||A Tabu Search Algorithm for Cluster Building
in Wireless Sensor Networks
We propose a novel data collection approach for sensor
networks that use energy maps and QoS requirements to reduce power consumption while increasing network coverage.The mechanism comprises two phases: during the first phase, the applications specify their QoS requirements regarding the data required by the applications. They send their requests to a particular node S, called the collector node, which receives the application query and obtains results from other nodes before returning them to the applications. The collector node builds the clusters, optimally using the QoS requirements and the energy map
information. During the second phase, the cluster heads must provide the collector node with combined measurements for each period. The cluster head is in charge of various activities: coordinating the data collection within its cluster, filtering redundant measurements, computing aggregate functions, and sending results to a node collector.
Algorithm / Technique used:
Tabu Search Algorithm.
Tabu search allows the search to explore solutions that do not decrease the objective function value only in those cases where these solutions are not forbidden. This is usually obtained by keeping track of the last solutions in term of the action used to transform one solution to the next. When an action is performed it is considered tabu for the next T iterations, where T is the tabu status length. A solution is forbidden if it is obtained by applying a tabu action to the current solution.
The main challenge when deploying sensor networks pertains to optimizing the energy consumption for data collection from sensor nodes. A new data collection mechanism based on a centralized clustering method distributed clustering method. It uses sensor network energy maps and applies QoS requirements in order to reduce energy consumption.
This paper proposes a new centralized clustering method for a data collection mechanism in wireless sensor networks, which is based on network energy maps and Quality-of-Service (QoS) requirements. The clustering problem is modeled as a hyper graph partitioning and its resolution is based on a tabu search heuristic. Our approach defines moves using largest size cliques in a feasibility cluster graph.
Â¢ System : Pentium IV 2.4 GHz.
Â¢ Hard Disk : 40 GB.
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Â¢ Operating system : - Windows XP Professional.
Â¢ Coding Language :- Java