Energy‐aware redundancy‐aware clustering in wireless sensor networks using Spined Loach Searching Optimization

Abstract

Wireless sensor networks play dominant role in data communication in many engineering applications and faces lot of challenges in its implementation. Energy conservation is also significantly required in these systems as the sensor nodes are battery operated. Hence, the energy efficient-based technique needs to be implemented in data transmission. The most widely used methods are clustering-based data transmission and energy efficient routing detection.In clustering, the proper selection of cluster head (CH) node is important to ensure energy balancing. The CH node should have more residual energy as it does many transmissions in the network. Moreover, the location of CH node should be nearer to all member nodes in the cluster. The clustering process must also perform the job of avoiding unnecessary transmissions. By considering these factors, the CH node can be optimally selected using Spined Loach Searching Optimization algorithm. The food searching behavior and environmental suitability for spiny loach fish living are deliberated to develop this bioinspired algorithm. In addition, the redundant transmissions inside the clusters can be reduced by employing a new technique called Least Difference Threshold Based Similarity Grouping. This work improves energy efficiency comparing with the conventional low energy adaptive clustering hierarchy protocol, and it is evaluated by setting the network in simulation environment. The network lifetime is increased; energy consumption, delay of transmission and the message cost are reduced.

Secure Clustering and Routing using Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm for Wireless Sensor Networks

“…Equation (6) shows the update of 𝑋 𝑖 . 𝑋 𝑖 (𝑡 + 1) = { 𝑋 𝑖 (𝑡) + 𝛼 ⨁ 𝐿𝑒𝑣𝑦 (𝜆) 𝑖𝑓 𝑓(𝑋 𝑖 (𝑡)) ≤ 𝑓(𝑉 𝑖 (𝑡 + 1)) 𝑉 𝑖 (𝑡 + 1) 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (6) Where, fitness value is denoted as 𝑓; dot multiplication is denoted using ⨁; step size control parameter is denoted as 𝛼; random search path is denoted as 𝐿𝑒𝑣𝑦 (𝜆) that required to satisfies the equation (7).…”

“…The sensor based devices used in WSN becomes intelligent and it has various functions such as intercommunication, calculation, sensing and decision making capacity which makes the WSN is powerful based on today's requirements [5]. The sensors are operated with the battery power, therefore the capacity of computation and data transferring is limited over the network [6]. The WSN is susceptible to the malicious attacks due to its energy constraint, lack of centralized administration and open deployment [7] [8].…”