Load-Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Srivastava, Ankita; Mishra, Pramod Kumar

doi:https://doi.org/10.1155/2023/4343404

Journal of Sensors

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Abstract Introduction Related Work System Model Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

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AI-Enabled Internet of Everything (IoE) in Next-Generation Wireless Sensor Networks

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Research Article | Open Access

Volume 2023 | Article ID 4343404 | https://doi.org/10.1155/2023/4343404

Load-Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Ankita Srivastava¹and Pramod Kumar Mishra¹

Academic Editor: Akhilesh Pathak

Received22 Jul 2022

Revised17 Oct 2022

Accepted31 Mar 2023

Published09 May 2023

Abstract

In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.

1. Introduction

Wireless sensor networks are the key step to any new technologies or applications as they can sense and monitor the environment. It collects the data, senses the data, and also makes a decision system for various applications [1, 2]. Sensor nodes have limited battery power and their replacement is not feasible. And it is a still challenge due to which network lifetime depleted and took more energy consumptions. Clustering is a useful approach in wireless sensor networks to increase network lifetime and improve energy efficiency. In clustering as shown in Figure 1, the sensor nodes were grouped into clusters, and from these clusters, CHs were chosen based on some parameters. After CH’s selection, the data were transferred to the base station from respective cluster heads [3]. Earlier various algorithms have been presented by researchers which are known to be the basic algorithms for clustering such as LEACH [4, 5], LEACH-C [6], and HEED [7, 8]. In LEACH, the cluster heads were selected based on probabilistic approaches, where CHs have been randomly selected, but later on, more advancement has been made to this approach. But sometimes, the selection of cluster heads was based on the probabilistic method due to which energy consumption increases which leads to overheads. There are various types of methods for the selection of cluster heads as some authors have taken distance from CHs and their residual energy, and some have taken the number of neighbor nodes and residual energy [9–14]. But deciding only on these parameters will not provide optimal CH selection. Thus, the multiattribute needs to be considered [15, 16] for cluster heads. Sensor nodes were the basic in all the emerging fields whether it is IoT [17, 18], digital image processing, cloud computing, or artificial intelligence. Everywhere, sensors were needed for sensing the data and then sending the data to the server. In today’s era, people were accessing various facilities such as smart home appliances, smart watches, smart TV, smart traffic systems [19], and smart healthcare systems [20] all of these with the help of emerging technologies where sensors play an important role. Considering multiattributes and making coordination among them by Multi-AttributeDecision-Making (MADM) approaches [21, 22] will enhance the network lifetime of the network. Various technologies need optimal node deployment where the performance of applications increases with efficient energy consumption.

In recent technologies, everything is online and sensor nodes were the central part of revolutionary technologies. In a hostile environment, it is believed that energized nodes must be alive for long period but know the fact that their battery is irreplaceable or not feasible to change [23]. This necessity of sensor nodes to be alive for a long time in the network leads to the advent of new alternative approaches for energy-efficient techniques for WSNs for resolving traditional issues such as network lifetime, connectivity, accuracy, latency, distance from the base station, power, and efficient energy consumption; at the same time, these are conflicting in nature too. Thus, these conflicting factors need to be considered in the approach, and proper coordination is needed for making efficient cluster head selection which is the main part of any clustering approach. Various issues and open challenges have been faced by WSNs such as routing, data, topology, coverage, and security, and various clustering approaches have been given to the researchers for resolving these issues [24]. The clustering approach has been used for an efficient energy data process. Some cluster heads (CHs) were chosen from the normal nodes such as in LEACH [25] and HEED [26] and somewhere chosen from the advanced node sometimes known as gateways such as in [27–29], and selected CH is responsible for sending the data back to the base station itself after data aggregation, filtering, or compression. When individual sensor nodes send their data to the base station, energy is not efficiently used. Thus, with the clustering approach, efficient energy utilization is possible, and thus, network lifetime will be enhanced. The cluster head algorithm is used for selecting cluster heads to transmit data to the base station in an efficient way. Some of the CH algorithms are LEACH, LEACH-C, HEED, EECS, and many variants of the LEACH algorithm. In CH selection, the primary goal is to select cluster heads, but for optimal cluster head selection, many factors need to be considered such as energy consumption, connectivity, coverage, load balance, distance to the base station, and distance to neighbor’s, but in earlier works, they only focus on one or two attributes, but with time, many updated algorithms were proposed. But in today’s scenario, we have to consider multiattributes, and making coordination among them is necessary for finding optimal cluster heads. So, these can be applied to many IoT-based applications and fulfill the current requirement of the users. Optimal cluster head selection [30] leads to efficient energy utilization; therefore, now, researchers were focussing on this, as recently every technology needs sensors for data collection, sensing, and monitoring. Many conflicting attributes play a vital role in efficient energy consumption for data collection, but these attributes were not discovered till now. But there is a need to make coordination among these conflicting attributes which will improve the efficiency of the network. Thus, Multi-AttributeDecision-Making (MADM) is used for making the coordination among the conflicting attributes and selecting the best alternatives among them.

