Probabilistic Energy Value for Clustering in Wireless Sensors Networks

Wireless sensors networks consist of a number of sensors nodes connected through a wireless network that collect data to be treated locally or relayed to the sink node using multi-hop wireless transmission. Several solutions were proposed to minimize the amount of information flowing within the network. Clustering algorithms is one solution and mechanism that enables the creation of sensor’s clusters;each sensor is dominated by elected routers. In order to limit energy consumption, the clustering around the sensor is established: sensors linked to the router transmit relayed data thereafter outward. The number of messages sent and the transmission range are thus reduced. This article tackles this issue by unveiling proposed techniques in the same line of researches and proposing a clustering mechanism based on the amount of energy remaining in the sensors. The simulation results show that proposed method can achieve higher network lifetime by comparison to original LEACH.

MED-BS Clustering Algorithm for the Small-Scale Wireless Sensor Networks

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.