Phenomena Detection in Mobile Wireless Sensor Networks

Environmental phenomena, such as fires, poisonous gases, and oil spills, can be detected by wireless sensor networks (WSNs) that cover the geographical area of the phenomena. These sensors collaboratively monitor the area to detect the sensors' readings that deviate from normal reading patterns after which a phenomena is declared. This research proposes a distributed algorithm to detect dynamic phenomena using mobile WSNs under the assumption that there is no centralized server to collect and aggregate the sensors data. Therefore, the sensors self-organize into disjoint groups by first electing a few sensors to be group heads (GHs) and then the rest of the sensors group themselves with the nearest GH. Each group of sensors detect phenomena locally. Then, the GHs communicate the detected local phenomena information among themselves to aggregate the information and detect the global phenomena. Moreover, the paper proposes two GH election algorithms, namely the Last Group Head election algorithm and the Distributed Group Head election algorithm. The experimental results show that the proposed election algorithms reduce the energy costs of the mobile WSN by 54-66 % as compared with the straightforward election algorithm. In addition, this paper proposes an optimization technique to further reduce the energy costs of reporting the global phenomena to about 33 % by reducing the size of the reported phenomena information. The proposed algorithms are validated through a comprehensive set of experiments conducted using the NS2 network simulator.