On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

This is the author accepted manuscriptWe live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures

Self-organizing Spatial Regions for Sensor Network Infrastructures

This paper focuses on sensor networks as shared environmental infrastructures, and presents an approach to enable a sensor network to self-partition itself, at pre-defined energy costs, into spatial regions of nodes characterized by similar patterns of sensed data. Such regions can then be used to aggregate data on a per-region basis and to enable multiple mobile users to extract information at limited and pre-defined costs

Self-organizing Spatial Regions for Sensor Network Infrastructures

This paper focuses on sensor networks as shared environmental infrastructures, and presents an approach to enable a sensor network to self-partition itself, at pre-defined energy costs, into spatial regions of nodes characterized by similar patterns of sensed data. Such regions can then be used to aggregate data on a per-region basis and to enable multiple mobile users to extract information at limited and pre-defined costs. 1