Low Energy Adaptive Clustering Hierarchy Protocol Research Articles

Ad hoc wireless network implementing BEE-LEACH

Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to cluster heads based on distance relative to the head. This algorithm achieves the goal of extending battery life and offers itself as a promising alternative to standard LEACH algorithms. The algorithm is tested by comparing sensor battery life, total sensors communicating at a given time, and sensors with residual energy. This paper addresses the strengths and vulnerabilities of the algorithm, as well as proposed work for further implementation for the following groups looking to create their own LEACH protocol.

Design and performance assessment of improved evolutionary computing based LEACH protocol for energy efficient and lifetime extension of wireless sensor network

The strengths of a Memetic Algorithm and an enhanced Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol are combined in this research study to propose a revolutionary method for improving the energy efficiency and longevity of Wireless Sensor Networks (WSNs). WSNs are widely used for a variety of applications, including industrial automation, healthcare, and environmental monitoring, where extending network lifetime and energy conservation are crucial. Although successful, conventional clustering algorithms like LEACH feature subpar cluster formation and premature cluster head failures. To get over these restrictions, a memetic algorithm is used to maximize cluster formation and reduce energy usage. This algorithm was influenced by natural evolution and cultural learning processes. The Memetic Algorithm iteratively improves the LEACH protocol in the proposed technique by enhancing dynamic clustering, energy-aware routing, and cluster head selection. These methods work together to increase energy efficiency and lengthen the life of the network. In comparison to conventional LEACH-based WSNs and other cutting-edge algorithms, the paper’s study of the simulation results shows how successful the suggested technique is. In light of network longevity, energy use, and data transmission efficiency, the results obtained demonstrate notable improvements.

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Improvement of Teach Protocol for Enhancing Features of WSN

. Wireless Sensor Network (WSN) has several applications such as military, industry. and environment. The importance of WSNs in current applications makes the WSN technology highly relevant and significant to the field of communication and computing. However, WSN's performance deals with number of challenges. Energy consumption is the most considerable because nodes use energy to collect, treat, and send data, but they have restricted energy- For this reason. numerous efficient energy routing protocols have been dev eloped to save the consumption or power. Low energy adaptive clustering hierarchy (LEACH) is considered the most attractive one in WSNs, in this projects we evaluate the LEACH approach effectiveness in the cluster-head (CHJ choosing and in data transmission, then we propose an enhanced protocol. proposed algorithm aims to improve energy Consumption and prolong the lifetime of WSN through selecting CHs depending on the remaining power, balancing the number of nodes in clusters. determining abandoned nudes in order to send their data to the sink. Then CHs choose the optimal path to reach the sink, we propose a new approach to achieve better enhancement of WSN in terms of network lifetime and data transmission time represented by reducing the packet delay time. Then, we compare the simulated result of the proposed algorithm with the basic LEACH protocol.

LEACH Protocol to Improve Energy Efficiency of Wireless Sensor Networks in Smart Agriculture

Smart agriculture is the application of technology to improve efficiency, productivity, and sustainability in agricultural practices. However, smart agriculture systems face major challenges related to connectivity and energy management. To address connectivity issues, the Wireless Sensor Network (WSN) architecture is utilized, consisting of sensor nodes to collect and transmit sensor data wirelessly. Despite the implementation of WSN, there are still issues related to high power consumption in smart agriculture systems. This can lead to reduced battery life for each sensor node in the WSN architecture. Therefore, increasing energy efficiency is crucial to optimizing the performance of smart agriculture systems. This study proposes the use of the LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol in smart agriculture to manage clusters within the WSN and reduce energy consumption in each sensor node. Experimental methods were conducted by building the WSN using the nRF24L01 as the sensor data transmitter and Arduino / Node MCU as the microcontroller. The use of the LEACH protocol aims to address energy issues. Additionally, data from each sensor is collected using the Message Queuing Telemetry Transport (MQTT) protocol to facilitate monitoring of sensor data transmission and battery power information. Test results show that the integration of the LEACH protocol into the WSN can be carried out at each stage, from Discovery-State to Steady-State, to Setup-State. These steps are aimed at significantly reducing energy consumption in sensor nodes by 13% over a 12-hour testing period. Furthermore, it can extend battery life and improve the overall system efficiency.

