Fuzzy Based Cluster Routing in Wireless Sensor Network

doi:10.21203/rs.3.rs-1532702/v1

Wireless sensor networks (WSNs) built for multiple purposes consist of a number of sensor nodes and base station (BS). In designing these networks, hierarchical cluster routing protocol is used as a common solution for the challenge of energy consumption and longer network lifetime. The strategy of proposed method (FBCR) is basead on zoning and a fuzzy-based cluster routing protocol. In this method, some of the nodes are selected as cluster heads (CHs) that are responsible for sending data to the BS. For fuzzy input in CH selection, the criteria were used, including “residual energy of the node”, “node distance to the center of each region”, and “node angle to the BS”. Fuzzy logic was used as an algorithm for modeling decision-making and human experiences, to maintain energy balance and better CH selection. In FBCR, with the aim of reducing network energy consumption, the responsibility of selecting CHs in each region, determining the region of nodes, calculating distance from each node to the center, and calculating angle of nodes were assigned to the BS. FBCR is improved in terms of network lifetime, stability Period, selection and number of CHs, and the mean residual energy compared to Farahzadi, LEACH and MSCR methods.

Clustering

Network lifetime

Wireless sensor network (WSN)

Fuzzy logic

Routing

Selection of cluster head (CH)

One of the major challenges of wireless sensor network is the issue of routing.
The network division into several regions and fuzzy logic were used in the proposed method.
"Residual energy of the node", "Node distance to the center of each region", and "Node angle to the BS" were considered in CH selection.
The network was evaluated based on "network lifetime", "stability period", "selection and number of CHs", and "the mean residual energy".

Each WSN (Wireless Sensor Network) includes a number of sensor nodes and BS (Base Station). Main parts of sensors consist of communication unit, processing unit and a sensing unit, which have their limitations[1].

In these networks, the sensors based on their special conditions and capabilities, transfer the data obtained from the workplace to the BS by the transmitter antenna after processing. In some WSNs where the nodes are constantly sending data to the BS, the nodes energy is consumed quickly and the so-called nodes die. One of the most common solutions to delay this process is to use routing, such as cluster routing protocols. In these protocols, the network is divided into a number of clusters, each of which has a node called CH (Cluster Head). CHs are generally responsible for sending data to the BS[2–4].

The use of fuzzy logic in various fields and issues, such as WSN is increasing. This method introduced by Lotfi A. Zadeh, without the need for complete information about the problem (uncertainty) and by using human experience and intuition, instead of numerical data based on fuzzy set, reaches the appropriate answer. In fuzzy logic, unlike the classical method, a value between 0 and 1 is defined for each variable, indicating its correctness and incorrectness [5–7].

WSNs designed under specific conditions and purposes, can be classified in various topics. Homogeneous networks have the same nodes; while in heterogeneous networks, the nodes are dynamic and generally mobile. In homogeneous networks, fuzzy based clustering has a good performance.

In this section, some examples of cluster routing protocols are introduced focusing on the CH selection process. Given that the proposed method is related to the fuzzy subject and it is used in routing process, some clustering protocols related to fuzzy logic were also expressed.

One of the most popular hierarchical cluster routing protocols is the Low Energy Adaptive Clustering Hierarchy (LEACH), which selects clusters based on the probability model and initially assumes that each node has an equal chance of becoming CH. This protocol is implemented in two phases, including setup phase and steady state phase. In the setup phase, CH selection and create clusters are performed and in the steady state phase, the data are transferred. In the setup phase a threshold equation is defined, each node selects a random number between 0 and 1. If the random number is less than the threshold value, the node has a chance to be CH in the current round. According to the threshold equation, nodes that are not selected as CH have more chance of being selected as CH in later rounds. Data collection and processing are done in the steady state phase. For each sensor, the data are transferred to CH at the allotted time. After receiving the data from its cluster members, CH aggregates and compresses them and finally transfers them to the BS[8, 9].

The authors in [10] developed the LEACH protocol and considered the CH selection problem. In this method, dividing the network into several regions leads to better CH distribution, so that there is at least one CH in each region. Also, a new threshold equation in the CH selection process was introduced based on two criteria of “residual energy of the node” and “node distance to the center of each region”, which shows the simulation study and the results indicating a reduction in energy consumption compared to another method.

LEACH-ERE method, which is one of the developed LEACH protocols that uses a system based on fuzzy logic to improve energy consumption. In this method, two parameters, including the expected residual energy after the end of the current round and the current residual energy as the fuzzy input are used to select CH. Energy consumption improvement of has been achieved according to the energy criterion compared to the LEACH protocol[11].

Fuzzy-based Hyper Round Policy (FHRP) method is a fuzzy-based algorithm for efficient and flexible cluster scheduling with the aim of reducing energy overhead. This method dynamically manages making cluster using fuzzy logic. In this method, clustering takes place at the beginning of each hyper round (HR) instead of each round. HR length is dynamically obtained according to the two criteria of “residual energy of the node” and “node distance to the center of each region” with the help of fuzzy logic. Therefore, re-clustering is done only in emergencies. The simulation results show the FHRP effectiveness in reducing the energy of clusters, increasing the network lifetime, and saving energy of the network nodes[12].

The improved particle swarm optimization based fuzzy clustering (IPSOFC) method is one of the developed particle swarm optimization (PSO). This method uses c-mean for fuzzy clustering to solve the problems of Early death and to reduce the efficiency of hot spot problem as well as the energy hole due to changes in the location of the nodes. Fuzzy inputs include parameters, such as residual energy, node density, and distance to the BS. The simulation results shown a large number of live nodes in IPSOFC compared to other methods[13].

Fuzzy based secure data gathering (FSDGA) is a method based on slot-based scheduling and asymmetric key encryption scheme. In this method, fuzzy logic is used to select CH to reduce energy consumption and network overhead. Moreover, due to the importance of choosing the appropriate route leading to reduction of latency and packet loss rate, two types of CH have been introduced called stable and dynamic. Stable CH is used to review and analyze route history, while dynamic CH focuses on routing and data gathering. After clustering and creating a secure route, the dynamic CH starts gathering data based on the allotted time, selects the CH fuzzy inference engine based on residual energy, node flexibility, connection ratio, and node stability. The simulation results shown that the proposed method provides more data collection, less latency and less control overhead compared to other methods[14].

The multidimensional secure clustered routing (MSCR) method proposed by Kiran et al. is a combined method of fuzzy clustering and multidirectional routing. In this method, residual energy and distance are used for clustering and selecting CH. Furthermore, to reduce energy consumption, various routes between the node and BS are examined and a route with the shortest distance and step is used to transfer information. The simulation results indicated an increase in the network lifetime[15].

The essential role of the CH node in data transmission and high workload lead to more energy consumption compared to other nodes; therefore, in the proposed method, FBCR (Fuzzy Based Cluster Routing), for better distribution of CH node, the network is divided into several regions and the CH selection process takes place in each region. In this method, CH is selected based on the three parameters, including the “Residual Energy of the Node”, “Node Distance to the Center of each Region”, and “Node Angle to the BS”. Fuzzy logic was used to maintain energy balance and better CH selection and rotation.

One of the most important reasons for using fuzzy logic in various fields is modeling human decision-making and human experiences, which is more accurate than the probability model. Fuzzy logic consists of 3 basic steps as follows.

Fuzzifier: In this step, the crisp inputs are converted to fuzzy linguistic variables.
Inference Engine: Analysis and inference are done according to the table of rules and fuzzy output is obtained.
Defuzzifier: In this step, the obtained fuzzy output is converted to crisp output using the appropriate method[5, 16].

In FBCR, the nodes are randomly distributed in the network and the network is divided into 4 regions (Fig. 1). CH was selected based on fuzzy logic by the BS. In this method, like most clustering protocols, the network lifetime is defined in the form of a number of rounds, each round consists of two phases, including setup phase and the steady state phase.

3.1. Setup phase

In FBCR method, the BS responsibilities include selecting CHs in each region, determining the region of each node, calculating the distance of each node to the center of its region, calculating the angle of each node to the BS, and sending information to all nodes. After randomly distributing the nodes in the network and dividing the network into several regions, the BS determines the region of each node. The distance of each node from the center of its region is calculated and stored based on the Euclidean distance given in Eq. (1).

$$\text{d}(\text{i},{\text{C}}_{\text{A}\text{j}})= \sqrt{{\left( {\text{X}}_{\text{i}}-{\text{X}}_{{\text{C}}_{\text{A}\text{j}}}\right)}^{2}+{\left({\text{Y}}_{\text{i}}-{\text{Y}}_{{\text{C}}_{\text{A}\text{J}}}\right)}^{2}}$$ (1)

C_Aj = coordinate of each region center in the network

i = coordinate of each node

j = region number

The number of CH, as an important topic in each region, was calculated based on Eq. (2).

$$\text{K}=\text{f}\text{l}\text{o}\text{o}\text{r}\left(\sqrt{\text{N}}\times \text{r}\text{a}\text{n}\text{d}\text{n}\right)$$ (2)

K = number of CH (if K = 0 then repeat the equation)

N = number of live nodes in each region

After calculating K value, the BS determines the CHs in each region based on fuzzy logic. The steps are shown in Fig. 2.

3.1.1. Fuzzification

The defined linguistic variable in the fuzzy system is three-variable (low, medium, high) for the inputs of "node distance to the center of each region" and "node angle to the BS" and five-variable (very low, Low, medium, high, and very high) for "residual energy of the node" (Fig. 3).

3.1.2. Table of rules and inference engine

The rules used by the inference engine are defined as IF-THEN based on Mamdani method. The results are sent to the defuzzification section. The fuzzy rules are shown in Table 1 determine the chance of each node being selected as CH, based on the maximum residual energy, the shortest distance to the center, and the lowest angle to the BS.

3.1.3. Defuzzification

To use inference engine results, they must be converted to the crisp values. At this stage, the CoA method was used as one of the Centroid methods. Figure 4 represents that the output membership function is five-variable (very low, Low, medium, high, and very high).

After selecting CH in the first round, the BS informs each node of the region where the node is located and the characteristics of the CHs in that region. Each node selects the closest CH using the received signal power then informs it; thus clusters are formed.

3.2. Steady state phase

In this phase, which begins after clustering, each CH dedicates time to each node according to the number of members by TDMA (Time Division Multiple Access) method to send its data to the CH during the allocated time. Each node is only active at a specific time slot and inactive at others to prevent energy consumption.

Since the BS needs the residual energy of each node to select CH in subsequent rounds, each node sends its residual energy to CH at its last time slot. The received information from the members is sent to the BS by the CHs after aggregation and compression, and at the last time slot, CH also transfers its own residual energy.

Given the importance of the energy consumption model in WSNs and it is clear that information transfer consumes considerable energy compared to other sensor tasks, Farahzadi et al.’s model was used.

Table 1 FUZZY RULES AND output Priority.

Input Variables			Output
Residual Energy of the Node	Node Distance to the Center of Region	Node Angle to the BS	Priority of Selecting CH
VL	L	L	L
VL	L	M	VL
VL	L	H	VL
VL	M	L	L
VL	M	M	VL
VL	M	H	VL
VL	H	L	L
VL	H	M	VL
VL	H	H	VL
L	L	L	H
L	L	M	L
L	L	H	VL
L	M	L	H
L	M	M	M
L	M	H	M
L	H	L	H
L	H	M	L
L	H	H	L
M	L	L	H
M	L	M	M
M	L	H	M
M	M	L	M
M	M	M	L
M	M	H	L
M	H	L	L
M	H	M	L
M	H	H	VL
H	L	L	VH
H	L	M	H
H	L	H	H
H	M	L	H
H	M	M	M
H	M	H	M
H	H	L	H
H	H	M	M
H	H	H	L
VH	L	L	VH
VH	L	M	VH
VH	L	H	H
VH	M	L	H
VH	M	M	H
VH	M	H	M
VH	H	L	M
VH	H	M	L
VH	H	H	L

In this section, the proposed method (FBCR), Farahzadi, MSCR and LEACH were simulated with MATLAB software to compare and evaluate their strengths and weaknesses. All nodes that are the same in processing and communicating, do not have GPS and are randomly distributed. The parameters of the simulation environment are given in Table 2.

