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Partition of networks into optimal set of clusters is the prominent technique to prolong the network lifetime of energy constrained wireless sensor networks. Enumeration search method cannot find optimal clusters within polynomial bounded time for large scale networks since the computational complexity of problem grows exponentially with the dimens...
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... The hybrid particle swarm optimization and simulated annealing-based polynomial time clustering methods in WSN were developed by reference [70]. There were two setup and steady-state phases for these clustering techniques. ...
... In order to obtain an unbalanced cluster in WSN, reference [70] provided the chemical reaction optimization (CRO). In order to choose the CHs according to the median sink distance, energy ratio, and node distance, the CRO was combined with a possible energy function and molecular encoding structure. ...
The design of underwater wireless sensor networks (UWSNs) faces many challenges, including power consumption, storage, battery life, and transmission bandwidth. UWSNs usually either use node clustering or multi-hop routing as their energy-efficient optimization algorithms. The cluster optimization technique will organize the sensor nodes into a cluster network, with each cluster led by a cluster head (CH). In contrast, the multi-hop optimization algorithm will create a multi-hop network by sending data to the base station (BS) while switching between different sensor nodes. However, the overburdens of CH nodes impact the performance of the cluster optimization method, whereas the overburdens of nodes close to the BS impact the performance of the multi-hop optimization algorithm. Therefore, clustering and routing procedures can be considered as a simultaneous NP-hard problem that metaheuristic algorithms can address. With this motivation, this paper proposes an energy-efficient clustering and multi-hop routing protocol using the metaheuristic-based algorithm to increase energy efficiency in UWSNs and lengthen the network life. However, the existing metaheuristic-based methods use two separate algorithms for clustering and multi-hop routing, increasing computational complexity, different initialization, and difficulty in hyperparameters’ tuning. In order to address the mentioned shortcomings, this paper proposes a novel hierarchical structure called hierarchical chimp optimization (HChOA) for both clustering and multi-hop routing processes. The proposed HChOA is validated using various metrics after being simulated using an extended set of experiments. Results are compared to those from LEACH, TEEN, MPSO, PSO, and IPSO-GWO to validate the impact of the HChOA. According to the findings, the HChOA performed better than other lifespan and energy usage benchmarks.
... These popular metaheuristic techniques offer advantages and disadvantages when used to choose a cluster head. Furthermore, the No Free Lunch (NFL) Theorem states that there is always a better optimization approach than the one now in use [8]. ...
Wireless sensor networks (WSNs) plays an important role in the advancement of the industrial 4.0/5.0 revolution, as they are integrated into IoT and other cyber-physical systems. Various techniques have been developed to optimize WSNs, but there are still some limitations. Clustering has been a highly effective method for efficient communication with low power and battery usage in high-speed communication systems. In this work, an Improved Squirrel Search Algorithm (I-SSA) is developed for selecting the cluster head (CH) node in WSNs. The improved I-SSA algorithm introduces a sine chaotic mapping strategy to boost population diversity, a backward learning mechanism to constrict the selection of excellent solution sets, and a cross-learning mechanism to enhance the accuracy of the algorithm optimization procedure in order to confront the drawbacks of the SSA algorithm, including such easy having fallen into local optimal solution and inadequate variability. The fitness function, which is employed to assess the performance of the solutions generated by the optimization algorithm, plays a key role in this cluster head selection process. Four parameters including residual energy, average intra-cluster distance, average sink distance, and CH balance factor are used in the fitness function. Network density analysis was performed by changing the number of sensor nodes (SNs) from 100 to 500 and randomly distributing them in the simulation region. For the simulation study, we utilize the most recent and stable release of MATLAB, version 2021a. Results from the simulation indicate that the proposed I-SSA based approach improves network performance and uses more stable energy compared to existing techniques such as SSO-CHST, ACO-based, and GA-based methods.
... The mechanism is well suited to maintain the lifespan of the network [56]. Many researchers use Ant Colony Optimization, Particle Swarm Optimization, and Butterfly Optimization Algorithm to optimize existing problems in wireless sensor networks, such as optimal route choosing, optimal quantity of cluster heads choosing per round, optimal quantity of nodes choosing per round, and elevating the packet transmission between the cluster head and the base station [57,58]. ...
