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With the spectacular progress of technology, we have witnessed the appearance of wireless sensor networks (WSNs) in several fields. In a hospital for example, each patient will be provided with one or more wireless sensors that gather his physiological data and send them towards a base station to treat them on behalf of the clinicians. The WSNs can be integrated on a building surface to supervise the state of the structure at the time of a destroying event such as an earthquake or an explosion. In this paper, we presented a Mobility-Energy-Degree-Distance to the Base Station (MED-BS) Clustering Algorithm for the small-scale wireless Sensor Networks. A node with lower mobility, higher residual energy, higher degree and closer to the base station is more likely elected as a clusterhead. The members of each cluster communicate directly with their ClusterHeads (CHs) and each ClusterHead aggregates the received messages and transmits them directly to the base station. The principal goal of our algorithm is to reduce the energy consumption and to balance the energy load among all nodes. In order to ensure the reliability of MED-BS, we compared it with the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm. Simulation results prove that MED-BS improves the energy consumption efficiency and constructs a stable structure which can support new sensors without returning to the clusters reconstruction phase.

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ABSTRACT

Energy consumption is an important parameter in the context of the wireless sensor networks (WSNs). Several factors can cause energy over consumption such as mobility, node position (relay or gateway), retransmissions... In this paper, we described a new Energy-Degree Distance(EDD) Clustering Algorithm for the WSNs. A node with higher residual energy, higher degree and closer to the base station is more likely elected as a clusterhead (CH). The inter cluster and intra cluster communications are realized on one hop. The principal goal of our algorithm is to minimize the energy consumption and balance energy consumption among all nodes. By comparing EDD clustering algorithm with LEACH algorithm, simulation results have shown its effectiveness in saving energy.