Clustering Protocol Research Articles

Energy efficient chaotic sparrow search algorithm based clustering protocol for wireless sensor network communications

Wireless sensor networks (WSN) include a collection of autonomous sensor nodes, which are generally deployed in a harsh or hostile environment to sense the physical parameters involved in the target region. It is highly utilized for target tracking, surveillance, and data gathering applications. Since the sensors in WSN operates on limited inbuilt batteries, it is needed to design energy efficient approaches to preserve energy and extend lifetime of the network. Earlier studies have reported that clustering is an effectual manner used for accomplishing energy efficiency and it is addressed with use of metaheuristic optimization algorithms. In this view, this paper present an energy efficient chaotic sparrow search algorithm based clustering (EE-CSSAC) technique for WSN. The proposed EE-CCSAC technique is dependent upon the integration of chaotic maps into the traditional sparrow search algorithm (SSA). In addition, the EE-CCSAC technique develops a fitness function with four variables to properly select the CHs and then organize clusters, thereby achieving energy efficiency. The experimental validation of the EE-CCSAC system takes place and the outcomes are related to recent clustering approaches. The simulation outcomes exhbited the effective outcome of the EE-CCSAC method on the other ones with respec to distinct estimation parameters.

IBRE-LEACH: Improving the Performance of the BRE-LEACH for Wireless Sensor Networks

Wireless sensor networks are extensively used in different fields, including severe and harsh environments such as battlegrounds, volcanic areas, healthcare, and so on. The major constraint of WSNs is the limited power supply, which impacts the lifecycle of the entire network. Clustering is a performing method used in WSNs to optimize power consumption and extend the lifetime of WSNs. LEACH is considered the first classical clustering protocol used in WSNs to optimize energy consumption. Many protocols have been developed to enhance the conventional LEACH protocol. BRE-LEACH (Balanced Residual Energy LEACH) is one of them. It is used to enhance the performance of the LEACH protocol. In this paper, we propose a new improved BRE-LEACH protocol called IBRE-LEACH (Improved Balanced Residual Energy LEACH), which improves the performance of the BRE-LEACH approach. The IBRE-LEACH combines clustering and multi-hop techniques. It selects CHs according to the residual energy and limits the maximum number of nodes in each cluster to balance the energy consumption. Thus, IBRE-LEACH allows the Abandoned Nodes to send their data to the BS. Furthermore, it routes the gathered data using CHs, ANs, and a root node, which is a node with maximum residual energy and minimum distance to the BS. The simulation results in MATLAB show that the IBRE-LEACH protocol increases the throughput, optimizes energy consumption, extends network lifetime, and ameliorates stability by 81.99%, 94.33%, and 91.47% compared to BRE-LEACH, LEACH-C, and LEACH, respectively.

Optimization of sink sojourn time for enhancing the lifetime of wireless sensor network

Reducing the energy holes by using multiple mobile sinks and reducing the one hop sensor energy consumption is one of the important research issues in wireless sensor network. The proposed work focus on deploying multiple mobile sinks to move across a predetermined trajectory for data collection. The network area is divided into triangular partitions and the mobile sinks are allowed to move along triangular partitions. The work proposes a clustering protocol to form data collection trees. The contribution is extended further by formulation an optimization model for improvising the sensor network lifetime. The mobile sinks are allowed to travel along the predetermined trajectory and stop at each sojourn location for an optimal time location. The optimization model considers the one hop residual energy and energy consumption of the individual sensor. Simulation experiments showcase better performance of the proposed work in comparison with existing model.

Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

SummaryBecause of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP‐MS) technique for IoT supported WSN. The EACMRP‐MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP‐MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type‐II fuzzy logic (T2FL) technique is used for the optimal selection of multi‐hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP‐MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.

An Insight on Clustering Protocols in Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSN) have drawn the attention of many researchers as well as general users in recent years. Since WSN has a wide range of applications, including environmental monitoring, medical applications, and surveillance, their usage is not limited. As energy is a major constraint in WSN, it is necessary to employ techniques that reduce energy consumption in order to extend the network’s lifetime. Clustering, data aggregation, duty cycling, load balancing, and efficient routing are some of the techniques used to reduce energy consumption. In this paper, we discuss in details about clustering, its properties, the existing clustering protocols. The clustering protocols that support data aggregation will also be discussed. The paper concludes with considering the impact of clustering and data aggregation in WSN.

