A new fuzzy multi-hop clustering protocol with automatic rule tuning for wireless sensor networks

Highlights

• FMSFLA aims to maximize the network lifetime and scalability based on application.

• FMSFLA considers effective parameters to select cluster heads and the parent node.

• The fuzzy rule table for clustering and multi-hop phases is optimized using SFLA.

• FMSFLA ensures proper distribution of clusters with the overlap optimized parameter.

• The simulation results show the proposed FMSFLA outperforms other protocols.

Abstract

In today’s world, a major challenge is to conserve and make optimal use of energy. This is a critical matter in wireless sensor networks due to their wide application in different areas. More importantly, scant attention has been paid to the use of node energy for certain applications in such networks. This study used the Shuffled Frog Leaping Algorithm (SFLA) to propose a Fuzzy Multi-hop clustering protocol (FMSFLA). The SFLA is used for automated configuration and optimization of the rule-base table in a fuzzy inference system and five adjustable parameters in two phases, i.e. Cluster Head (CH) selection and parent selection, based on application features. The proposed protocol (FMSFLA) considers effective parameters including energy, distance from the base station (BS), the number of neighboring nodes, real node distance from the BS, mean route load, delay, overlap, and the problem of hot spots, to achieve the best application-based performance. The FMSFLA includes rounds, in each round the phases of CH selection, parent selection, cluster formation, and steady state are performed. In the CH selection phase, CHs are selected from candidate nodes based on the fuzzy output and energy threshold (i.e. a control parameter) with respect to the overlap rate of adjacent CHs. In our protocol, the parent selection phase began by determining the levels of CHs in the network. At the end of this phase, the parent of each CH is determined on the basis of the greatest fuzzy output based on application. In the cluster formation phase, the clusters are formed on the basis of the determined CHs. Finally, the information received by CHs is sent through their parents to the BS in the steady state phase. The FMSFLA is evaluated against the LEACH, LEACH-EP, LEACH-FL, ASLPR, SIF, and ERA protocols in terms of the number of alive nodes, received packets, and cluster heads in addition to their appropriate distribution rates and other parameters pertaining to the network lifetime and protocol scalability using three application-oriented scenarios. According to the simulation results, the FMSFLA functioned far better than the other protocols in all scenarios with respect to goals and application features.

Introduction

Wireless sensor networks (WSNs) represent a new generation of networks consisting of many inexpensive sensor nodes, which are linked through wireless waves. The goal of WSNs is to collect information from the surrounding environment of network sensors. Energy is a major challenge to WSNs, with numerous methods proposed to reduce the energy of sensor nodes and lengthen the network lifetime due to dischargeable and irreplaceable batteries. Clustering is one of the most efficient methods for this purpose [1], [2], [3], [4]. In clustering, sensor nodes are classified into certain groups, called clusters. Every cluster has a cluster head (CH) which is responsible for sending collected information from cluster members to a base station (BS) on single-hop or multi-hop routes through other CHs [5], [6], [7], [8], [9]. Finally, information is collected and processed in the BS so that the actual values of the relevant parameters are estimated fairly accurately [10], [11], [12], [13]. Clustering offers numerous advantages such as reducing bandwidths, reducing overhead, preventing the redundancy of message exchange among nodes, and implementability of administrative strategies in networks [14], [15], [16].

There have been many technological advances in the manufacturing of small nodes, development of wireless sensor networks, and increased applications of such networks in industries [17], medicine [18], military [19], [20], agriculture [21], etc. Therefore, such networks have been widely used to increase the welfare and comfort of human life. In this paper, the SFLA is employed to propose a multi-hop fuzzy clustering protocol with the main purpose of reducing network energy and prolonging network lifetime. Given the configurations of the SFLA objective function designed on the basis of application, the proposed FMSFLA prolonged the network lifetime based on an application. Then CHs are selected from candidate nodes having the greatest fuzzy outputs based on the overlap rate of adjacent CHs based on application, resulting in the appropriate distribution of CHs in the network. In addition, the redistributor (parent) nodes are selected in the FMSFLA from candidate nodes having the greatest fuzzy outputs based on application. There are also two fuzzy rules base tables developed and optimized by the SFLA prior to the network operations to select CHs and parent nodes along with five parameters controlling the clustering and routing processes. Then they are used in the current network process. The proper distribution of CHs, the right number of CHs, and their appropriate activity radius are determined in the FMSFLA based on application. Finally, they result in a steady workload, reduce the energy consumption, and prolong the network lifetime. In fact, the SFLA determine and optimize the minimum and maximum overlap rates of adjacent CHs and their activity radius based on different scenarios, their distances to the BS, and the threshold determining the energy level of candidate nodes, all regarded as control parameters based on application.

In this paper, Section 2 reviews the related literature. Section 3 presents hypotheses, a network model, an energy consumption model, and the proposed protocol in detail. Section 4 introduces the fuzzy logic model thoroughly with relevant inputs in the FMSFLA. In Section 5, the SFLA is used for describing the details of the FMSFLA optimization process. Section 6 analyzes the complexity of the proposed algorithm. Section 7 evaluates the proposed algorithm. Finally, Section 8 presents the conclusion.