Part I New Sensing Technologies for Societies and Environment
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1 Part I New Sensing Technologies for Societies and Environment
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3 Introduction New ICT-Mediated Sensing Opportunities Andreas Hotho, Gerd Stumme, and Jan Theunis During the last century, the application of sensors has emerged in a large variety of domains: industrial processes are controlled by sensors measuring temperatures, pressures, filling levels and flow rates; weather stations measure wind speed and direction, air temperature, humidity, and rain-gauge; and induction loops measure road traffic. With increasing network coverage and decreasing sensor sizes and production costs, this technology has become broadly available for interested citizens. This includes not only simple sensors like temperature but also more advanced ones, e.g., sensors for gas or radiation. These days, for instance, semi-professional weather stations are available in most hardware stores, and everyone can contribute to networks such as wetter.com, 1 which are used for weather forecasts. With the rise of mobile applications (in particular GPS and smartphones), spatial coverage has increased. Interested citizens have started with systematic observations of their environment. Probably the most prominent and successful example is the creation of OpenStreetMap, 2 a map generated by two million people using the A. Hotho ( ) DMIR Group at Chair VI, Department of Mathematics and Computer Science, University of Wuerzburg, Wuerzburg, Germany hotho@informatik.uni-wuerzburg.de G. Stumme Department of Electrical Engineering and Computer Science, University of Kassel, Kassel, Germany J. Theunis VITO Flemish Institute for Technological Research, Mol, Belgium
4 4 A. Hotho et al. Global Positioning System (GPS). In many locations, its coverage already surpasses commercial online maps such as Google Maps or Bing. 3 With every new generation, smartphones come along with an extended list of sensors. Today, almost every smartphone is equipped with a microphone, a camera, GPS, an accelerometer and a gyroscope. The different connectivity modes of a phone (GSM, WiFi, bluetooth) can also serve as sensors, as they are able to determine both the location of the device and the proximity of other devices. Additionally, smartphones can be extended by external sensors, for instance for measuring radioactivity or air pollution in the environment, but also for observing the biological state of the user (e.g. step counter, heart rate, sleeping state). New smartphone generations do not only contain new sensors but also bring advanced connectivity features. Nearly everywhere in the world devices can connect to the internet and, thus, can send and receive information. This connectivity can be used to advance the process of collecting sensor measurements on a central server and allows to directly collect user feedback. It plays a central part in the upcoming process of a ubiquitous collection of distributed information and is the basis of a new way of sensing. The combination of novel sensing technologies, developments in ICT, new ways of information collection and intelligent data processing techniques leads to new concepts of decentralised and widespread data collection by non-experts, often described as human sensing, participatory sensing, urban sensing, crowdsourcing or citizen science. Their common thread is that new opportunities arise to collect and analyse novel data to understand the world surrounding us with increasing involvement of the general public in the process. In the following we will shortly introduce the main concepts and some characteristics of collective sensing. Part I of this book deals with these new sensing technologies for societies and environment, and focuses on their technological possibilities and constraints. The term sensing can relate to sensor. According to the Oxford English Dictionary asensorisa device which detects or measures a physical property and records, indicates, or otherwise responds to it. But it can also relate to sense which is defined as a faculty by which the body perceives an external stimulus, referring in the first place to the traditional senses sight, hearing, taste, smell and touch. Both sensors and senses detect physicochemical properties of the environment. However, in an extended meaning sensing also relates to the psychosocial environment, such as in sensing danger, sensing tension in a group of people or sensing someone s mood. In this meaning sensing refers to a higher level of integration and interpretation of different external and internal signals. In a similar manner we will use sensing to refer to detecting and recording signals that contain information on people s physical, natural and social environment. This includes both physicochemical signals, such as sound, radiation or images, and psychosocial signals such as behavior, opinions or moods. We define sensing as collecting observations relating both to facts (objective) and to interpretations, 3 allows for directly comparing both maps.
5 Introduction 5 opinions and moods (subjective) with the help of a sensor. Thus, we extend the definition of a sensor to include technological sensors as well as human sensors both reflected and discussed in this part. The above-mentioned definition of a technological sensor refers to a device capturing physicochemical signals and translating them in a meaningful value, such as a microphone translating a pressure into a noise level or a gas sensor translating a change in resistance into a gas concentration. Nowadays, however, the tremendous technological development in data communication, data storage and computing power allows to generate and analyse streams of tags, comments, votes, ratings, opinions or counts. Thus, we also define human sensors as the tools used to collect and extract meaning and information on the physical, natural or social environment from information registered by humans through text, numerical values or categories without the use of technological sensors. This information can relate to objective facts, such as a count of the number of birds that passed by, or a tag specifying that a sound is produced by an idling car, or it can relate to subjective impressions, opinions and moods. Note, that while human sensors can capture direct reports of such subjective information, they can also make use of tracks that are left inadvertently, e.g. in twitter feeds or web search activities. Novel types of sensing data can be collected in a purpose-oriented way with dedicated sensors such as air quality sensors, an app recording noise levels or a camera providing pictures. Purpose-oriented data collection can make use of targeted data collection campaigns in which data are collected along specified lines according to a specific goal. This will in most cases need active and conscious participation from the person collecting the data. In opportunistic data collection campaigns, people are collecting data during their normal daily routines without any specific guidelines. But data that is initially intended for one purpose, can also be re-used or exploited for a purpose that is entirely different from the initial context, e.g. twitter feeds can be used to extract mood information, local environmental measurements from different sources can be mashed up to extract broader patterns, internet query logs can be used to extract information on spread of disease and GPS data from car navigation systems can be used to extract information on traffic speed, congestion and travel times. In all these cases data are sourced from available data stores, and data and text mining techniques are used to derive meaning from the data. The different chapters of Part I of this book give an overview of the state-ofthe-art in different sensing domains and sensing technologies, and illustrate their potential as well as the challenges with examples. They deal with sensing systems that can be used actively (possibly with some training) by the general public, or to which the general public contributes by leaving their data (knowingly or unknowingly), and as such create new opportunities for collecting novel data, improving monitoring, and understanding the environment, human behavior, opinions and moods.