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor networks (WSNs), as the key component of IoT, are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.

A multi-hop routing protocol for an energy-efficient in wireless sensor network

The low-energy adaptive clustering hierarchy (LEACH) protocol has been developed to be implemented in wireless sensor networks (WSNs) systems such as healthcare and military systems. LEACH protocol depends on clustering the employed sensors and electing one cluster head (CH) for each cluster. The CH nodes are changed periodically to evenly distribute the energy load among sensors. Updating the CH node requires electing different CH and re-clustering sensors. This process consumes sensors’ energy due to sending and receiving many broadcast and unicast messages thus reduces the network lifetime, which is regarded as a significant issue in LEACH. This research develops a new approach based on modifying the LEACH protocol to minimize the need of updating the cluster head. The proposal aims to extend the WSN’s lifetime by maintaining the sensor nodes’ energy. The suggested approach has been evaluated and shown remarkable efficiency in comparison with basic LEACH protocol and not-clustered protocol in terms of extending network lifetime and reducing the required sent messages in the network reflected by 15%, and, in addition, reducing the need to reformatting the clusters frequently and saving network resources.

Optimization of LEACH Protocol for WSNs in Terms of Energy Efficient and Network Lifetime

Wireless Sensor Network (WSN) is a group of small, intelligent sensors with limited resources. WSN has limited energy restrictions, so, the network lifetime is the major challenge that directly affect the efficiency of the network. This work presents an energy-saving clustering hierarchical algorithm for WSNs; it is an improvement of Low-Energy adaptive Clustering Hierarchy (LEACH) algorithm. The aim of this algorithm is to minimize power consumption by the appropriate election of new cluster heads in every data transfer round and avoid network collisions. This goal achieved by using an efficient function to select the best cluster heads nodes in each round, which takes into account the current energy in the sensors. The proposed algorithm improves the cluster formation process by relying on the shorter distance to the base station. The Time Division Multiple Access (TDMA) mechanism also utilized to schedule the transmission of data packets to cluster heads nodes and to avoid data packet collisions at the base station. Experiments conducted in MATLAB R (2020a) software showed that the suggested algorithm extended the network lifetime by 14.5%, and improved the network throughput by 16.8% compared to the LEACH algorithm. That means, the proposed energy-saving clustering hierarchy algorithm has improved the performance of the LEACH algorithm in term of enhancing network lifetime and increasing network throughput.

Enhancing Parameters of LEACH Protocol for Efficient Routing in Wireless Sensor Networks

The increasing popularity of wireless sensor networks can be attributed to their unique characteristics, which include high density, low power consumption, software programmability, and long-term accuracy. This paper presents a thorough analysis of key parameters for sensor network applications, including network lifetime, packet delivery ratio, energy-efficient transmission, and dead node ratio. This paper examines the effectiveness of clustering in wireless sensor networks for achieving efficiency. The research focuses on the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol and proposes an enhanced algorithm that improves network lifetime, throughput, and the number of living nodes. This paper evaluates the performance of the improved LEACH protocol and compares it to other protocols currently in use. The results indicate that the proposed method significantly improves the performance of the protocol, making it a promising method for efficient routing in wireless sensor networks. This study is an important contribution to the field of wireless sensor networks and can aid in the creation of more efficient and effective protocols for various sensor network applications.

