LEACH Algorithm Research Articles

DEVELOPMENT OF A METHODOLOGY FOR DATA NORMALISATION AND AGGREGATION TO ENHANCE SECURITY LEVELS IN INTERNET OF THINGS INTERACTIONS

The number of interacting devices is increasing every day, and with this constant innovation, serious security challenges arise. The concept of the Internet of Things is being actively applied in both domestic and industrial settings. Researchers are increasingly highlighting the challenges and importance of network security. Data preprocessing plays an important role in security by transforming the input data corresponding to algorithmic criteria and thereby contributing to the prediction accuracy. The data preprocessing process is determined by many factors, including the processing algorithm, the data, and the application. Moreover, in Internet of Things interactions, data normalisation and aggregation can significantly improve security and reduce the amount of data used further decision making. This paper discusses the challenges of data normalisation and aggregation in the IoT to handle large amounts of data generated by multiple connected IoT devices. A secure data normalisation and aggregation method promotes successful minimised data transfer over the network and provides scalability to meet the increasing demands of IoT deployment. The proposed work presents approaches used in data aggregation protocols that address interference, fault tolerance, security and mobility issues. A local aggregation approach using the run-length encoding algorithm is presented. The proposed technique consists of data acquisition, data preprocessing, data normalisation and data aggregation steps. Data normalisation was performed via the Z-score algorithm, and the LEACH algorithm was used for data aggregation. In the experimental study, the percentage of faulty nodes reached 35%. The performance of the proposed solution was 0.82. The results demonstrate a reduction in resource consumption while maintaining the value and integrity of the data.

Comparison and Enhancement in Cluster Based Algorithms in MANET

Building a dynamic MANET network based on mesh technology to improve wireless communication QoS is the main objective of this project. It will be interesting to see how the service performs on mobile ad hoc networks, which are wireless networks without infrastructure management or pre-existing content. The rapid development of mobile, real-time, and multimedia technologies has increased the need for effective programming that completely supports all energy usages. This article compares three algorithms—LEACH, HEED, and MODLEACH—using a variety of comparison techniques. There is a significant amount of difference between the energy levels in MOD-LEACH which is 0.0374092J ,0.0270755J and 0.0270759J in LEACH and HEED algorithms. We can also check MOD-LEACH's dead node count as only 15 as compared to dead nodes in LEACH and HEED which are 34 and 47 respectively. Less dead nodes suggest that MOD-LEACH has the majority of living nodes in the cluster. For the packet loss parameter, MOD-LEACH loses the least number of packets ranging from 120-140 which is less that the ranges 145-165 and above 150 in HEED and LEACH algorithms.

Mathematical Modeling and Statistical Analysis of Leach Algorithm Based Cluster Head Selection Approach for Wireless Sensor Networks

In this paper, we present a comprehensive design and analysis of our proposed improved fuzzy logic-based algorithm. The algorithm utilizes a fuzzy logic system to assess node attributes such as residual energy, distance to the base station, and connectivity. By considering these parameters, the algorithm dynamically evaluates the most suitable candidates for cluster head roles, effectively distributing the energy load and promoting equitable cluster formation. An extensive performance evaluation of proposed algorithm has been conducted through simulations and comparisons with the original LEACH protocol. Our results reveal that the improved fuzzy logic-based algorithm outperforms the conventional LEACH in terms of network lifetime, energy consumption, and overall network stability. In this study, we delve into the mathematical modeling and statistical analysis of the proposed algorithm, focusing on its impact on energy efficiency, network stability, and data routing in WSNs. The Improved Fuzzy Logic-Based Cluster Head Selection Algorithm employs fuzzy logic to intelligently select cluster heads, considering parameters like residual energy, distance to the base station, and historical data. We develop mathematical models to describe the algorithm's behavior and analyze its performance through extensive simulations and statistical evaluations. The algorithm demonstrates enhanced adaptability to varying network conditions and node heterogeneity, showcasing its potential to significantly extend the operational duration of WSNs in diverse deployment scenarios. In conclusion, this paper contributes a novel perspective to the domain of WSNs by introducing an advanced fuzzy logic-based cluster head selection algorithm. The algorithm enhances the foundation of the LEACH protocol, effectively addressing energy optimization concerns and promoting prolonged network lifetime. The presented design and analysis substantiate the algorithm's superiority, offering valuable insights for researchers and practitioners working on energy-efficient routing protocols and wireless sensor network management.

Improved LEACH algorithm based on Cuckoo search mechanism

Improved LEACH algorithm based on Cuckoo search mechanism

Intrusion Detection System Fog Security Model for the Smart Cities and Urban Sensing

In smart cities, Intrusion Detection Systems (IDS) play a critical role in enhancing cybersecurity and safeguarding the interconnected network of devices and systems. Smart cities leverage various technologies, such as the Internet of Things (IoT), sensors, and communication networks, to optimize urban services and improve overall efficiency. However, this increased connectivity also introduces vulnerabilities that could be exploited by malicious actors. This paper presents a thorough exploration of contemporary developments in the domains of security and optimization within the context of smart cities. The rapid integration of Internet of Things (IoT) devices and technologies in urban environments necessitates robust security frameworks and efficient resource management strategies. The paper begins by scrutinizing Intrusion Detection Systems (IDS) tailored for smart city networks, evaluating their efficacy in mitigating diverse cyber threats. Novel approaches, such as Anomaly Detection and Firewall systems, are analyzed alongside traditional IDS, providing a comprehensive overview of the security landscape. In parallel, the paper introduces innovative clustering techniques, with a focus on the proposed Flora Optimization Weighted Clustering (FOWC) model. Inspired by biological growth mechanisms, FOWC presents a unique paradigm for optimizing sensor placements in smart cities. A comparative analysis with existing clustering algorithms like LEACH and Genetic Algorithm underscores FOWC’s superior performance in terms of Detection Rate, Precision, Recall, and F1 Score, positioning it as a promising solution for urban sensor network optimization. Beyond security and optimization, the paper addresses broader challenges in the realm of smart cities, including secure IoT applications, intrusion detection for IoT-enabled environments, and the integration of artificial intelligence for enhanced cyber defense. The research studies presented collectively contribute to a comprehensive understanding of the evolving landscape of smart cities, offering insights with practical implications for policymakers, urban planners, and technologists. Keywords: Smart cities; internet of things (iot); intrusion detection system (ids); fog computing; security

A Design Study on the Design of Customer Claims Management System for Qinghai Electric Power Company

Abstract This paper utilizes the SOA framework to realize the system structure design, combined with the total flow of the claim processing, the design of the acceptance and audit processing, responsibility determination and reminder management of 4 modules, constituting the customer claim management system as a whole. The improved LEACH algorithm is utilized to control the network topology, and the cluster tree combined with data fusion is used to reduce the energy loss of the nodes in the network. Based on the weighted queuing scheduling algorithm, the feedback processing module is designed to determine the grouping rules and calculation methods, and the decision tree algorithm is used to build the demand management prediction model. The accuracy of the prediction of the decision tree algorithm is verified to realize the case information management statistics. Analyze the utility of the improved LEACH algorithm and test the system’s performance. The experimental results show that the improved LEACH algorithm is more balanced in the range of [0,100] dead nodes, and the energy consumption is well balanced. The total delay is 16.13-time slots, which is the lowest delay among the four algorithms. The node death time is 1045 rounds, having the longest network life cycle. The system performance is tested using IO and memory-intensive loads. Before writing a file of 16GB, the system time responsiveness stays around 80%, and the system CPU utilization and disk IO utilization increase as the file is written. The system performance is good.

UASN‐3D: An energy efficient localization based on LEACH‐BR algorithm (EELBL‐BR)

AbstractUnderwater acoustic sensor networks (UASN) research is gaining popularity as it has various applications such as military communication, oil rig maintenance, animal survival, linking submarines to land, gathering data for water monitoring, and other commercial fields. The two most critical requirements for the application's proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. The proposed energy efficient localization based on the LEACH‐beacon and reinforced node (EELBL‐BR) algorithm satisfies both the requirements in 3D‐UASN. The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I‐LASP (improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) (Yadav N, Khilar PM. Trans Emerg Telecommun Technol. 2023;34:e4772.) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced, and member nodes. The proposed clustering LEACH‐BR (low‐energy adaptive clustering hierarchy‐beacon and reinforced nodes) algorithm is based upon the LEACH algorithm which provides accurate location of all the sensor nodes, improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL‐BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%, 51.91%, 51.47%, and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by Rizvi et al. (Wirel Pers Commun. 2022;124(4):3725–3741.) and thus outperforms the existing algorithm (Rizvi HH, Khan SA, Enam RN. Wirel Pers Commun. 2022;124(4):3725–3741.).

Research on Hierarchical Clustering LEACH Protocol Optimization Algorithm Based on Chebyshev Distance

For the problems of premature death of cluster head nodes, short network life cycle and fast energy consumption in hierarchical clustering LEACH protocol in wireless sensor networks, an improved routing protocol K-means LEACH distance optimization algorithm based on LEACH is proposed in this paper. The improved routing algorithm takes into account the problems such as random selection of cluster heads and tedious calculation of distance formula in LEACH algorithm, so K-means clustering algorithm is introduced. The specific scheme is to first divide the data into K classes, then select the appropriate cluster heads, and introduce the Chebyshev distance formula on the basis of K-means clustering algorithm. From the comparative analysis of the simulation results, the K-means LEACH distance optimization algorithm compared to the original LEACH algorithm wireless network node life cycle by 64%, node energy consumption by 26%, which is conducive to improving the stability of WSN, reducing node energy consumption and extending the network survival time.

IMPROVING THE SECURITY BASED ROUTING PROTOCOL FOR WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) have become one of the most popular wireless communication systems in the world. Various attacks have been launched against the WSN individual nodes over the past few years, and the overall security of the network has gradually deteriorated. In this research, we propose a secure uneven clustering method for WSN. The proposed method is based on a trust score evaluation model with the objective of identifying malevolent users in WSNs. The trust score is used to determine whether or not a node is malicious. The results show that the proposed Trust Algorithm outperforms the currently used LEACH algorithm, the TASRP algorithm and the FLSO algorithm in terms of the percentage of packets that are successfully delivered across all use cases and density of nodes.

Cluster Based Energy Efficient Routing Protocol using SA-LEACH to Wireless Sensor Networks

Objectives: To develop an energy efficient routing protocol using SA-LEACH to Wireless Sensor Networks (WSN). Methods: The Simulated Annealing (SA) and LEACH algorithms are used to optimize the energy and maximize the active period of the network nodes. Here, initially the selection of the cluster head will be chosen by LEACH protocol; however, it is further investigated by SA algorithm to get better neighboring node (if exist) when compared with the selected one cluster head. Findings: There is an improvement in the throughput by 5.25%, 30% when compared with EE-LEACH and LEACH algorithms respectively. Also, energy is improved by 11.11% and 23.07% when compared to EE-LEACH and LEACH respectively. The performance of the designed algorithm has well suited for Coal mine industry, in which large number of sensor nodes is deployed to monitor the situation of the coal mine, continuously. Novelty: The designed algorithm is simple and effective. Furthermore, the proposed approach can enhance the rural healthcare monitoring system. Keywords: WSNs; Energy Optimization; LEACH Protocol; SA; Routing; MATLAB

Improvement of life time for wireless body sensor networks using optimal clustering and routing protocol

Medical advancements are being made in order to extend the lifespan of mankind. In the medical field, the penetration of Wireless Sensor Networks (WSN) can aid doctors in diagnosing patients accurately and prescribing the medications accordingly. In recent times, several people have permanent implants such as face makers and it is threatening to life to keep altering this body enhancement as well as it is required to possess a system in place to improve the performance of the Wireless Body Sensors. Transmission loss and route loss are two important elements that will drag the battery energy and minimizes its life span. This research proposes optimal clustering and path selection protocol to enhance the lifetime of wireless body sensor networks. Initially, the data is collected from each body sensor through a clustering method called Glow-worm Swarm Optimization (GSO) and the Fruit-fly technique is applied to find the best path. Here, the cluster head is selected with the help of GSO that minimizes the energy consumption as well as enhances the lifetime of WBSN. Further, the best path is identified by the FFO using the fitness value that is measured within the nodes on the basis of the distance. Since hybrid technology is used here, the routing accomplished is shown to be better. The results reveal that the proposed model has improved the sensor life term (95 sec) while compared with other existing methods like PSO with FFO (78 sec), ACO with FFO (77 sec), GA with FFO (76 sec), and LEACH (68 sec) algorithm for 500 nodes.

Retracted: Parameter Detection of an On‐Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Retracted: Parameter Detection of an On‐Chip Embedded Debugging System of Wireless Sensor Internet Based on LEACH Algorithm

Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi‐Attribute Decision‐Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH‐C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.

МОДЕЛЮВАННЯ ТА УДОСКОНАЛЕННЯ СЕНСОРНОЇ МЕРЕЖІ СИСТЕМИ ОБЛІКУ СПОЖИВАННЯ ЕНЕРГЕТИЧНИХ РЕСУРСІВ У МІКРОРАЙОНІ

The research is aimed at the development of new algorithms for the selection of a main node and methods of placement of sensor nodes for the effective construction of wireless sensor networks (WSNs), therefore it is relevant.The analysis of the literature made it possible to establish that many scientists were engaged in the research of methods of building smart energy systems for a smart city management. However, the researchers did not analyze the potential and real threat of data transmission from energy consumption control sensors.The purpose of the research is to improve the accuracy of control and accounting of energy resources consumed by residents for the neighborhood using wireless sensor networks.The article analyzes the state of the art in the field of research on wireless sensor networks (WSNs), determines the most important characteristics and structure of WSNs in the organization of the automated system of control and accounting of energy resources (ASCOE). An analysis of the existing algorithms for routing, self-organization and selection of ta main node of the cluster in wireless sensor networks of sensors for controlling the consumption of energy parameters by the consumer was carried out. The duration of the network's life cycle, the period of stability and the bandwidth of the BSM was evaluated based on the ratio between the radius of coverage and the radius of the communication range.Studies have shown that the applied algorithm for selecting the main node of the cluster based on fuzzy logic using Voronoi diagrams significantly increases the life cycle of the sensor network compared to the LEACH and Fuzzy C-Means algorithms

RSU Cluster Deployment and Collaboration Storage of IoV Based Blockchain

The Internet of Vehicles is increasingly becoming an indispensable platform to make interactions among vehicles, humans, and roadside infrastructures, and it is continuing to evolve with improvements on its application scenarios and service robustness. Academia and industry have also been concerned with the security issue of Internet of Vehicles (IoV). The Blockchain technology provides a new solution to improve security of the IoV, and it has drawn increased attention. Blockchain technology deals with the network model, identity authentication, trust management, and access control. However, there are insufficient studies on strategy of nodes deployment in the existing BIoV (Blockchain-based IoV). Based on the principle of partition, this paper studies the Blockchain-based IoV model in which the vehicle network system consists of vehicles and Road Side Unit (RSU) nodes. The Blockchain network is formed by the RSU nodes. By optimizing the LEACH algorithm, we partition RSUs and select cluster heads in BIoV, which has good scalability while maintaining a reasonable scale of Blockchain. Simulation results indicated that the improved-LEACH algorithm (I-LEACH) is more effective than the LEACH, DEEC, and the improved-DEEC algorithms (I-DEEC) with respect to the network life cycle and data transmission. Additionally, in order to reduce dependence of the storage resources from BIoV, we designed an intra-cluster collaborative storage scheme by adopting three algorithms for comparison—the genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and the model quantum genetic algorithm (QGA). It revealed that the intra-cluster collaborative storage scheme is effective to ease the bottleneck of storage space demanded in BIoV, and verified by the simulation experiments.

Efficient Clustering Based Routing for Energy Management in Wireless Sensor Network-Assisted Internet of Things

Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR’s performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.

AE-LEACH: An Incremental Clustering Approach for Reducing the Energy Consumption in WSN

In recent years, optimizing sensor nodes energy appears to be a strong element in improving the performance of the Wireless Sensor Network (WSN). In the focus of these different approaches, substantial outputs regarding energy optimization are revealed by applying clustering-based object tracking techniques. Clustering is achieved through an enhanced adaptive LEACH algorithm, named Advanced Efficient Low-Energy Adaptive Clustering Hierarchy (AE-LEACH) algorithm. The cluster head (CH) is adopted through firm descriptors like residual energy and reserve distance from the Base Station (BS) in the clustering process. The selected CH predicts the target trajectory using Particle Filter (PA) algorithm and selectively activates from the next round of sensor nodes to continuously track the target. The target nodes’ predictable locations are routed to the base station through the backbone that lies in the cluster head. Gini index is used to assess the energy efficiency of the clustering algorithm . Thus, the proposed performance metrics stated on network timespan, residual energy, energy consumption, and Gini index for remaining energy and achieves significantly lower Gini index of upto 78.38%, 86.11%, and 85.92% as compared to K means clustering , HEED clustering, and LEACH clustering algorithm, respectively. Moreover, time accuracy is directly proportional to the network lifetime and energy consumption.