6 6 A. Hotho et al. In Chap. 1 Human Sensors on the Move D. Ferreira, V. Kostakos, and I. Schweizer highlight the opportunities created by the omnipresence of mobile phones to generate information on several aspects such as human mobility (based on bluetooth, WiFi, GPS), air pollution (based on gas sensors), and noise. Smartphones are turned into information gatekeepers making intelligent inferences about what its sensors capture and providing information when needed. The authors discuss the practical problems that have to be addressed to turn a smartphone in a truly mobile sensing device. Smartphone embedded sensors are cheap and generally of low quality, and they need calibration. Another crucial issue is the high energy consumption for continuous sensing. Finally, the authors present middleware frameworks that allow for easier development of sensor applications on top of smartphones. In Chap. 2 Sensing the Environment, J. Theunis, M. Stevens, and D. Botteldooren specifically address the prospects for environmental sensing with technological sensors. New sensors and apps create opportunities for more detailed environmental monitoring, as compared to official monitoring networks, and for involving the general public through participatory data collection and monitoring schemes. However, proper monitoring often requires important efforts in developing and validating sensing devices and in processing the collected data. The authors illustrate this with two environmental parameters that recently received a lot of interest, air quality and sound, and discuss the possible added value, the technical challenges and future prospects in these domains. Low-cost sensors have to be optimised for environmental monitoring which involves know-how on sensing technology, electronics, software development and data processing, as well as a thorough knowledge of the dynamics of the parameters that are monitored. They also point out that features, such as air quality or noise, can be highly variable both in space and time. The spatial and temporal resolution of such measurements has to be in line with this variability. Besides environmental monitoring the increasing presence and use of technological sensors such as GPS or RFID, (or radio signals used for communication such as Bluetooth or WiFi) in daily life leads to new opportunities to track and analyse human behavior. In Chap. 3 Observing Human Activity through Sensing, S. Gautama, M. Atzmueller, V. Kostakos, D. Gillis discuss how human mobility patterns can be detected, ranging from traffic control by induction loops and manual counting (e.g. for car occupancy rates) over camera networks to bluetooth scanning of pedestrians and users of public and private transport. In a set of case studies, Chap. 3 shows how floating car data are used for the monitoring of traffic flow, how smartphone data are used for the monitoring of dynamic, multimodal crowd behavior, and how WiFi signals are exploited for analysing tourist mobility patterns in a city. A complementary perspective on the relationship between users and sensors is taken by V. Kostakos, J. Rogstadius, D. Ferreira, S. Hosio, and J. Goncalves analyse in Chap. 4 Human Sensors. They discuss how humans are not only the target of sensing activities, but also take over the role of the sensors themselves. The authors
7 Introduction 7 focus on three domains: collecting human contributions through crowdsourcing platforms, data mining of online social media (in particular for crisis response), and the collection of data and opinions in urban and in-situ systems that collect data from pedestrians. Privacy, trust management and incentives for participation are important issues for participatory sensing applications. These issues and their dependencies are discussed by Mehdi Riahi, Rameez Rahman, and Karl Aberer in Chap. 5 Privacy, Trust and Incentives in Participatory Sensing. As an example, anonymising the collected data will improve the users privacy, but will make trust management more difficult. Collecting sensor information together with user input is one of the key factors to allow for a proper analysis of the data. Challenges like storing, visualizing and analyzing data addressed in web-based platforms are the topic of Chap. 6 Collective Sensing Platforms Martin Atzmueller, Martin Becker and Juergen Mueller. As the amount of data is increasing rapidly, issues like big data processing and sensor cloud storage are becoming more and more important which is also reflected in the platform design. In addition to this, the technological challenges are discussed resulting from the full cycle of collecting data with smartphones to processing and visualizing them on a web system. The last chapter of this part is Applications for Environmental Sensing in EveryAware by Martin Atzmueller, Martin Becker, Andrea Molino, Juergen Mueller, Jan Peters, and Alina Sirbu. It focuses on the technical basis of the EveryAware platform. Two example applications, namely AirProbe for measuring air quality and WideNoise for recording noise pollution are introduced. Beside the challenges of AirProbe specific sensing hardware and the WideNoise smartphone based sensing technology, the focus of this chapter is on the features of the web server component. Specific features like real-time tracking, data storage, analysis and visualizations are discussed along with the two applications. With this application specific chapter we conclude this part of the book about new ICT-mediated sensing opportunities. With further advances of sensing and processing technology, we envision another big step in this area towards more detailed insights into our environment. The combination of objective sensor measurements and subjective impressions of users are two sides of the same coin and will lead to new ways of understanding the current environmental situation in our daily life.
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