Abstract
Wireless sensor network (WSN) is a low-powered prestigious network fashioned by sensor nodes that treasures application in civilian, military, visual sense models and many others. Reduced energy utilization is an exigent task for these sensor networks. By the data aggregation procedure, needless communication between sensor nodes, cluster head and the base station is eluded. An evaluation of energy efficient optical low energy adaptive clustering hierarchy has been performed and the enactments have been compared with the prevailing low energy adaptive clustering hierarchy algorithm, between two detached wireless sensor network fields. The proposed clustering procedure has been primarily implemented to join two distinct wireless sensor fields. An optical fiber is used to join two reserved wireless sensor fields. This distributed clustering methodology chiefly targets in exploiting the parameters like network lifetime, throughput and energy efficiency of the whole wireless sensor system.
Highlights
Wireless Sensor Networks (WSN) finds applicable in many real-world applications like military, target tracking, environmental monitoring and civilian applications
A wireless sensor node is powered by limited- powered battery
Energy efficiency can be accomplished at different levels starting from physical layer, MAC layer and routing protocols up to the application level
Summary
Wireless Sensor Networks (WSN) finds applicable in many real-world applications (figure 1) like military, target tracking, environmental monitoring and civilian applications. In order to trim down the data transmission time and energy consumption, the sensor nodes are clustered into a number of small groups called clusters. The main advantages of clustering are that it transmits the aggregated data to the sink or base station (BS), provides scalability for large number of nodes and diminishes the energy consumption. Distributed clustering mechanism is used for some exclusive reasons like sensor nodes prone to failure, better collection of data and minimizing redundant information [2]. These distributed clustering mechanisms have highly self-organizing capability.
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