A study on group head election for detecting phenomena in mobile WSN

An important application of Wireless Sensor Networks (WSN) is to detect phenomena from the data gathered from the sensors. Traditional phenomena detection techniques are not directly applicable to WSN due to the nature of sensor data and specific requirements and limitations of the WSNs. The most critical requirement in WSN applications is extending the networks' life time by reducing the energy consumption while maintaining connectivity and accuracy. To meet this requirement, many approaches has been developed taking into consideration a static and dynamic WSNs. In this paper, we propose an algorithm to elect group heads that are used to detect phenomena in data gathered from mobile sensor nodes while minimizing the energy expenditure. The algorithm elects group heads based on the collected information of the global reported phenomena. The algorithm makes use of the phenomena location information to effectively select group heads. The paper shows the feasibility of the proposed algorithm by comparing it with a naïve approach for electing group heads in terms of energy consumption.