Data interpolation for participatory sensing systems
Pervasive and Mobile Computing
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A new class of wireless sensor networks has recently appeared due to the pervasiness of cellular phones with embedded sensors, mobile Internet connectivity, and location technologies. This mobile wireless sensor network has the potential to address large-scale societal problems and improve the people’s quality of life in a better, faster and less expensive fashion than current solutions based on static wireless sensor networks. Ubiquitous Sensing is the umbrella term used in this dissertation that encompasses location-based services, human-centric, and participatory sensing applications. At the same time, ubiquitous sensing applications are bringing a new series of challenging problems. This dissertation proposes and evaluates G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks, and addresses several new problems related to location-based services, participatory sensing, and human-centric sensing applications. G-Sense features the critical point algorithms, which are specific mechanisms to reduce the power consumption by continous sensing applications in cellular phones, and reduce the amount of data generated by these applications. As ubiquitous sensing applications have the potential to gather data from many users around the globe, G-Sense introduces a peer-to-peer system to interconnect sensing servers based on the locality of the data. Finally, this dissertation proposes and evaluates a multiobjective model and a hybrid evolutionary algorithm to address the efficient deployment of static wireless sensor nodes when monitoring critical areas of interest.