LEACH Protocol Research Articles

Application of nature inspired optimization algorithm on hierarchal LEACH protocol for improved performance

In today’s scenario Wireless Sensor Network is playing a dynamic role with the advancement in IOT for mission critical applications due to the fact that these applications are either event driven or time driven applications. Sensor network comprises of battery operated tiny sensors for sensing any change in the environment since the battery life is limited so is the sensor life. One solution to overcome this problem is the use of clustering protocol. A lot of optimization techniques are employed for performance improvement of the hierarchal LEACH routing protocol. This paper explores the three types of Foraging behavior for cluster head selection which means the process to search for food or the energy of the node so that it can be elected as CH. A fitness function is defined relating to residual energy, CH distance to Base station, position of base station. Theses algorithm progresses on the basis of the energy of sensor node in the deployed network. Here the higher energy of node is analogous to food for which the search goes on; in doing so the best CH is selected. The algorithm uses the best location of the node. The overall performance of the network is improved in terms of Energy consumption; packet delivery ratio metrics the results are compared with the existing LEACH protocol.

Path Generation Protocol to Improve Lifetime of WSN

In WSN sensor nodes are connected to their neighbor nodes by a wireless channel and these all nodes are operated on a battery to perform any operations. So improving the lifetime of WSN is a major research challenge by effective utilization of each and every node's energy. The lifetime of WSNs can be improved in three steps: path creation, path selection, and power saving. In existing works, path selection has the major role to utilize and enhance lifetime. In the proposed PStack algorithm, path generation is a major concern, and routing is applied. It takes advantage of and solves the limitations of existing algorithms using the stack concept in WSN. The proposed algorithm shows a better result than the improved LEACH protocol because it uses the data structure with graph theory.

Intrusion Detection in Novel WSN-Leach Dos Attack Dataset using Machine Learning based Boosting Algorithms

Security poses a substantial challenge in the context of wireless sensor networks (WSNs). WSNs, which are frequently employed for real-time monitoring in various Internet of Things (IoT)-based applications, are particularly vulnerable to a range of Denial of Service (DoS) attacks. These attacks represent a significant security concern and are among the most prevalent issues confronting WSNs today.In our research, we have introduced a WSN DoS dataset using the Matrix Laboratory (MATLAB) tool. This dataset was developed through the utilization of the Leach Protocol and incorporates various types of DoS attacks, including Wormhole, Blackhole, Grayhole, Flooding, and Time Division Multiple Access (TDMA) attacks. In our study, we assessed the performance of several machine learning techniques using our dataset. We employed various performance evaluation metrics that gauge both the classification accuracy and the complexity of these machine learning models. These metrics included parameters such as the number of accurately classified and misclassified instances, along with the time required for constructing the model. This comprehensive evaluation aimed to demonstrate the effectiveness of our proposed approach.The results of our analysis revealed that the Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Bagging algorithms exhibited strong performance on our dataset. They achieved impressive accuracy levels of up to 99.16%, 99.16%, and 99.8%, respectively, when using 14 features. Additionally, these algorithms achieved accuracy rates of 99.16%, 98.75%, and 99.8%, respectively, with 5 features, and 95.41%, 95%, and 93.75%, respectively, when considering only 3 features such as node ID and Energy-Consumed, Attack_type. Notably, LightGBM outperformed the others in terms of speed, as it employs a leaf-wise growth strategy, consumes less memory, and requires less time for model construction. Consequently, LightGBM emerged as the most efficient choice for our WSN DoS dataset.

An exploration of integration of IoT and 5G networks and a fuzzy approach for clustering to enhance the lifetime of such networks

Medium access control protocols play an important role for allocating the medium to the smart devices connected on the networks. These networks include IoT and sensor networks, IoT and 5G networks, IoT and optical networks using 5G communication etc. MAC protocols ensure the efficient and fair communication through these networks from devices to base station. Wireless sensor networks are considered as the back bone for the internet controlled applications. These networks set up link between virtual and real world. The selection of the suitable MAC protocol can help in improving the network life time so the data collected by the nodes is delivered to its destination. So this paper explores the various MAC protocols available for IoT and 5G applications and .The parameters of study for these MAC protocols used for the real time applications includes residual energy, throughput, packet delivery ratio, latency and network lifetime. The paper also explores the usages of machine learning and AI used in MAC protocols for these high speed IoT networks. The work addresses the improvement of network lifetime and residual energy by applying Fuzzy approach for the selection of cluster head . The energy consumption increases if the clusters are not balanced in terms of the nodes so we have used the grid formation of the clusters. We used the Fuzzy Logic Inference system (FIS) in the cluster formation technique. The probabilistic method for election of cluster head does not consider the parameters such as residual energy, node position and base station distance. This result in the improper selection of the cluster head .The use of multiple parameters in the FIS improves the network lifetime and residual energy as compared to the classical LEACH protocol. Fuzzy logic approach solves the uncertainties occurring in the system especially when it is handling the larger amount of data. The fuzzy approach shows the prolonged network lifetime in terms of first node die and the Last node Die. We have also used the statistical regression approach to calculate the residual energy and network life time in terms of alive nodes. Finally this chapter ends with the discussion of challenges and future scope of the MAC protocols used for the real time applications. The work is important as it gives opportunity for the research areas in high speed IoT and 5G networks for using AI and ML intelligent to develop QoS MAC protocols.