MADM (Multi-Attribute Decision-Making) [31] is an approach applied to solve a problem where the selection of the best alternative can be done from the given alternatives. MADM specifies how we can process the information of the multiple attributes to give the ranking among the given alternatives. In this paper, coordination among the multiple attributes has been done for finding the optimal solution. Sensor nodes were deployed almost in every field according to the applications, but seeing today’s user requirement, we need to focus on the multiple attributes which lead to efficient energy which is lacking. We have considered multiconflicting attributes of sensor nodes for the cluster head selection. Results validate that making coordination among conflicting attributes is the better way to choose the cluster heads. In this paper, we have proposed MADM-based method for cluster head selection where network lifetime enhancement and load balance among the sensor nodes were obtained. The principle objective of our proposed work is as follows: (i)To explore the multi-attribute-based cluster head selection by collaborating with the conflicting attributes. The enhancement of network lifetime and efficient energy consumption was evaluated in terms of FND, CHD, and LND(ii)To conjoin among conflicting attributes and then to decide the selection of CHs which enhances the network lifetime and efficient energy consumption. The load among the sensor nodes was also balanced with optimal load balancing for sensor nodes(iii)To evaluate the performance of the network using a multicriteria decision-making approach

The rest of this paper has been arranged as follows: the related work has been discussed in Related Work. The energy model considered for the simulation and parameters used for the experiment have been discussed in Assumption and System Model. The detail of the proposed algorithm and method has been discussed in Evolution Methods for the Selection of CHs Using MADM. Multiattributes taken in this research paper with their detailed description have been discussed in Attributes Considered for the Proposed Work. In Data Set Generation, the generation of data using MATLAB has been discussed. Simulation results and experimentation with case studies have been discussed in Simulation Results, and also, the analysis of the obtained results with their case studies has been discussed. Concluding remarks on the future scope have been discussed in Conclusion and Future Scope.

Finding an energy-efficient data collection process in WSN is a big challenge. Data collection needs to be optimized as direct data collection will increase the communication heads which leads to less network lifetime. While facing this problem, some of the clustering solutions have been given by researchers [32, 33]. The clustering approach can be defined in several ways such as the CH selection methods (random, deterministic), objective of clustering (coverage, energy, and efficiency), clustering methods (distributed, centralized), or the architecture of the network for doing the communication (multihop, single hop). We can also classify the clustering methods into heuristic and metaheuristic methods. In this paper, we are doing single-hop communication for the wireless network.

LEACH [4] is a classical clustering algorithm that uses the probabilistic method for data collection based on the random number selection of nodes. Many LEACH algorithm variants have been developed for different purposes but have one important objective which is energy conservation. The main objective of the LEACH algorithm is efficient energy consumption by selecting the cluster heads on a rotation-based using a random number. There are several rounds in LEACH where each round is divided into two phases: the set-up phase and the steady phase. The concept used in the LEACH protocol is that it enforces less communication between the sensor node and the base station which increases the network lifetime. LEACH-C [13] is a variant of the LEACH protocol where all the decisions whether it is CH selection, distribution, or cluster formation are taken by the base station. LEACH-DCHS [34] is used for prolonging the network lifetime. Another protocol of LEACH is SLEACH [35] where the energy was harvested from the external source to the sensor node and the concept of solar power can be applied to distribute or centralize clustering. SLEACH [36, 37] is the first protocol that added the concept of security by using the SPIN protocol. This protocol uses the lightweight cryptographic technique in WSNs as this is a challenging task due to limited resources for the sensor nodes. ME-LEACH [38–40] means more energy-efficient LEACH extending the LEACH protocol by minimizing the distance between the sensor nodes and base station. EP-LEACH [41, 42] has improved the lifetime of the LEACH algorithm by using EH-WSN where the sensor nodes have a rechargeable battery that is charged from the environment itself.

