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

Analytic modeling of detection latency in mobile sensor networks

An envisioned usage of sensor networks is in surveillance systems for detecting a target or monitoring a physical phenomenon in a region. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. In many applications, if the monitored region is relatively large compared to the sensing range of a node, a large number of nodes are required in the region to achieve high coverage. Using mobile nodes in such situations can be an attractive alternative. Mobility of sensor nodes has been studied in sensor networks for many purposes such as power saving, data collection, and packet delivery. However, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper investigates the problem of detecting the presence/absence of a target using mobile sensor networks. It presents an analytic method to evaluate the detection latency based on a collaborative sensing approach using nodes with uncoordinated mobility. We verify the analytic model through simulations. The analytic method provides a simple way of analyzing the tradeoff between number of nodes and detection latency in a mobile sensor network. The analysis is also used to compare the performance of mobile and stationary sensor networks with respect to these measures. Results show that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably less as compared to that of stationary networks with the same number of nodes