Table 2 Simulation parameters.

Parameter	Value
The size of the simulation environment (x_m ,y_n)	100×100 m²
The number of nodes (N)	100
Base station location (BS)	(50,175)
Initial energy	0.5 J
Energy data aggregation (EDA)	5 nJ/bit
Data packet length	6400 bit
Control packet length	200 bit
Round time (T)	20 s
Free space (ℰ_fs)	10 pJ/bit/m²
Multi-path (ℰ_mp)	0.0013 pJ/bit/m⁴

5.1. Network lifetime

Network lifetime is defined as a number of rounds, and the higher number, the longer lifetime. The simulation results of the mentioned methods in terms of network lifetime are shown in Fig. 5.

Figure 5 indicates that the nodes run out of energy very quickly in the LEACH protocol due to inattention to the issue of energy and the lack of a specified system in CH selection. Farahzadi method, which considers “residual energy of the node” and “node distance to the center of each region”, has improved by 100% compared to the LEACH protocol. The MSCR method has a longer lifetime than the previous two methods because the combination of fuzzy logic with the shortest route selection. By choosing better CH in FBCR method, the number of round has increased by 136%, 18%, and 8% compared to LEACH, Farahzadi, and MSCR methods, respectively.

5.2. Network stability Period

Network stability is one of the important parameters of WSNs, indicating the efficiency of the networks. Before the first node dies (FND index), the network is operating in its best possible condition without the slightest disruption. Figure 6 shows that the stability Period in the proposed method is higher than others, and FND is 835, 710, 692, and 390, respectively, in FBCR, MSCR, Farahzadi, and LEACH. This figure also shows first node dies (FND), half node dead (HND), and last node dead (LND) for each method.

5.3. Selection and number of CHs

Because CH is responsible for transferring information to the BS, consumes more energy than other nodes, so the number of CHs and their selection are important.

LEACH protocol uses a threshold equation for change CHs; therefore, CH selection has a vague and random mechanism. After zoning the network, Farahzadi et al. selected CH in each region based on “residual energy of the node” and “node distance to the center of each region” using a threshold equation. MSCR method uses “residual energy of the node” and “node distance to the BS” as two input criteria to fuzzy logic in CH selection. In FBCR method, after dividing the network into several regions, CHs are selected using fuzzy logic of the three inputs lead to a better distribution of CH throughout the network.

5.4. Mean network residual energy

The mean residual energy per round, indicating the energy consumption trend in WSNs, is shown in Fig. 7. As mentioned in Table 1, the nodes mean energy was initially 0.5 J and the network activity process continues until remaining of 5 alive nodes. According to HND, which is obtained from Fig. 6, the mean residual energy of the network for FBCR, MSCR, Farahzadi, and LEACH, is 0.136 J, 0.152 J, 0.119 J, and 0.112 J, respectively.

The issue of longer network lifetime and battery limitations of sensors in WSNs has led designers to come up with different ideas, such as hierarchical cluster routing. FBCR is a combined method dividing the network into several regions and uses fuzzy logic to better select CH. Three-variable inputs were considered for "node distance to the center of each region" and "node angle to the BS" and five-variable input was defined for "node residual energy" in the fuzzy system. Five-variable was also considered for the output membership function in the defuzzification stage.

One of the advantages of FBCR was that the responsibility of selecting CH in each region was assigned to the BS, which reduced the computational load of the nodes, thus reducing the energy consumption of the entire network, given that the BS has no energy limits.

In this method, the number of CHs was obtained based on a new equation, which in addition to using the number of live nodes in each region, ensures the existence of at least one CH in each region. The rules of the inference engine, defined using Mamdani method, determine the chance of nodes being selected as CH.