A mathematical optimization of the lifetime of the wireless sensor network using ML- based energy dependent clustering and tiered routing algorithm

Significant research work is being done in the field of WSN and standardization of many aspects is being looked into. The most well-known procedures for optimising energy in WSNs are using hierarchical topology. LEACH is a hierarchical topology which was introduced initially and served as the basis for achieving energy efficiency in WSN. However, LEACH has certain limitations which has led to a lot of research in improving the conventional LEACH performance. The current study is based on the use of certain algorithms and strategy to enhance the performance of the conventional LEACH protocol. By focusing on parameters such as cluster head formation, energy expenditure in transmission etc., the performance such as throughput and network life may be extended using this enhanced LEACH.

Remote Identification for Smart-meter Operation Error based on a Wireless Sensor Network in a Smart City

This paper presents a remote identification method for smart-meter operation errors based on a wireless sensor network (WSN) in a smart city to reduce the bad operation of smart meters. In the WSN, ZigBee communication technology was used to collect data from smart meters to enhance the reliability of information. The LEACH protocol can effectively extend the network life cycle and was selected as a communication protocol for WSNs to collect operation data from intelligent meters. Based on the operation data of a smart meter collected by a WSN, a remote identification model of operation error of a smart meter was established. The limited memory least square algorithm was adopted to construct the remote identification model of operation error for a smart meter to realize the remote identification of a smart-meter error in a smart city. The proposed method can use a WSN to collect operation data from a smart meter and remotely identify the mean absolute percentage error (MAPE) and root mean square error (RMSE) of a smart meter’s operation error, which were less than 1%.

基于无线传感器网络的农业温室环境的低功耗监测方法

In order to improve the reliability of wireless sensor networks and reduce the power consumption in the monitoring process, the low power consumption monitoring method of agricultural greenhouse environment based on wireless sensor networks is studied. The terminal node of wireless sensor network is constructed by using CO2 sensor, temperature and humidity sensor and illumination sensor. In the sensor network layout stage, in order to reduce the node power consumption, considering the number of nodes and network coverage, the cuckoo search algorithm is used to optimize the node layout model. The communication module uses LEACH protocol to transmit the environmental data collected by the terminal node to the monitoring center. The cluster head link is selected in LEACH protocol to save energy. After receiving the environmental data collected by the terminal node, the monitoring center uses multi-dimensional data to identify and locate abnormal environmental data according to the correlation between multi-modal data streams in the same node, so as to realize the environmental monitoring of agricultural greenhouse. The experimental results show that the data transmission of the research method is stable and reliable, and the node’s power consumption in the process of internal environment monitoring of the research object is effectively reduced.

Smart Hybridized Routing Protocol for Animal Monitoring and Tracking Applications

Wireless sensor networks (WSN) have been exploited for {countless} application domains, most notably the surveillance of environments and habitats, which has already become a critical mission. As a result, WSNs have been implemented to monitor animal care and track their health status. However, excessive energy utilization and communication traffic on packet transmissions lead to system deterioration, especially whenever perceived information captured in the monitoring area is transferred to the access point over multiple dynamic sinks. Further to manage the energy and data transmission issue, the energy consumption and location aware routing protocol has been architected on the wireless Nano sensor nodes. In this article, a novel hybrid energy and location aware routing protocol to cloud enabled IoT based Wireless Sensor Network towards animal health monitoring and tracking has been proposed. However proposed data routing protocol incorporates the trace file for path selection for data transmission to base station using sink node. Trace file has been obtained on processing the cluster heads established in the network. Therefore, clustering of node in the network has to be achieved using LEACH protocol which enhances the network scalability and network lifetime by clustering the nodes with Metaheuristics constraints like location or node density comparability. The objective of the proposed model is to enhance the network scalability and energy consumption by establishing the multiple node clusters with high density cluster head through Metaheuristics Node Clustering optimization techniques. Metaheuristics based node clustering is been obtained using Improved Particle Swarm Optimization. Further it is employed to compute the optimal path for sensed data transmission to base station. Node clustering provides high energy consumption among the sensing nodes and to establish the high energy clusters towards sensed information dissemination to base station on dynamically reforming the nodes clusters with respect to Node density and node location. Simulation analysis of the proposed energy efficient routing protocol provides high performance in energy utilization, packet delivery ratio, packet loss and Average delay compared against the conventional protocols on propagation of the data through sink node to base station

