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Wireless Sensor Network constitutes sensor nodes that cooperatively monitor different parameters such as humidity, noise levels, vibration, temperature, motion etc, at different locations. Energy constraint is a fundamental limitation in the lifetime of Wireless Sensor Network. Here, we propose three routing approaches for data packets transmission. The data packets are routed either through the established route done with the help of relay nodes which act as an intermediate nodes or data packets routed through high energy based clusters irrespective of its distance and it also finds another way, such that the data packets routed through clusters based on localization so that it selects shortest path to reach the destination with minimal energy consumption. Our work involves fault node recovery mechanislns under all three different scenarios. The results obtained with the help of NS-2 simulation software concludes that Localization based routing approach outperforms High energy clusters based and Relay nodes based routing with best aggregation quality by taking delivery rate, overhead, delay and energy as parameter metrics into consideration. IN1RODUCTIONWireless Sensor Networks (\VSNs) plays a vital role in various applications, such as environmental monitoring, forest fire detection, disaster recovery, and military surveillance [1]. Sensor nodes are energy-constrained devices. Most of the energy is dissipated by the transceiver unit. WSNs are datacentric networks, so huge amount of information needs to be routed to sink node. Sink node acts as a gateway to the monitoring centre. Routing plays a prominent role in data gathering process. In order to optimize the routing task, it is efficient to utilise the significance of relay nodes. To provide reliable data gathering mechanism with a minimum use of available resources, smart configuration of sensor nodes to forward data by making local decisions is essential. It concludes that data aggregation is an effective technique for 978-1-4799-3671-7/43©2014 IEEE 1v1s. A. Asha Rajalaksluni Engineering College, asha. a@rajalaksluni.edu.in ]\Jr. C. Arun IDv1K College of Engineering and Technology.saving energy in WSNs. The most challenging task in WSN is to ensure the delivery of sensed data even in the presence of dead nodes and interruptions in the communication. Data aggregation becomes more critical when dead nodes are along the routing path from source to sink [2]. Data aggregation aware routing protocols should have some desirable characteristics to accomplish reliable data transmission. To provide balanced energy in the network, routing is done by forwarding the data packets towards the sink through high energy areas in order to protect other nodes with low residual energy. From the measurement of energy density field, it is ensured that the packets are always forwarded only through high energy area. Thus low energy nodes are protected [1]. It also finds another way for balancing the energy in the network by routing the data packets towards the potential cluster tha...

Summary Wireless Sensor Network (WSN) plays an essential role in consumer electronics, remote monitoring, an electromagnetic signal, and so forth. The functional capacity of WSN gets enhanced everyday with different technologies. The rapid development of wireless communication, as well as digital electronics, provides automatic sensor networks with low cost and power in various functions, but the challenge faced in WSN is to forward a huge amount of data between the nodes, which is a highly complex task to provide superior delay and energy loss. To overcome these issues, the development of a routing protocol is used for the optimal selection of multipath to perform efficient routing in WSN. This paper developed an energy‐efficient routing in WSNs utilizing the hybrid meta‐heuristic algorithm with the help of Hybrid African Vultures‐Cuckoo Search Optimization (HAV‐CSO). Here, the designed method is utilized for choosing the optimal cluster heads for progressing the routing. The developed HAV‐CSO method is used to enhance the network lifetime in WSN. Hence, the hybrid algorithm also helps select the cluster heads by solving the multi‐objective function in terms of distance, intra‐cluster distance, delay, inter‐cluster distance, throughput, path loss, energy, transmission load, temperature, and fault tolerance. The developed model achieved 7.8% higher than C‐SSA, 25.45% better than BSO‐MTLBO, 23.21% enhanced than AVOA, and 1.29% improved than CSO. The performance of the suggested model is validated, and the efficacy of the developed work is proved over other existing works.

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