implementations of sensor and actuator networks. Adapting ambitions to these predicted scenarios can strengthen current ambitions and form a basis in decision making in, for example, city redevelopment.

3.2 Taxonomic structure Sensors and actuators are subdivided in different types as shown in Figure 3.2 to represent the hierarchical nature of the structure of sensors and actuators. For both sensors and actuators, a distinction has been made between humans as sensors/actuators and physical sensors/actuators. We discuss the human and physical sensor taxonomy in Section 3.3 and actuators in Section 3.4.


page 11/41

Figure 3.2 Taxonomy structure sensors and actuators

3.3 Sensors Sensors are transducers that convert physical stimulus from one form into a more useful form to measure the stimulus, which is used to collect data about events or changes of the environment in which it is located. This data coming from sensors is used in smart, intelligent systems to autonomously observe the environment. Nowadays, numerous sensors of various types are ubiquitously available to monitor all sorts of processes and therefore useful for improving smart and aware environments. The era of pervasive sensing has begun. Not only physical sensors, static and/or mobile, can observe the environment, also humans can act as sensors by using their natural senses such as eyes, ears, nose, tongue, and tactile nerves (Figure 3.3) in combination with their brains.


page 12/41

3.3.1 Human Sensors Human perception is difficult to capture and analyze but is nevertheless important as it provides information to create a livable environment. “The IoT allows people and things to be connected anytime, anyplace, with anything and anyone, ideally using any path/network and any service” (Guillemin & Friess, 2009). The rise of the IoT and the social media services that are now deeply rooted in our society (Doran, Gokhale & Dagnino, 2013), enable us to enlarge insights into human perception of their environments. According to Kaplan and Haenlein (2010), Social Media is a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 (referring to the internet being an interactive medium) and that allow the creation and exchange of User Generated Content. The development of Web 2.0, mobile devices and corresponding platforms in the past ten years, gave rise to an enormous growth of online social media and the explosion of a wide spectrum of user-generated content on the web (Crowley et al., 2012). Enormous amounts of data are largely retrieved by mobile devices which can be georeferenced and therefore the data retrieved via these devices includes geographical data (Sagl, Resch & Blaschke, 2015). This geographical data can be seen as anthropocentric sensor data, where humans or calibrated hardware sensors within the mobile devices carried by humans are the sensors measuring in-situ human perception or factual data of an environment. The anthropocentricity of this sensor subgroup enables the capturing of both objective and subjective georeferenced data by using different human sensing techniques: people as sensors, citizen science and collective sensing.

People as sensors Citizens as sensors, citizen sensing, human sensing, human sensors, humans as sensors, physiological sensors, participatory sensing, volunteered Geographic information (VGI), are all terms related or identical to the term ‘people as sensors’ that is used throughout this report (Sagl, Resch & Blaschke, 2015). People as sensors is a new, innovative concept of a data capturing model, in which measurements are taken from individuals instead of using physical sensors. Individual human measurements include the sensation, current perceptions or personal observations (Resch, 2013) of their environment or an event within their environment. These measurements are captured with non-technical sensors, our sense organs. Measuring the environment based on human sensing is not something new, also sharing our senses is not something new but the extend in which this is currently done is. Up until the development of Web 2.0, sharing our senses with the whole world was limited. Nowadays, due to both hardware (mobile devices) and software (special applications) technology developments that took place in the last years that are omnipresent in citizens’ lives, we can share our observations, perceptions and senses with the whole world, allowing for expressing feelings virtually at any location and time.

Figure 3.3 - Human sensors

Figure 3.4 Human sensor data (Georeferenced Panoramio photos, YouTube movies, Twitter messages, source: Google)


page 13/41

The human factor has been neglected for many years, even though the question of how people perceive a city and how they feel about it has been an important issue in urban planning and management over the past years, as it provides information about, for example, air quality impressions, street damages, weather observations, or statements on public safety submitted via dedicated mobile or web applications (Resch, 2013). Traditionally, data from people as sensors was retrieved by surveys or interviews, which are obsolete techniques, as they cannot provide real-time data. Instant in-situ subjective data from people as sensors is needed to make cities smart cities. As it has been widely accepted to share our perceptions on the Internet, data of these perceptions is available via the platforms where they have been posted. Since the sharing is on a voluntarily basis, no added installation of cost-intensive physical sensor networks have to be deployed (Resch, 2013). Another advantage of using people as sensors is the ability to retrieve measurements that are created through personal and subjective observations and therefore providing different types of information that is not retrievable with physical sensors. This advantage can, at the same time, be a disadvantage as subjective data is depending on human opinions instead of facts and the data retrieved has therefore limited comparability and interpretability. Citizen Science There are multiple obvious intersections between people as sensors and citizen science. In some cases, citizen science is considered to be a part of people as sensors but as it has some disparities it is considered as being a different subtype in this report. Citizen science refers to the personal and local experiences of citizens across their lives, similar to human as sensors, but citizen science focuses on and aims to the exploiting and elevating expertise of these citizens (Resch, 2013). Therefore the main difference between citizen science and human as sensors is that in case of citizen science the data is based on data stemming from citizens with expertise and knowledge in a certain area about the object, event or environment, ranging from bird sighting to air pollution reports, of which real-time data is shared. The sensors paired with personal mobile phones can be used in addition to the citizen’s expert knowledge to better formulate and understand experiences. Due to the rise of the smartphone, expertise citizens are easily invited to participate in collecting and sharing measurements of their everyday environment that matter to them, although, the end users keep responsible for interpretation and sharing.

Collective sensing Collective sensing is a method in which aggregated anonymous data coming from collective networks or the mobile phone network is analyzed (Resch, 2013). This method is not focused on data coming from individuals but data coming from groups of people, the user-generated content based data or data coming from crowd-sourcing approaches. Collective sensing is an approach based on an infrastructure that tries to retrieve contextual information data by using existing networks. For example, analyzing the mobile phone network and use it as an indicator, when traffic increases it might be the presence of a dense crowd of people (Reades et al., 2007).

3.3.2 Physical Sensors Not only the use of human sensors have evolved over the last years due to the rise of IoT and joint technologies, also the physical sensors have evolved. Both the hardware and software needed for physical sensors have developed enormously so that a broad spectrum of sensors is available nowadays. In case of smart devices, the devices to which the sensors are mounted are connected to the internet and therefore real-time data can easily be shared. Sensors in smart devices can measure in-situ, which is measuring with direct contact with an object or medium, or they can measure remotely, which is measuring with indirect contact with an object or medium. In-situ Sensing ‘In-situ’ refers to on site or in position. Therefore ‘in-situ sensing’ means sensing or measuring the immediate surroundings of the sensors (Sagl, Resch & Blaschke, 2015), direct sensing with an object or medium. The sensors used for in-situ sensing are divided in two sub-categories, fixed and mobile sensing.


page 14/41

Fixed sensors are static sensors that do not have the ability to move around. These sensors are mounted to fixed objects, e.g. buildings or fixed object (poles, bridges). Examples of fixed sensors are e.g. weather stations, sensors fixed on bridges that measure the water level, security cameras on buildings, etcetera. Mobile sensors are the sensors that are not static and therefore have the ability to move around. These sensors can be mounted on vehicles but can also be wearable sensors that are carried by humans or animals. This might suggest that these types of sensors are also human sensors; nevertheless it differs from human sensors. Where smartphones, in case of human sensors are used to share the data or to support human input, in case of mobile in-situ sensors the smartphones, i.e. the sensors mounted within smartphones, are used for the sensing itself. An example of mobile sensing is using bike-mounted sensors to retrieve measurements (Sagl, Resch & Blaschke, 2015). Remote Sensors ‘Remote’ refers to distance between the sensor and the object. Therefore ‘remote sensing’ is an acquisition method in which data about the environment, an event or an object is retrieved without making physical contact with the object, i.e. indirect sensing. Remote sensing often relies on aerial sensor technologies, where an active or passive system is used to measure objects on Earth. Examples of remote sensing are classic airborne and space-borne optical systems (Sagl, Resch & Blaschke, 2015), but nowadays also drones can be classified as remote sensors.

3.4 Actuators The term actuator refers to the responsible source, i.e. the driving factors of the remote or in-situ control of objects. An actuator is (a part of) a device that activates something from distance (remote control) or in-situ. Activating by actuators is based on data retrieved from the sensors that has been used to measure physical quantities such as temperature, pressure, or flow, and the therefrom performed analysis and predictions, usually determined by a directly coupled readout device, to steer and monitor an environment by applying system corrections to change the reading closer to a desired value (Frank, 2013). Controlling the environment by adapting to the monitored environment uses actuators in which a type of motor is implemented to make movement or other activation possible. Examples of actuators are opening bridges after ship detection, activating air-raid alarm after disaster detection, et cetera. In some cases an actuator is considered being a human but generally actuators in smart devices as part of smart cities are physical actuators. In this report, unless stated otherwise, the term actuators refers to physical actuators.

3.4.1 Human Actuators In case humans have to activate a system we speak about human actuators. Activating by humans is not a new concept but due to the developments in retrieving information the activations done by humans have been optimized. Systematic notifications as a result of analyzed sensor data have to be sent to the people involved in the activation of something. For example, a sensor placed in a bin in a public area measures the bin being full, a notification is sent to the cleaner responsible for emptying the bins in that area to make him or her aware, so that he or she can take action and empty the bin.

3.4.2 Physical Actuators Physical actuators have been created in addition to the human actuators. Physical actuators are (parts of) hardware devices that convert a controller command signal, an input signal, into a change in a physical parameter (SlideShare, 2015). In contrast to human actuators where muscles are the driving force of activation, physical actuators are often supplied with a control system within the device that acts upon a signal. In for example electronic engineering, physical actuators, as a subpart of transducers, are frequently used as mechanisms to support motion. The acting of the control system often involves the steering of a source of energy and to convert the energy in motion or other type of activation. Different types of actuators can be distinguished. A basic classification is hydraulic, pneumatic, mechanical and electrical.


page 15/41

All these techniques and methods are developed and interwoven within the ‘smart device’ to activate something. As visualized in the taxonomy structure (Figure 3.2), the subgroup physical actuators consists of different types of physical actuators based on type of activation to which they are responsible. Motion actuators Motion actuators have to start a movement of an object by activating a motor. For example, the opening of a cage or the turning of cameras to better observe an environment. Sound actuators Sound actuators have to spread sound and are often used to warn people for possible danger, as they are useful to provide awareness of conditions for unattended or dangerous situations. For example, activating the air-raid alarm after a disaster, spreading words by a speaker or activation of the fire alarm when the detector measures fire/smoke. Heat actuators Heat actuators are used together with temperature sensors and temperature controllers to control the (indoor) temperature in automated equipment. An example of such a heat controller is a thermostat where the heating system adapts to the preferred temperature. By using a smart thermostat, controlling the temperature becomes more advanced and more sensors than just a thermometer are influencing the actuator. Light actuators Light actuators (LED, LCDs, gas plasma display, CRT, light bulbs) are used in almost all machines to indicate the status of the machine and provide feedback to the operator by light but are also used to provide people with light during darkness. Examples of light actuators are the actuators that active streetlight in times of darkness.


page 16/41

Chapter 4 Sensor and Actuator Networks in the Netherlands

4.1 Introduction In this chapter an inventarisation of existing (operational) and planned sensor and actuator networks is given. We will order the descriptions of sensors and actuators on different spatial scale levels. We start with social media in Section 4.2 since these networks operate on an international level. From thereon, we zoom in to the Netherlands and describe sensor and actuator networks on national (Section 4.3), regional (Section 4.4) and local scale (Section 4.5). Applications and specifications of each of these networks are described and we also include information about whether the data or service is public or private. Further, in Section 4.6 we explain the existence of data brokers and give examples of these platforms that collect, aggregate and redistribute sensor data. As developments are going rapidly in the areas of Open data, Big Data and IoT, it is virtually impossible to give a complete overview of all sensor networks currently in existence. This report nevertheless attempts to present the reader with an overview of sensor networks in the Netherlands on (inter-)national, regional and local level. Next to a concise description of the networks, the overview also lists whether collected data is publicly available (Open) or not (Closed), how many sensors are involved (where applicable) and how data can be accessed (for instance, via an API or a portal website). Also the hyperlinks to sources for more information are included. This overview is included in Appendix A of this report and is delivered as a separate pdf document.

4.2 International networks There are many (sensor and actuator) networks that stretch across the boundaries of the Netherlands. Social media networks are an important example. With social media we mean basically all sorts of media that offer the ability to interact, i.e. two-way information flows. For instance, you not just visit a website to click and browse, but the website asks you to comment on something, thus interacting with you. Facebook is an example of a social media network. Other social media network examples are Twitter (all sorts of communication), LinkedIn (social media platform for professionals), Pinterest (to share creative hobby ideas) and Flickr (social media platform for sharing photos). What these social media platforms have in common, is that they ask you to register and fill in general questions about who you are. Depending on the platform, this can be limited to basic information (name & email), or very broad (hobbies, gender, opinions). A lot of platforms are also available on mobile devices and ask access to you photos, location information and more. The companies behind these services harvest your personal data, interactions and behavior, to build a very detailed profile. Often, they then sell this information to third parties, either in aggregated form or as is, depending on privacy laws. Some social media platforms, for instance, Twitter and Flickr, offer Application Programming Interfaces (APIs) with which you can freely harvest a limited amount of data from their platform yourself, allowing you to do some data analysis and visualization yourself. See, for instance, Figure 4.1 for a map of Rotterdam that is composed solely out of combined Twitter and Flickr data feeds. As soon as you want more data than the limit, you will have to pay, turning the social media platform into a data broker (see Section 4.6). Geographic Twitter data has become popular in the domain of social geography, leading to the new term ‘Tweetography’.


page 17/41

Figure 4.1 ‘See something or say something’, merge of Twitter and Flickr geographic data by Eric Fisher (Source: The Guardian, 2011) Another nice example is Opentopia.com, a website that gives you access to thousands of live webcams which are found through clever search techniques. In many cases, these webcams are view-only, but in some cases, you can also control the camera. Opentopia even lists a Dutch traffic cam (Figure 4.2), but the still images somehow are all black. Live video-feeds of about 30 cameras alongside Dutch highways are already available from the Verkeers Informatie Dienst (VID) via www.vid.nl/Camera/list.