HEED is another popular heuristic algorithm based on the single-hop transmission which does not depend upon the density of the sensor network. HEED algorithm considers residual energy and the number of neighboring nodes for selecting the cluster heads. This residual energy of sensor nodes is considered to be the primary attribute for selecting the cluster heads, and the average minimum reachable power works as a tie-breaker between the sensor nodes. The enhanced algorithm of HEED is named DWECH [43] which has the same primary parameter for the selection of cluster heads, but it also takes care of overlapping and unbalanced size when selecting the cluster heads. HEED has a good distribution of cluster heads over the network but has the disadvantage of not covering all nodes in the network. Both HEED and DWECH consume lots of energy due to overhead costs. FLOC [44, 45] is another heuristic algorithm that takes care of sensor nodes not getting overlapped and also creates an almost equal size of clusters such that each has one hop distance to the respective cluster heads. Energy-efficient clustering scheme (EECS) [46] is also another heuristic algorithm that reduces the unbalanced consumption of energy by considering the three attributes and also considering the respective weight cost factor for the sensor node. EEHC (energy-efficient heterogeneous cluster scheme) [47] provides the election probability weights that are directly related to the residual energy of the sensor node, whereas BEENISH [48] (balanced energy-efficient network-integrated super heterogeneous) protocol is also a clustering algorithm that assigns one of the four energy levels to the sensor node and uses this energy level for selecting the cluster heads. Enhanced developed distributed energy-efficient clustering for heterogeneous network (EDDEEC) [49] classifies nodes as normal nodes and advanced nodes and then changes the probability of becoming cluster heads.

Some of the metaheuristic algorithms were also proposed by the researchers in wireless sensor networks. Among them, the genetic algorithm is one of the most important algorithms used in the clustering approach for sensor networks where it reduces the communication distance of the target [50]. In [51], the authors propose a genetic algorithm-based fuzzy-optimized reclustering scheme to overcome the network lifetime failure, fixed routing path problem, and energy saving for the sensor network for the revolutionary area. The simulation results validate that the proposed algorithm for the network lifetime extension is 3.64-fold by preserving energy efficiency. In [52], the authors proposed a genetic algorithm for the dynamic clustering approach in IoT applications, and the simulation results validate that it has overcome the problem of a dynamic cluster relay node in terms of throughput and standard deviation for the data transmission. In [53], the authors propose the EEWC (energy-efficient weighted clustering) based on a genetic algorithm, and the proposed algorithm modifies the steady-state phase of LEACH and considered three attributes for the optimization which shows that the proposed algorithm is better than ERP, SEP, and IHCR. Some of them also use MADM-based approach for cluster head selection by considering 2 or 3 attributes. In [54], the authors propose an enhanced AHP-TOPSIS-based clustering algorithm for high-quality live video streaming flying in the ad hoc network. The proposed algorithms were simulated on OMNET++. It shows that video quality, UAV energy consumption, and the number of cluster heads needed have been improved when they used two models, namely, random waypoint and paparazzi. In [55], TOPSIS multicriteria decision-making algorithm has been used by OPNET software, and the proposed algorithm proved that it is suitable for clustering and selecting the cluster heads. The data transmission between the nodes has also been used for transmitting the files with improved efficiency of the network and sustainable routing path. In [56], the authors have proposed an ordered clustering based on PROMETHEE and the fuzzy c-mean clustering method. The author has finally proposed OFCM (ordered fuzzy c-mean clustering) for solving the problem of human development indexes, and comparison analysis also validates the efficiency of the OFCM approach.

But these approaches consider two or three factors in clustering which do not guarantee optimal clustering; thus, we need to consider more conflicting factors for achieving the optimal clustering for enhancing network lifetime. Less number of intermediate nodes for data transmission consumes more energy; thus, in [57], the authors suggested using an optimal number of intermediate nodes for the transmission of data enhancing the network lifetime. The authors in [58] have reviewed the renewable energy sources which will help WSN for recharging the battery of sensor nodes. They also discuss issues/challenges and provide future direction for the researcher to work on. In [59], the authors have proposed load-balanced adaptive position update (LAPU) for routing techniques which balances the load among sensor nodes in the selected path. Basically in this approach, the sensor nodes select the two best next hops for the data transmission based on the length queue and mobility of nodes and transmit the data to both selected nodes for balancing the load among sensor nodes. In [60], the authors have proposed a two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multihop wireless sensor networks (TTDFP) for enhancing the network lifetime by combining the efficiency of routing and clustering phases along with two-tier fuzzy logic for tuning the parameters. In [61], a modified CLONal selection algorithm has been proposed for improving the energy efficiency of rule-based fuzzy systems. Here, CLONALG has been modified for constrained approximation problems. In [62], the author proposed hybrid gray wolf optimization (HGWO) for resolving the constrained resource problem in WSNs. These resources can be in any form such as bandwidth and energy consumption.