A CLUSTERING OPTIMIZATION FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORK USING K-MEANS ALGORITHM

The limitation of sensors energy make energy efficiency still a priority issue in Wireless Sensor Network (WSN) technology. One effort that can be done to overcome this problem is to design the right data transmission path (or better known as routing). Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most widely used cluster-based routing protocols because it is considered capable of minimizing the amount of energy consumption through the formation of clusters or groups of nodes. Unfortunately, this protocol will experience a significant decrease in energy as the amount of data transmission increases. This is partly due to the clustering process which is carried out randomly and causes an imbalance in the distribution of the number of nodes between clusters. This study proposed a method to optimize the clustering process in the LEACH protocol by integrating the K-Means algorithm, which is called LEACH-KMe. A simulation was conducted to determine the effectiveness of the proposed method by considering 4 main parameters, namely total energy consumption, number of alive nodes, number of dead nodes, and residual energy. The test results proved that the LEACH-KMe protocol provides better performance than the conventional LEACH protocol (more even distribution of nodes, less total energy consumption and number of dead nodes, as well as a larger number of alive nodes and residual energy).

Ameliorate the performance using soft computing approaches in wireless networks

<span lang="EN-US">Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.</span>

OPTIMIZATION OF CLUSTER HEAD SELECTION AND ENHANCING ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS USING NOVEL METAHEURISTIC ALGORITHMS

Clustering-based routing is preferred to support fault tolerance, load balancing, reliable communication, and to prolong the network lifetime in a wireless sensor network (WSN). The low-energy adaptive clustering hierarchy (LEACH) is the most popular routing technique, introduced for the first time for homogeneous WSNs. However, the random selection of cluster heads (CHs) in LEACH protocols results in poor performance in real network deployments due to the faster rate of energy depletion at CHs. The dynamic selection of CHs based on a heuristic approach can minimize the energy consumption at CHs and enhance the network lifetime. In this paper, a metaheuristic algorithm called grey wolf optimization (GWO) and its enhanced versions are proposed in selecting the optimal CH. The fitness function is defined based on sink distance to CH and residual energy at the sensor node. The optimal values of fitness function give an efficient CH selection and cost-effective routing. The primary goal of this paper is to maximize the network lifetime of WSNs by optimal selection of CHs using the improved GWO (IGWO) algorithm. The proposed IGWO-based LEACH protocol confirmed the optimal selection of CH with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by balancing the number of alive and dead nodes in WSN.

Performance analysis of multi-layered clustering network using fault tolerance multipath routing protocol (MRP-FT) in a wireless sensor network (WSN)

Wireless sensor networks (WSNs) are ad hoc and self-configuring networks having the possibility that any sensor node can connect or leave the network. With no central controller in WSN, wireless sensor nodes are considered responsible for data routing in the networks. The wireless sensor nodes are very small in size and have limited resources, therefore, it becomes difficult to recharge or replace the battery of the sensor nodes at far places. The present study focused on reducing the battery consumption of the sensor nodes by the deployment of the newly proposed Fault Tolerance Multipath Routing Protocol (MRP-FT) as compared with the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol under particle swarm optimisation based fault tolerant routing (PSO-FT) technique. The proposed algorithm of MRP-FT-based on the dynamic clustering technique using Boltzmann learning of the neural network and the weights were adjusted according to the area of networks, number of nodes and rounds, the initial energy of nodes (E0), transmission energy of nodes (d < d0), data reviving energy (ERX), data aggregation energy (EDA), energy dissipation on free space (εfs), energy dissipation of multi-path delay (ε mp) and the packet size. The results of the present study revealed that the packet heads remains constant during the initial time period (up to 2500 seconds), and exhibited a sharp increase thereafter. The network energy consumption remains constant up to 2300 s and exhibited a sharp increase thereafter. High energy use after 2300 seconds describes the faulty occurrence in the network and leads to decreased reliability (%) of the existing protocol. The energy consumption was substantially reduced by 15 J (38.5%) due to the implementation of the newly proposed MRP-FT, compared with the existing PSO-FT protocol. The reduced delay of 22 packets was achieved with MRP-FT protocol, compared with the existing PSO-FT technique-based LEACH protocol. Nonetheless, the MRP-FT enhanced the packet overhead of 10.8% over the current protocol due to deploying more uniform clustering. Additionally, a 12% increase in reliability was achieved with the implementation of MRP-FT protocol emphasizing that network lifespan was prolonged efficiently with the proposed algorithm.

