Abstract
Minimization of the number of cluster heads in a wireless sensor network is a very important problem to reduce channel contention and to improve the efficiency of the algorithm when executed at the level of cluster-heads. In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Long communication distances between sensors and a sink in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime. Simulation results show that our algorithm can quickly find a good solution.
Highlights
Recent advances in micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics increased the development of low-cost, low-power and multifunctional sensor nodes that are small in size and communicate in short distances [1], [16], [10]
A wireless sensor network usually has a base station that can have a radio relation with other sensor nodes in a network
One method for clustering the network based on the genetic algorithm was introduced
Summary
Recent advances in micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics increased the development of low-cost, low-power and multifunctional sensor nodes that are small in size and communicate in short distances [1], [16], [10]. Designing efficient protocols for using energy is the necessity for wireless sensor networks By using these protocols the whole consumed energy in a network will decrease, and the load of consumed energy will be distributed among the network nodes monotonously. The tasks of these head clusters are gathering the sending data from the nodes of the cluster, omitting the repetitious data, mixing the data and sending these data to sink In these protocols selecting a node as a cluster head and mixing the data are greatly efficient in increasing the scalability and longevity of a network. We suggest a GA for clustering sensor nodes and selection of the minimum of clusters In this way the total of connecting distances and the use of energy will decrease effectively and the longevity of network will increase.
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