Energy Efficiency in Wireless Sensor Networks using Advanced LEACH Protocol

The wireless sensor network is the best research subject matter with huge applications in various domains. By and large, a wireless sensor network involves hundreds to thousands of sensor nodes, which transmit and communicate with each other by the utilization of radio transmissions or signals. A portion of the difficulties that exist in the sketch of wireless sensor networks are confined computation power, storage capacity, battery, and transmission transfer speed or bandwidth. To determine these issues, clustering and routing algorithm have been introduced. Clustering and routing processes are viewed as an optimization issue in wireless sensor networks which can be settled by the utilization of swarm intelligence-based approaches. This article presents an original multitude of insight-based grouping and multihop routing protocol for wireless sensor report giving a new swarm optimization technique applied for picking the cluster heads and organizing the cluster capably. Then, at that point, the grey wolf optimization algorithm-based routing process takes place to choose the ideal ways in the network. The introduced better particle swarm optimization-grey wolf optimization approach consolidates the advantages of both the clustering and routing processes which prompts the greatest energy efficiency and network lifetime. The proposed model is reproduced under a broad arrangement of experiments, and the outcomes are approved under a few measures. The acquired trial result exhibited the predominant qualities of the improved particle swarm optimization–grey wolf optimization method under all the test cases. It enhances LEACH protocols in terms of energy efficiency, network Lifetime, and throughput. Maintaining the Energy Efficiency of a wireless sensor network has been a great concern nowadays. The main aim is to overcome the drawback of Improvement of the energy efficiency of a wireless sensor network. It may be improved with the performance of an optimization algorithm using swarm Intelligence. The novelty of the project is Network lifetime and Energy Consumption. The future of this project is to share the complete information which is present in the cluster head without any interruption

Modulated relay based stable election protocol for large‐scale wireless sensor networks

SummaryInternet of things (IoT) applications based on wireless sensor networks (WSNs) have recently gained vast momentum. These applications vary from health care, smart cities, and military applications to environmental monitoring and disaster prevention. As a result, energy consumption and network lifetime have become the most critical research area of WSNs. Through energy‐efficient routing protocols, it is possible to reduce energy consumption and extend the network lifetime for WSNs. Using hybrid routing protocols that incorporate multiple transmission methods is an effective way to improve network performance. This paper proposes modulated R‐SEP (MR‐SEP) for large‐scale WSN‐based IoT applications. MR‐SEP is based on the well‐known stable election protocol (SEP). MR‐SEP defines three initial energy levels for the nodes to improve the network energy distribution and establishes multi‐hop communication between the cluster heads (CHs) and the base station (BS) through relay nodes (RNs) to reduce the energy consumption of the nodes to reach the BS. In addition, MR‐SEP reduces the replacement frequency of CHs, which helps increase network lifetime and decrease power consumption. Simulation results show that MR‐SEP outperforms SEP, LEACH, and DEEC protocols by 70.2%, 71.58%, and 74.3%, respectively, in terms of lifetime and by 86.53%, 86.68%, and 86.93% in terms of throughput.

An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches

Wireless sensor network (WSN) is widely acceptable communication network where human-intervention is less. Another prominent factors are cheap in cost and covers huge area of field for communication. WSN as name suggests sensor nodes are present which communicate to the neighboring node to form a network. These nodes are communicate via radio signals and equipped with battery which is one of most challenge in these networks. The battery consumption is depend on weather where sensors are deployed, routing protocols etc. To reduce the battery at routing level various quality of services (QoS) parameters are available to measure the performance of the network. To overcome this problem, many routing protocol has been proposed. In this paper, we considered two energy efficient protocols i.e. LEACH and Sub-cluster LEACH protocols. For provision of better performance of network Levenberg-Marquardt neural network (LMNN) and Moth-Flame optimisation both are implemented one by one. QoS parameters considered to measure the performance are energy efficiency, end-to-end delay, Throughput and Packet delivery ratio (PDR). After implementation, simulation results show that Sub-cluster LEACH with MFO is outperforms among other algorithms.Along with this, second part of paper considered to anomaly detection based on machine learning algorithms such as SVM, KNN and LR. NSLKDD dataset is considered and than proposed the anomaly detection method.Simulation results shows that proposed method with SVM provide better results among others.

CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN

The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.