Neural Network-Based Routing Energy-Saving Algorithm for Wireless Sensor Networks.

With the evolvement, standards have changed, mobile Internet technology has also been upgraded, and it has also driven the development of smart objects mobile. With the continuous development of smart objects mobile, the bottleneck of small node size and low battery energy storage has not been solved in the end, which makes the research of wireless sensor network energy-saving technology become the focus, and the improvement of routing technology is an effective way to improve energy-saving technology. From the data transmission energy consumption of smart objects mobile, the routing algorithm of smart objects mobile is discussed and analyzed and the classical representative LEACH is the object of in-depth research. Routing algorithms can easily and reliably process network data and make the network work well and are widely used in highly secure military systems and smaller commercial networks. Aiming at these deficiencies, a corresponding improved algorithm is proposed, and it is tested through simulation and specific experiments to verify the correctness and the system's reliability. The SMPSO-BP algorithm converges when the number of iterations is about 600, which is earlier than the LEACH algorithm and the improved LEACH algorithm, so the SMPSO-BP algorithm is due to the other two algorithms. In the wireless sensor network routing energy consumption experiment, in addition, the SMPSO-BP algorithm uses less energy than the other two methods. Therefore, the energy-saving algorithm under the neural network data fusion mechanism is still feasible.

Разработка алгоритма повышения эффективности протокола маршрутизации С-LEACH

In the process of studying wireless sensor network protocols and communication mechanisms, the extension of network service life and minimization of node power consumption have become important indicators. In order to reduce the energy consumption caused by communication in a wireless sensor network, improve network scalability and extend the network lifecycle, it is important to intelligently connect network nodes using a certain clustering algorithm. The paper proposes an improved routing protocol for wireless sensors with low power consumption LEACH, based on data aggregation (C-LEACH). The Kalman filter algorithm was used to solve the problem of data accuracy on cluster member nodes. The principle of operation of the Kalman filter is explained by the example of the noise signal of the sensor. The above data modification model can be performed continuously to achieve the optimal evaluation and filtering effect. Thus, the volume of data sent by the node is reduced. Experimental results have shown that the proposed LEACH protocol based on data fusion (C-LEACH) effectively reduces data redundancy, reduces the power consumption of sensor nodes and prolongs the life cycle of wireless sensor networks compared to the traditional LEACH algorithm, significantly improves network integrity.

An efficient cluster head selection for wireless sensor network-based smart agriculture systems

With the increasing availability of high-resolution satellite and drone images and the Internet of Things (IoT) has begun transforming remote sensing of agriculture by improving accessibility and frequency of updates. Modern IoT-based smart agriculture systems use Wireless Sensor Networks (WSNs) to gather information from an ecosystem that regulates the quantity of water in agricultural fields could be one of these activities. The WSNs remained a challenge to transfer data to drones for analysis purposes. These are composed of tiny sensory architectures organized together to bring efficiency and scalability features to a network. WSN nodes are controlled and managed by a cluster. It is quite difficult to design an efficient leader election protocol. The computation power, storage space, and energy supply of sensor nodes make them unable to frequently switch to a different cluster. The WSN cluster-head election process requires a lot of energy (evaluation and computational process to select the most appropriate node with the least impact on network fragmentation in energy consumption of selected node). Then it is necessary to formulate a mechanism where WSNs utilize the least energy to coordinate with the remote sensing sources. This study presents a cluster election algorithm using the fuzzy logic inference system. It uses a coordinates system to map network nodes and map them based on prioritized scheduling. Lifetime augmentation in wireless sensor networks has always been of great interest. During data transmission from normal sensor nodes to the base station (sink), excess energy is dissipated. Optimizing the energy dissipation of WSNs through the selection of cluster heads is a powerful way to increase the lifespan. By electing more efficient nodes as cluster heads, the proposed method extends the network's lifetime by reducing the number of unimportant communications between nodes. With the utilization of network resources efficiently, the network's lifetime is extended. The proposed algorithm is evaluated with the LEACH (Low Energy Adaptive Clustering Structure) algorithm and FCA method based on the remaining energy and the number of active nodes. The simulation results show that the proposed algorithm utilizes less energy for communication with remote sensory equipment for intelligent agriculture. The performance of the method improved for remaining energy by 9%, the number of active nodes rate by 24%, and indirectly network resource utilization than other states of the art solutions.