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Wireless Sensor Network (WSN) contains huge amount of small devices namely sensor nodes which are located in the physical environment to gather the desired information (Haseeb et al. 2016). The nodes in the WSN are connected together and to the Base Station (BS). Since, the node uses wireless communication and it is generally powered by the battery source (Kaur & Mahajan 2018; Mohajerani, & Gharavian 2016). The nodes deployed in the WSN contains transmitter, receiver, sensor and processor. In general, the node senses, measures and gathers the information from the environment and the data’s are transmitted to the desired users by considering the local decision procedure (Prasad et al. 2017). There are various physical phenomena measured using the distributed and cooperative sensing of the network. Some of the physical parameters are vehicular movement, acoustic signal, temperature, vibrations, humidity, pressure, etc (Moh’d Alia, 2018;Arjunan 2018). WSN used in various applications such industrial failure detection, health monitoring, target tracking, agriculture monitoring, fire management, surveillance missions, energy management, home automation, traffic monitoring and environmental monitoring (RejinaParvin & Vasanthanayaki 2019).
The data transmission between the node to the BS is carried out in different methods such as Single-hop transmission, Multi-Hop transmission, Cluster based transmission, Tree based and Chain based transmissions (RejinaParvin, & Vasanthanayaki 2015). The challenges in the WSN are given as follows: The main issue in the WSN are nodes with insufficient power source. The battery replacement is impossible in the WSN, because the nodes are located in an unattended hostile environment (Rao et al. 2017). In addition, the balancing of network load and reduction of energy utilization is also considered as main issue in the WSN (Xie et al. 2015). The WSN is also affected due to various concerns like link bandwidth, insufficient computing power, time-varying fading channels, communication failure, etc (Sabet, & Naji 2015). In order to overcome the aforementioned limitation, it is significant to design the structure which utilizes less energy to transmit the data to the BS. The energy consumption is improved by designing the hierarchical structure for WSN (Mazinani et al. 2019). The clustering and routing methods are developed for establishing the hierarchical structure. Some of the existing cluster based WSN are given as follows: genetic algorithm based routing protocol (Mittal et al. 2019), gravitational search algorithm (Morsy et al. 2018) and Particle Swarm Optimization (Mekonnen et al. 2017). In this research, the Ad hoc On-Demand Distance Vector (AODV) routing protocol is selected because it supports both the unicast and multicast transmissions. Furthermore, the AODV routing protocol provides better performance in dynamic nature of WSN. The major contributions of this research paper are given as follows:
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An energy efficient WSN is designed by combining both the clustering and routing algorithms. Here, the optimal path is generated for routing the data packets to the destination.
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The mahalanobis distance based Cluster Head (CH) selection is used to obtain an optimal CH through the group of nodes. The main advantage of using mahalanobis distance is the usage of covariance during distance calculation.
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Due to less computation and higher access speed, the Ad hoc On-Demand Distance Vector (AODV) algorithm is used to identify the route among the CHs to the BS.