In [63], the authors have proposed energy-aware clustering and efficient cluster head selection by dividing the network into grids. This cluster head selection was based on only residual energy, distance to the base station, and distance to neighbors. In [64], the authors proposed a low energy clustering hierarchy for mobile sensor networks to not only enhance network lifetime but also reduce packet loss. In [65], the authors proposed a new routing technique for efficient consumption and increased network lifetime by optimal selection of cluster head. But here, cluster head selection is done only based on residual energy and distance to the base station. In [66], fuzzy-based clustering algorithms have been proposed where two types of sensors used, namely, free sensors for communicating to the base station and clustered sensor that sends sensed data to CH and the base station which were based on four parameters. In [67], an extension of the energy prediction with fuzzy logic has been proposed for increasing the network lifetime.

But these factors do not guarantee the optimal solutions in WSNs because many conflicting factors affect the energy consumption such as if it selects cluster heads (CHs) near the base station based on higher residual energy, but it may be possible that some sensor nodes have higher distance from the base station to send the data which consume more energy, which will degrade the efficiency of the sensor networks. We have already seen the problem of resource-constrained in the form of bandwidth and energy consumption which will drastically affect the network lifetime. So we need to focus on cooperating among the conflicting attributes to choose optimal cluster heads which enhance the network lifetime by properly and efficiently utilizing the resources. Thus, MADM is an emerging approach for applications based on WSNs by considering some important factors too. This paper focuses on the cooperation among the many conflicting attributes and then deciding on optimal cluster heads for efficient energy consumption.

3. Assumption and System Model

In this paper, we have assumed some predetermined values of the parameters and also made some assumptions for our simulation. In the simulation, we have taken an energy model during the data collection process. The considered parameter values and assumption made for simulation purposes have been discussed in this section as follows:

3.1. Assumptions

The following assumptions have been made in our simulation: (i)Sensor nodes send the data from cluster heads to the base station(ii)Here, sensor nodes are homogeneous(iii)Here, the random uniform distribution of sensor nodes has been assumed(iv)Some gateways were selected as cluster heads and sent the data to the base station(v)Gateways are approximately six times higher energy than the normal sensor nodes(vi)The base station knows the location of sensor nodes, gateways, and vice versa(vii)The base station is considered to be the hefty node having the capability of communicating and computing without having any restriction on energy consumption(viii)Sensor nodes were having the potential to transmit the data to the fluctuating energy level based on the distance from the sensor nodes(ix)Sensor nodes are static

3.2. Energy Model

In WSN, most of the sensor nodes sense the data from the environment from their vicinity and send back data to the respective CHs, and CHs send this data to the base station by operating on the data. We need an energy model for transmitting the data, so we have taken the classical energy model for performing the operation mentioned above. The transmitter consumes energy for operating the radio electronics with amplifier power and only the receiver for operating the radio electronics.

In our experiment, we have considered both the free-space channel and multipath model. These models depended on the distance between the transmitter and receiver. Here, appropriate settings have been provided for preventing energy loss and providing power control at the power amplifier, i.e., if the distance for the transmission is less than which is the threshold distance that a free-space path will be used else a multipath model will be used. Here is an energy model; if bit packets send at a distance , then the required energy for the transmission is

The energy consumption for receiving the message at the receiver end is given as

Here, is the electronic energy based on some factors such as filtering, signal spreading, modulation, and digital coding. And the amplifier energy is known as or ; this is based on the receiver distance and bit error rate. For our experimental analysis, these energy parameters for transmission purpose have been set as follows: , , and . For data aggregation, the energy consumption has been taken as 5 pJ/bit/signal. The optimal number of cluster heads (CHs) can be calculated as

Here, is known as the distance from cluster heads (CHs) to the base station (BS), is the network deployment area, and is the number of sensor nodes.

3.3. Parameters and Energy Model for the Simulation

Parameters used in the simulation for energy dissipation have been given in Tables 1 and 2 showing the parameters used for the repeated simulations in our experiment.