Figure 4.2 Traffic cam in North-Holland, Opentopia.com (Source: www.opentopia.com/webcam/9462) In terms of international infrastructure networks, LOFAR should be mentioned. LOFAR stands for Low Frequency Analysis and Recording. It is a collection of low-cost antennas who act as sensors. These sensors are organized in stations. There are currently 36 stations being build in the Netherlands and “[...] they are distributed over an area about one hundred kilometers in diameter (located in the North-East of the Netherlands). Several international stations are to be built in Germany (5), Sweden (1), the UK (1) and France (1). When finished, LOFAR can be used for advanced monitoring. “In the geosciences field, for instance, it should be possible, to extend the understanding of natural and induced seismicity, subsidence, and water management.” (source: www.lofar.org) Another international network of sensors are weather stations. Before, only professional satellite and professional weather stations data from meteorological services were used. Nowadays, also weather hobbyists are integrated in the network. For instance, the website hetweeractueel.nl collects and presents weather data from Belgium and the Netherlands using sensor stations from connected weather hobbyists. Geonovum has it’s own weather stations on top of their headquarters in Amersfoort, see justobjects.nl/into-the-weather-part-3/.


page 18/41

The airline industry also has opened up some of their data to the public. Using this public data access, the website Flightradar24 shows live air traffic: the actual whereabouts of aircraft worldwide, for instance around Schiphol Amsterdam Airport (Figure 4.3).

Figure 4.3 Live Air Traffic around Schiphol Amsterdam Airport (source: FlightRadar24.com, last visited 21-12-2015)

4.3 National networks There is a whole range of national data networks managed by the government. Some are open, like the National Data Warehouse Road traffic data (NDW), and available (almost) in real-time, others are only limited available, e.g. in aggregated form or with a (big) delay, or not open at all. The following national networks are currently being managed by the government (Table 4.1):


page 19/41

Network Description Responsible

Landelijk Meetnet Water (LMW) (Combination of 3 former networks: MSW, MNZ & ZEGE)

Hydrological and meteorological measurements related to Dutch water, i.e. the rivers and including buoys as sensors in the Noordzee

RWS

Landelijk Meetnet Zwemwaterkwaliteit (LMZ)

Surface water, Water quality, swim water RWS

Landelijk Meetnet Luchtkwaliteit (LML) Air quality RIVM

Nationaal Meetnet Radioactiviteit (NMR)

Radiation, radioactivity RIVM

Landelijke Meetnetten Grondwaterkwaliteit en -kwantiteit

Ground water quality RIVM

Landelijk Meetnet Bodemkwaliteit (LMB)

Soil quality RIVM

Landelijk Meetnet Effecten Mestbeleid (LMM)

RIVM

Meetnet Hemelhelderheid Nederland (MHN)

RIVM

Table 4.1 Overview of national government data networks The National Monitoring Programme for Water (LMW) is a facility that is responsible for the gathering, storage and distribution of water resources data. The data is then delivered to different clients, responsible for closing the floodgates, the high and low water coverage, as well as processes such as operational water level management, fulfillment of international and regional agreements, determine hydraulic conditions, and navigating shipping traffic . In 1990 Rijkswaterstaat Monitoringnetwork Infrastructure (RMI) building blocks are designed to combine and achieve standardization within the three former wet automated measurement information networks of Rijkswaterstaat (MSW, MNZ & ZEGE). Through more than 600 locations across the canals, rivers, the North Sea, tidal water data are collected, processed and stored in a data center. According to Ghafarian & Gorte (2015) who performed quick-scan research on recent available data from Copernicus satellites for Rijkswaterstaat, show interesting results in favor of operational services performed bij Rijkswaterstaat. It could mean that a lot of physical locations could become redundant and cost efficient. At the moment the maintenance of physical locations are outsourced to two contractors (BAM and Venko/Lohmans). For maintenance on the North Sea the contractors hire helicopters which are very expensive. Replacementof physical sensors by satellite data could cut serious costs by at least minimizing the scope of work to several critical location for which Rijkswaterstaat want to ensure data delivery, in case satellite information data are unreliable or unavailable. Some of these national networks, that are in majority managed by RWS and RIVM are accessible for the public via the data portal of the Dutch government, data.overheid.nl and/or through the national geospatial data infrastructure Nationaal Georegister. Development of quite a few of these networks are driven by national and European laws and regulations. See the table in Appendix A for more detailed information, including accessibility, hyperlinks and whether law and regulations are driving forces behind these networks. The RIVM has developed a portal entitled sensors observation service (SOS)Pilot. “The main goal of the SOSPilot project is to take raw Air Quality Measurements from the Dutch RIVM Landelijk Meetnet Luchtkwaliteit and publish this data using various public standards in particular those related to INSPIRE and Eionet. For this purpose a technical platform, "SOSPilot" has been developed. This website provides links to various elements of the SOSPilot platform. In addition, a weather station has been added to the platform. In 2015, Air Quality data from the Smart Emission project (via CityGIS.nl) was added to the platform.” (source: http://sensors.geonovum.nl/, 14-12-15). The RIVM also provides WMS-access to real-time Air Quality Measurements via their geoserver (http://geodata.rivm.nl/geoserver/ows, Figure 4.4).


page 20/41

Figure 4.4 RIVM - real-time Air Quality Measurement data as a WMS-layer in QGIS (source: twitter.com/SimeonNedkov/status/671646685286834176/photo/1)

4.4 Regional networks Like the national government, provinces are also quite active in measuring all sorts of things. A.o. groundwater quantity and quality, surface water and other environmental measurements are carried out in various provinces. Often, these initiatives are driven by national and/or European legislation. Around Schiphol Amsterdam Airport, next to the national sound charge network managed by RIVM, three regional networks exist: NOMOS, Luistervink and Geluidsnet. NOMOS is used by Schiphol and other airports, Luistervink has been developed by the City council of Amsterdam and Geluidsnet is a crowd-sourced citizen initiative. The different networks each have their respective (dis-)advantages. Opinions differ as to what approach is the best (see volkskrant.nl/opinie/-burgersysteem-voor-vliegtuiglawaai-net-zo-goed-als-systeem-overheid~a3304732/) Another nice example of how publicly available data can be used through an API is ovzoeker.nl (figure 4.5). This web service presents real-time information, including location, about local and regional public transport vehicles (bus, tram, light rail and train).


page 21/41

Figure 4.5 OVzoeker.nl, real-time public transport information. Data source: NDOVloket.nl

4.5 Local networks and ad-hoc initiatives A nice example of a creative local initiative is Stratumseind 2.0. This street will be linked to a Living Lab initiative, whereby Philips will use light to influence the mood of the crowd. Sensors, cameras and other measurement devices are used to gather and aggregate data that is used as input to change light color and/or intensity.

Figure 4.6 Living Lab Eindhoven - Stratumse Eind 2.0 (source: (brainport.nl/high-tech-systems-materials/living-lab-laat-ander-licht-schijnen-op-stratumseind)


page 22/41

Nijmegen smart emissions is an interesting example of a project where citizens are involved as sensors to measure local differences in environmental indicators (e.g. air quality and pollutants) on a scale level that can never be covered by the national environmental sensors networks. It is a test case to see whether participative monitoring can lead to more informed decision-making on a local level. (ru.nl/publish/pages/774337/smartemission_nijmegen_pitch_v10_nist_washingtonchicago_smallsize.pdf). Further, The city of The Hague has launched various Smart Pilot projects, involving safety around big events (see denhaag.nl/home/bewoners/to/Smart-City-Pilotprojecten.htm). Another test case in The Hague involves using smart sensors for elderly care (Figure 4.7).

