A Systematic Review on the Energy Efficiency of Dynamic Clustering in a Heterogeneous Environment of Wireless Sensor Networks (WSNs)

Abstract

There are a variety of applications for wireless sensor networks (WSNs), such as military, health monitoring systems, natural disasters, smartphones, and other surveillance systems. While the primary purpose of sensor nodes is to collect unattended data in hostile environments, many are placed in large numbers and operate independently. Due to limited capabilities, power is often limited. Therefore, these nodes are grouped into clusters to increase communication efficiency. In WSNs, two different routing protocols are possible: apartment and hierarchical or clustering protocols. Due to their significant role in minimizing energy consumption, hierarchical methods have become very popular in clustering. In cluster-based methods, nodes are organized into clusters, and the sensor node with the most resources is appointed as the cluster head (CH). In this paper, we present a Systematic Literature Review (SLR) explaining the difficulties in developing cluster-based methods, critical factors for clustering, and hierarchical clustering protocols. The most important factor of a routing protocol for WSN is the energy consumption and lifetime of a network. Focusing on energy consumption, different cluster-based methods were analyzed to determine which technology should be deployed by analyzing specific criteria to support the selection process. Additionally, the pros and cons of different protocols are listed with their relevance in specific scenarios. To identify these protocols, a systematic literature review was conducted using research studies published from 2010 to 2021, with 30 papers analyzed in the final phase. Based on the results of this SLR, several issues need to be further investigated with respect to the interaction of the potential technology with the Internet of Things (IoT) and Vehicular Ad-Hoc Networks (VANETs).

1. Introduction

Wireless communication has accelerated scientific advancements in the electrical and communication engineering domain. WSNs comprise a collection of geographically dispersed micron-sized nodes that communicate with one another over a wired or wireless channel to establish an independent connection [1]. It has been used in an extensive range of applications such as intelligent transportation systems, smart management systems, and e-voting systems. It also allows receiving information on the status of workers in hazardous environments such as mines, tracking climate change in the oceans, and keeping track of firefighters’ status in large fires [2]. In WSN, Sensor Nodes (SNs) consist of transceivers and low-power microcontrollers that carry out various network operations [3]. The data is collected from SNs locations, then deployed and broadcasted to the so-called Base Station (BS) through Cluster Heads (CHs). BS is the area that sends sensed data to the recipient in a specific physical medium conveniently. There are two methods of SNs connectivity, which are single and multi-hop. In single connectivity, sending data from the field directly to SNs takes a long time and consumes lots of energy. In multi-hop connectivity, nodes far from the base station (BS) do not have to send information directly over long distances by creating multi-level clusters. When many SNs attempts to connect a specific BS, each method will overcrowd the BS. Because of its high energy consumption, the single-level method is rarely used. Clustering is a more secure method of saving energy in a single SN. A network’s lifetime can be extended by arranging nodes in such a way that they form a cluster model while successfully minimizing energy consumption [4]. Each sensor node in the cluster is wirelessly connected to the Cluster Head (CH), which is in turn wirelessly connected to the Base Station (BS) through wireless inter-cluster communication and the CH as well as between the BS and the CH [5]. Each cluster contains CHs that collect data from other nodes in the cluster and transmit it to the base station directly or indirectly via other nodes [6].

Clustering is generally defined as dividing a specified territory into small sectors and assigning a single node as the CH. CH is a critical component in developing an energy-efficient WSN model. In practice, the CH may change during certain iterations to improve network performance [7]. Furthermore, with the assistance of clusters, specific specialist nodes such as CHs make all major decisions on behalf of SNs. Clustering can be classified as multi-hop or single-hop. The disadvantages and advantages of single-to-multi hop communications are numerous; for example, the energy losses of single-hop communication due to increased range [8,9]. Meanwhile, the multi-hop approach can improve energy consumption to handle the scalability issue in the wireless sensor network, hence enhancing both energy consumption and scalability.

The energy-efficient protocols for WSN can be classified into four classes: reliable routing, topology-based, network structure, and communication model as shown in Figure 1 [10,11]. These four techniques are used to route messages while considering energy consumption. Furthermore, how they minimize the energy consumption and the lifetime of the networks are also taken into consideration.

The routing protocols for the network structure are categorized as flat or hierarchical The communication model can be either Query-based, Coherent and non-coherent based, or Negotiation-based. Location-based/Mobile Agent-based are considered in the category of topology-based protocols [12]. Lastly, QoS-based or Multipath-based belongs to the reliable routing protocols. Classification of routing algorithms is broken down into additional sub-categories as depicted in Figure 1.

The identification of CH selection parameters is the main objective of clustering. Low-Energy Adaptive Cluster Hierarchy (LEACH) is a probabilistic and hierarchical technique that has shown great performance in clustering [13]. LEACH has been proposed as a standard clustering technique for topology control. The CH was chosen at random in a cyclic method so that the energy is distributed evenly across the nodes. The purposes are to increase the network lifespan and at the same time decrease energy consumption.

The cluster head is chosen at random to maximize energy efficiency. Numerous research in the literature has been conducted to modify LEACH, which results in a variety of different configurations to increase network performance. However, the node degree and energy metrics were not taken into account during multi-hop data transmission across the CHs [14,15]. Among the approach that has been proposed to solve the issue are classical methods [16] and numerous meta-heuristic algorithms [17,18,19,20]. The authors in [21,22] studied the homogeneous and heterogeneous routing protocols for WSNs. However, energy consumption is not considered in his works.

