A new fuzzy multi-hop clustering protocol with automatic rule tuning for wireless sensor networks
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.
Section snippets
Literature review
According to a simple classification, clustering-based routing protocols can be categorized as single-hop and multi-hop methods. Fig. 1 shows the difference between these two categories of methods.
FMSFLA clustering protocol
The FMSFLA is proposed to achieve three main goals, the first of which is to develop an energy-efficient clustering and routing protocol. Second, the proposed protocol is supposed to be adjustable and usable based on different applications of the wireless sensor network. Third, it should be scalable. Fig. 2 presents an overview of the proposed FMSFLA protocol phases.
Parameters and fuzzy logic model
In this section, the fuzzy logic model is presented thoroughly along with the input and output parameters of the CH selection and parent selection phases in the proposed protocol (FMSFLA), with details on how to calculate, and the type of membership functions for each, are fully described.
Optimization of the FMSFLA protocol via SFLA
This section presents the process of optimizing the fuzzy rule table of the FMSFLA through the SFLA optimization algorithm thoroughly. Fig. 14 shows the optimization flowchart of the proposed FMSFLA protocol.
Analysis of the complexity of the online process of FMSFLA
The time complexity of the FMSFLA protocol includes the time complexity of the CH selection phase (clustering) and the parent selection phase (multi-hop information transfer to the BS). The fuzzy rule tables, and the five adjustable parameters are optimized and saved for the current process of the network operation (online). Since the FMSFLA is a centralized protocol, all CH selection and parent selection processes are done in the BS. The time complexity of this protocol is discussed here.
The
Performance evaluation
This section draws a comparison between the scenarios and details of configuring FMSFLA and other similar protocols in simulations. The simulation results are then compared and a brief account of procedures is given for each protocol.
Conclusion
This paper presents a fuzzy multi-hop clustering protocol (FMSFLA), which not only is energy-efficient, but it can also manage energy consumptions of nodes for optimization based on FIS rule-base table and five control parameters by using the shuffled frog leaping algorithm (SFLA) for application management. The proposed protocol considers effective parameters including energy, distance to the BS, the number of neighboring nodes, real distance between a node and the BS, mean route load, delay,
Declaration of Competing Interest
No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.asoc.2020.106115.
Acknowledgments
The authors would like to express their thanks to the anonymous referees for their valuable comments and suggestions that improved the paper.
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2022, Expert Systems with ApplicationsCitation Excerpt :Fuzzy logic (Zadeh, 1965) is an approach for mapping an input space to an output space by using fuzzy rules and a collection of fuzzy membership functions. Since the efficiency of a network is influenced by various related factors, the fuzzy approach has been applied to manage the complicated characteristics of different factors and to compute the probability for the appropriate CHs and NHs in many clustering protocols (Bagci & Yazici, 2013; Baranidharan & Santhi, 2016; Fanian & Rafsanjani, 2018, 2020; Lata et al., 2020; Liu & Chang, 2019; Radhika & Rangarajan, 2019; Zahedi et al., 2016). Because of the benefits of the fuzzy approach, we applied this technique as the fundamental idea behind the overall EFC-ISFLA method consisting of the following four main processes: