LEACH Algorithm Research Articles

Clustering and data aggregation scheme in underwater wireless acoustic sensor network

Underwater Wireless Acoustic Sensor Networks (UWASNs) are creating attentiveness in researchers due to its wide area of applications. To extract the data from underwater and transmit to watersurface, numerous clustering and data aggregation schemes are employed. The main objectives of clustering and data aggregation schemes are to decrease the consumption of energy and prolong the lifetime of the network. In this paper, we focus on initial clustering of sensor nodes based on their geographical locations using fuzzy logic. The probability of degree of belongingness of a sensor node to its cluster, along with number of clusters is analysed and discussed. Based on the energy and distance the cluster head nodes are determined. Finally using using similarity function data aggregation is analysed and discussed. The proposed scheme is simulated in MATLAB and compared with LEACH algorithm. The simulation results indicate that the proposed scheme performs better in maximizing network lifetime and minimizing energy consumption.

Energy Efficient Chain Based Routing Protocol for Orchard Wireless Sensor Network

Wireless sensor network nodes have limited energy, how to employ limited energy efficiently to realize effective data transmission has become a hot topic. Considering the characteristics of orchard planting in rows and shade caused by sparse random features, to improve energy efficiency of the orchard wireless sensor network and prolong network lifetime, we propose an improved chain-based clustering hierarchical routing (ICCHR) algorithm based on LEACH algorithm. The ICCHR algorithm investigates the formation of clusters, cluster head election, chain formation as well as the data transmission process, and further simulated with E-LEACH, PEGASIS-E, LEACH-1R PEGASIS and P-LEACH algorithms through MATLAB. The simulation results show that for BS at (50, 175), from the point of view of all sensor nodes death metric, the network lifetime for ICCHR algorithm prolongs about 3.29, 8.78, 35.53, and 43.11% compared with E-LEACH, PEGASIS-E, LEACH-1R PEGASIS and P-LEACH algorithms. The average energy consumption per round of the ICCHR algorithm is lower than E-LEACH, PEGASIS-E, LEACH-1R PEGASIS and P-LEACH algorithms about 4.73, 9.04, 35.60, and 43.31%. This research can provide theoretical references for the orchard complex environment wireless networking.

Metode Routing Protokol LEACH pada Jaringan Sensor Nirkabel Studi Kasus Sistem Pemantauan Suhu dan Kelembaban Udara

Wireless Sensor Network (WSN) is a wireless network consisting of a group of nodes scattered in a certain area. Each node has the ability to gather data around it and communicate with other nodes. In WSN, energy efficiency is important to maintain the lifetime of a network. A WSN consisting of several clusters and having a Cluster Head (CH) in each cluster requires a CH change mechanism in each cluster so that the network lifetime is longer.The LEACH algorithm is implemented on a system consisting of 9 sensor nodes and 1 sink node. Each sensor node monitors the temperature and humidity of the surrounding air. System testing is done by varying the number of data snippets per CH selection process in each LEACH cycle. Based on the results of testing, the application of the LEACH algorithm can increase network lifetime. The LEACH algorithm with 70 data snippets is an optimal state that results in a network lifetime of 7,387 seconds, whereas with the same number of data snippets when using a non-LEACH algorithm the network lifetime can only reach 5,565 seconds.

Energy Consumption Reduction in Wireless Sensor Network Based on Clustering

One of the important issues in the routing protocol design in Wireless Sensor Networks (WSNs) is minimizing energy consumption and maximizing network lift time. Nowadays networks and information systems are one of the main parts of modern life that without them, people cannot live. On the hand, the impairment of these networks leads to great and incalculable costs. In this paper, a new method based on clustering has presented that problem of energy consumption is solved. The proposed algorithm is that energy-based clustering can create clusters of the same energy level and distribute energy efficiency across the WNS nodes. This proposed clustering protocol classify network nodes based on energy and neighbourhood criteria and attempts to better balance energy in clusters and ultimately increase network lifetime and maintain network coverage. Results are shown that the proposed algorithm is on average 40% better than LEACH algorithm and 14% better than IBLEACH algorithm.

An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks

Wireless sensor networks are being used in almost all remote monitoring applications which involve sensing of information from remote locations and transmitting them to the control base center in a wireless medium. This has gained wide spread importance and attention especially with the integration of Internet of things which effectively help in transmission of real-time data and multimedia content. Especially in transmission of multimedia content, energy of nodes is a critical issue as the power provided to the nodes is limited in nature. Hence, this necessitates an intelligent and query-based utilization of energy for the applications or transmission on demand basis. A cluster-based evolutionary algorithm evolutionary multipath energy-efficient routing protocol has been proposed in this research article with a network size of 200 nodes and experimented. The performance with respect to network lifetime and network efficiency has been compared against conventional clustering routing schemes, namely LEACH, PEGASIS and TEEN algorithms. An evolutionary algorithm in the form of cuckoo search algorithm has been used to optimally select the cluster head in the clustering process with attributes taken in terms of energy efficiency.

Energy Consumption Reduction in Wireless Sensor Network Based on Clustering

One of the important issues in the routing protocol design in Wireless Sensor Networks (WSNs) is minimizing energy consumption and maximizing network lift time. Nowadays networks and information systems are one of the main parts of modern life that without them, people cannot live. On the hand, the impairment of these networks leads to great and incalculable costs. In this paper, a new method based on clustering has presented that problem of energy consumption is solved. The proposed algorithm is that energy-based clustering can create clusters of the same energy level and distribute energy efficiency across the WNS nodes. This proposed clustering protocol classify network nodes based on energy and neighbourhood criteria and attempts to better balance energy in clusters and ultimately increase network lifetime and maintain network coverage. Results are shown that the proposed algorithm is on average 40% better than LEACH algorithm and 14% better than IBLEACH algorithm.

A hybrid soft computing: SGP clustering methodology for enhancing network lifetime in wireless multimedia sensor networks

The sensor network that contains sensor nodes equipped with cameras, microphones and other sensors producing multimedia content is known as wireless multimedia sensor network (WMSN). In WMSN, battery energy and network lifetime are the real requirements of research for the transmission of multimedia information needs more energy. This research paper proposes a soft computing-based approach for optimizing the cluster head selection process to decide upon the optimal path of transmission, and SGP approach is used for cluster formation. A three-layer ANN model trained using back propagation has been proposed in this paper, and comparative analysis is performed among existing techniques such as LEACH, Q-LEACH, C-LEACH and SGP algorithms. It is found from the observations that proposed neural hybrid approach exhibits superior performance while exhibiting marginal improvement over SGP due to the training process where the error is minimized.

Notice of Violation of IEEE Publication Principles: Energy Saving of Wireless Sensor Network Based on Topology Control Algorithm

Wireless sensor networks are an important part of the Internet of Things (IoT). For energy-constrained wireless sensor networks, research on topology control with energy saving is a difficult problem. The influence of topology on the overall performance of the network plays an important role, and the topology control algorithm plays a decisive role in the formation of the topology. Based on the research of hierarchical topology control algorithm, an improved algorithm based on energy-saving clustering algorithm (CAEC) is proposed. The algorithm changes the cluster area and performs communication stability by selecting cluster head nodes. The simulation experiments show that the clustering process of the CAEC algorithm is more reasonable than the LEACH algorithm, which prolongs the survival time of the network. The number of surviving nodes in the monitoring area, network energy consumption, and BS receiving data volume are different degrees of improvement during our study.

Clustering Routing Algorithm and Simulation of Internet of Things Perception Layer Based on Energy Balance

Low-energy adaptive clustering routing protocol is a low-power adaptive hierarchical routing protocol designed for IoT, which has the function of extending network life time. In order to balance the energy consumption of wireless sensor network nodes and extend the life cycle of the whole network, the cluster heads are randomly selected in the classical clustering LEACH algorithm of the wireless sensor network of the Internet of Things, and the residual energy, node position and node density of the nodes are not considered. The problem is solved by the research of LEACH protocol, the selection of cluster head and the communication mechanism between cluster head and Sink. An improved clustering routing algorithm of LEACH protocol is proposed. The simulation experiment proves that the improved LEACH algorithm has more energy consumption than the comparison algorithm, the lowest power consumption of the cluster head node, and better network connectivity and reliability.

Game theory based distributed clustering approach to maximize wireless sensors network lifetime

One of the most significant difficulty in Wireless Sensors Network (WSN) is the development of an effective topology control method that can support the quality of the network, respect the limited memory and at the same time increase the lifetime of the network. This paper introduces a new approach by mixing a non-cooperative Game Theory technique with a decentralized clustering algorithm to address the problem of maximizing the network lifetime. More precisely, this approach uses Game Theory techniques to control the activities of a sensor node and its neighbors to limit the number of the forwarding messages and to maximize the lifetime of the sensor's battery. In other words, the approach will decrease the energy consumed by the WSN by decreasing the number of forwarded packets and improve the network lifetime by harvesting energy from the environment. The simulations results show that the performances in terms of energy saving and increasing the number of data packets received by base station outperforms those with distributed based clustering algorithms without GT, such as low energy and location based clustering LELC and LEACH algorithms.

Framework for Cross Layer Energy Optimization in Wireless Sensor Networks

Cross-layer routing technique interacts among the various layers of the OSI model and exchanges information among them. It enhances the usage of network resources and achieves significant performance improvements in Quality of Service (QoS) parameters. The Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) routing algorithm consumes higher energy due to communication overhead and thus, a hierarchical model-based routing protocol named Cross-Layer Energy Efficient Scalable-Low Energy Adaptive Clustering Hierarchy Protocol (CLEES-LEACH) is proposed. This increases scalability using the Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol between the intermediary node and cluster head, with the overhead of latency. A Linear Programming model is used, which further makes use of scheduling to overcome latency. Energy efficiency and latency are addressed with the proposed cross-layer routing algorithm CLEESLEACH. The cross-layer design establishes Physical, Media Access Control (MAC), and Network layer interactions in the proposed algorithm. The present LEACH algorithm also increases the network overhead as there is no mechanism for communication among the network layer and consumes high energy. In the proposed algorithm CLEES-LEACH, latency is reduced to 25% and throughput is maximized to 20% compared to existing Energy-Efficient Distributed Schedule Based protocol (EEDS) and Integer Linear Programming (ILP) protocols. The energy consumption is also reduced to 20 % and the scalability is increased to 10 % compared to the existing LEACH and CL-LEACH protocols. These results are shown by using NS3 simulation.

A Reliability Optimization Algorithm for Wireless Sensor Network

Aiming at the reliability optimization algorithm based on wireless sensor network, a data fusion algorithm based on extreme learning machine for wireless sensor network was proposed according to the temporal spatial correlation in data collection process. After analyzing the principles, design ideas and implementation steps of extreme learning machine algorithm, the performance and results were compared with traditional BP algorithm, LEACH algorithm and RBF algorithm in simulation environment. The simulation results showed that the data fusion optimization algorithm based on the limit learning machine for wireless sensor network was reliable. It improved the efficiency of fusion and the comprehensive reliability of the network. Thus, it can prolong the life cycle and reduce the total energy consumption of the network.

Reducing Energy Consumption of the SEECH Algorithm in Wireless Sensor Networks Using a Mobile Sink and Honey Bee Colony Algorithm

Purpose – Clustering, routing, and data dissemination are an important issue in wireless sensor networks. The basic functions of wireless sensor networks are phenomena controlling in the physical environment, and the reporting of sensed data to the central node called sink, in which more operations can be done on the data. The most important limitation of wireless sensor networks is energy consumption. There are several ways to increase the lifetime of these networks, that one of the most important is the using proper clustering method. The aim of this study is to reduce energy consumption using an effective clustering algorithm and for this purpose, the honeybee colony metaheuristic method was used for cluster heads selection.
 Methodology/approach/design – The simulation in this paper was done using MATLAB software and the proposed method is compared with the LEACH and SEED approach.
 Findings – The results of simulations in this research indicate that the research has significantly reduced the energy consumption in the network than LEACH and SEED algorithms.
 Originality/value – Given the energy constraints in the wireless sensor network, providing such solutions and using metaheuristic algorithms can dramatically reduce energy consumption and, consequently increase network lifetime.

IoT coverage based on intelligent algorithm

In order to improve the coverage of IoT, according to the characteristics of mass data, the data fusion in IoT coverage was hierarchically classified. At the same time, the LEACH routing algorithm and fuzzy algorithm were introduced in detail. The data was fused correctly. The routing algorithm was improved. The improved LEACH algorithm and fuzzy algorithm were simulated and the results were analyzed. The results showed that when the massive data was fused, the improved intelligent algorithm maximized the IoT coverage. Therefore, the improved algorithm can greatly reduce the node energy consumption. At the same time, it saves time.

Load-balanced routing algorithm based on cluster heads optimization for wireless sensor networks

In order to solve the problem of unbalanced load consumption of nodes for wireless sensor networks (WSNs), this paper proposes a load-balanced routing algorithm based on cluster heads optimization for wireless sensor network. The proposed algorithm first applies first-order wireless transmission model to calculate the optimal number of clusters, then calculate nodes competitiveness rating by fuzzy algorithm considering the residual energy of node and distance from the node to base station, cluster head selection uses unequal clustering algorithm according to the competitiveness of nodes. By node competitiveness and energy management mechanism which cooperate with each other to select the best cluster heads. Use connected optimization between clusters to search multi-hop paths base station for reducing energy consumption of node, and consider transmission energy consumption, residual energy, transmission distance and other factors. The experimental results show that the proposed algorithm compared with LEACH and UCDP algorithm, can balance loading and effectively extend the life cycle of wireless sensor network.

Fuzzy Logic-Based Clustering Algorithm for Multi-hop Wireless Sensor Networks

To prolong the life of wireless sensor networks(WSNs) and reduce energy consumption, a clustering protocol based on fuzzy logic for Multi-hop WSNs(FLCMN) is proposed. The algorithm considers the residual energy of nodes, the number of neighbor nodes and the average residual energy of neighbor nodes. According to the pre-set fuzzy rule base, the fuzzy system is used to evaluate the satisfaction of the elected cluster head(CH). In addition, the average residual energy of neighbor nodes is taken into consideration, which improves the hotspot problem and balances the energy consumption between clusters. At the same time, in order to improve the hot issues among clusters, a multi-hop transmission scheme based on Fibonacci sequence is proposed to prolong the lifetime of the network. Through simulation, FLCAMN algorithm outperforms LEACH, EAMMH and DFLC algorithms in terms of network’s survival time and energy consumption.

Performance analysis of artificial bee colony optimization based clustering protocol in various scenarios of WSN

Most primaryconcern in wireless sensor networks (WSN) is management of energy of the small nodes deployed for sensing physical or environmental conditions of an area. However appropriate use of routing protocols can increase the network life. Protocols based on clustering such as LEACH is a competent approach, in which cluster has been planned; each cluster has plentiful nodes and lone cluster head (CH). Node transmits their sensed information to CH; CH cumulates that information and forwards that to sink. Soft-computing (SC) technique such as nature inspired algorithms are use to deal with the complex constraints in WSNs. One of such technique is Artificial Bee Colony Optimization (ABCO) that is based on swarm intelligence. In this paper, ABCO based LEACH algorithm is tested broadly on diverse scenarios of WSNs, changing maximum number of rounds (rmax) as well as number of sensor nodes (n). Multiple numbers of parameters such as dead nodes per round, alive nodes per round and packet to base station (BS) per round, are taken into concern for performance evaluation. Comparison of each parameter from conventional LEACH is also presented. The simulation has been done in MATLAB.

An Energy-Efficient Ring Cross-Layer Optimization Algorithm for Wireless Sensor Networks

In wireless sensor networks, cross-layer optimization is a hot topic. In order to balance energy consumption a clustering based cross-layer optimization model is established in this paper. In this model, the network layer could get the related information from physical layer and the datalink layer to effectively route packets. Based on the cross-layer optimization model, an energy-efficient ring cross-layer optimization algorithm is proposed and a new routing algorithm called Leach-Cross-Layer Optimization (CLO) is also proposed for a ring monitoring domain. This Leach-CLO routing algorithm is based on the typical Leach algorithm. The experiment results show that this routing algorithm could effectively and efficiently balance the energy consumption in wireless sensor networks compared with related algorithms.

Distributed Joint Optimization Routing Algorithm Based on the Analytic Hierarchy Process for Wireless Sensor Networks

As a crucial issue for routing algorithms in wireless sensor networks (WSNs), balancing energy consumption is a vital strategy to extend the network lifetime. Considering the node energy property or the node distance property, some conventional WSNs’ routing algorithms unevenly distribute traffic over the nodes, which are not optimal to maximize the network lifetime. In this letter, a distributed joint optimization routing algorithm based on the analytic hierarchy process (DORAHP) is proposed to extend WSNs lifetime. The analytic hierarchy process is applied at the transmitter side to select the next hop node by defining three key decision criteria including the residual energy of the node, the distance from the node to the base station, and the degree of the node, which aims to extend WSNs lifetime through balancing energy consumption. At length, extensive simulation experiments for DORAHP are performed and compared with the LEACH, HEED, EERC, and GASONeC algorithms. The results indicate that DORAHP has superior performance on balancing energy consumption and on extending WSNs lifetime.

Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach

The life time extension in the wireless sensor network (WSN) is the major concern in real time application, if the battery attached with the sensor node life is not optimized properly then the network life fall short. A protocol using a new evolutionary technique, cat swarm optimization (CSO), is designed and implemented in real time to minimize the intra-cluster distances between the cluster members and their cluster heads and optimize the energy distribution for the WSNs. We analyzed the performance of WSN protocol with the help of sensor nodes deployed in a field and grouped in to clusters. The novelty in our proposed scheme is considering the received signal strength, residual battery voltage and intra cluster distance of sensor nodes in cluster head selection with the help of CSO. The result is compared with the well-known protocol Low-energy adaptive clustering hierarchy-centralized (LEACH-C) and the swarm based optimization technique Particle swarm optimization (PSO). It was found that the battery energy level has been increased considerably of the traditional LEACH and PSO algorithm.