4. Attributes Considered for the Proposed Work

Here, we have considered multiattributes for our proposed work, and these are conflicting in nature shown in Figure 2. These attributes have an important impact on cluster head selection. Attributes considered in this paper were given below in the figure. These are conflicting attributes; thus, proper coordination among them is necessary for further use in other applications.

5. Data Set Generation

For the simulation purpose, we have used MATLAB for modeling the WSN. In our simulation, we have generated a random population of 20 for preserving the difference between the 20 alternative populations. The more difference helps us to understand the proposed APRO algorithm in a better way. The population generated in our experiment has been done by using the above equations for every given alternative. Table 3 shows the brief descriptions about the attributes considered in our proposed work, whereas Table 4 shows the computed values of the attributes for modeled WSN by using the CHs selected for each alternative:

6. Evolution Methods for the Selection of CHs Using MADM

Many methods can be applied to select the best cluster heads (CHs). In this section, we have applied MADM-based methods such as AHP and PROMETHEE for ranking the alternative and selecting the best cluster heads (CHs) among them. Here, each method with its respective results has been discussed.

6.1. AHP (Analytical Hierarchy Process) Method for CH’s Selection

Step 1: the first step is to normalize the data set () by using the following equation; we can denote the matrix by , and the normalized data has been presented in Table 5

There are two types of factors: one is a beneficial factor and the other is a nonbeneficial factor. For the beneficial factor, we have to select the max value of each factor to compute the normalized value to the column and .

And for nonbeneficial, the min value for each factor has been calculated , where .

The values of the normalized matrix lie between 0 and 1.

Step 2: after this, the relative importance matrix will be generated

Step 3: geometric mean (GM) of each has been computed by using the following equation, and it is represented in Table 6

Step 4: in this step, a weighted matrix will be calculated . Table 7 denotes the weighted matrix of the attributes

And also

Step 5: check the consistency: (i)Compute the matrix and it is represented in Table 8.(ii)Compute the matrix and it is represented in Table 9.(iii)Compute :

Consistency index (CI) is as follows: (iv)Compute consistency ratio:

Here, is the random index.

Step 6: calculate the value of by using the SAW method. In our experiment, we used the SAW method for selecting the CH. The value is presented in Table 10

Step 7: in this step, we finally rank the alternative according to the higher value of . And the rank is presented in Table 11

6.2. PROMETHEE Method for CH Selection

Step 1: calculate the denomination matrix for each attribute and alternatives matrix. . For each attribute, the denomination matrix has been given in Tables 12–34. In Table 35, we have presented the abbreviation of attributes used in the simulation

Step 2: now, the corresponding weights of each attribute are multiplied by each denomination matrix, and the final matrix is calculated by doing the summation of each matrix. And the final denomination matrix is presented in Table 33where .

Step 3: now, compute and by adding rows and columns. This is presented in Table 36

Step 4: compute the net flow by using the following equation as presented in Table 37:

Step 5: lastly, rank the alternative according to the higher value of , and the rank is given in Table 38

7. Simulation Results

This section evaluates the proposed APRO algorithm against other clustering algorithms under a different scenario. In this, MATLAB is used for the simulation, and we have compared the proposed algorithm by LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET. We have performed the proposed APRO algorithm under different scenarios using three metrics FND, CHD, and Network_Dead. Here, network dead means when 75% of the nodes are dead. A total of five scenarios Table 39–43 have been considered where the simulation area, number of nodes, the initial energy of the sensor nodes, and base station position values are considered different. In all five scenarios, it is shown that the proposed algorithm preserves the energy of sensor nodes and outperforms the other algorithms. Here, we have taken five scenarios by changing the values of the simulation area, the number of nodes, and the base station position, and in all scenarios, our proposed algorithm (Table 44) performs better than LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET. The performance has been measured in terms of the first node dead, the cluster head dead, and the last node dead. We measured network lifetime in terms of dead nodes, as network lifetime means how much time a network sustains.

The proposed APRO method is a hybrid approach of AHP and PROMETHEE, and the time complexity of the algorithm is and , respectively, and the overall proposed APRO hybrid algorithmic complexity is .

The confidence interval of the proposed approach is 99% which can be calculated as

Figure 3 shows that in all the scenario whether we change the simulation area, increase the number of sensor nodes, or change the base station position, the proposed APRO algorithm performs better than other algorithms. In the figure, we have also shown the simulated area with sensor nodes in each scenario so that we can better understand the scenarios. In our proposed APRO algorithm, applying multiattributes for cluster head selection shows that network lifetime has been increased with efficient energy consumption. Also considering every aspect of sensor nodes, load balance between nodes and thus efficient energy consumption helps in increasing the network lifetime.