FBCR performed better than Farahzadi, LEACH and MSCR methods according to the studied criteria. Regarding network lifetime criterion as one of the important indicators, the results showed that FBCR has improved by 136%, 18%, and 8%, respectively, compared to LEACH, Farahzadi, and MSCR methods.

It seems that this method is also useful for reducing energy consumption in networks with mobile nodes. It is also suggested that other network criteria, such as QOS (Quality of Service), latency and overhead be considered in future studies.

Ethical approval

This study was supported by Shahid Sadoughi University of Medical Sciences (Ethics Code: IR.SSU.SPH.REC.1399.146).

Funding details

We have not received any funding support.

Conflict of interest

There is no conflict of interest related to the publication of this manuscript.

Informed Consent

Not applicable.

Authorship Contributions

AliReza Naderloo: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Visualization, Supervision. Seyed Ali Fatemi Aghda: Validation, Formal analysis, Data Curation, Writing review & editing. Mahdi Mirfakhraei: Methodology, Software, Investigation, Data Curation, Writing review & editing, Visualization, Supervision, Project administration.

Kaur, T. and D. Kumar, Particle swarm optimization-based unequal and fault tolerant clustering protocol for wireless sensor networks. IEEE Sensors Journal, 2018. 18(11): p. 4614-4622.
Kiran, W., S. Smys, and V. Bindhu, Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks. Soft Computing, 2020. 24(15): p. 11805-11818.
Sah, D.K. and T. Amgoth, Renewable energy harvesting schemes in wireless sensor networks: a survey. Information Fusion, 2020. 63: p. 223-247.
Arulmurugan, A., S.F. Waris, and N. Bhagyalakshmi. Analysis of Cluster Head Selection Methods In WSN. in 2021 6th International Conference on Inventive Computation Technologies (ICICT). 2021. IEEE.
Sood, P., A fuzzy logic based clustering algorithm for WSN to extend the network lifetime. Journal of Global Research in Computer Science, 2018. 9(6): p. 19-25.
Lin, D., W. Min, and J. Xu, An energy-saving routing integrated economic theory with compressive sensing to extend the lifespan of WSNs. IEEE Internet of Things Journal, 2020. 7(8): p. 7636-7647.
Aghda, S.A.F. and M. Mirfakhraei, Improved routing in dynamic environments with moving obstacles using a hybrid Fuzzy-Genetic algorithm. Future Generation Computer Systems, 2020. 112: p. 250-257.
Heinzelman, W.R., A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks. in Proceedings of the 33rd annual Hawaii international conference on system sciences. 2000. IEEE.
Akyildiz, I.F., et al., Wireless sensor networks: a survey. Computer networks, 2002. 38(4): p. 393-422.
Farahzadi, H.R., et al., An improved cluster formation process in wireless sensor network to decrease energy consumption. Wireless Networks, 2021. 27(2): p. 1077-1087.
Lee, J.-S. and W.-L. Cheng, Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 2012. 12(9): p. 2891-2897.
Neamatollahi, P., M. Naghibzadeh, and S. Abrishami, Fuzzy-based clustering-task scheduling for lifetime enhancement in wireless sensor networks. IEEE Sensors Journal, 2017. 17(20): p. 6837-6844.
Bhowmik, T., I. Banerjee, and A. Bhattacharya. An improved PSO based fuzzy clustering algorithm in WSNs. in 2019 IEEE 16th India Council International Conference (INDICON). 2019. IEEE.
Samydurai, G., G.K. Bojan, and B. Dharmarajan, Fuzzy based Secure Data Gathering Approach for Ad hoc Sensor Networks. Journal of Scientific and Industrial Research (JSIR), 2020. 79(05): p. 391-394.
Fang, W., et al., MSCR: multidimensional secure clustered routing scheme in hierarchical wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2021. 2021(1): p. 1-20.
Taheri, H., et al., An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 2012. 10(7): p. 1469-1481.

Fuzzy Based Cluster Routing in Wireless Sensor Network

Status:

Journal Publication

Version 1

Abstract

Figures

Highlights

1. Introduction

2. Background

3. Proposed Method