Figure 4.7 Smart elderly care in Woonzorgcentra Haaglanden (source: welzijnservices.eu/nieuws-faq/nieuws/slimme-sensoren) The city of Rotterdam has installed a smart trash bin in a public square, the so-called Big Belly. The Big Belly measures whether it is almost full. Since it is internet-connected, it can send a message to the municipality that it needs to be emptied (Figure 4.8)


page 23/41

Figure 4.8 Big Belly, smart trash bin (source: rotterdam.nl/nieuweenbakvoor600literafval)

More and more local initiatives appear, really too many to include all of them. The local projects are in various stages of realization, from project-initialization stage until fully up-an-running. City councils seems to be quite active, desiring to adopt their profile and become a Smart City. Some examples of local initiatives are Assen smart city, Dordrecht wants to become a smart city (bewonersaanzet.nl/nieuws-en-publicaties/meer-nieuws/archief/february-2015/dordrecht-een-slimme-stad). Next, in the city of Groningen bikers’ waiting time is reduced when it rains because traffic lights are equipped with rain sensors (fietsen.123.nl/fietsnieuws/regensensoren-op-fietsstoplichten-in-groningen). Also, Groningen and Eindhoven are building crowd-sourced Internet of Things data networks (thethingsnetwork-groningen.org/ & gadgetgear.nl/2015/11/draadloos-eindhoven-lanceert-eigen-lora-iot-netwerk/). Another nice example of citizen participation in monitoring is Verbeterdebuurt.nl. People act as sensors and can use the app on their mobile phone or use a website to submit reports of, for instance, broken lantern posts.

Figure 4.9 Verbeterdebuurt.nl We also see a strong increase in the number of WhatsApp-neighborhood groups that aim at keeping their neighborhood safe.


page 24/41

4.5.1 Ad-hoc initiatives More and more, tests are being done on a more or less ad-hoc basis with emerging technologies to see how these can be used for sensor monitoring. UAV is such an emerging technology. UAV stands for Unmanned Aerial Vehicle. Drones are the best known examples of UAVs. Large companies and government institutes are experimenting with the possibilities of drone technology. To name a few eye-catching examples: ● the Dutch Cadaster has used drones to take high-resolution aerial photographs of the new

Hanzelijn rail infrastructure (youtube.com/watch?v=UnrMOH96GwA); ● Prorail has performed test flights with drones inspecting the condition of old, concrete overhead

gantries that hold the power cables for the trains (Figure 4.10);

Figure 4.10 Prorail uses drones to check gantries (prorail.nl/nieuws/luchtproef-boven-het-spoor)

● Prorail has also equipped drones with infrared cameras to check the heating systems of switches on the railroad tracks;

Figure 4.11 Prorail uses drones to check switch heating systems (prorail.nl/nieuws/proef-met-drones-

controleren-wisselverwarming-met-infraroodcamera-s) ● The Dutch police force is more and more using drones for surveying. From 2009 to 2013, this

happened at least 132 times and the number of drone flights has been increasing since then (nu.nl/binnenland/3372322/politie-zet-steeds-vaker-drones-in.html ).To give a local example, the police force of Almere has performed 19 test flights with a drone to monitor activity in specific


page 25/41

areas of the city. In one occasion, the use of the drone has led to the arrest of a burglar (omroepflevoland.nl/Nieuws/105717/almere-inzet-drone-levert-een-arrestatie-op).

The law requires that drones have to be operated on-site, so in our taxonomy (see Chapter 3), drone sensor applications can be categorized as mobile, in-situ. Exceptions are made for the military and the police. They may also control drones remotely. Other initiatives and tests that are planned for the near future are, for example, the test by Dutch Railways (NS) to equip personnel with bodycams in order to increase their safety and have extra eyes - i.e. next to their already extensive fixed camera network - available to monitor situations in and around stations and trains. (beveiligingnieuws.nl/nieuws/geweld/ns-start-proef-met-bodycams-voor-personeel). Also, NS wants to install cameras in the cabins of all trains so that in case of a collision, they can remotely evaluate whether it was an accident or not instead of having to send someone on-site to investigate and interview the engineer. NS expects to save valuable time with this experiment.

4.6 Publish – Find – Bind for sensor data APIs (Sensor Catalogs) Most of the national, regional, and local sensor networks mentioned in the previous paragraphs provide open sensor data. In many cases this data is provided as downloadable files (e.g. spreadsheets with time series), but increasingly also in a more modern way through (non-standardized and standardized) web services (web APIs). For instance, most national sensor networks (KNMI, RIVM, en RWS incl. NDW) have web services. To stimulate the “cross-boundary” (between nations, regions, cities) exchange and use of sensor data, and to stimulate innovation with sensor data (e.g. hackathons), national, regional and local sensor network web services need to be findable. It is therefore very important that governmental sensor network web services are published in governmental metadata (service) catalogs, such as the (OGC-CS-W 2.0) catalog service of the National Geo Register (NGR) (www.nationaalgeoregister.nl). The NGR already provides functionality for the publication of standardized (OGC-SOS/SPS) and non-standardized sensor network web services. In the NGR, the ISO 19119 “metadata for services” standard is used to describe the sensor web services. In this way the Service Oriented Architecture (SOA) principle of ‘Publish – Find – Bind (=“use”)’ can be operationalized for sensor data.

4.7 Sensor Data Broker Platforms A recent development in sensor data collection and distribution is the emerging of commercial sensor data broker platforms. These commercial platforms aim to collect, enhance (e.g. spike removal, aggregation, interpolation, extrapolation) and subsequently sell sensor data. Further they provide real-time data analysis and visualization functionality for the collected sensor data. The business model is that individuals can subscribe their private sensors (e.g. private weather station, outdoor air quality sensor, etc.) in the platform and use the platform functionality (visualization and analysis) through a web interface for free. However, using an API to access your own private data is not for free. And the collective data of all subscribed sensors is only commercially available (data selling). Good examples of this kind of platforms are Xively, Thingworx, and Thingspeak.


page 26/41

Figure 4.12 Example architecture Sensor Data Broker Platform (source: Xively) The strength of this kind of platforms is that once enough private sensors of a certain type are subscribed and available in a certain area, the collective dataset really starts to have value. As an example, if enough citizens in a city provide weather data through their private weather stations, one is able to calculate and predict local weather variations e.g. the spatial-temporal extent of urban heat islands in a city. The downside of this kind of commercial platforms is that sensor data ends up in a commercial databases.


page 27/41

Chapter 5 Standardization initiatives

5.1 Introduction The development of Big Data is an almost complete autonomous on-going phenomenon. Due to its increasing extend, the challenge of using data can be found in the selection and obtainment of useful data but also in linking different datasets to retrieve different and supplemented data in addition to single dataset data, i.e. the transformation of Big Data into Smart Data. Geographical knowledge, context and especially the use of standards are important to be able to do this transformation. “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” (ISO, 2015). Advantages coming along with the use of open standards according to Bray and Ramage (2011) are:

- Interoperability, therefore more parties can work on finding solutions; - Transparency, accountability and manageability, to secure information of the data; - Sustainability, the ability to reuse the data.

The standardization process poses considerable challenges when human as sensor data is used, in terms of interoperability involving data formats, service interfaces, semantics and measurement uniformity (Boulos et al., 2011). International (OGC™, ISO, W3C), and National institutions responsible for the development of standards, concerning sensor data, are discussed in this chapter.