The Heterogeneous Wireless Sensor Networking (HWSN) protocols are proven for network efficiency. Over the years, three hierarchical HWSN protocols have been proposed which are EEPCA, EDFCM, and MCR, together with another two existing conventional routing protocols SEP and LEACH [23]. The proposed protocols adapt frameworks to improve WSN energy efficiency and lifetime. There is an emerging technology called Cross-Technology Communication (CTC) which is a software-only solution that enables direct communication between wireless assets that use heterogeneous standards, such as IoT standards, which inevitably suffer from low spectrum efficiency. Therefore, it is essential to simplify the configuration and obtain a cost-effective and energy-efficient structure [24,25]. In contrast, the challenges lie in the use of wireless technologies used by IoT devices, which are becoming more diverse while mobile devices are becoming more constrained. This makes these devices vulnerable to data collection by the Internet of Things (IoT) devices such as ZigBee and LoRa [26]. Many studies have shown that the mobility of these low-power Internet of Things devices is critical. G-Bee is designed to provide communication in large networks with minimal delays. In contrast, G-Bee exclusively detects error-free communication and provides energy efficiency concurrently [27]. The researchers in [24] proposed a software-only X-MIMO for the ZigBee commodity. Enabling successful IoT connectivity using multi-user MIMO technology is critical due to the massive growth of IoT and the inherent resource constraints of the wireless medium. X-MIMO is the work to implement MU-MIMO technology in commodity devices. This will expand the potential of wireless sensing for low-power IoT devices. In another study by Woojae et al. [28] SDR-Lite was proposed, as the first software-only SDR receiver with excellent WiFi built without additional hardware or firmware. By simulating (faking) a packet’s header, the principle that works when a packet is missing or not there can be tested. SDR-Lite offers SDR receiver capability over a standard WiFi network. In terms of heterogeneous, this wireless technology is the most recent and least expensive solution for interoperability with all mobile devices, WiFi access points, and IoT at the service level and device dynamics. However, it is not covered extensively in this systematic review.

On the other hand, few studies have been conducted on QoS and related communication requirements. There has been limited research available to address QoS and communication demands in the context of resource-constrained wireless sensor networks. If pathway maintenance is not addressed effectively it may result in excessive use of energy. The algorithm has been widely applied to WSN applications such as motion control of automobiles, target positioning, and clinical care of the patient [29] Accordingly, a study is required to analyze the movement of the sink and sensor nodes in the context of WSNs. Energy efficiency, CH selection, safety, and application-specific are the performance metrics used to evaluate the efficacy of current state-of-the-art methods. The innovative routing algorithms support the meta-heuristic algorithms by considering network architecture and application requirements [30]. Meta-heuristic techniques are a vital method for handling real optimization issues with high computational complexity in WSNs. Complex optimization problems that are not quite solvable are designed to be solved rapidly using these approaches. Various techniques such as Ant Colony Optimization (ACO) algorithm have been used by both long-standing and modern metaheuristic algorithms to tackle different problems in wireless sensor network Energy. It is based on the behavior of actual ants. A troop of ants has been seen to find jointly the quickest route between their nest and food. Pheromones, which are volatile molecules, are used for communication between ants [18,30].

Furthermore, Artificial Bee Colony (ABC) is a significant bio-inspired algorithm stimulated by the collective foraging activity of honey bees [31]. Similar to the ACO, it explores big data sets for the best numerical solution. They may achieve their mission via the social collaboration of three sorts of bees: user bees, spectator bees, and scout bees. Worker bees are responsible for foraging near the food source and communicating this information to spectator bees. Species of bees then choose excellent food sources among those identified by worker bees [32]. In the dynamic environment, energy efficiency is also an issue that needs to be addressed in WSNs. The vital issues can be concluded from the literature:

• the clustering structure disturbances due to the mobility of the nodes
• the energy consumption in heterogeneous mobile nodes
• The communication path between nodes
• an energy-efficient multi-clustering approach that adapts hierarchal techniques based on meta-heuristic algorithms

Furthermore, to address the WSN routing, management of energy, etc. Problems with more recent meta-heuristic algorithms have emerged. The authors Mirjalili and Lewis [33] presented the whale optimization algorithm (WOA) as one of the meta-heuristics used in nature. It emulates the WOA process of humpback whale hunting in temperate environments. In this little timeframe due to its simplicity of installation.

Recent SLR studies have been conducted in the area of optimization and energy efficiency of WSNs. Shafiq et al. in [34] presented a systematic study on the energy efficiency of routing algorithms for wireless sensor networks. The article followed the Kitchenham process. The techniques are classified into eight categories, the most important of which are energy-efficient clustering, energy-efficient distribution, energy-efficient secure routing algorithms, and energy-based network lifetime. The study is based on several general criteria for each type of wireless sensor network and its challenges. Another study by Gouda et al. [35] examined the issues in wireless sensor network algorithms and the type of model used (modified, standard, or hybrid) in terms of optimization and energy. A PRISMA scheme was used in the study. In addition, the challenges of routing and clustering protocols, which are the most significant compared to the other protocols, were studied in general. According to the results of the study. In addition, Rawat and Chauhan [36] presented a systematic literature review based on the features and concentration on clustered protocols and the majority of existing algorithms. The findings indicate that the majority of the evaluated algorithms did not prioritize the mobility factor, but rather attempted to improve the cluster head selection factor. Researchers in [37] presented a systematic review on clustering-based protocols in WSN should be instructed to examine and evaluate cluster and recent protocols with energy efficiency, the review identified few publications addressing this issue.

This systematic review was conducted to improve the effectiveness and efficiency of clustering routing protocols in the area of WSNs regarding energy efficiency, cluster routing methods that put an accent on essential aggregation features instead of the overall purpose of aggregation should be given consideration. In order to conduct research on the diverse environment, problems, and research direction of the mobility issue, more of these technologies must be deployed. This encourages researchers to provide pertinent information in cluster-based models in WSNs.