WSN consist of tiny sensors that are distributed over the entire network and have the capability of sensing the data, processing it, and conveying it from one node to another. The purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, nature-inspired mechanisms are used to minimize the power utilization of the network. In the proposed study, the Butterfly Optimization Algorithm (BOA) is used to choose the optimal quantity of cluster heads from the dense nodes (available nodes). The parameters to be considered for the choice of the cluster head are: the remaining power of the node; distance from the other nodes in the network; distance from the base station; node centrality; and node degree. The particle swarm optimization (PSO) is used to form the cluster head by choosing certain parameters, such as distance from the cluster head and the BS. The path is chosen by means of the Ant Colony Optimization (ACO) Mechanism. The route is optimized by the distance, node degree, and the chosen remaining power. The inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the base station, and overall power utilization of the network. The results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, and EDEEC (Khan et al. in World Appl Sci J, 2013; Arora and Singh in Soft Comput 23:715–734, 2019; Saini and Sharma in 2010 First international conference on parallel, distributed and grid computing (PDGC 2010), 2010; Elbhiri et al. in 2010 5th International symposium on I/V communications and mobile network, 2010) and correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, and FLION. This review will help investigators discover the applications in this arena.
... Kuila and Jana [22], a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. WSN polynomial temporal clustering algorithms named common scrambling algorithm (CSA) and chaotic particle swarm optimization (CPSO) relying respectively on simulated annealing (SA) and particle swarm optimization (PSO) have been discussed in [23], [24]. ...
Wireless sensor networks (WSNs) contain an inordinate number of sensor nodes that are spatially distributed. The network is composed of entities that determine its lifetime. The WSN nodes are equipped with a battery whose autonomy is limited in duration. In this paper, different solutions are introduced to improve the overall energy consumption of the network in order to improve its lifetime. Contrary to many works considering the clustering algorithm as one potential candidate to improve the network's lifetime, this study has investigated the firefly algorithm optimization where an optimal cluster head is selected from a group of nodes. The set-up process of the cluster head is based on a set of conditions. To measure the performance of the proposed approach, the number of dead nodes and data packets received by the base station (BS) or sink node are considered. The results are tested on 100 nodes for 5000 transmission rounds, the amount of data transported is 20 million bits a little more than the other methods. It has been shown that the proposed solution outperformed the traditional low energy adaptive clustering hierarchy (LEACH), threshold sensitive energy efficient sensor network (TEEN), and developed distributed energy-efficient clustering (DEEC) approaches.
... The mechanism is well suited to maintain the lifespan of the network [74]. Trend of the literature Fig 3. shows that ACO, PSO and BOA is used by the many researchers to optimize the extant problem in wireless sensor network such as optimal route choosing, optimal quantity of CH choosing per round, optimal quantity of nodes is chooses per round and elevate the packet transmission in between the CH and the BS [42] [51]. ...
... For choosing the appropriate mechanisms, various factors have been considered such as problem type, time constraint, availability of resources, accuracy desired. To achieve the better performance of the network, the researchers have used many approaches in nature inspired mechanisms such as, classical approaches [28][29][30][31][32] and swarm intelligence-based approaches [33][34][35][36][37][38][39][40][41][42][43]. There are various types of mechanisms present in the literature focused on the different types of problem faced by the wireless sensor networks named as Optimal Scope, Data Aggregation, Power Efficient Clustering, Power Efficient Routing mechanisms, Sensor lateralization [75][76]. ...
WSN consist of tiny sensors which are distributed over the entire network having capabilities of sensing the data, processing it and convey it from one node to another node. The purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, the nature inspired mechanisms are used to minimize the power utilization of the network. In the proposed study Butterfly Optimization Mechanism is used to choose the optimum quantity of CH from the dense nodes. The parameter is to be considered for the choosing of the CH is the remaining power of the node, interspace from the other nodes in the network, interspace from the BS, node centrality and node degree. The PSO is used to form the CH by choosing certain parameters such as interspace from the CH and the BS. The path is choosing by means of the Ant Colony Optimization (ACO) Mechanism. The route is optimized by the interspace, node degree and the choose remaining power. The inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the BS and the overall power utilization of the network. The results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, EDEEC [50–53] and also correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, FLION. This review will help investigators to discover the applications in this arena.