Non-cooperative Game Leach for Cluster Distribution Routing Method on Wireless Sensor Network (WSN)

A wireless sensor network (WSN) is a group of sensor nodes that take data with specific measurement parameters and then send the data wirelessly to a central node or server for data processing. WSN nodes are designed to be compact; therefore, the energy of sensor nodes usually depends on the integrated battery. The sensor node must work as efficiently as possible so that energy use can be more efficient and WSN life can be prolonged. The longer the lifespan of the WSN, the higher the throughput. One of the many ways to extend the life of the WSN is to use the concept of grouping nodes or the so-called cluster method. This study proposes the The Division Non-Cooperative Game LEACH (DivNCGL) routing method, a modified method based on Low Energy Adaptive Clustering Hierarchy (LEACH) by distributing cluster heads (CHs). The distribution process is performed by dividing the distribution area of the nodes into subregions. The subregion division process uses the probability value obtained by the non-cooperative game method based on the remaining active nodes, the total remaining energy, and the energy required for transmission. The simulation results, the lifespan of WSN using the DivNCGL method increases up to 30% with stable energy dissipation compared to the LEACH protocol. The increasing lifespan of the WSN also increases the amount of data transmitted, thereby increasing the received throughput. Received data increases up to 70% using the DivNCGL method compared to LEACH.

An Enhanced Energy Efficient Routing Protocol over Wireless Sensor Networks

Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further enhance the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs.

A Secure LEACH Protocol for Efficient CH Selection and Secure Data Communication in WSNs

Due to the applications of wireless sensor networks (WSNs) in distinct fields, like civilian and military, environmental monitoring, and target tracking, etc., it became the important research area for researchers. Therefore, an efficient organization of network topology is needed for multi-hop data aggregation based on the energy constrained WSNs with large scale deployment for making the routing task simpler, balancing the load efficiently, and enhancing the network lifetime. The low-energy adaptive clustering hierarchy (LEACH) is an architecture of application-specific protocol, which is used for WSNs. However, this LEACH protocol suffered from more energy consumption in network without considering the cluster heads (CHs) distribution on a rotation basis. In addition, the complex security mechanisms are not sufficient due to limited bandwidth and other restrictions that the sensor nodes have. Thus, it is essential to improve the energy efficiency, CH stability and secure data transmission in WSN’s, which is the main objective of this work. Motivated from these three challenges, this article proposes a stable and secure LEACH (SS-LEACH), in which a new and improved protocol of LEACH is proposed for enhancing the stability of CH and energy efficiency as it considers the nodes’ consumed energy ratio (CER) for CH selection and random number generation, here after the proposed LEACH is named as CER-LEACH. In addition, it aims to prevent the previously elected CH node and it will get another chance in the current round. This method correlates the threshold used in traditional LEACH with the energy consumption ratio of each node. Further, it also introduces a hybrid reputation-based data transmission (HRDT) scheme for secure data transmission. With the proposed CER-LEACH protocol, WSNs achieves better performance in terms of secured communication, network lifetime, and energy consumption based on the analysis of simulation results as compared to state-of-art approaches.

Energy-efficient Query-Driven Clustering protocol for WSNs on 5G infrastructure

In the last decades, the low-energy adaptive clustering hierarchy (LEACH) protocol has become one of the most prominent protocols in wireless sensor networks (WSNs). Based on different application scenarios, researchers have come up with various modifications of the LEACH protocol, which are classified as clustering protocols. Nevertheless, in current clustering protocols, sensor nodes usually propagate messages over a large scale for cluster formation and discovering an optimal inter-cluster route, which consumes a large amount of energy. In this study, we introduce a Query-Driven Clustering (QDC) protocol to improve the energy efficiency in WSNs. The proposed QDC includes four sequential parts. Firstly, a size-balanced network partitioning algorithm is to split large-scale WSN into several small sub-networks with the help of 5G infrastructure. Secondly, an estimation-based mechanism aims to keep energy consumed in centralized sub-network information maintenance at a low level. Thirdly, in each sub-network, centralized query-driven clustering algorithms are introduced for clustering the query targets, which saves the high energy consumption for clustering nodes on a global scale. Lastly, as for inter-cluster routing in each sub-network, an energy-efficient and load-balanced routing scheme is developed. Through extensive experiments, QDC exhibits much higher energy efficiency and thus enhances the network lifetime, compared with other protocols in WSNs.

