A clustering routing protocol for wireless sensor networks based on type-2 fuzzy logic and ACO

Abstract

Aiming at the problem of load balancing and lifetime prolonging for wireless sensor networks (WSNs), and considering complex uncertainties existed in WSNs, this paper proposes a clustering routing protocol CRT2FLACO for WSN based on type-2 fuzzy logic and ant colony optimization (ACO). Specifically, in the cluster set-up phase, a type-2 Mamdnai fuzzy logic system (T2MFLS) is built to handle rule uncertainty better and balance the network load, in which three important factors-residual energy, the number of neighbor nodes and the distance to the base station (BS) of a node-are considered as inputs, and the probability of the node to be a candidate cluster head (CH) and the CH competition radius as outputs of our T2MFLS, to select the final CHs; in the steady-state phase, in order to reduce the transmission consumption, all the CHs are linked into a chain using ACO algorithm, then each CH send its data packet to the leader along link, which is a CH eventually transmitting packets to the BS. The simulation results show that the proposed routing protocol can effectively balance network load and reduce the transmission energy consumption of CHs, thus greatly prolong the lifetime of WSN.Index Terms-Wireless sensor network, clustering algorithm, unequal competition radius, type-2 fuzzy logic, ant colony optimization I. INTRODUCTIONAs a product of the development and combination of the sensor technology, embedded computer technology, wireless communication technology and distributed information processing technology, wireless sensor networks (WSNs) provide a kind of brand-new information acquisition and processing method, and have broad application prospects in military, environmental protection, agriculture and health and other fields [1]. WSN is commonly composed of tiny, cheap, low power consumption sensor nodes which are deployed in monitoring area with wide coverage. The nodes are generally tiny embedded systems, which have relatively weak processing capacity, storage capacity and communication ability, carry energy limited battery and not easy to replace or recharge. Therefore, the main challenge for WSN is balance the network load and reduce the overall energy consumption, and consequently prolong the network lifetime.To solve this problem, researchers have proposed many routing protocol to extend the life cycle of WSN. In presence, clustering routing protocol is commonly used, in which each cluster is formed by a number of adjacent nodes, consists of

Eigenvector centrality based cluster size control in randomly deployed wireless sensor networks

“…As compared to Yu, Qi, Wang & Gu (2012) our method can be linked to any routing protocol and is applicable irrespective of the nature of deployment, sink location and shape of the deployment region. While Zheng et.al, (2013), Mao, Zhao, Zhou & Ye, (2013) & Zhang, Sun & Zhang (2014) have proposed new inter cluster routing methodologies, our method can also be applied on these methods to further improve the energy efficiency. Thus, our method addresses the more realistic situation of random and irregular deployment of nodes and has better applicability.…”

“…The proposed method has used ACO to find the optimal path between cluster-head and base station. Zhang, Sun & Zhang (2014), have proposed a type-2 fuzzy logic and Ant Colony Optimization (ACO) based clustering routing protocol CRT2FLACO for WSN. In the proposed method, type-2 Mamdani fuzzy logic system (T2MFLS) has been used to compute competition radius and probability of the node to be cluster-head.…”

Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks

“…Maximize network lifetime A clustering routing protocol for WSNs based on type-2 fuzzy logic and ACO [33].…”

Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs

“…As a computing technology that can quickly and efficiently solve complex problems, swarm intelligence has been applied to UWSNs clustering routing algorithm [ 6 ]. Swarm intelligence has various techniques, such as the cat swarm optimization algorithm (CSO), the fish swarm algorithm, the ant colony algorithm (ACO), and the particle swarm optimization (PSO) algorithm, among others [ 7 , 8 , 9 , 10 , 11 ]. In general, swarm intelligence is easy to understand, has low complexity and strong commonality.…”

“…In general, swarm intelligence is easy to understand, has low complexity and strong commonality. Zhang et al [ 7 ] proposed a clustering routing algorithm based on type-2 fuzzy logic and ACO. In particular, the type-2 fuzzy logic was used to solve the network uncertainty and balance the energy load.…”