... Energy efficiency is the main problem in which the operation is dependent on the lifecycle of sensor nodes battery (Zhang et al., 2014a). Some of the well-known algorithms for the energy-efficient clustering include 'Low-Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Distributed Energy-Efficient Clustering (DEEC), Probabilistic Selection of Cluster-Head Based on the Nearest Possible Distance of Cluster-Head (PSCND) (Masaeli et al., 2013), Stochastic Election of Appropriate Range Cluster Heads (SEARCH) (Wang et al., 2015), Cluster Optimisation' based on heuristics (Mekonnen & Rao, 2017), enhancement approach for energy reduction (Elshrkawey et al., 2018), Energy-Aware Routing (Arasu & Ganesan, 2018), and Clustering routing based on mixed-integer programming (Li et al., 2018;Zhang et al., 2017). The algorithms like 'Threshold Sensitive Energy Efficient Network (TEEN) and Hybrid Energy-Efficient Distributed clustering (HEED)' make use of the hierarchical strategy in the homogeneous network. ...
The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in WSN. This paper plans to derive a multi-objective function with the constraints like energy, distance, delay, stability period, and intents to attain the objective by developing a new well-performing meta-heuristic algorithm called Opposition-based Elephant Herding Optimisation (O-EHO). The objective function diminishes the energy consumption of sensor nodes by optimum selection of cluster heads that leads to maintain the energy balance between the normal nodes. In this way, there is a remarkable enhancement in the performance parameters such as throughput, stability period, and network lifetime. It is proved that the network lifetime is enhanced by the stability period and thus it is considered as the most significant parameter. The experimental analysis proves the competitive performance of the proposed model over other heuristic methods.
... Due to the modification in LEACH method which is done by the author of publication [38], its consumption of power becomes better and the inactive nodes reduced by eighty eight percent. For the consumption of energy in an efficient manner in [39][40][41][42][43][44][45], SEP protocol is bringing in to use. The main disadvantage of this protocol is that it fails to maintain energy usage in support of some specific tasks.In table five those methods are discussed in an absolute manner which provides output and criticism in relation to those transmission protocols which has been established on the basis of group from various methods. ...
... However, fault tolerance capability of this unequal clustering scheme was considered to require significant improvement. Simulated Annealing (SA) and Particle Swarm Optimization (PSO)-based cluster scheme was proposed based on the phases of Setup and Steady-state phases [10]. In the setup phase, the CH election and cluster development are operated, and in the second phase, the modes of better nodes are allocated as sleep mode, until computing data transmission. ...
Wireless Sensor Network (WSN) is the one of the hot area of research in which energy stability and network lifetime are considered to be the twin challenges during its application. Clustering is the optimum energy efficiency strategy that organizes the sensor nodes into potential groups for the objective of attaining energy stability and network lifetime. In this energy potent clustering process, cluster head selection is determined to be highly significant in order to balance energy among the nodes sensor nodes. Moreover, two-tier cluster head selection that includes temporary and final cluster head is identified to be challenging in WSNs. In this paper, Hybrid Fuzzy Logic and Artificial Flora Optimization Algorithm (FL-AFA)-based Two Tier Cluster Head Selection is proposed for improving energy efficiency and prolog network lifetime. This FL-AFA scheme achieved the cluster head selection in two stages, such as, i) Temporary Cluster Head (TCH) selection using FL and, ii) Final Cluster Head (FCH) selection using AFA. In the first stage, the concept of fuzzy logic applied over the input parameters of residual energy (RE), distance to BS (DTBS), and node degree (NDE). In the second stage, the benefits of AFA is employed for computing the fitness function through distance to nearby nodes (DNN), cluster compactness estimation factor (CCEF), and position estimation (PE). Simulation experiments of the proposed FL-AFA scheme and the benchmarked schemes are conducted based on the evaluation metrics of energy efficiency, network lifetime, average delay, and packet delivery ratio (PDR) under the impact of different sensor nodes. .