5.2 International

5.2.1 Open Geospatial Consortium The Open Geospatial Consortium is an international non-profit organization, committed to make quality open standards for the global geospatial community. The standards of the OGC™ are made through a consensus process and freely available for anyone to improve sharing of geospatial data and used in a wide variety of domains including environment, defense, health care, agriculture, meteorology, sustainable development, etc. (OGC™, 2015a). Nowadays, 484 companies, government agencies and universities participating in the consensus process to develop publicly available standards and it has a broad user community and alliance partnership with more than 30 standards organizations, including ISO, W3C, and OASIS (van der Zee & Scholten, 2014). The OGC™ has become involved in the sensor web standards effort since this technology domain needs standards that address a broad set of critical real world information interoperability demands, including information about location (OGC™, 2015b). The OGC™ has identified the need for standardization interfaces for sensors and actuators in the Internet of Things and for that started initiatives over the past years of which the following three are elaborated in this report: Sensor Web Enablement initiative For the sensor and actuator networks and the IoT, the OGC™ has developed the Sensor Web Enablement (SWE) 2.0 standard suite (van der Zee & Scholten, 2014). The aim of OGC™'s SWE standards is to enable developers to make all types of sensor systems and observations given as sensor data discoverable, accessible and useable via the Web. The process of enabling these possibilities asks for certain criteria including sensor capabilities, quality of measurement, accessible sensor parameters for geo-referencing the data, retrieval of real-time data, etc. that have to be determined in advance (Boulos et al., 2015). An overview of SWE is given in Figure 5.1. The developed SWE has recently gained importance through its broad support from research and industry (Boulos et al., 2015).


page 28/41

Figure 5.1 SWE overview (source: OGC™, 2015b) The OGC™ standards of SWE that are implemented or expected to be implemented in the nearby future, include the following seven subcategories as given by the OGC™, (2015b):

1. “Observations & Measurements (O&M) –The general models and XML encodings for observations and measurements;

2. Sensor Model Language (SensorML) – Standard models and XML Schema for describing the processes within sensor and observation processing systems;

3. PUCK Protocol Standards – Defines a protocol to retrieve a SensorML description, sensor "driver" code, and other information from the device itself, thus enabling automatic sensor installation, configuration and operation;

4. Sensor Observation Service (SOS) – Open interface for a web service to obtain observations and sensor and platform descriptions from one or more sensors;

5. Sensor Planning Service (SPS) – An open interface for a web service by which a client can 1) determine the feasibility of collecting data from one or more sensors or models and 2) submit collection requests;

6. SWE Common data model – Defines low-level data models for exchanging sensor related data between nodes of the OGC™ Sensor Web Enablement (SWE) framework;

7. SWE Service Model – Defines data types for common use across OGC™ Sensor Web Enablement (SWE) services. Five of these packages define operation request and response types.

INSPIRE “INSPIRE is a directive that aims to create a European Union (EU) spatial data infrastructure to enable sharing of environmental spatial information among public sector organizations and better facilitate public access to spatial information in Europe (INSPIRE, 2015a).” The directive is introduced in the year 2007 to improve policy making across Europe. INSPIRE is not a standard itself, it is a document mentioning the standards necessary for successful implementation and is therefore the driver behind the enabling of the European Spatial Data Infrastructure (SDI) (Bray & Ramage, 2011). Expected is that due to a successful implementation of INSPIRE in the future, many public sector bodies in Europe will be involved in sharing public sector geospatial data (Bray & Ramage, 2015). Standards used for the INSPIRE directive are the OGC™ standards and therefore the OGC™ is the most important partner in the process of INSPIRE. Good communication and cooperation between INSPIRE and the OGC™ is therefore crucial to recognize the requirements of the standards given by


page 29/41

INSPIRE and to include these in the overall international standards developing process by the OGC™ (Bray & Ramage, 2011), so that data sets can be harmonized and shared. In the scope of the INSPIRE Annex III theme “Environmental Monitoring Facilities”, the OGC™ SWE standards are used to disseminate real-time sensor data. The location and operation of environmental monitoring facilities includes observation and measurement of emissions, of the state of environmental media and of other ecosystem parameters (biodiversity, ecological conditions of vegetation, etc.) by or on behalf of public authorities. Recently, the Joint Research Center (JRC) of the European Union, together with Geonovum and RIVM have implemented a SOS web service to evaluate the usability of the OGC™ SWE standards. The OGC™ SWE standards are often considered complex, therefore, OGC developed the easy-to-use SensorThings API. The OGC™ (Open Geospatial Consortium) SensorThings API is an OGC™ candidate standard for providing an open and unified way to interconnect IoT devices, data, and applications over the Web. The SensorThings API is an open standard, builds on web protocols and the OGC™ Sensor Web Enablement standards, and applies an easy-to-use REST-like style. The result is to provide a uniform way to expose the full potential of the Internet of Things.

5.2.2 ISO Standards The over 20.500 ISO International Standards, are standards to ensure that products and services are safe, reliable and of good quality. For business, they are strategic tools that reduce costs by minimizing waste and errors, and increasing productivity. They help companies to access new markets, level the playing field for developing countries and facilitate free and fair global trade (ISO, 2015a). The ISO standards can also be used for the interoperability of sensor data. Two examples of ISO standards, useful when using sensor data, are discussed in this report. ISO 37120:2014 ISO 37120:2014 is the so-called ‘smart city standard’ launched in 2014 to improve data sharing with regard to sustainable development of communities by giving indicators for city services and the quality of life (ISO, 2016b). The standard is applicable to any city, municipality or local government that undertakes to measure its performance in a comparable and verifiable manner, irrespective of size and location (ISO, 2016b). ISO/IEC TR 27019:2013 ISO/IEC TR 27019:2013 is the ‘information technology standard’ launched in 2013 to improve data sharing with regard to information security management applied to process control systems (ISO, 2015c). This includes for example the digital protection and safety systems and the distributed component of future smart grid environments (ISO, 2015c).

5.2.3 W3C Standards In the W3C domain, there are also initiatives regarding sensors. The Semantic Sensor Network Incubator Group within W3C has performed a research to develop an ontology to describe sensors and sensor networks for use in sensor network and sensor web applications, and to study and recommend methods for using the ontology to semantically enable applications developed according to available standards such as OGC™ SWE. The results of this research are found in the report “The Semantic Sensor Network XG Final Report” (source http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/). One of the main outcomes of the research is the Semantic Sensor Network (SSN) ontology. as described in the report “the ontology is based around concepts of systems, processes, and observations”. It supports the description of the physical and processing structure of sensors. Sensors are not constrained to physical sensing devices: rather a sensor is anything that can estimate or calculate the value of a phenomenon, so a device or computational process or combination could play the role of a sensor. The representation of a sensor in the ontology links together what it measures (the domain phenomena), the physical sensor (the device) and its functions and processing (the models)”.


page 30/41

A graphical representation of the SSN ontology structure is presented in figures 5.2, 5.3 and 5.4.

Figure 5.2 Overview of the Semantic Sensor Network ontology modules

Figure 5.3 Overview of the Semantic Sensor Network ontology classes and properties


page 31/41

Figure 5.4 Enumeration of the measurement, environmental and survival properties The complete semantic description of the SSN ontology can be found at http://www.w3.org/2005/Incubator/ssn/ssnx/ssn and is described in Table 5.1.

