F5N‐based unequal clustering protocol (F5NUCP) for wireless sensor networks

Abstract

Summary Wireless sensor networks (WSNs) became very popular and still remains an active research because of diverse applications. Minimizing the energy dissipation and maximizing the network lifetime is an important design issue in WSN. Clustering is the widely used energy effective technique to lessen the overall energy consumption of WSN. An appropriate selection of cluster heads (CHs) and cluster size is a crucial process in clustered WSN. The CHs nearer to base station (BS) suffer from hot spot problem and die earlier than the normal lifetime. This paper contributes a five input fuzzy‐based unequal clustering protocol (F5NUCP) for selecting CHs and determining the appropriate cluster size to prevent the network from hot spot problem. Unequal clusters eliminates the hot spot problem by producing smaller clusters for the nodes located near the BS and larger clusters for the nodes located far away from the BS. F5NUCP uses a nonprobabilistic approach for selecting tentative CHs by introducing a back‐off timer where the timer value is set using the remaining energy of the node. The proposed method uses five input parameters, namely, remaining energy, distance to BS, distance to its neighboring nodes, link quality, and node degree. The output fuzzy parameters used in this research are cluster size and the probability of becoming CHs. The proposed method is implemented in MATLAB. The simulation results show that F5NUCP performs well when compared with LEACH, DEEC, TEEN, and EAUCF in terms of network lifetime and reduced energy consumption.

Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

“…The solution gives a direction from every gateway in the direction of the BS during after that subsequent gateway in the network. Every gateway is initiated with an arbitrary number (E q, p ) = Rnd(0, 1) where 7 N i is the first solution. The element p is the gateway number in the particular solution.…”

“…Update wolves position. To attain the preys, every wolf has to know its place based on the location of delta, beta and alpha wolves are template in equations (7) and (8). In GWO-dependent method, alpha wolves are the wide resolution in the solution put; the beta wolves are the optimal solutions from the before iteration; and the delta wolves are the optimal solution from the present iteration.…”

“…Routing is approved with regarding the smallest distance traversal and the least count of hops. 7 Consequently, the lesser on the whole distance crossed, and the numbers of hop, the maximum the fitness value for the result, which means that distances and numbers of hop are in reverse relative to the routing fitness. The solution with the maximum fitness value is the optimal solution in the population.…”

“…Some other techniques involving the inter-cluster communication are also presented in the literature. 5,7 This article presents a new swarm intelligence (SI)based clustering and with multihop routing protocol for WSN. Initially, improved particle swarm optimization (IPSO) technique is applied for deciding the CHs and organizes the clusters proficiently.…”

“…6 A sensor network consists of sensing unit, communication unit, memory unit and communication unit which are limited in nature. 7 WSN is deployed in unmanned environments that damage the nodes for replacement or more expensive nodes. Hence, in many cases, the wireless node must be computed for longer period of time without battery.…”