An Improved Multi-hop LEACH Protocol Based on Fuzzy Logic for Wireless Sensor Networks

<p>As an important kind of clustering protocols in wireless sensor networks (WSNs), LEACH and its variants have been demonstrated to efficiently balance the energy consumption, simplify the routing task and extend the network lifetime. Especially, the multi-hop LEACH type protocols have proved to be effective approaches for high energy efficiency, reliability as well as scalability. However, data transmission in hop-by-hop mode increases the energy consumption and end-to-end delay. In this paper, an improved multi-hop LEACH protocol based on fuzzy logic called IMF-LEACH is proposed, which uses a fuzzy logic controller with residual energy, length of data, and distance to BS as fuzzy descriptors to determine the hop count. Moreover, the hop count is used for each CH to find its optimal next-hop intermediate CH with more residual energy and less number of members. Simulations are conducted in MATLAB to evaluate the performance of the proposed protocol, and the results show that IMF-LEACH maximizes the network lifetime and outperforms its counterparts consistently.</p> <p> </p>

ESCVAD: An Energy-Saving Routing Protocol Based on Voronoi Adaptive Clustering for Wireless Sensor Networks

An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks

<p><span>Improving the network lifetime is still a vital challenge because most wireless sensor networks (WSNs) run in an unreached environment, and offer almost impossible human access and tracking. Clustering is one of the most effective methods for ensuring that the relevant device process takes place to improve network scalability, decrease energy consumption and maintain an extended network lifetime. Many research have been developed on the numerous effective clustering algorithms to address this problem. Such algorithms almost dominate on the cluster head (CH) selection and cluster formation; using the intelligent type1 fuzzy-logic (T1-FL) scheme. In this paper, we suggest an interval type2 FL (IT2-FL) methodology that assumes uncertain levels of a decision to be more efficient than the T1-FL model. It is the so-called energy-efficient interval type2 fuzzy (EEIT2-F) low energy adaptive clustering hierarchical (LEACH) protocol. The IT2-FL system depends on three inputs of the residual energy of each node, the node distance from the base station (sink node), and the centrality of each node. Accordingly, the simulation results show that the suggested clustering protocol outperforms the other existing proposals in terms of energy consumption and network lifetime.</span></p>

An Energy Efficient Clustering Protocol Using CM-YSGA Optimization Approach in WSN

Abstract: While dealing with the wireless sensor networks (WSNs), one of the greatest factors that must be taken into consideration is energy consumption of nodes. One of the most effective common way of preserving energy in sensor nodes is clustering technique in which CH selection is of great importance. In this manuscript, an improved energy efficient clustering protocol is proposed in which chaotic mapping algorithm is clubbed along with the advanced variant of Yellow Saddle Goatfish Algorithm (YSGA). The main objective of the proposed model is to reduce the energy consumption of nodes which ultimately prolongs the lifespan of wireless network. Initially, cluster are formed in the network by selecting the nodes randomly. After this, four important parameters which included, residual energy, distance to sink, average distance to CH neighbor node and delay are taken into consideration and on the basis of these factors’ fitness value is calculated by the chaotic-YSGA technique. The node whose fitness value came out to be highest of all other nodes, is selected as the CH in that particular cluster. In addition, the Huffman data compression technique is used in the proposed system to reduce energy consumption in nodes. Finally, the performance of the proposed Chaotic-YSGA model is analyzed and compared with conventional energy efficient models in MATLAB software in terms of alive node, dead nodes, throughput, residual energy, FND, HND and LND. Keywords: WSN, Energy Efficiency, CH selection, Optimization Algorithms.

A weighted Markov-clustering routing protocol for optimizing energy use in wireless sensor networks

Interest has increased in wireless sensor networks (WSNs) in fields such as healthcare, industrial control and environmental monitoring. More recently, WSNs have been widely deployed in Internet of Things (IoT) based applications. They are considered as an essential part of IoT networks. The expanded use of WSNs raises daunting technical challenges, not the least of which is sensor battery energy conservation. The design of efficient sensor clustering strategies to reduce energy consumption by data transmission throughout the WSN has become crucial. Several applications of WSNs induce a random deployment of sensors such as the monitoring of conflict zones, the study of natural phenomena in hostile zones or rescue operations. In this context, energy conservation should be ensured according to the non-uniformity of the resulting sensor distribution in different areas of the network. In this study, a novel weighted Markov clustering protocol that considers sensor abundance for cluster-head and queried sensor selections is presented. The new protocol aims to decrease intra-cluster energy consumption by reducing the sending of redundant data in sensor-dense regions. In addition, it attempts to prolong sensor-sparse regions lifetime by limiting the number of queried sensors. This protocol combines a Markov clustering of sensors with a sensor weighting based on residual energy and sensor abundance in the network. The proposed protocol is a significant improvement of an existing unweighted Markov clustering protocol. The unweighted Markov clustering protocol is based on sensor residual energy and sensor location without taking into account the sensor abundance in different areas of the network. Simulations affirm that the new protocol handles more appropriately the non-uniformity of sensor distribution and enhance the durability of wireless sensor networks. Indeed, simulation results show that the proposed clustering protocol outperforms its unweighted ancestor and other well-known clustering protocols in terms of energy conservation and network lifetime. The number of expired sensors and the average dissipated energy are reduced, whereas, the average sensor lifetime is prolonged compared to the unweighted ancestor, or HEED, LEACH, PEGASIS, and TEEN.

Novel dynamic route aware clustering (DRAC) protocol for enhancing energy efficiency by clustering concept in WSN

"Wireless Sensor Networks (WSNs)" have started to recognize clustering as among the best effective ways of saving energy. The Cluster-Heads (CHs) probably have spent higher energy than typical nodes in a WSN based on clustering, resulting in higher consumption of energy and a shorter network lifespan. To lower the consumption of energy of CHs, many energy-efficient routing techniques had already been developed and demonstrated. It is possible to reduce the consumption of energy of CHs by increasing the number of CHs nearby. Earlier research has not taken into account these conditions. Poorly balanced CH consumption of energy is also a major design concern, resulting in increased misspent energy and initial network downfall. These situations need re-creation of the shortest path route if the previously found routed path is faulty or interrupted. Extra node consumption of energy may exhaust the sensor's energy supply as a result of this reconfiguration. Researchers in the last several years also aimed to enhance the overall balance of energy through the use of huge and costly energy harvesting equipment but nothing had solved this issue.

HPCP-QCWOA: High Performance Clustering Protocol based on Quantum Clone Whale Optimization Algorithm in Integrated Energy System

Combined with highly developed technologies such as cloud computing and the Internet of Things, the integrated energy system (IES) has realized the coordinated scheduling and utilization of various energy resources with its superiority of comprehensive control and high applicability. Meanwhile, approaches to enhance the quality of service (QoS), e.g. network lifetime, energy distribution, and data transmission delay, of an indispensable component of IES, i.e., integrated energy system wireless sensor networks (IESWSNs), have become an urgent research issue. To tackle the issue, a high-performance clustering protocol termed quantum clone whale optimization algorithm (HPCP-QCWOA) is proposed for IESWSNs. By designing a new quantum operator and a new layering clone operator, HPCP-QCWOA can find an approximate optimal clustering scheme to improve the QoS performance of IESWSNs. Unlike other population-based clustering protocols, a new objective function that embraces remaining energy, intra-cluster distance, BS distance, and data transmission delay is designed in the HPCP-QCWOA, which renders its clustering performance more desirable than some other state-of-the-art clustering protocols, i.e., O-LEACH, LDIWPSO, and ARSH-FATI-CHS. Empirically, simulation experiments under different scenarios illustrated that the lifetime of the HPCP-QCWOA-based clustering has increased by 28.78%, 25.50% and 11.22% respectively compared with O-LEACH, LDIWPSO, and ARSH-FATI-CHS.

Energy aware fault tolerant clustering with routing protocol for improved survivability in wireless sensor networks

Wireless Sensor Networks (WSN) remain a hot research topic due to its wide range of application areas like environmental monitoring, military surveillance, healthcare, and disaster management. Network survivability is one of the challenging issues in WSN that needs to be maximized for long-run operation of the network. It can be proficiently improved by effective utilization of available energy since the nodes in WSN operate only via in-built batteries. The studies conducted earlier have mainly focused on the design of clustering and routing protocols to reduce energy consumption in the network. With this motivation, the current research paper presents a novel Energy-Aware Fault Tolerant Clustering with Routing for Improved Survivability (EAFTC-RIS) technique in WSN. The proposed EAFTC-RIS technique intends to select the Cluster Heads (CHs) and optimal routes to the destination in an optimal manner with fault tolerant mechanism. The proposed EAFTC-RIS technique primarily designs Moth Flame Optimization (MFO)-based clustering scheme for CH selection and cluster construction. Also, fault tolerant mechanism is considered to further enhance the survivability of the network. In addition, Social Spider Optimization (SSO)-based routing approach is also employed for optimal selection of routes in WSN. Clustering, routing, and fault tolerant mechanism designs, with multiple input variables, result in enhanced network survivability. The proposed EAFTC-RIS technique was validated for its performance and the outcomes were inspected under various aspects. The simulation outcomes demonstrate the superiority of EAFTC-RIS technique over recent approaches namely, EAFTC-RIS, FUCCAR-GWSO, FBECS, MLSLEEP, and Bee Cluster.

Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review

Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms.

Survey on energy efficient dynamic sink optimum routing for wireless sensor network and communication technologies

SummaryWireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid‐based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering‐based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid‐based dynamic sink routing scenario. The significance of clustering‐based protocols for improving lifetime of the network is evaluated as case study. The clustering‐based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks.

The Key Technology of Wireless Sensor Network and Its Application in the Internet of Things

At present, sensor networks and the Internet of Things have made rapid development and received more and more attention. Wireless sensor network technology is widely applied in many industries. As an important part of IoT sensor layer, it is an important infrastructure for IoT development. The purpose of this paper is to enhance the existing wireless sensor networks on the basis of existing technologies for better application in IoT. In this article, we introduce the structural features, key techniques, and some specific applications of wireless sensor networks in the IoT. Secondly, this paper studies and analyzes the principle of the cell membrane algorithm and its own characteristics. Aiming at the problem of energy constraint in wireless sensor network, an energy equilibrium clustering algorithm based on the cell membrane optimization algorithm is proposed in order to realize energy balance of nodes and distribution of network cluster heads. This algorithm divides the nodes by concentration and energy factors and divides them into globally balanced clusters by combining distance factors, which can well solve the problems of uneven distribution of cluster heads and unbalanced global power usage in sensor networks. On the basis of a lot of previous researches, this paper presents a QoS model, which is based on the routing protocol, clustering protocol, and data fusion scheme discussed in this paper. Through analysis and experiments, it is proved that this scheme improves energy consumption and flexibility by 78% compared with the previous one and achieves better network performance.

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

Low-Energy Clustering Protocol for Query-Based Wireless Sensor Networks

Nowadays, clustering protocols have been widely used to reduce energy consumption in wireless sensor networks (WSNs). However, when existing clustering protocols are used for query-based WSNs, the energy efficiency becomes limited because in such networks, clusters are formed without considering the positional distribution of query targets, and the procedures of node clustering, cluster head selection and inter-cluster routing are performed in a distributed way, which is energy-consuming. In this paper, a centralized clustering protocol, called Low-Energy Dynamic Clustering (LEDC), is developed to enhance the energy efficiency in query-based WSNs. The main contributions of LEDC include: (1) a novel prediction-based scheme is raised to make the centralized network information maintenance energy-efficient and scalable; (2) centralized optimal dynamic cluster formation algorithms are proposed to cluster the query targets in a centralized way, which not only helps the distribution of clusters match with that of query targets but also saves the high cost incurred in the distributed procedures of node clustering, cluster head selection and inter-cluster routing; (3) an energy-aware forwarding strategy is suggested to improve the energy efficiency and balance the energy load among nodes in inter-cluster communication; (4) a parallel processing algorithm is raised to increase the network throughput and reduce the latency of queries. The performance of LEDC is then compared to the latest clustering protocol. Simulation results demonstrate that LEDC considerably saves the energy of nodes and prolongs the network lifetime.

Improved cluster to normal ratio protocol for increasing the lifetime of wireless sensor networks

<span>The wireless sensor network (WSN) is a decentralized network that allows sensor nodes to freely join and quit the network, making it a unique network type that varies from centralized systems in which the fusion center decides on entry and exit. However, one of the key drawbacks of these networks is that the sensor nodes are small and located in remote areas. As a result, energy usage must be efficiently managed. In order to limit energy consumption, an efficient clustering protocol approach is necessary, which may be performed by dividing the networks into clusters and selecting cluster heads depending on the remaining energy and sensor distance. The energy hole problem has an impact on the performance of an energy-efficient technique. As a result, this work aims to extend the lifespan of WSNs, the goal of this research is to make the cluster to normal ratio (CTNR) Protocol better. Data forwarding nodes are added to the CTNR protocol hierarchy to enable hopping and avoid consuming nodes in order to solve the energy hole problem (sink). The proposed method is implemented in Matlab, and the results are compared to literature on energy usage and the amount of dead and living nodes. In terms of the performance indicators given, the results reveal that our proposed scheme beat others.</span>

Congestion Management of GSTEB Routing Protocol Using K-Means Algorithm in Wireless Sensor Network

A sensor node comprises of enormous nodes which can a variety of tasks such as recognize, indulgence, communication, and influence resource. Data transmission is the main aspect from starting to target. Once it is transferred between the node and within-cluster, the congestion will arise either node or in a resource allotment. So that it will affect the unremitting delay of reaching data, flow, the defeat of information to the destination, and the unfavorable use of a significant quantity of energy in the node. In this work, the impact of machine learning algorithms is studied using the GSTEB hierarchical protocol. By altering the type of input parameters, K-Means clustering on node energy usage can be performed. If congestion occurs, our proposal outlines a strategy for dealing with it and improving the CHPSCCC (Clustered Highly Prioritized Application-Specific Congestion Control Clustering Protocol) approach for congestion management. Simulation results show that in the proposed system there is significant growth of rises in the lifetime, energy, throughput, and packet loss.