Section Module Classes Properties

DUL DUL DesignedArtifact, Event, InformationObject, Method, Object, PhysicalObject,Process, Quality, Region, Situation

describes, hasLocation, hasPart, hasParticipant, hasQuality, hasRegion, includesEvent,includesObject, isDescribedBy, isLocationOf, isObjectIncludedIn, isParticipantIn, isQualityOf,isRegionFor, isSettingFor, satisfies

Skeleton Skeleton FeatureOfInterest, Observation, Property, Sensing, Sensor, SensorInput,SensorOutput, Stimulus

detects, featureOfInterest, forProperty, hasProperty, implementedBy, implements, isPropertyOf,isProxyFor, observationResult, observedBy, observedProperty, ofFeature, sensingMethodUsed

Model System System hasSubSystem

Model Process Input, Output, Process hasInput, hasOutput, isProducedBy

cc cc license

Sensor Measuring SensingDevice, SensorDataSheet

observes

Sensor MeasuringCapability Accuracy, DetectionLimit, Drift, Frequency, Latency, MeasurementCapability,MeasurementProperty, MeasurementRange, Precision, Resolution, ResponseTime,Selectivity, Sensitivity

hasMeasurementCapability, hasMeasurementProperty


page 32/41

Observation Observation madeObservation, observationResultTime, observationSamplingTime, qualityOfObservation

Deploy Deployment Deployment, DeploymentRelatedProcess

deployedOnPlatform, deployedSystem, deploymentProcessPart, hasDeployment, inDeployment

Deploy PlatformSite Platform attachedSystem, onPlatform

Deploy OperatingRestriction MaintenanceSchedule, OperatingProperty, OperatingRange, SurvivalProperty,SurvivalRange, SystemLifetime

hasOperatingProperty, hasOperatingRange, hasSurvivalProperty, hasSurvivalRange

Base Data ObservationValue hasValue

Base Time endTime, startTime

Base ConstraintBlock Condition inCondition

Device Device Device

Energy EnergyRestriction BatteryLifetime, OperatingPowerRange

Table 5.1 The Semantic Sensor Network Ontology (source: W3C) Recently, the W3C produced a interesting draft document (12 January 2016 - https://www.w3.org/TR/dwbp/#dataAccess) on the openness and flexibility of the Web which creates new challenges for data publishers and data consumers. In contrast to conventional databases, data on the Web allows for the existence of multiple ways to represent and to access data. This initiative sets out a series of best practices that will help publishers and consumers face the new challenges and opportunities posed by data on the Web and is designed to help support a self-sustaining ecosystem. Data should be discoverable and understandable by humans and machines. Where data is used in some way, whether by the originator of the data or by an external party, such usage should also be discoverable and the efforts of the data publisher recognized. In short, following these best practices will facilitate interaction between publishers and consumers.

5.3 National The previous mentioned standards are international standards that can be widely used and implemented. In addition to the use of international standards, national (Dutch) standards exist and can be used if the process concerns a Dutch process. Geonovum is an OGC™ member that is coordinating the build-up of the Dutch NSDI (Bray & Ramage, 2011). Together with private companies and knowledge institutes, Geonovum is exploring, testing and developing useful standards and agreements by monitoring international developments (Geonovum, 2015). Digital data, coming from a broad variety of sources and devices, asks for the use of open standards to create interoperability of the data. This is important for almost all social sectors and is influencing the data streams in which governmental organizations act. Geonovum helps in technical-content of these standards and agreements (Geonovum, 2015). As for sensor network standards, Geonovum, as part of the INSPIRE directive and Dutch Smart City initiatives, facilitates implementation pilots in which OGC™ SWE and W3C Semantic Sensor Network standards are being deployed and tested in real-world use cases.


page 33/41

Chapter 6 Conclusions and Recommendations

6.1 I-Strategy In the coming years, the Netherlands faces some major challenges in terms of accessibility, safety, and livability. These challenges can only be realized with the further development of information services which serves the primary tasks of Rijkswaterstaat, as has been defined in the policy document ‘RWS NEXT 2020’. The necessary measures are defined in the Rijkswaterstaat ‘i-Strategy’. This strategy contains a clear set of themes and choices, and provides a solid base for the further developments of information services within the Rijkswaterstaat for the coming years. Rijkswaterstaat has thereby two operational objectives, namely; the availability of the networks (including information services), and to be a reliable partner. Regarding this report, there are two important measures within the I-strategy, which are directly related on how Rijkswaterstaat wants to accomplish their information goals related to sensor information. These measures are “the future development of IV networks” (measure 12 in the I-strategy) and “RWS as a Data broker within the national government and society” (measure 9 in the I-strategy). These will be briefly discussed. For the purpose of digital data, there are many different communication networks (copper, glass, WIFI, 3G, 4G, etc.). Because of the importance of uninterrupted communication between traffic centers and roadside systems, the RWS fiber connections have been identified as a strategic asset. These networks are not mutually exclusive, but complementary. The different networks are suitable for more and more services. Also, more and more devices are accessed through these networks. These devices can send, receive, and share information. The potential of the Internet of Things (IoT) for RWS is very large. It offers new opportunities for the gathering of data and offers new innovative ways for the monitoring and control of the networks. With relatively 'unreliable' sensors, there can be realized a high data reliability through the large amount of (big) data. RWS will actively use the opportunities of these developments. The nationwide network of RWS will be developed into a nationwide sensor network. The nationwide fixed network RWS will form the core which will be complemented by wireless services. It is expected that by 2025, half of all the RWS objects will be using wireless connection. The main objective for this measurement is that RWS has a robust nationwide network IV for uninterrupted communication, consisting of a fixed core network with secure wireless connectivity features and that the nationwide network of RWS IV will be developed into a nationwide sensor network. The value added services for such functionality is to be able to monitor the availability of the networks and, where necessary, take measures where availability is below the agreed service levels. This in order to achieve a sufficient available IV network, and to provide reliable service. Moreover, this will also be the basis for creating new services (data collection and full utilization of sensor data through new features/systems). Rijkswaterstaat and their partners (citizens, businesses and other government agencies) have together an enormous amount of data on the status and use of road- and waterway networks, and on the effects on the environment. Clever combinations of these data sources can provide new insights into how we can keep the Netherlands accessible, safe, and livable. However, the distance between theory and practice is still wide. If we want the use and availability of data to meet the required quality, the management of data needs to be professionalized. The organization of professional data management is a major challenge through the fragmentation of tasks. There are many departments, directorates, departments, and organizations involved with data management. This measure involves the professional development for a role of Rijkswaterstaat as a data broker: an intermediary between data collectors and end users. At the level of RWS and partners, there already exists a National Data Warehouse for Traffic Information (NDW) and Information House Water (IHW). Through the arrival of the ‘Laan van Leefomgeving’, there also will be created different 'informatie huizen’. Another important development is that brokers not only need to provide structured data but also unstructured data (such as text, sound, images). And not only for the utilization for governments but also serve citizens and businesses. Also sensor information will play here an increasing important role. The main challenge of this measure is that RWS plays an important role in the development of the necessary infrastructure for the provision of digital data relating to the condition and (the effects of) the use of roads and waterways, broadly in two ways. Firstly, by taking responsibility for one or several ‘informatie huizen’. Secondly by providing the information and communications technologies that are required to process, manage, edit, and provide structured and unstructured data.