... Previous mechanisms have been kept in mind during evaluation of proposed work. These mechanisms are CRHS [40], BERA [41], CPSO [42], ALOC [43], FLION [44] and BOAACO [45]. Results have been obtained by comparing all these mechanisms in various scenarios are shown below: Proposed work 200 129 160 200 200 100 100 100 400 77 128 200 200 100 100 100 600 43 114 200 200 100 100 100 800 30 74 200 200 100 100 100 1000 17 52 200 200 100 100 www.turkjphysiotherrehabil.org 3581 way, battery energy plays an important role in the WSNs lifespan. ...
... The performance analysis of proposed algorithm has been done on the basis of alive nodes, remaining energy. The performance of proposed work has compared with CRHS [40], BERA [41], CPSO [42], ALOC [43], FLION [44] and BOAACO [45]. The residual energy and alive nodes are higher in the proposed algorithm. ...
Wireless Sensor Networks (WSNs) contains lot of sensor nodes which are used for networking. These are connected through wireless medium, which work to collect physical information from the environment. The sensor nodes are operated through a battery, but the battery is lost the power after some time. In this way, battery energy plays an important role in the WSNs lifespan. The objective of this work is to improve the lifetime of the wireless sensor networks. Many swarm intelligence techniques have been used in existing research to improve the network's life time. There are a lots of drawbacks inside existing techniques such as week routing, slow convergence, improper balancing between exploitation and exploration phases. Hybrid ACO algorithm is uses for optimal path selection. This proposed algorithm is integration of ACO and PSO. The performance analysis of proposed algorithm has been done on the basis of alive nodes, remaining energy. The performance of proposed work has compared with CRHS, BERA , CPSO, ALOC, FLION and BOAACO. The residual energy and alive nodes are higher in the proposed algorithm.
... Hence, several works related to classical methods [89][90][91][92][93][94][95][96][97][98], Optimization approaches [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115] and Machine Learning techniques [116][117][118][119][120][121][122][123][124] with respect to cluster formation and CH selection, routing protocols, reliability and security are comprehensively discussed and analyzed with various parameters in Section 5 and Unequal clustering in Section 6. Research findings, challenges, open issues, and future directions are also addressed based on all these aspects. Moreover, the comprehensive solution is by providing a state-of-the-art classification of clustering for wireless sensor networks based on different dimensions, such as the taxonomy of clustering, strategies, clustering aspects, challenges, open issues, and research findings, drive this survey different from the existing other surveys and is considered as the novelty of this work. ...
... A clustering algorithm based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO) finds optimal CHs and prolongs the energy efficiency in WSNs [110]. However, only a minimal number of performance parameters are analyzed. ...
Wireless Sensor Networks (WSNs) have attracted various academic researchers, engineers, science, and technology communities. This attraction is due to their broad research areas such as energy efficiency, data communication, coverage, connectivity, load balancing, security, reliability, scalability, and network lifetime. Researchers are looking towards cost-effective approaches to improve the existing solutions that reveal novel schemes, methods, concepts, protocols, and algorithms in the desired domain. Generally, review studies provide complete, easy access or solution to these concepts. Considering this as a driving force and the impact of clustering on the deterioration of energy consumption in wireless sensor networks, this review focus on clustering methods based on different aspects. This study’s significant contribution is to provide a brief review in the field of clustering in wireless sensor networks based on three different categories, such as classical, optimization, and machine learning techniques. For each of these categories, various performance metrics and parameters are provided, and a comparative assessment of the corresponding aspects like cluster head selection, routing protocols, reliability, security, and unequal clustering are discussed. Various advantages, limitations, applications of each method, research gaps, challenges, and research directions are considered in this study, motivating the researchers to carry out further research by providing relevant information in cluster-based wireless sensor networks.