7.1. Statistical Analysis

Here, Table 45 represents the statistical significance of analyzing the performance of the network. If we choose the alternative based on maximum residual, maximum coverage, and maximum connectivity, then the 1^st alternative is for maximum coverage, the 3^rd for maximum connectivity, and the 11^th for maximum residual energy. Although the rank of the best alternatives has been given the solution by our proposed algorithm, these alternatives are 8^th, 18^th, and 11^th (PROM ranking) and 8^th, 10^th, and 14^th (AHP ranking). If we choose alternative 1^st based on maximum coverage, then it will show that sensor nodes are near to the base station and they will consume less energy, but it will not guarantee that distance from CH to the base station is less and CHs are having higher residual energy for the data transmission. The chosen alternative shows that the solution is not optimal as it consumes more energy for data transmission which lowers network lifetime as other attributes were not optimal. The rank given to this alternative by PROMETHHE and AHP is 8^th.

If we choose 3^rd alternative based on maximum connectivity, then it will assure that CH is near the base station but not guarantee higher residual energy. The sensor nodes other than the CH will be far away from the base station. As normal sensor nodes were at a higher distance, they require higher residual energy for data transmission. In this case, the network lifetime and residual energy will be lower. The rank given by the proposed algorithm is 6^th and 7^th, respectively. If we choose the 8^th alternative based on higher maximum residual energy, then it will give higher residual energy to sensor nodes. But sensor nodes were not equally distributed and sensor nodes’ loads were also not balanced which causes a lower network lifetime. The rank given by our proposed APRO algorithm to this alternative is 11^th and 14^th. The other attribute values prove that the above results were not optimal; thus, we can say that with many limitations, the solutions were not optimal. Thus, we have to consider other attributes for CH’s selection for data transmission. Such selected CHs should have a maximum lifetime with evenly distributed sensor nodes and have efficient energy consumption. The proposed algorithm provides optimal CH selection where all attributes have their optimal values and the simulation results were evaluated in terms of FND, CHD, and LND.

8. Conclusion and Future Scope

The proposed APRO algorithm provides a load-balanced and increased network lifetime for the selection of cluster heads by considering the twenty-one attributes. All the attributes were considered and synchronized among them for the data collection process. The selected cluster heads (CHs) have a balanced load among the sensor nodes with optimal energy consumption for the data transmission to the base station. The results validate the outcome of our proposed algorithm which verify consume optimal energy consumption for data transmissions. Results show that the considered 21 attributes play an important role in efficient energy consumption and increased network lifetime. As far as energy consumption is considered to be the most important factor in sensor networks, thus sensor nodes consume less energy and also balance the load among the sensor nodes. So, our proposed algorithm favored the data transmission and collection for a longer time in the network. Also, we have taken some scenarios in which we have changed the number of sensor nodes, deployment area, and base station position, where we can see that our proposed algorithm performs well compared to other algorithms. This shows that our proposed algorithm is scalable to small or large deployment areas and applications. We conclude that our proposed algorithm performs better than the other algorithms and our results validate as well.

In the future automatic weight, an assignment can be done using the fuzzy logic approach in place of relative weight. Automatic weight assignment can be done for enhancing the performance of the network. Further, we can move to the multihop transmission of data.

Data Availability

Data will be made available after making reasonable request from the author.

Conflicts of Interest

There is no conflict of interest among the authors.

Authors’ Contributions

The authors have given consent for the publication.

Acknowledgments

The first author is thankful for the UGC-BHU-NET fellowship. The corresponding author is grateful for IoE grant of the Banaras Hindu University.

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Copyright © 2023 Ankita Srivastava and Pramod Kumar Mishra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Journal of Sensors

AI-Enabled Internet of Everything (IoE) in Next-Generation Wireless Sensor Networks

Load-Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Abstract

1. Introduction

2. Related Work

3. Assumption and System Model

3.1. Assumptions

3.2. Energy Model

3.3. Parameters and Energy Model for the Simulation

4. Attributes Considered for the Proposed Work

5. Data Set Generation

6. Evolution Methods for the Selection of CHs Using MADM

6.1. AHP (Analytical Hierarchy Process) Method for CH’s Selection

6.2. PROMETHEE Method for CH Selection

7. Simulation Results

7.1. Statistical Analysis

8. Conclusion and Future Scope

Data Availability

Conflicts of Interest

Authors’ Contributions

Acknowledgments

References

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