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Clustering the Wireless Sensor Networks (WSNs) is the major issue which determines the lifetime of the network. The parameters chosen for clustering should be appropriate to form the clusters according to the need of the applications. Some of the well-known clustering techniques in WSN are designed only to reduce overall energy consumption in the network and increase the network lifetime. These algorithms achieve increased lifetime, but at the cost of overloading individual sensor nodes. Load balancing among the nodes in the network is also equally important in achieving increased lifetime. First Node Die (FND), Half Node Die (HND), and Last Node Die (LND) are the different metrics for analysing lifetime of the network. In this paper, a new clustering algorithm, Genetic Algorithm based Energy efficient Clustering Hierarchy (GAECH) algorithm, is proposed to increase FND, HND, and LND with a novel fitness function. The fitness function in GAECH forms well-balanced clusters considering the core parameters of a cluster, which again increases both the stability period and lifetime of the network. The experimental results also clearly indicate better performance of GAECH over other algorithms in all the necessary aspects.

Wireless sensor networks are a web of sensor nodes with a set of processor and limited memory unit embedded in it. Reliable routing of packets from the sensor node to its base station is the most important task for the networks. In wireless sensor networks, routing is bit more complex than other wired or wireless networks. The routing protocols applied for the other networks cannot be used here due to its battery powered nodes. Unlike other wireless networks routing in WSN should be the energy efficient one. This paper gives an overview of the different routing strategies used in wireless sensor networks and gives a brief working model of energy efficient routing protocols in WSN. We have also compared these different routing protocols based on metrics such as mobility support, stability, overlapping. The study concludes with the recommendations to the future direction in the energy efficiency model for the sensor networks.

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