In-network Collaborative Mobile Crowdsensing

International audienceOur work aims to make opportunistic crowdsensing a reliable means of detecting urban phenomena, as a component of smart city development. We believe that the optimal method for achieving this is by enforcing the cost-effective collection of high quality data. We then investigate a supporting middleware solution that reduces both the network traffic and computation at the cloud. To this end, our research focuses on defining a set of protocols that together implement "context-aware in-network collaborative mobile crowdsensing" by combining: (i) The inference of the crowdsensors' physical context so as to characterize the gathered data; (ii) The context-aware grouping of crowdsensors to share the workload and filter out low quality data; and (iii) Data aggregation at the edge to enhance the knowledge transferred to the cloud

Distributed, Low-Cost, Non-Expert Fine Dust Sensing with Smartphones

Diese Dissertation behandelt die Frage, wie mit kostengünstiger Sensorik Feinstäube in hoher zeitlicher und räumlicher Auflösung gemessen werden können. Dazu wird ein neues Sensorsystem auf Basis kostengünstiger off-the-shelf-Sensoren und Smartphones vorgestellt, entsprechende robuste Algorithmen zur Signalverarbeitung entwickelt und Erkenntnisse zur Interaktions-Gestaltung für die Messung durch Laien präsentiert. Atmosphärische Aerosolpartikel stellen im globalen Maßstab ein gravierendes Problem für die menschliche Gesundheit dar, welches sich in Atemwegs- und Herz-Kreislauf-Erkrankungen äußert und eine Verkürzung der Lebenserwartung verursacht. Bisher wird Luftqualität ausschließlich anhand von Daten relativ weniger fester Messstellen beurteilt und mittels Modellen auf eine hohe räumliche Auflösung gebracht, so dass deren Repräsentativität für die flächendeckende Exposition der Bevölkerung ungeklärt bleibt. Es ist unmöglich, derartige räumliche Abbildungen mit den derzeitigen statischen Messnetzen zu bestimmen. Bei der gesundheitsbezogenen Bewertung von Schadstoffen geht der Trend daher stark zu räumlich differenzierenden Messungen. Ein vielversprechender Ansatz um eine hohe räumliche und zeitliche Abdeckung zu erreichen ist dabei Participatory Sensing, also die verteilte Messung durch Endanwender unter Zuhilfenahme ihrer persönlichen Endgeräte. Insbesondere für Luftqualitätsmessungen ergeben sich dabei eine Reihe von Herausforderungen - von neuer Sensorik, die kostengünstig und tragbar ist, über robuste Algorithmen zur Signalauswertung und Kalibrierung bis hin zu Anwendungen, die Laien bei der korrekten Ausführung von Messungen unterstützen und ihre Privatsphäre schützen. Diese Arbeit konzentriert sich auf das Anwendungsszenario Partizipatorischer Umweltmessungen, bei denen Smartphone-basierte Sensorik zum Messen der Umwelt eingesetzt wird und üblicherweise Laien die Messungen in relativ unkontrollierter Art und Weise ausführen. Die Hauptbeiträge hierzu sind: 1. Systeme zum Erfassen von Feinstaub mit Smartphones (Low-cost Sensorik und neue Hardware): Ausgehend von früher Forschung zur Feinstaubmessung mit kostengünstiger off-the-shelf-Sensorik wurde ein Sensorkonzept entwickelt, bei dem die Feinstaub-Messung mit Hilfe eines passiven Aufsatzes auf einer Smartphone-Kamera durchgeführt wird. Zur Beurteilung der Sensorperformance wurden teilweise Labor-Messungen mit künstlich erzeugtem Staub und teilweise Feldevaluationen in Ko-Lokation mit offiziellen Messstationen des Landes durchgeführt. 2. Algorithmen zur Signalverarbeitung und Auswertung: Im Zuge neuer Sensordesigns werden Kombinationen bekannter OpenCV-Bildverarbeitungsalgorithmen (Background-Subtraction, Contour Detection etc.) zur Bildanalyse eingesetzt. Der resultierende Algorithmus erlaubt im Gegensatz zur Auswertung von Lichtstreuungs-Summensignalen die direkte Zählung von Partikeln anhand individueller Lichtspuren. Ein zweiter neuartiger Algorithmus nutzt aus, dass es bei solchen Prozessen ein signalabhängiges Rauschen gibt, dessen Verhältnis zum Mittelwert des Signals bekannt ist. Dadurch wird es möglich, Signale die von systematischen unbekannten Fehlern betroffen sind auf Basis ihres Rauschens zu analysieren und das "echte" Signal zu rekonstruieren. 3. Algorithmen zur verteilten Kalibrierung bei gleichzeitigem Schutz der Privatsphäre: Eine Herausforderung partizipatorischer Umweltmessungen ist die wiederkehrende Notwendigkeit der Sensorkalibrierung. Dies beruht zum einen auf der Instabilität insbesondere kostengünstiger Luftqualitätssensorik und zum anderen auf der Problematik, dass Endbenutzern die Mittel für eine Kalibrierung üblicherweise fehlen. Bestehende Ansätze zur sogenannten Cross-Kalibrierung von Sensoren, die sich in Ko-Lokation mit einer Referenzstation oder anderen Sensoren befinden, wurden auf Daten günstiger Feinstaubsensorik angewendet sowie um Mechanismen erweitert, die eine Kalibrierung von Sensoren untereinander ohne Preisgabe privater Informationen (Identität, Ort) ermöglicht. 4. Mensch-Maschine-Interaktions-Gestaltungsrichtlinien für Participatory Sensing: Auf Basis mehrerer kleiner explorativer Nutzerstudien wurde empirisch eine Taxonomie der Fehler erstellt, die Laien beim Messen von Umweltinformationen mit Smartphones machen. Davon ausgehend wurden mögliche Gegenmaßnahmen gesammelt und klassifiziert. In einer großen summativen Studie mit einer hohen Teilnehmerzahl wurde der Effekt verschiedener dieser Maßnahmen durch den Vergleich vier unterschiedlicher Varianten einer App zur partizipatorischen Messung von Umgebungslautstärke evaluiert. Die dabei gefundenen Erkenntnisse bilden die Basis für Richtlinien zur Gestaltung effizienter Nutzerschnittstellen für Participatory Sensing auf Mobilgeräten. 5. Design Patterns für Participatory Sensing Games auf Mobilgeräten (Gamification): Ein weiterer erforschter Ansatz beschäftigt sich mit der Gamifizierung des Messprozesses um Nutzerfehler durch den Einsatz geeigneter Spielmechanismen zu minimieren. Dabei wird der Messprozess z.B. in ein Smartphone-Spiel (sog. Minigame) eingebettet, das im Hintergrund bei geeignetem Kontext die Messung durchführt. Zur Entwicklung dieses "Sensified Gaming" getauften Konzepts wurden Kernaufgaben im Participatory Sensing identifiziert und mit aus der Literatur zu sammelnden Spielmechanismen (Game Design Patterns) gegenübergestellt