The next section of this paper will detail the systematic review steps conducted for this study. Next, a detailed review of different routing protocols on energy-efficient techniques for WSNs is presented. Section 4 will discuss the findings of the study. Open issues and research directions are presented in the next section. The last section will be discussed in the conclusion of the study.

2. SLR Methodology

A systematic literature review is designed to explore, characterize, and study a particular research topic. This study follows the SLR guidelines suggested by Kitchenham and Charters [38]. Three main steps for the SLR study are planning, conducting the review, and reporting the results Figure 2 each of the steps will be explained in the next section.

2.1. Planning the Review

This phase is primarily involved with the development of the protocol that will be used to carry out the systematic review. Protocol development takes into account the following steps:

Review Objective and Research Questions

The purpose of this systematic investigation is to identify and assess energy-efficient routing algorithms for WSNs. The performance of protocols in a heterogeneous environment adopting single and multi-clustering techniques will be evaluated. The protocol analysis is led from the perspective of energy efficiency technology. There are two types of energy efficiency measures that need further investigation which are energy balancer and energy reduction [39]. As shown in

Table 1. Research questions.

RQs	Research Questions
RQ1	What are the algorithms proposed in the literature that enable both energy reduction and network load balancing?
RQ2	What algorithms have been examined in the literature for a heterogeneous environment that incorporates SN and CH mobility in single-level clustering?

Research questions were formulated to determine the application of single and multi-clustering approaches in the mobile SNs context.

2.2. Search String

The comparison, population, outcome, context, and intervention criteria are used to create the search string [40]. The research that cites energy-efficient techniques is aimed at the general public. Boolean expressions were used to construct refined search strings for SLR (using ANDs and ORs). This review’s meta string is as follows:

(Energy Efficient OR Energy Effectiveness) AND (Routing) AND (Protocols OR Algorithm) AND (Wireless sensor networks OR WSN) AND (Mobility) AND (Heterogeneity)

2.3. Database Selection

Details of the search sources are presented in ACM Digital Library, IEEE Explorer, Springer, and Wiley. The resource analysis Conference papers, journals, and workshop papers. The research is published between 2010 and 2021.

2.4. Inclusion Criteria

The most important part after executing the search query is setting the inclusion and exclusion criteria for identifying the most relevant research. The three criteria indicated below were used to include the relevant papers to answer the research questions.

The research paper must be published in a “journal” or at a “conference” and The research paper must be written entirely in English.

Along with the formulation of inclusion criteria, the quality criteria given by CASP [40] are employed in this systematic review.

2.5. Selection of Primary Studies

The primary studies were identified by running the search string through the five most widely used research databases. The reference section of the studies is examined and evaluated based on the identification of primary studies to locate more relevant research. The execution of inclusion and exclusion criteria for selecting the most relevant research is the most important part. After the inclusion and exclusion criteria were applied, the number of papers was reduced to 30 as shown in

Table 2. Number of papers selected for SLR.

Databases	Total Papers	Selected Papers	Rejected Papers	Snow Bowling	Final Selection
ACM	15	7	8	0	7
IEEE Xplore	13	4	22	1	5
Science Direct	26	8	9	1	9
Springer	64	9	55	0	9
Grand Total	118	28	94	2	30

.

After executing the selection step, a list of primary studies was established. As part of this review, researchers also examine the introduction, conclusion, and sometimes suggested strategy for clarity in the contribution of the study. From the observation, the majority of publications were published between 2015 and 2020. Most studies were retrieved from the Springer database. Figure 3 depicts the distribution of papers based on the databases.

3. Review of Energy Efficiency in WSNs

3.1. Energy Efficient Routing Protocols for WSN

In the domain of WSNs, routing protocols are evolving rapidly. The application needs and the underlying network architecture are considered when developing routing protocols. The amount of energy required to capture by the sensors will affect the lifetime of the underlying network. Jia et al. [41] proposed a DCHS for WSNs to extend network lifetime and reduce energy consumption. The analytical results demonstrate that this strategy can balance network efficiency in two phases and has a longer network surviving life span. Ramin and Naser Hashemi [42] proposed an approximation factor-based Fixed Parameter Tractable (FPT) solution for the Load-Balanced Clustering Problem (LBCP). The energy-balanced routing strategy and an energy-efficient technique for routing between the sink and the CHs have been developed. Combining routing and clustering models into a precise chromosome has resulted in a genetic algorithm-based technique for calculating overall energy consumption [43]. Load parameters were assigned using the fitness function to adjust energy consumption among nodes. According to the findings of this study, appropriate load balancing across the CHs was achieved in a range of circumstances. The UCRA-GSO for WSNs has also been used to address the problem of energy balance. To choose the best clustering strategy, the GSO model considers the CH proximity distance, the CH energy, the CH density, and the cluster compactness [44].

The Stable Election Protocol (SEP) was the second generation of HWSN [45]. It is made up of two distinct nodes. Within SEP, CH selection is based on weighted election probability (WEP). However, when it came to multi-level heterogeneous WSNs, SEP revealed a lack of SN efficiency. Differences in data creation rate (traffic) determine the computational heterogeneity Energy Dissipation Forecast and Clustering Management (EDFCM) [46] incorporates both traffic and energy heterogeneity in highly specialized two-level WSNs. Furthermore, EDFCM uses extra nodes (management nodes) to limit cluster growth, which throws into doubt its intrinsic distributed localized decision-making

3.2. Energy Efficiency in Heterogeneous Protocols

Zhou et al. [47] presented a novel clustering approach called Energy Centers Searching using Particle Swarm Optimization (EC-PSO). For selecting suitable CHs, this approach searches for energy centers and avoids energy gaps. A geometric technique is used to choose the CHs. The nodes were then clustered using EC-PSO after the network’s energy dispersed. An improved PSO approach is used to discover the energy center. The nodes in the area are then selected to serve as Central Hubs (CH). Data is collected by a mobile data collector. A protection mechanism will prevent low-energy nodes from operating as forwarders.