Performance Analysis and Evaluation of a Multi-Hop Routing Protocol for Wireless Multimedia Sensor Networks

Researchers in wireless multimedia sensor networks (WMSNs) are constantly interested in designing and developing energy-efficient routing protocols. Numerous clustering routing protocols have been developed in recent years to overcome the limitation of consuming less energy and prolong the lifetime of sensor nodes in homogeneous and heterogeneous WMSNs. Existing approaches depend on single-hope communication, in which sensor nodes consume more energy and die faster. Inter-communication nodes can play an important role in reducing energy by transmitting data in a multi-hope fashion to the sink. An energy-efficient multi-path clustering with load balancing routing protocol for wireless multimedia sensor networks (EEMCL) is proposed in this study. In this protocol, the main cluster heads (MCHs) are preselected in each network cluster and filled with more energy than normal sensor nodes. The two secondary cluster heads (SCHs) with the highest energy nodes will be selected by the main cluster head algorithm. Moreover, inter-cluster multi-hop routing with the help of MCHs can enhance the network lifetime when the sink is located at the corner of the sensing field. The simulation results verify that the proposed multi-hop technique provides better performance than existing LEACH, LEACH-C, SEEN, and IEE-LEACH routing protocols in scenario 1 and when compared with CPMA in scenario 2. The last node dead (LND) for the proposed protocol is at round 5721, 2225 for LEACH, 2300 for LEACH-C, 2900 for SEEN, and 3160 for IEE−LEACH in scenario 1, while in scenario 2, the LND for the proposed protocol is at round 3820 and 435 for the CPMA routing protocol.

An Energy-Efficient Protocol Based on Leach for Smart Home

In Smart Home (SH) the devices and appliances can be controlled automatically and remotely via the internet. Sensors in a smart home detect changes, which are processed and used to make intelligent decisions, such as automatically turning on the lights. To be effective, a smart home's sensors must be connected, establishing a wireless sensor network as the backbone for the smart home's functionalities. For many researchers, the performance of WSN faces several challenges. Power consumption is the most crucial factor since nodes require power to collect, process, and send data but have limited energy. As a result we propose an improved protocol for WSN in smart homes called EEP-LEACH (Energy Efficient Protocol - Low Energy Adaptive Clustering Hierarchy) based on the LEACH protocol. Matlab was used for the algorithm's performance analysis, and the simulation result showed that, compared to others protocols the death time of the nodes was longer, thus managing to balance the consumption of energy of the network nodes. Python and Arduino are used in this research to perform some simulations.

Performance analysis of energy efficient improved LEACH protocol in IoT networks

AbstractThe Internet of Things incorporates embedded technologies, wireless sensor networks, control and automation technologies, and wearable devices to enable integrated buildings, enterprises, intelligent homes, and wearable devices. Because sensor nodes have limited power, the energy consumption of the Internet of Things network is critical. Low‐energy adaptive clustering hierarchy is a green protocol that divides the network into clusters of a fixed size. Cluster heads are assigned to each cluster so that information can be relayed to the base station. This protocol is a dynamic clustering protocol that changes cluster heads after each round within the community. The cluster heads are chosen in the network entirely based on the energy of each node and the distance between the sensor node and the base station. When sensor nodes aggregate data to the cluster head, energy is also consumed. The work proposed in this paper reduces wireless sensor network energy consumption by improving proposed protocol. The cache nodes combine data from the cluster heads node and send the extra data to the base station. The results show that the proposed work reduces the number of dead nodes in the network by 83 percent and increases the node energy level by 1.66 times.

Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

In today’s world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node; after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-II systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

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.

Reducing Energy Consumption in Sensor-Based Internet of Things Networks Based on Multi-Objective Optimization Algorithms

Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks.

Routing Protocol for Wireless Sensor Networks Based on Archimedes Optimization Algorithm

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime.

A Comprehensive Review on Successors of LEACH Protocols in Wireless Sensor Networks

Background: LEACH protocol, even after 20 years, is of interest due to the consideration of the examination network, using wireless sensor networks (WSNs) that demonstrate the significance of the LEACH protocol. Professionals have thought of different types of the LEACH convention. Beneficiaries of the LEACH convention are presently accessible from single hop to multi-hop positions. Methods: The present paper reviews different variations of LEACH steering conventions introduced and examines their upgradation and working. We have examined 65 unique variations of LEACH and inspected how they improve imperativeness usage in WSNs and augmentation framework's lifetime. This study classifies conventions in two areas, to be specific, single-hop correspondence and multihop correspondence, dependent on information communication from the cluster head to the base station. Results: A complete investigation utilizing eight unique criteria’s, for example, energy efficiency, scalability, delay, overhead, etc, has been given in an ordered manner. Conclusion: A complete investigation utilizing eight unique features, such as energy efficiency, scalability, delay, overhead, etc., has been given in an ordered manner.