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

In-network Collaborative Mobile Crowdsensing

International audienceOur work aims to make opportunistic crowdsensing a reliable means of detecting urban phenomena, as a component of smart city development. We believe that the optimal method for achieving this is by enforcing the cost-effective collection of high quality data. We then investigate a supporting middleware solution that reduces both the network traffic and computation at the cloud. To this end, our research focuses on defining a set of protocols that together implement "context-aware in-network collaborative mobile crowdsensing" by combining: (i) The inference of the crowdsensors' physical context so as to characterize the gathered data; (ii) The context-aware grouping of crowdsensors to share the workload and filter out low quality data; and (iii) Data aggregation at the edge to enhance the knowledge transferred to the cloud

Distributed sensing with low-cost mobile sensors towards a sustainable IoT

Cities are monitored by sparsely positioned high-cost reference stations that fail to capture local variations. Although these stations must be ubiquitous to achieve high spatio-temporal resolutions, the required capital expenditure makes that infeasible. Here, low-cost IoT devices come into prominence; however, non-disposable and often non-rechargeable batteries they have pose a huge risk for the environment. The projected numbers of required IoT devices will also yield to heavy network traffic, thereby crippling the RF spectrum. To tackle these problems and ensure a more sustainable IoT, the cities must be monitored with fewer devices extracting highly granular data in a self-sufficient manner. Hence, this paper introduces a network architecture with energy harvesting low-cost mobile sensors mounted on bikes and unmanned aerial vehicles, underpinned by key enabling technologies. Based on the experience gained through real-world trials, a detailed overview of the technical challenges encountered when using low-cost sensors and the requirements for achieving high spatio-temporal resolutions in the 3D space are highlighted. Finally, to show the capability of the envisioned architecture in distributed sensing, a case study on air quality monitoring investigating the variations in particulate and gaseous pollutant dispersion during the first lockdown of COVID-19 pandemic is presented. The results showed that using mobile sensors is as accurate as using stationary ones with the potential of reducing device numbers, leading to a more sustainable IoT