page 34/41

6.2 Conclusions

6.2.1 Overall conclusions Sensor networks, especially national ones by law, exist already for a long time. Due to better communication networks and cheaper hardware, sensing is becoming easier and cheaper every day. This facilitates sensing on increasingly larger (city, regional) scales with increasingly dense measure networks. It also takes sensing technology out of the expert domain, and makes it available for the general public (crowd sourced sensing, citizen science). More and more local and regional sensing citizen sensing initiatives are initiated (e.g. noise pollution network Schiphol, earthquake network Groningen, and in Japan the crowd sourced sensing initiative for radioactivity after the Fukushima reactor meltdown), partly because citizens do not trust official governmental measurements for various reasons. It is not always clear if these emerging citizen network violate privacy laws. The Dutch Government, and also Rijkswaterstaat, has an open data policy. Therefore, governmental sensor data should preferably be provided as open data through open APIs. A good example is the Nationaal Data warehouse Wegverkeergegevens (NDW) API of Rijkswaterstaat, that provides sensor data of the Dutch highway network. But maybe more networks of Rijkswaterstaat can be made public. It is important that the right standards and protocols are used to disseminate real-time sensor data on the national, regional and local levels. In the INSPIRE context, the OGC™ SWE standards are used to expose sensor data to end-user applications. OGC™ standards are often considered complex, therefore OGC™ has developed lightweight standard for Smart Environment use cases. W3C has developed an ontology for Semantic Sensor Networks, which provides semantic definitions that can be used in discussions about sensor and actuator networks. Also important is the discovery of sensor data APIs. For this, a sensor catalog needs to be build. The Nationaal GeoRegister (http://nationaalgeoregister.nl) and data.overheid.nl (http://data.overheid.nl) play an important role in the realization of such a catalogue. If the standards are applied in the right way, sensor network data becomes interoperable (standardized and easy accessible) and the step towards real-time monitoring and controlling the environment becomes possible, thus creating a smart environment.

6.2.2 Conclusions related to the research questions What is Big Data and the Internet of Things (IoT)? Big data can be defined in many ways, but in the context of sensor networks and internet connected smart “things” it is related to the collection, storage, analysis and visualization of sensor data (event) streams. How can sensor & actuator types be categorized? This report provides a categorization of sensors and actuators. The categorization distinguishes between human sensors (subjects) and physical sensors (objects). It concerns a broad overview, subdivided in (sub)categories. However, while drawing up the inventory of sensor networks in chapter 4, it shows that the different subcategories don’t exclude each other and that these are sometimes used interchangeably. For example the iSpex-network: this network concerns physical sensors (i.e. hardware add-ons on an iPhone), but is sometimes in the media referred to as a form of citizen science, which in our taxonomy is covered by human sensing. Which sensor & actuator networks are currently available in the Netherlands? In this report, an overview of current national, regional, and local sensor network initiatives have been presented. This overview does not pretend to be complete, as many new initiatives are emerging in the current situation. Which standardization initiatives of sensor networks are developed? Internationally, the OGC™ consortium and W3C consortium have developed standards and ontologies that provide a sound basis on which sensors can be described and sensor data can be disseminated though standardized APIs. Standardization is important to obtain interoperability between national, regional and local sensor network initiatives in the current smart environment context.


page 35/41

6.3 Recommendations This report focusses on the availability of sensor data through sensor network APIs. These sensor networks create an enormous amount of real-time sensor data. To become meaningful (i.e. information that can be used to initiate actions), the raw sensor data events (measurements) have to be transformed into meaningful events (see figure 6.1).

Figure 6.1 Event processing (source: unknown) As part of the framework contract “Raamovereenkomst betreffende samenwerking en kennisuitwisseling op het gebied van ruimtelijke informatievoorziening” it is recommended that the VU Amsterdam together with Rijkswaterstaat is to establish an operational environment for experimenting with the collection of real-time data streams from APIs as mentioned in this document, big data analysis (event stream processing), actuating scenario’s, and the use of sensor and actuator standards provided by W3C, OGC™ and ISO. Focus should also be put on the application of semantic web (Linked Open Data) technology (“web of data”) related to sensing, and research in the domain of sensor catalogs. Also important is the study of event stream processing and real-time spatial analysis, definition of good event rules (“patterns”) and in this context machine learning (self-improving of decision making by the event processing engine). Rijkswaterstaat should actively monitor the emerging and evolution of current sensor network initiatives. This makes it easier to determine a strategy towards and possible involvement in these initiative, as an information chain partner. To stimulate the “cross-boundary” (between nations, regions, cities) exchange and use of sensor data, and to stimulate innovation with sensor data (e.g. hackathons), national, regional and local sensor networks web services need to be findable. It is recommended that Rijkswaterstaat publishes its sensor network web services in a governmental metadata (service) catalog, such as the (OGC-CS-W 2.0) catalog service of the National Geo Register (NGR) (www.nationaalgeoregister.nl). In this way the Service Oriented Architecture (SOA) principle of ‘Publish – Find – Bind (=“use”)’ can be operationalized for sensor data.


page 36/41

References Allwinkle, S., & Cruickshank, P. (2011). Creating smarter cities: An overview. Journal of Urban Technology, 18(2), 1-16. Boulos, M. N. K., et al. (2010). Social Web mining and exploitation for serious applications: Techno social Predictive Analytics and related technologies for public health, environmental and national security surveillance. Computer Methods and Programs in Biomedicine, 100(1), 16-23. Boulos, M. N. K., et al. (2011). Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC™ standards and application examples. International journal of health geographics, 10(1), 67. Boyd, J. R. (1987). A discourse on winning and losing. publisher not identified. Bray, A., & Ramage, S. (2012). OGC™ Market Report Open Standards and INSPIRE. Open Geospatial Consortium, 25(04) Campbell, A. T., et al. (2008). The rise of people-centric sensing. Internet Computing, IEEE, 12(4), 12-21. Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of urban technology, 18(2), 65-82. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. Cook, D., & Das, S. (2004). Smart environments: technology, protocols and applications (Vol. 43). John Wiley & Sons. Crowley, D. N., et al. (2012). Gamification of citizen sensing through mobile social reporting. In Games Innovation Conference (IGIC), 2012 IEEE International (pp. 1-5). IEEE. Crowley, D. N., Curry, E., & Breslin, J. G. (2014). Leveraging Social Media and IoT to Bootstrap Smart Environments. In Big Data and Internet of Things: A Roadmap for Smart Environments (pp. 379-399). Springer International Publishing. Dey, A. K. (2001). Understanding and using context. Personal and ubiquitous computing, 5(1), 4-7. DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., & Wentz, E. (2007). Introducing the first edition of geographic information science and technology body of knowledge. Cartography and Geographic Information Science, 34(2), 113-120. Doran, D., Gokhale, S., & Dagnino, A. (2013). Human sensing for smart cities. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. pp. 1323-1330. ElectricalEasy (2015). How a DC motor works. Working principle of a DC motor. Available form: http://www.electricaleasy.com/2014/01/basic-working-of-dc-motor.html [16 December 2015]. Wikipedia (2015). Actuator. Available from: https://en.wikipedia.org/wiki/Actuator [16 December 2015]. Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer- Mediated Communication, 13(1), 210-230. Evans, D. (2011). The internet of things: How the next evolution of the internet is changing everything. CISCO white paper, 1, 14. Francisco, R. & Arsenio, A. (2014). Intelligent Multi-platform Middleware for Wireless Sensor and actuator Networks. In Giaffreda, R. et al. (Ed.), Internet of Things: user-centric IoT: first international Summit, IoT360. pp.16-22. New York, USA: Springer Frank, R. (2013). Understanding smart sensors. Artech House.


page 37/41

Frick, D., Hoefert, H. W., Legewie, H., Mackensen, R., & Silbereisen, R. K. (Eds.). (1986). Quality of Urban Life: Social, Psychological, and Physical Conditions. Walter de Gruyter. Giffinger, R., et al. (2007). Smart cities-Ranking of European medium-sized cities. Vienna University of Technology. Geonovum (2015). Geonovum: Waar wij voor staan. Retrieved at 21 December 2015, from http://www.geonovum.nl/over-geonovum/waar-wij-voor-staan Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211-221. GSMA (2013). Guide to Smart Cities: The Opportunity for Mobile Operators. GSMA, London

Guillemin, P. & Friess, P. (2009). Internet of Things strategic research roadmap. The Cluster of European

Research Projects, Tech. Rep. Available from: http://www.internet-of-things-

research.eu/pdf/IoT_Cluster_Strategic_Research_Agenda_2009.pdf [07 December 2015].