Enhanced balanced energy efficiency network-integrated super-heterogeneous routing (E–BEENISH) protocol was introduced to evaluate the number of energy clusters used to communicate with each other [48]. At the same time, the broad range of energy levels in heterogeneous WSNs was also considered. E-BEENISH assigns a weighted probability to each node as the cluster leader based on the residual power and the distance between the node to the sink. Energy usage is also affected by node heterogeneity.

A variety of routing strategies with dynamic nodes based, including an efficient centroid-based routing protocol (ECBRP), are used in WSNs to reduce energy consumption [49]. The majority of data in sensor nodes comes from a densely traveled area, which causes collisions. Multi-channel communication is one way for reducing sensor node conflict. different algorithms with their characteristics and features presented in

Table 3. Comparison of different issues of clustering algorithms.

Ref.	Methodology	Features	Limitations
[50]	Fuzzy-based balanced cost CH selection	Consider the node’s residue energy and distances between nodes balance energy consumption	Ignore the possibility of CH failure High energy consumption
[51]	Distributed CH scheduling	Use real data Dynamic CH selection based on the intensity of receiving signals	small size of the system under study Constant status of sensors
[52]	Adelson-Velskii and Landis tree rotation clustering model	Minimize network energy consumption by producing two different models	Disruption by losing one or more nodes high computational calculations for using multi models
[31]	Improved ABC algorithm with an enhanced solution	Improve convergence with modified search space Balancing the energy of nodes in different clusters	Did not consider the dynamic state in changing the structure of clusters Improper performance in presence of noise
[53]	Pareto optimization-based approach	Improving the quality of communication Using real data	Creating multiple interfaces to establish communication between nodes Need to search for optimal power to transfer data
[54]	Clustering algorithm basis of k-mean	Balancing to increase the lifetime of nodes	Complexity in estimating the k-value Significant time delay due to high computational efforts

. such as clustering, CH failure, and high energy usage, are also present.

The energy consumption model is a probabilistic method that includes CPU prediction for wireless sensor nodes [55]. The power consumption was considered during several stages of the CPU’s operation which included standby, power-up, idle, and active. The energy consumption can be determined accurately by calculating the quantity of CPU consumption and the expenditure of time. This research has focused on the routing methods as classified into three categories: hierarchical, flat, and location-based. Additionally, heterogeneous and multi-path-based routing protocol techniques have been discussed.

3.3. Lifetime Metric and Energy Efficiency in WSN

Among the popular in WSNs are balancing and minimizing power usage [56]. An algorithm has been proposed to determine the path of following other nodes. The algorithm dynamically allocates dispersed energy to high-energy nodes to act as source routing nodes. PEGASIS provides an energy proficient mechanism for sensor information systems [57,58]. It connects sensor nodes in a chain so that each node sends and receives information from its neighbor. One of the chain’s links is then chosen to transfer data to the base station (sink). The technique reduces the overhead necessary to construct several CHs.

This consumption increased lifetime from 8% to 14% while also achieving the goal of saving resources [59]. The adaptive duty cycle and an encoding method are used to synchronize the conversion and encoding. Because the sink node contains the majority of the bottleneck, this method is used there. The results showed that when the suggested adaptive duty-cycled network coding was used, sensor network lifespan increased by 2.5% to 9.5 %. Wireless Multimedia Sensor Networks (WMSNs) have been demonstrated to be an underutilized and inadequate energy-efficient routing mechanism. The routing protocols for WMSNs are plagued by design issues, which are exacerbated by the problems inherent in existing techniques designed primarily for managing non-multimedia data transmissions. Few studies have focused on various energy-efficiency issues in WSNs. Although it is mostly generated from efficient energy-saving technologies, it is also reliant on QoS Assurance for WMSNs [60].

The Energy Efficient Credit Based technique allows the selection of cluster leaders based on the relative importance of relay nodes [61]. This protocol maximizes the packet loss is greater and minimizes the data delivery rate. The protocol use number of nodes as candidates for data transfer. Directed Diffusion (DD) is adopted for flat routing and cluster architecture for hierarchical routing. DD adds the nodes that are not in the route of sink nodes to minimize energy waste.

The energy hole issue in sensor nodes has been identified in the situation where the nodes are located proximately to the sink [62]. The failure of nodes is caused by undistributed network loads [63]. Based on forwarding and routing selection data, the entire network employs data forwarding and routing selection techniques. Therefore, it is important to maximize the efficiency of the energy sources.

In order to achieve this, energy use solutions are introduced through algorithms that help close the power consumption gap of moving sensors. These algorithms also use unbiased energy, and by constructing cooperative clusters among themselves, these mitigation models help to reduce network breakdown and stability. These models strike a compromise between load provision and energy efficiency. Finding of the review.

Various alternative methods for creating hierarchical clustering protocols based on the application’s needs have been discussed in the literature. Important aspects such as energy management and total network life should be considered in the protocols. The majority of reviews in WNSs routing protocols focused on alternative methods to hierarchical clustering. Besides that, constraints in building clusters and CH selection are also discussed. The benefits and drawbacks of each protocol were presented. The hierarchical grouping was categorized as grid-based and cluster-based which will be discussed in the next section.

Energy-efficient composite event detection is intended to improve energy efficiency while handling massive amounts of data [64,65]. Until recently, the majority of current techniques had been applied to composite event identification [66,67], consuming a significant amount of energy. The effectiveness of WSNs can be measured using a variety of criteria [68]. Connectivity, SN deployment type, latency, network area lifetime, scalability, packet delivery ratio, reliability, energy conservation, and energy utilization cost are some of the parameters that could be configured [66].

4. WSN Clustering

Because of the high energy consumption, sensor nodes fail prematurely. Direct communication between sensor nodes and the base station (or communication between sensor nodes and multiple hops to the base station) is not feasible to solve this problem because it would consume more energy as shown in Figure 4 below. Due to WSN’s limitations, long-distance transmission is not possible. The most significant disadvantage of direct communication is the significant energy consumption. Problems also occur when duplicate data is sent from sensor nodes that are close to each other, with minor differences, resulting in the furthest nodes dying quickly. To address these issues, two-tier transmission is used, in which clusters of nodes are interconnected using a hierarchical method. The leader node, also known as the cluster head, coordinates data collection and forwarding to the BS.

A two-level hierarchy is frequently formed, with the top level for cluster leaders and the bottom level for nodes. At regular intervals, some lower-level nodes send data to their respective CHs. When the cluster head obtains the aggregated data, it sends it to BS. For a long time, the CH node used more energy than others, especially when transmitting data over long distances [69]. Furthermore, after a certain number of rounds, the chosen CH may have depleted all of its energy, forcing it to withdraw. Because it is necessary to dynamically balance the energy consumption of the sensor nodes, the function of CH must be switched regularly [55,56]. Figure 5 depicts intra-cluster communication within a cluster and inter-cluster communication between clusters.

Cluster-based and grid-based hierarchical methods are the most commonly used [70]. Several distribution mechanisms for energy-efficient routing such as adaptive periodic threshold sensitive, low energy adaptive mechanisms have been proposed [71]. A comprehensive review reveals that energy consumption is the most important topic in WSN, but it is largely ignored by researchers and practitioners. Literature also proposed the adoption of computing, sensing, and data transmission mechanisms to the Base Station (BS) with the help of gateways to reduce energy consumption. Figure 6 illustrated the energy efficiency of cluster-based protocols in wireless sensor networks according to analyzing and results of the algorithms shows panel protocol is the highest one with energy efficiency. As such most of the developed protocols take in this aspect with advanced WSN and its applications for many areas; as a result, there is a persistent need to conducting an overview on such heterogeneous algorithms and clustering based.

4.1. Clustering Models Hierarchies

Clustering simplifies network administration by lowering energy consumption, increasing scalability, and improving load balance, resilience, and data aggregation. Nodes are used to organize clusters. The CH node collects data from member nodes (MN) and adds it to the BS directly or through an intermediary CH. Instead of broadcasting data from the entire cluster sensing nodes, CH communicates only collected data to reduce the number of packets transmitted and also the amount of power used by a network. The data from a CH node is displayed at the BS and made available to end users. The location of the BS can vary; it can be deployed in the field or outside of the network area [56].

Most of the time, BS is located outside of the sensor nodes at a significant distance from them. A gateway (CH) connects the sensor node to the base station (BS). A multi-level clustering architecture can be used by more than one BS in a network [60]. Many studies have been conducted to improve energy efficiency using various clustering approaches by addressing the challenges of efficient cluster creation, CH selection and re-selection, equitable load distribution, and cluster restructuring. However, only limited techniques were being discussed.

4.2. A Low-Energy Adaptive Clustering Structure

A lightweight yet low-energy adaptive clustering architecture is the most common energy-efficient routing protocol proposed in WSNs [72]. In LEACH, CH is selected from many rotating nodes to distribute the energy consumption. It is divided into two phases: the configuration phase and the stationary phase.

When a node enters the configuration phase, it decides whether to become a CH for the current round or not. The decision also depends on the suggested CHs by considering if it has been a CH node in the previous rounds. An integer between 0 and 1 is chosen at random. If the value is less than the threshold, it will be considered a cluster leader node. The chosen CH broadcasts messages to the entire network. The nodes will decide which cluster to join and which affiliation message to transmit based on the signal power received from the chosen CH. To make better use of available energy, the role of CH has been reversed.

In the stationary phase, nodes were discovered. The collected data from the notes are sent to the appropriate CH for accumulation and transmitted directly to the BS. The use of TDMA/CDMA MAC will prevent collisions. Because of the distributed nature of LEACH’s technique, it does not require global information.

4.3. K-Means Algorithm

K-means algorithm is an unsupervised algorithm as it groups points that are close to one another [73]. The position of a new point within a given group is determined by its Euclidean distance from all other groups. The data is then regrouped, with the number of clusters that produces the least variance being optimal. This iterative procedure continues until the stated number of repetitions is reached, at which point the repeat terminates. Yong and Lee [74] devised the K-means algorithm-based KCED protocol for determining the cluster header in order to reduce the power consumption of wireless sensor networks. The operating idea is to only seek an update from the first and subsequent rounds when a node has died. To build the cluster, the most potent and nearby nodes are chosen. The study’s findings demonstrated that the proposed strategy was successful at selecting an appropriate cluster head and extending the network’s lifespan. In another work by Khandelwal and Kumar [73] the K-means clustering algorithm was utilized. Where the SAW and WPM methods were used to segment the performance of the decision-making process. In terms of selection of CHs, process distribution, package delivery time, and energy usage, the results demonstrate that the proposed method outperforms the alternatives. Presented cluster head selection strategies to reduce the power consumption of nodes and extend the whole network lifespan by improving energy consumption of nodes as cluster-based techniques used.

The authors in [75] suggested MR-LEACH model in the whole procedure was split into three stages (build clustering, re-clustering of nodes, and CH selection). The LEACH procedure is used to decide the selection of the first CH. The network is divided into clusters, depending on the Euclidean distance nodes connecting their closest CH. Once the nodes are attached to the CH, the center of each cluster is given an ID depending on the distance from the center. The node nearer to the center has a lower number. CH is rotated, and a new CH is chosen for the next comparable closer node to the center. Compared to other clusters, it increases overall network life, but frequent cluster reforms provide extra overhead networks and high energy consumption. In addition, since clusters are randomly generated, it may originally lead to sub-optimal clusters and unfair load distribution. Some of these modifications include LEACH-B [76], and ER-LEACH version [77]. Due to its probabilistic approach in selecting the cluster heads, LEACH has various downsides, especially increased energy demand in the sub-optimal CH node. A bad network’s performance is a side effect of the dynamic overhead clustering and non-uniform CH distribution, both of which require more energy. Furthermore, compares various clustering routing models proposed for WSNs. The summary of classes and clustering routing protocol differences is presented in Table 4.

Table 4. Cluster-based protocols’ comparison.

Protocol	Probabilistic CH Selection	Distributed	Pros	Cons	Ref.
LEACH	Yes	Yes	Load balancing to a certain level. Unnecessary collision avoidance. Energy conservation.	No multi-hop intranet communication. Energy depletion and holes. Communication overhead.	[78]
LEACH-C	Yes	No	Whole networks’ global view. Load balancing. Power conservation.	Communication overhead. Resources’ over utilization.	[15]
CAF	yes	Yes	Optimal routing. Enhanced network lifetime. Best performance in small-scale and static networks.	optimal CH selection. Communication overhead. Performance degradation in large-scale networks.	[79]
K-means clustering	Yes/Centroid-based	Yes	Easy-going. Enhanced network lifespan.	Episodic cluster reformation. No load balancing. Centroid distance-based re-selection.	[80]
CHEF	Yes/No	Yes	Whole networks’ global view in the selection of CH. Best selected CH.	Network overhead. Energy overhead.	[81]
UCS	Yes/No	Yes	Energy conservation. Performs well in homogeneous networks.	The variable number of nodes. Optimal CH numbers (in quantity).	[82]
NUDND	No	Yes	Load balancing. Energy Efficient. Optimal working in fixed nodes’ position.	Fails in randomized deployments. Fails in dynamic environments.	[83]
EADC	No	Yes	Load balancing. Energy-efficient.	Energy and message overheads. Not suitable in random deployments.	[84]
LEACH-MAC	Yes	Yes	Randomized CH count. Load balancing. Energy-efficient.	Randomized CH selection. No multi-hop intra-cluster communication. Energy-efficient.	[85]
EADUC	No	Yes	Energy-efficient. Enhanced network lifespan. No equal clustering.	Message overhead. Energy overhead. Lower network performance.	[86]

Table 4. Cluster-based protocols’ comparison.

Protocol	Probabilistic CH Selection	Distributed	Pros	Cons	Ref.
LEACH	Yes	Yes	Load balancing to a certain level. Unnecessary collision avoidance. Energy conservation.	No multi-hop intranet communication. Energy depletion and holes. Communication overhead.	[78]
LEACH-C	Yes	No	Whole networks’ global view. Load balancing. Power conservation.	Communication overhead. Resources’ over utilization.	[15]
CAF	yes	Yes	Optimal routing. Enhanced network lifetime. Best performance in small-scale and static networks.	optimal CH selection. Communication overhead. Performance degradation in large-scale networks.	[79]
K-means clustering	Yes/Centroid-based	Yes	Easy-going. Enhanced network lifespan.	Episodic cluster reformation. No load balancing. Centroid distance-based re-selection.	[80]
CHEF	Yes/No	Yes	Whole networks’ global view in the selection of CH. Best selected CH.	Network overhead. Energy overhead.	[81]
UCS	Yes/No	Yes	Energy conservation. Performs well in homogeneous networks.	The variable number of nodes. Optimal CH numbers (in quantity).	[82]
NUDND	No	Yes	Load balancing. Energy Efficient. Optimal working in fixed nodes’ position.	Fails in randomized deployments. Fails in dynamic environments.	[83]
EADC	No	Yes	Load balancing. Energy-efficient.	Energy and message overheads. Not suitable in random deployments.	[84]
LEACH-MAC	Yes	Yes	Randomized CH count. Load balancing. Energy-efficient.	Randomized CH selection. No multi-hop intra-cluster communication. Energy-efficient.	[85]
EADUC	No	Yes	Energy-efficient. Enhanced network lifespan. No equal clustering.	Message overhead. Energy overhead. Lower network performance.	[86]

4.4. Centralized Low-Energy Adaptive Clustering Structure

The low energy adaptive clustering centralized hierarchy (LEACH-C) [15] is the most recent version of the LEACH algorithm. Although a base station is included in LEACH-C, each node in LEACH is responsible for configuring the cluster. All of the energy and position information of the entire member nodes is received by BS. Routing is centralized when BS estimates the overall network energy for several nodes with higher energy states. A CH is picked from among the list of nodes in order to ensure that the nodes chosen have sufficient energy to serve as CH. As a result, the nodes are evenly distributed across the network, allowing the strain to be transferred gradually. Re-selection choices must be made by BS under the centralized technique, which means that overhead communication will lead to an increase in the number of times CH is selected. In addition to sending requests, each cluster will use a large amount of energy.

4.5. Cluster Fuzzy-Based Algorithm

Cluster Algorithm and A-Star with Fuzzy algorithm (CAF) [79] improved the efficiency and performance of weighted metric networks. The results of these measures are used to select a group of CHs. Data is exchanged between cluster members and their respective CHs via instant contact. To communicate between clusters, a routing chain of selected CHs is formed, with each CH passing data to its surrounding CH until it reaches the centralized server (BS). Although increases the overall network’s longevity, the reelection of CH increased message overheads due to the non-optimized selection of CH. Furthermore, intra-cluster transmission is direct, resulting in uneven energy consumption across the cluster.

4.6. Cluster Head Election Using Fuzzy Logic

Each round in Cluster Head Election using Fuzzy Logic (CHEF) used a random number to select temporary CHs [81]. The selected CH makes use of two fuzzy parameters: power level and proximity. Proximity in terms of distance is mainly the distances between all nodes. Each CH calculates and broadcasts its probability value using fuzzy conditional rules. The CH with the highest probability value is chosen as CH and advertised for the addition of the remaining member nodes. When compared to previous methods, CHEF extends network life but introduces overheads such as traffic and network overheads due to periodic notifications. Furthermore, the process of cluster head election is costly in terms of energy consumption because it is performed across the entire network, resulting in significant energy consumption.

4.7. Unequal Clustering Size Model (UCS)

A clustering sensor of the variable size called Unequal Clustering Size (UCS) is proposed for a network of wireless sensors [82]. The sensing field is considered to be circular and divided into two-layer. Layer One clusters have the same structure and form, whereas Layer Two clusters come in a variety of shapes and sizes. The UCS model addresses the issue of unbalanced energy consumption. To use consume energy as minimal as possible, the CH must be placed in or near the center of a cluster. The cluster area in each layer can be changed by adjusting the layer radius near the BS, which changes the density of a specific cluster. The proposed model works in homogeneous networks and provides better energy use via an uneven clustering method, especially for large-scale networks. However, the number of nodes per cluster varies greatly in WSNs deployment. Furthermore, the optimal CH number per layer is an issue because the method addresses multiple layers.

4.8. Nonuniform Deterministic Node Distribution

The limitations of uniform clustering were highlighted in Non-Uniform Deterministic Node Distribution (NUDND) [83]. However, the proposed algorithm may result in a network energy hole. A novel non-uniform model for the distribution of deterministic nodes is presented, in which node density grows into a sink node. Because nodes closer to the BS are used more frequently than other network nodes, a simplified decentralized method is used to provide balanced information collection. The proposed method may work in specified node locations, but it is frequently dispersed in random deployment nodes, resulting in an energy-hole problem.

4.9. Energy-Aware Distributed Clustering (EADC)

A Cluster-Aware Energy-Aware Distributed Clustering (EADC) [84] has been proposed for the non-uniform distribution of sensor nodes. The purpose of EADC is to distribute the network load equally to all parts of the network. The issue of energy gaps is addressed by constructing uneven clusters. For ensuring load balancing in CHs, the routing algorithm assigned the nodes with the most energy and the fewest hops to membership nodes. Cluster heads are selected based on how much residual energy remains among the nodes’ neighbors and how much energy each node has remaining. Duplicate sensor nodes required more energy to operate in some cases, which was overlooked by EADC. The redundant nodes were turned off according to the timetable, resolving the problem. Furthermore, unnecessary sensing and transmission were avoided, resulting in a reduction in total energy usage.

4.10. LEACH-MAC

The low energy adaptive clustering hierarchy-media access control (LEACH-MAC) is used to control randomness in cluster headcount in [85]. The problem with LEACH is a node with a value less than the threshold is selected randomly to be a CH. The randomness problem has been solved by utilizing information from the media access control (MAC) layer. To save energy, the LEACH-MAC chooses the CH at uniform random intervals to keep the CH count stable. Even though the CH count has stabilized, CH still is selected based on the threshold value. As a result, critical selection parameters are still overlooked.

4.11. Energy-Aware Distributed Unequal Clustering

The Energy-Aware Distributed Unequal Clustering (EADUC) handled the issue of energy clusters [86]. For example, to fill up an energy gap, nodes with differing energy resources are examined and clusters of varying sizes are formed. The outcomes were greater in comparison to LEACH for the purpose of increasing network lifespan and reducing energy costs. EADUC creates uneven cluster structures to improve energy efficiency. Although sparse data is taken into consideration, over redundancy of dense causes excessive energy consumption. Consequently, this will negatively impact the network lifespan.

4.12. Comparison of Different Clustering Routing Protocols in WSNs

The development of routing protocol in an energy-constrained network is influenced by WSNs characteristics. Among the characteristics that need to be taken into consideration are energy constraints, limited hardware constraints, scalability, sensor node deployment, mobile node information, and latency. The most significant challenges to be addressed in WSNs are mobile node information and energy constraints [87]. For the multiple categories of protocols, a performance-based comparison was conducted using factors such as battery friendliness, cluster durability, scalability, reaction time, load distribution, model complexity, and stability period. To aid others in their future investigations of research into cluster sensor networks, the effectiveness of various clustering approach types is thoroughly analyzed and reported as shown in Table 5.

Table 5. Comparative analysis of cluster-based energy-efficient routing protocols.

Protocol	Protocol Type	Mobility	Battery Friendly	Cluster Durability	Expandability	Latency	Load Balancing	Model Complexity	References
LEACH	Cluster-based	no	Minimal	Medium	Minimal	Minute	Medium	Less	[13]
HEED	Cluster-based	no	Medium	High	Medium	Medium	Medium	Medium	[88]
DWEHC	Cluster-based	Not specified	Maximum	High	Medium	Medium	Very Good	Medium	[89]
CH-PANEL	Cluster-based	Yes	Medium	Less	Less	Medium	Good	High	[90]
TL-LEACH	Cluster-based	Yes	Less	Medium	Medium	Small	Bad	Less	[91]
EECS	Cluster-based	Yes	Medium	High	Less	Small	Medium	Maximum	[92]
ACE	Cluster-based	No	Medium	Minimal	Medium	Small	Medium	Maximum	[93]
E-LEACH	Cluster-based	Yes	High	Medium	Medium	Small	High	Less	[94]
PEGASIS	Cluster-based	Not Specified	Less	Less	Minimal	Large	Medium	High	[57]
TEEN	Cluster-based	No	Maximum	High	Less	Small	Good	High	[95]
APTEEN	Cluster-based	Yes	Medium	Minimal	Less	Small	Medium	Maximum	[96]
CCS	Cluster-based	No	Less	Less	Less	Large	Very Bad	Medium	[97]
EELRP	Cluster-based	Yes	Less	High	Maximum	Medium	Bad	Less	[98]

Table 5. Comparative analysis of cluster-based energy-efficient routing protocols.

Protocol	Protocol Type	Mobility	Battery Friendly	Cluster Durability	Expandability	Latency	Load Balancing	Model Complexity	References
LEACH	Cluster-based	no	Minimal	Medium	Minimal	Minute	Medium	Less	[13]
HEED	Cluster-based	no	Medium	High	Medium	Medium	Medium	Medium	[88]
DWEHC	Cluster-based	Not specified	Maximum	High	Medium	Medium	Very Good	Medium	[89]
CH-PANEL	Cluster-based	Yes	Medium	Less	Less	Medium	Good	High	[90]
TL-LEACH	Cluster-based	Yes	Less	Medium	Medium	Small	Bad	Less	[91]
EECS	Cluster-based	Yes	Medium	High	Less	Small	Medium	Maximum	[92]
ACE	Cluster-based	No	Medium	Minimal	Medium	Small	Medium	Maximum	[93]
E-LEACH	Cluster-based	Yes	High	Medium	Medium	Small	High	Less	[94]
PEGASIS	Cluster-based	Not Specified	Less	Less	Minimal	Large	Medium	High	[57]
TEEN	Cluster-based	No	Maximum	High	Less	Small	Good	High	[95]
APTEEN	Cluster-based	Yes	Medium	Minimal	Less	Small	Medium	Maximum	[96]
CCS	Cluster-based	No	Less	Less	Less	Large	Very Bad	Medium	[97]
EELRP	Cluster-based	Yes	Less	High	Maximum	Medium	Bad	Less	[98]

5. Open Issues

This study investigates the methods for facilitating hierarchical clustering by considering its related parameters. A comprehensive comparison of the current hierarchical clustering methods area was also analyzed. Additional research involving the interaction of technology with others such as IoT, VANETs, and so on is still needed. Due to the open nature of the network, WSNs are difficult to secure. Conventional cryptographic methods cannot be used for secure data transfer since limited resources are available in WSNs. Therefore, it is important to propose a lightweight protocol that provides secure communication with minimum energy consumption.

The existing hierarchical and non-hierarchical routing protocols with sink mobility for data collection were discussed in this literature. The sink mobility pattern, protocol overhead, hotspot severity, etc., are analyzed, therefore overcome to the issues related to frequent changes in Mobile WSN (MWSN) topologies. In the following some issues can be addressed by researchers:

• In VANETs, sensors are installed on each vehicle. The sensor nodes are responsible for detecting events. The primary goal is to improve road safety for all users. Data aggregation in VANETs is difficult to handle particularly when sensors are used with high-level mobility vehicles
• IoT devices will use sensors to connect and transmit data wirelessly. Because everything is linked, there is a good chance that a large volume of data will be generated. As a result, data accessibility, retrieval, and storage will become more difficult. Big data refers to large datasets, and powerful deep learning algorithms are required to search for and extract information from them.
• Multimedia sensors are frequently used in today’s world to capture images in a specific area or to trigger an event. They are used for security and surveillance purposes. Due to its importance, delay and fault tolerance should be prioritized when sensors are deployed.

6. Conclusions

As interest in WSNs has developed, the technology has been widely implemented in numerous civilian and military applications. Among the challenges are building, implementing, and scaling routing protocols for WSNs due to their inefficiency, resilience, and scalability. However, the limitations and design challenges of WSNs are resolved by employing clustered routing algorithms. In this situation, it is proven that over the years works have been conducted to improve the effectiveness and efficiency of clustering routing protocols in the area of WSNs. Therefore, this study discussed the overview of clustering routing techniques. In addition, a new taxonomy of clustering routing approaches is proposed that focuses on significant clustering properties rather than the clustering’s overall objective. Using the defined taxonomy considering essential criteria, a long-term study was conducted to analyze classic WSNs clustering routing algorithms scientifically.

Identifying and resolving the QoS challenges in routing clustering, particularly in real-time circumstances such as target tracking and emerging event monitoring is important for future research. In the WSN resource-constrained context, limited studies have been conducted on QoS requirements. Further investigation needs to be conducted on the movement or stability of the sink and sensor nodes. Various node mobility applications such as war simulations need a comprehensive analysis of the overhead issues associated with node mobility and topology changes. The growth of network size in WSNs will result in the creation of duplicate data. Therefore, a degree of redundancy is important to increase network reliability. There is still an unresolved issue of whether a trade-off exists between redundancy reduction and redundancy usage.

The outcome of this study is to motivate more research on WSNs by considering various properties of clustered routing methods. The new protocols should utilize clustered routing technologies effectively. WSNs, informally known as less well-known small networks, have great potential in the implementation of router-assisted clustering routing systems due to their increasing adoption in more complicated applications. For future work, we will study the cross-technology communication, and its applications in heterogeneous WSN, which changes the structure of the WSN topology, which in turn, yields the inefficiency of energy consumption especially with the IoT devices.