Mobile Sensor Networks for Air Quality Monitoring in Urban Settings

With urban populations rapidly increasing and millions of deaths being attributed annually to air pollution, there is a critical need for a deeper understanding of urban air quality. The locality of urban emissions sources, and the specific topography of cities lead to a very heterogeneous pollutant concentration landscape, the details of which cannot be captured by traditional monitoring stations. Although highly accurate, these systems are large, heavy and very expensive, which leads to a very sparse distribution of measurements. Mobile sensor networks hold the potential to allow a paradigm shift in our understanding of urban air pollution, through an augmentation of the spatial resolution of measurements. The road to achieving reliable high quality information from these type of systems is, however, full of challenges. These start from the system design, as the task of developing robust mobile sensing networks for continuous urban monitoring is arduous in itself. The limitations of existing sensor technology is another important source of hard problems. Chemical sensors suffer from many issues that make their use in a mobile scenario non-trivial. These include: instability, cross-sensitivity, low signal-to-noise ratios, and slow dynamic response. The latter problem, in particular, is a tough challenge when considering a mobile scenario, as it leads to significant measurement distortion. The question of maintaining the calibration of chemical sensors is another essential issue that derives from their instability. Finally, the development of appropriate modeling techniques that would enable us to generate high-resolution pollution maps based on mobile sensor network data is a highly difficult problem due to the inherently dynamic and partial coverage of such systems. The aim of this thesis is to show the feasibility of mobile sensor networks for monitoring air quality and their ability to achieve the goal of pushing our understanding of urban air pollution. We have taken a holistic approach, by studying the end-to-end system, and addressing each of the aforementioned challenges. Using public transportation vehicles for mobility, we have developed and deployed a full-scale mobile sensor network for monitoring the air quality in the city of Lausanne, Switzerland. We have carefully considered all steps of the system design process, starting from the choice of targeted pollutants, sensor selection, node design, server architecture, and system operation. For addressing the problem of mobility-caused distortion, we created a rigorous wind tunnel experimental set-up to study this effect and the techniques for mitigating it. We propose using deconvolution for recovering the underlying pollutant signal. Since the performance of this approach is limited by the signal-to-noise ratio of the measurements, we propose using an active sniffer to enhance the quality of the raw signal. On the topic of sensor calibration, we propose two improvements to online rendezvous calibration methodology. The first one is a model-based approach, which considers the use of more sophisticated sensor models, which are more faithful to the complex behavior of chemical sensors. The second one proposes the use of a pre-processing step, in which the mobile data is deconvolved. Finally, we study the problem of generating high-resolution maps based on mobile data. We propose five statistical modeling methods that use a heterogeneous list of explanatory variables

Impact of Random Deployment on Operation and Data Quality of Sensor Networks

Several applications have been proposed for wireless sensor networks, including habitat monitoring, structural health monitoring, pipeline monitoring, and precision agriculture. Among the desirable features of wireless sensor networks, one is the ease of deployment. Since the nodes are capable of self-organization, they can be placed easily in areas that are otherwise inaccessible to or impractical for other types of sensing systems. In fact, some have proposed the deployment of wireless sensor networks by dropping nodes from a plane, delivering them in an artillery shell, or launching them via a catapult from onboard a ship. There are also reports of actual aerial deployments, for example the one carried out using an unmanned aerial vehicle (UAV) at a Marine Corps combat centre in California -- the nodes were able to establish a time-synchronized, multi-hop communication network for tracking vehicles that passed along a dirt road. While this has a practical relevance for some civil applications (such as rescue operations), a more realistic deployment involves the careful planning and placement of sensors. Even then, nodes may not be placed optimally to ensure that the network is fully connected and high-quality data pertaining to the phenomena being monitored can be extracted from the network. This work aims to address the problem of random deployment through two complementary approaches: The first approach aims to address the problem of random deployment from a communication perspective. It begins by establishing a comprehensive mathematical model to quantify the energy cost of various concerns of a fully operational wireless sensor network. Based on the analytic model, an energy-efficient topology control protocol is developed. The protocol sets eligibility metric to establish and maintain a multi-hop communication path and to ensure that all nodes exhaust their energy in a uniform manner. The second approach focuses on addressing the problem of imperfect sensing from a signal processing perspective. It investigates the impact of deployment errors (calibration, placement, and orientation errors) on the quality of the sensed data and attempts to identify robust and error-agnostic features. If random placement is unavoidable and dense deployment cannot be supported, robust and error-agnostic features enable one to recognize interesting events from erroneous or imperfect data

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig