Abstract
In Wireless Sensor Networks (WSN), energy is one of the most important resources since each node collects, processes and transmits data to its base station. Most of the traditional works in WSNs are consisted of static nodes and one base station. Recently, some mobile data gathering methods are proposed to prolong the operation time of sensor networks. One or more mobile collectors are used to gather sensed data from sensor nodes at short transmission ranges. This paper presents to find optimal visiting points and data gathering path for a mobile sink within clusters. With defining an optimal clustering and data gathering path, this method improves the data collection performance as well as the network lifetime extension of sensor networks. The network lifetime is increased to 20% when sensor nodes are divided into from 4 clusters to 15 clusters.
Highlights
When Wireless Sensor Networks (WSNs) are deployed at areas of interest, they are generally composed of hundreds or thousands of sensor nodes
The network lifetime is increased to 20% when sensor nodes are divided into from 4 clusters to 15 clusters
When the mobile sink approaches to a data gathering point, it will communicate with sensor nodes in the visited cluster and gets data from them by using Time Division Multiple Access (TDMA) method in one hop
Summary
When Wireless Sensor Networks (WSNs) are deployed at areas of interest, they are generally composed of hundreds or thousands of sensor nodes These devices perform three basic tasks: sample a physical quantity from the surrounding environment, process the acquired data, and send it to a sink node or a base station [1]. Mobile sinks have been proposed as a solution for data gathering in WSN to balance the energy consumption geographically among the sensors throughout the network and deal with isolated regions [7, 8]. We decide optimal visiting points and a data collection path for a mobile sink to manage wireless sensor networks efficiently in energy aspect. To find the optimal data gathering point and make clusters, K-means algorithm is used It makes to decrease intra-cluster communications and to gain energy efficiency for sensor nodes.
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