Han, D. M. et al. (2014). Smart home energy management system Including Renewable Energy Based on ZigBee and PLC, IEEE Transactions on, 56(3), 198-202. Hancke, G. P., Silva, B. & Hancke Jr, G. P. (2012). The role of advanced sensing in smart cities. Sensors, 13(1), 393-425. Huisman, O., & de By, R. A. (2009). Principles of geographic information systems. ITC Educational Textbook Series, 1. INSPIRE (2015a). About INSPIRE. What is the INSPIRE directive? Available from: http://inspire.ec.europa.eu/index.cfm/pageid/48 [17 December 2015] ISO (2015a). Standards. What is a Standard. Available from: http://www.iso.org/iso/home/standards.htm [17 December 2015]. ISO (2015b). ISO 37120:2014. Sustainable development of communities – Indicators for city services and quality of lift. Available from: http://www.iso.org/iso/catalogue_detail?csnumber=62436 [17 December 2015] ISO (2015c).ISO/IEC TR 27019:2013. Information technology – Security techniques – Information security management guidelines base on ISO/IEC 27002 for process control systems specific to the energy utility industry. Available from: http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=43759 [17 December 2015] ITU (2012). Measuring the Information Society. International Telecommunication Union, Geneva, Switzerland Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14. Van Loenen, B., & Verdonk, Y. (2012). Open data: van ideaal tot realiteit. Nederlandse Commissie voor Geodesie. Lohr, S. (2012). The age of big data. New York Times, 11. Lukowicz, P., Choudhury, T., & Gellersen, H. (2011). Beyond context awareness. Pervasive Computing, IEEE, 10(4), 15-17. van Manen, N., Scholten, H. J., & van de Velde, R. (2009). Geospatial technology and the role of location in science (pp. 1-13). Springer Netherlands. Manyika, J. et al. (2011). Big data: The next frontier for innovation, competition, and productivity.


page 38/41

Moin, A.H. (2014). Sense- Deliberate-Act Cognitive Agents for Sense-Compute-Control Applications in the Internet of Things and Services. In Giaffreda, R. et al. (Ed.), Internet of Things: user-centric IoT: first international Summit, IoT360. pp.23-28. New York, USA: Springer OGC™ (2015a). Welcome to the OGC™. Available from: http://www.opengeospatial.org [17 December 2015] OGC™ (2015b). Sensor Web Enablement (SWE). Available from: http://www.opengeospatial.org/ogc/markets-technologies/swe [17 December 2015] OGC™ (2015c). Open GeoSMS Standard – Core. Available from: http://www.opengeospatial.org/standards/opengeosms [17 December 2015] Paulos, E., Honicky, R., & Hooker, B. (2008). Citizen science: Enabling participatory urbanism. Urban Informatics: Community Integration and Implementation. Preuveneers, D. (2014). Foreword. In Big Data and Internet of Things: A Roadmap for Smart Environments (pp. v-vi). Springer International Publishing. Reades J, et al. (2007). Cellular census: explorations in urban data collection. IEEE Pervasive Comput 6(3):30–38 Resch, B., Mittlboeck, M., & Lippautz, M. (2010). Pervasive monitoring—An intelligent sensor pod approach for standardized measurement infrastructures. Sensors, 10(12), 11440-11467. Resch, B. (2013). People as sensors and collective sensing-contextual observations complementing geo-sensor network measurements. In Progress in Location-Based Services (pp. 391-406). Springer Berlin Heidelberg. Resch, B. et al. (2015). Crowdsourcing physiological conditions and subjective emotions by coupling technical and human mobile sensors. GI_Forum. Sagl, G., et al. (2012). Ubiquitous geo-sensing for context-aware analysis: Exploring relationships between environmental and human dynamics. Sensors, 12(7), 9800-9822. Sagl, G. (2012). Towards adaptive geo-monitoring: Examining environmental and social dynamics and their relationships for holistic process understanding. In Seventh International Conference on Geographic Information Science (GIScience) (pp. 18-21). Sagl, G., Resch, B., & Blaschke, T. (2015). Contextual sensing: Integrating contextual information with human and technical geo-sensor information for smart cities. Sensors, 15(7), 17013-17035. Schmidt, A., Beigl, M., & Gellersen, H. W. (1999). There is more to context than location. Computers & Graphics, 23(6), 893-901. Serbanati, A., Medaglia, C. M., & Ceipidor, U. B. (2011). Building blocks of the internet of things: State of the art and beyond. INTECH Open Access Publisher. Sheth, A. (2009). Citizen sensing, social signals, and enriching human experience. IEEE Internet Computing, (4), 87-92. SlideShare (2015). Sensors and Actuators. Available from: http://www.slideshare.net/nazibhmd/sensors-and-actuators-14216258 [16 December 2015]. Snidaro, L., Garcia, J., & Corchado, J. M. (2015). Context-based information fusion. Information Fusion, 21, 82-84. Srivastava, M., Abdelzaher, T., & Szymanski, B. (2012). Human-centric sensing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 370(1958), 176-197. Steenbruggen, et al. (2013). Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities. GeoJournal, 78(2), 223-243. Steenbruggen, et al. (2013b). Data from telecommunication networks for incident management: An exploratory review on transport safety and security. Transport policy, 28, 86-102.


page 39/41

Sullivan, B. L., et al. (2009). eBird: A citizen-based bird observation network in the biological sciences. Biological Conservation, 142(10), 2282-2292. Swan, M. (2012). Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator Netw. 1, 217–253. The Guardian, 13 July 2011. Mapping the World with Twitter and Flickr, http://www.theguardian.com/technology/pda/2011/jul/13/flickr-twitter-maps, date last visited: 15 December 2015 United Nations (2004). ST/ESA/SER.A/236 World Population to 2300. United Nations Publication United Nations (2012). ESA/P/WP/224 World Urbanization Prospects: The 2011 Revision. United Nations Publication Weiser, M., Gold, R., & Brown, J. S. (1999). The origins of ubiquitous computing research at PARC in the late 1980s. IBM systems journal, 38(4), 693-696. Xia, F., et al. (2012). Internet of things. International Journal of Communication Systems, 25(9), 1101. van der Zee, E., & Scholten, H. (2014). Spatial dimensions of big data: Application of geographical concepts and spatial technology to the internet of things. In Big Data and Internet of Things: A Roadmap for Smart Environments (pp. 137-168). Springer International Publishing. The internet sources and references used in Chapter 4 are included in the table in Appendix A and are therefore not repeated here.


page 40/41

Appendix A - Overview of sensor networks See the table in the separate pdf document that comes with this report for the overview of sensor networks. In this table, the yellow networks consist of physical sensors, whereas the blue networks consist of human sensors.

Documents

Eindrapport Sensor and Actuator Networks in the Netherlands...Iris Theunisse 0.2 concept 03-12-2015 Chapter 1 and 2 v1 added Iris Theunisse 0.3 concept 15-12-2015 Review Chapter 1-2,