Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

An approach to pervasive monitoring in dynamic learning contexts : data sensing, communication support and awareness provision

It is within the capabilities of current technology to support the emerging learning paradigms. These paradigms suggest that today’s learning activities and environments are pervas ive and require a higher level of dynamism than the traditional learning contexts. Therefore, we have to rethink our approach to learning and use technology not only as a digital information support, but also as an instrument to reinforce knowledge, foster collaboration, promote creativity and provide richer learning experiences. Particularly, this thesis was motivated by the rapidly growing number of smartphone users and the fact that these devices are increasingly becoming more and more resource-rich, in terms of their communication and sensing technologies, display capabilities battery autonomy, etc. Hence, this dissertation benefits from the ubiquity and development of mobile technology, aiming to bridge the gap between the challenges posed by modern learning requirements and the capabilities of current technology. The sensors embedded in smartphones can be used to capture diverse behavioural and social aspects of the users. For example, using microphone and Bluetooth is possible to identify conversation patterns, discover users in proximity and detect face-to-face meetings. This fact opens up exciting possibilities to monitor the behaviour of the user and to provide meaningful feedback. This feedback offers useful information that can help people be aware of and reflect on their behaviour and its effects, and take the necessary actions to improve them. Consequently, we propose a pervasive monitoring system that take advantage of the capabilities of modern smartphones, us ing them to s upport the awarenes s provis ion about as pects of the activities that take place in today’s pervas ive learning environments. This pervasive monitoring system provides (i) an autonomous sensing platform to capture complex information about processes and interactions that take place across multiple learning environments, (ii) an on-demand and s elf-m anaged communication infras tructure, and (ii) a dis play facility to provide “awarenes s inform ation” to the s tudents and/or lecturers. For the proposed system, we followed a research approach that have three main components. First, the description of a generalized framework for pervasive sensing that enables collaborative sensing interactions between smartphones and other types of devices. By allowing complex data capture interactions with diverse remote sensors, devices and data sources, this framework allows to improve the information quality while saving energy in the local device. Second, the evaluation, through a real-world deployment, of the suitability of ad hoc networks to support the diverse communication processes required for pervasive monitoring. This component also includes a method to improve the scalability and reduce the costs of these networks. Third, the design of two awareness mechanisms to allow flexible provision of information in dynamic and heterogeneous learning contexts. These mechanisms rely on the use of smartphones as adaptable devices that can be used directly as awareness displays or as communication bridges to enable interaction with other remote displays available in the environment. Diverse aspects of the proposed system were evaluated through a number of simulations, real-world experiments, user studies and prototype evaluations. The experimental evaluation of the data capture and communication aspects of the system provided empirical evidence of the usefulness and suitability of the proposed approach to support the development of pervasive monitoring solutions. In addition, the proof-of-concept deployments of the proposed awareness mechanisms, performed in both laboratory and real-world learning environments, provided quantitative and qualitative indicators that such mechanisms improve the quality of the awareness information and the user experienceLa tecnología moderna tiene capacidad de dar apoyo a los paradigmas de aprendizaje emergentes. Estos paradigmas sugieren que las actividades de aprendizaje actuales, caracterizadas por la ubicuidad de entornos, son más dinámicas y complejas que los contextos de aprendizaje tradicionales. Por tanto, tenemos que reformular nuestro acercamiento al aprendizaje, consiguiendo que la tecnología sirva no solo como mero soporte de información, sino como medio para reforzar el conocimiento, fomentar la colaboración, estimular la creatividad y proporcionar experiencias de aprendizaje enriquecedoras. Esta tesis doctoral está motivada por el vertiginoso crecimiento de usuarios de smartphones y el hecho de que estos son cada vez más potentes en cuanto a tecnologías de comunicación, sensores, displays, autonomía energética, etc. Por tanto, esta tesis aprovecha la ubicuidad y el desarrollo de esta tecnología, con el objetivo de reducir la brecha entre los desafíos del aprendizaje moderno y las capacidades de la tecnología actual. Los sensores integrados en los smartphones pueden ser utilizados para reconocer diversos aspectos del comportamiento individual y social de los usuarios. Por ejemplo, a través del micrófono y el Bluetooth, es posible determinar patrones de conversación, encontrar usuarios cercanos y detectar reuniones presenciales. Este hecho abre un interesante abanico de posibilidades, pudiendo monitorizar aspectos del comportamiento del usuario y proveer un feedback significativo. Dicho feedback, puede ayudar a los usuarios a reflexionar sobre su comportamiento y los efectos que provoca, con el fin de tomar medidas necesarias para mejorarlo. Proponemos un sistema de monitorización generalizado que aproveche las capacidades de los smartphones para proporcionar información a los usuarios, ayudándolos a percibir y tomar conciencia sobre diversos aspectos de las actividades que se desarrollan en contextos de aprendizaje modernos. Este sistema ofrece: (i) una plataforma de detección autónoma, que captura información compleja sobre los procesos e interacciones de aprendizaje; (ii) una infraestructura de comunicación autogestionable y; (iii) un servicio de visualización que provee “información de percepción” a estudiantes y/o profesores. Para la elaboración de este sistema nos hemos centrado en tres áreas de investigación. Primero, la descripción de una infraestructura de detección generalizada, que facilita interacciones entre smartphones y otros dispositivos. Al permitir interacciones complejas para la captura de datos entre diversos sensores, dispositivos y fuentes de datos remotos, esta infraestructura consigue mejorar la calidad de la información y ahorrar energía en el dispositivo local. Segundo, la evaluación, a través de pruebas reales, de la idoneidad de las redes ad hoc como apoyo de los diversos procesos de comunicación requeridos en la monitorización generalizada. Este área incluye un método que incrementa la escalabilidad y reduce el coste de estas redes. Tercero, el diseño de dos mecanismos de percepción que permiten la provisión flexible de información en contextos de aprendizaje dinámicos y heterogéneos. Estos mecanismos descansan en la versatilidad de los smartphones, que pueden ser utilizados directamente como displays de percepción o como puentes de comunicación que habilitan la interacción con otros displays remotos del entorno. Diferentes aspectos del sistema propuesto han sido evaluados a través de simulaciones, experimentos reales, estudios de usuarios y evaluaciones de prototipos. La evaluación experimental proporcionó evidencia empírica de la idoneidad del sistema para apoyar el desarrollo de soluciones de monitorización generalizadas. Además, las pruebas de concepto realizadas tanto en entornos de aprendizajes reales como en el laboratorio, aportaron indicadores cuantitativos y cualitativos de que estos mecanismos mejoran la calidad de la información de percepción y la experiencia del usuario.Postprint (published version

Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. We find a systematic relationship between the road length and type and the parking area obtained. We suggest that our results are informative to those needing information on parking in structurally similar regions