A Secure and Efficient Method to Protect Communications and Energy Consumption in IoT Wireless Sensor Networks

The rapid growth of technology has resulted in the deployment of a large number of interconnected devices, resulting in a wide range of new societal services. Wireless sensor networks (WSNs) are a promising technology which is faced with the challenges of operating a large number of sensor nodes, information gathering, data transmission, and providing a means to act in different scenarios such as monitoring, surveillance, forest fire detection, and many others from the civil to military spectrum. The deployment scenario, the nature of the sensor-equipped nodes, and their communication methods make this architecture extremely vulnerable to attacks, tampering, and manipulation than conventional networks. Therefore, an optimal solution to ensure security in such networks which captures the major constraints of the network in terms of energy utilization, secured data transmission, bandwidth, and memory fingerprint to process data is required. This work proposes a fast, reliable, and secure method of key distribution and management that can be used to ensure the integrity of wireless sensor networks’ communications. Moreover, with regards to efficient energy utilization, an improvement of the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm (a cluster routing protocol that is mainly used in WSN) has been proposed to enhance the networks’ energy efficiency, simplicity, and load-balancing features. Therefore, in this paper, we propose a combination of a distributed key exchange and management methods based on elliptic curve cryptography to ensure security of node communication and an improved routing protocol based on the LEACH protocol to demonstrate better performance in parameters such as network lifespan, dead nodes, and energy consumption.

Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning.

The Wireless Sensor Network is a network formed in areas human beings cannot access. The data need to be sensed by the sensor and transferred to the sink node. Many routing protocols are designed to route data from a single node to the sink node. One of the routing protocols is the hierarchical routing protocol, which passes on the sensed data hierarchically. The Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the hierarchical methods in which communication happens in two steps: the setup phase and the steady-state phase. The efficiency of the LEACH has to be optimized to improve the network lifetime. Therefore, the k-means clustering algorithm, which comes under the unsupervised machine learning method, is incorporated with the LEACH algorithm and has shown better results. But the selection of cluster head needs to improvise because it will transfer the summed-up data to the sink node, so it is to be efficient enough. So, this paper proposes the modified k-means algorithm with LEACH protocol for optimizing the Wireless Sensor Network. In the modified k-means algorithm, the weight of the cluster head is tested and elected, and the clusters are formed using the Euclidean distance formula. The proposed work yields 48.85% efficiency compared to the existing protocol. It is also proven that the proposed work showed more successful data transfer to the sink node. The cluster head selection process elects the more efficient node as the head with less failure rate. The proposed work optimistically balanced the whole network in terms of energy and successful data transfer.

Secured Clustered Internet of Things Wireless Sensor Network

The Internet of Things (IoT) is a swiftly evolving technology that will have a significant impact in the future. The Internet of Things (IoT) is a huge collection of interconnected networks, such as the Wireless Sensor Network (WSN), that make use of fast evolving devices and technologies. The risk of confidentiality of information and protection has increased as a result of the indefinite interconnection of a variety of applied sciences in IoT. WSN employs Low Energy Adaptive Clustering Hierarchy (LEACH) to create an energy-efficacious network that is vulnerable to a wide range of assaults, including the HELLO flood. This work employs AS-LEACH (Abundant Secure LEACH) as an extension to the LEACH protocol to protect cluster heads from Hello flood attacks. For the purpose of validating a sensor node as CH, AS-LEACH employs RBG color cube numbers, a distinct Abundant number, and each sensor node has its own unique ID. The NS2 network simulator is utilized to implement AS-LEACH and verify its efficiency in this proposed work.