BIG DATA Sensor and Actuator Networks in the

Transcription

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
STATUS
VERSION
AUTHORS
DATE
VERSION
Definitief
1.0
drs. E van der Zee
ir. Iris Theunisse
prof. dr. H.J. Scholten
dr. Jasper Dekkers
dr. ir. John Steenbruggen
19.12.2015
VERSION
STATUS
DATE
DESCRIPTION
AUTHOR(S)
0.1
concept
13-11-2015
Definition table of contents
0.2
concept
03-12-2015
Chapter 1 and 2 v1 added
Erik van der Zee
Iris Theunisse
Iris Theunisse
0.3
concept
15-12-2015
Jasper Dekkers
0.4
concept
17-12-2015
Review Chapter 1-2, List of References added,
Chapter 4 v1 added
Chapter 3 v1 added
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
HISTORY
Iris Theunisse
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
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
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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.
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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.
2.
3.
4.
What is Big Data and the Internet of Things (IoT)?
How can sensor & actuator types be categorized?
Which sensor & actuator networks are currently available in the Netherlands?
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.
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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.
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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, contextaware 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.
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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).
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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
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(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.
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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.
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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.
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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 nontechnical 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)
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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.
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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.
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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.
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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’.
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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 NorthEast 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/.
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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 21-122015)
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):
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Network
Landelijk Meetnet Water (LMW)
Description
Responsible
Hydrological and meteorological
RWS
(Combination of 3 former networks: MSW, measurements related to Dutch water, i.e. the
MNZ & ZEGE)
rivers and including buoys as sensors in the
Noordzee
Landelijk Meetnet Zwemwaterkwaliteit Surface water, Water quality, swim water
RWS
(LMZ)
Landelijk Meetnet Luchtkwaliteit (LML) Air quality
RIVM
Nationaal Meetnet
Radiation, radioactivity
RIVM
Radioactiviteit (NMR)
Landelijke Meetnetten
Ground water quality
RIVM
Grondwaterkwaliteit en -kwantiteit
Landelijk Meetnet
Soil quality
RIVM
Bodemkwaliteit (LMB)
Landelijk Meetnet
RIVM
Effecten Mestbeleid (LMM)
Meetnet Hemelhelderheid Nederland
RIVM
(MHN)
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).
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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-voorvliegtuiglawaai-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).
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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/livinglab-laat-ander-licht-schijnen-op-stratumseind)
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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.p
df).
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/nieuwsfaq/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)
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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-slimmestad). 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-ingroningen). Also, Groningen and Eindhoven are building crowd-sourced Internet of Things data
networks (thethingsnetwork-groningen.org/ & gadgetgear.nl/2015/11/draadloos-eindhoven-lanceerteigen-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.
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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-dronescontroleren-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
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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-voorpersoneel). 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 onsite 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 (nonstandardized 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 (OGCCS-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 nonstandardized 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 realtime 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.
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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.
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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).
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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 28/41
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-touse 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-ssn20110628/).
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 29/41
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 30/41
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
Sensor
Measuring
SensingDevice,
SensorDataSheet
Sensor
MeasuringCapability
Accuracy, DetectionLimit,
hasMeasurementCapability,
Drift, Frequency, Latency,
hasMeasurementProperty
MeasurementCapability,Mea
surementProperty,
MeasurementRange,
Precision, Resolution,
ResponseTime,Selectivity,
Sensitivity
license
observes
page 31/41
Observation
Observation
madeObservation, observationResultTime,
observationSamplingTime,
qualityOfObservation
Deploy
Deployment
Deployment,
DeploymentRelatedProcess
deployedOnPlatform, deployedSystem,
deploymentProcessPart, hasDeployment,
inDeployment
Deploy
PlatformSite
Platform
attachedSystem, onPlatform
Deploy
OperatingRestriction
MaintenanceSchedule,
hasOperatingProperty,
OperatingProperty,
hasOperatingRange, hasSurvivalProperty,
OperatingRange,
hasSurvivalRange
SurvivalProperty,SurvivalRan
ge, SystemLifetime
Base
Data
ObservationValue
Base
Time
Base
ConstraintBlock
Condition
Device
Device
Device
Energy
EnergyRestriction
BatteryLifetime,
OperatingPowerRange
hasValue
endTime, startTime
inCondition
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 32/41
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 33/41
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 34/41
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 (selfimproving 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 35/41
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1980s. IBM systems journal, 38(4), 693-696.
Xia, F., et al. (2012). Internet of things. International Journal of Communication Systems, 25(9), 1101.
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The internet sources and references used in Chapter 4 are included in the table in Appendix A and
are therefore not repeated here.
page 39/41
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.
page 40/41

BIG DATA Sensor and Actuator Networks in the

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