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Top1. Introduction
Wireless sensor networks (WSN), are geographically distributed autonomous sensors which are deployed either arbitrarily or using some predefined provision. It is used to monitor the physical or environmental characteristics such as temperature, pressure, humidity, sound etc. shown in Figure 1. The collected data is cooperatively forwarded to the base station for application specific decisions. Furthermore sensor nodes have serious limitations in terms of battery lifetime, memory constraints and computational and communication capabilities.
Lots of works have been proposed in the field of energy efficient clustering and routing but they have serious limitations in terms of implementation complexity, load balancing, data fusion and the energy conservation.
In a WSN most of the energy is consumed in transmission. In order to maximize network lifetime we divide the network into virtual groups and CHs send their data to the BS either directly or using multi-hop routing. We have several ways for the selection of CHs, here we have assumed our CHs are some special nodes having more energy than normal nodes. In fact these special nodes are also battery operated and consequently power constrained, thus play a vital role in network lifetime. Cluster formation is very important and improper design may cause overload. Thus, overloaded CHs increases latency in communication and consumes more energy in turn minimizes the performance of the network.
In literature, we have several methods for load balancing, such as GA based load balanced clustering problem for wireless sensor networks (GALBC) protocol (Kulia et al., 2013). They have used GA for minimizing the maximum load of each gateway. The proposed algorithm differs from the traditional GA because it generates children chromosomes that ensures better load balancing where as in traditional GA in which mutation point is selected randomly. The proposed strategy of generating initial population makes the proposed algorithm converges faster than the traditional GA but, it has a serious drawback. This method balances the load of the gateways without considering their residual energy. The proposed method is not practical because it forcibly balances the load and in turn chooses the incorrect node which may create network failure.
Our presented algorithm Energy Efficient Load Balanced Clustering Algorithm for WSN is a GA based load balanced clustering-based protocol which is better than GALBC because it balances the load by considering the residual energy. We have used the same method and design as GALBC but we have added residual energy as a constraints load balancing. We have conducted grievous simulations in our indigenous tool developed in C programming language. The result shows that our proposed method is better than GALBC in terms of energy consumption, number of active sensor nodes and network life. The rest of the paper is organized as follows. The related work is explained in Section 2. Section 3 describes the proposed model. Simulation results are presented in Section 4. Finally, Section 5 concludes the paper.
TopLots of works have been proposed in the field of clustering and energy efficiency for WSNs. Liu X (2012) proposed a survey on clustering routing protocols in wireless sensor networks. Here, we are presenting some of the review and research work on this topic.
Low Energy Adaptive Clustering Hierarchy (LEACH) protocol (Heinzelman, 2000) is a popular TDMA based MAC protocol which improves the lifespan of WSN. LEACH protocol uses two phases namely set-up phase and steady phase. It balances the load of routing by dynamically rotating the workload of the CHs between the sensor nodes. On the other hand, the limitation of this approach is that it selects a node as CH without considering its residual energy. In addition to that in LEACH a CH communicates with the base station in a single hop.
Some of the algorithms (Liu et al., 2008; Ali et al., 2008; Al-Refai et al., 2011; Tyagi & Kumar, 2013; Kulia & Jana, 2012; Gupta et al., 2017; Han et al., 2017; Nayak & Vathasavai, 2017) have been proposed for clustering and routing to improve clustering protocol but it has serious connectivity issues with CHs.