Cluster Building in Wireless Sensor Networks

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. During data collection, two mechanisms are used to reduce energy consumption: message aggregation and filtering of redundant data. These mechanisms generally use clustering methods in order to coordinate aggregation and filtering.

Such a clustering mechanism is used to collect data in sensor networks. The first original aspect of this investigation consists of adding these constraints to the clustering mechanism that helps the data collection algorithm in order to reduce energy consumption and provide applications with the information required without burdening them with unnecessary data. Centralized clustering is modeled as hypergraph partitioning. Finally, results show that a tabu search-based resolution method provides quality solutions in terms of cluster cost and execution time.