A New Clustering Routing Algorithm for WSN Based on PSO

2013 ◽  
Vol 850-851 ◽  
pp. 689-692
Author(s):  
Li Fu Wang ◽  
Jian Ding ◽  
Zhi Kong
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Long Distance ◽  
Wireless Sensor Nodes ◽  
Spatially Distributed

A wireless sensor network (WSN) consists of spatially distributed wireless sensor nodes. The node power constrains the development of WSN. Employing techniques of clustering can reduce energy consumption of wireless sensor nodes and prolong the network lifetime. Therefore, in the study a new clustering routing algorithm is presented. The clustering algorithm uses the double-layer sensor nodes to communicate. And in order to optimize power energy consumption for WSN node energy, PSO algorithm is employed to find cluster head in each layer. Simulation results show that the algorithm not only can equal power energy of node, but also can reduce consumption in the long distance data transmission.

Routing Algorithm based on Clustering for Increasing the Lifetime of Sensor Networks by Using Meta-Heuristic Bee Algorithms

2020 ◽  
Vol 10 (1) ◽  
pp. 25-36
Author(s):  
Mohammad Sedighimanesh ◽  
Hesam Zand Hesami ◽  
Ali Sedighimanesh
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Cost Effective ◽  
Sensor Nodes ◽  
Wireless Sensor

Background: Nowadays, the use of wireless sensor networks is developing rapidly. these networks are applicable in many fields, including military, medical, and environment. these networks use hundreds or thousands of cheap sensor nodes with low power-low and low energy to perform large tasks. These networks have limitations that can lead to inefficiency or not cost - effective. Among these limitations, consumption of energy and issues related to the lifetime of the network. One of the solutions that can assist the load balancing between sensor nodes, increased scalability, improving energy consumption and consequently, increasing network lifetime, clustering of sensor nodes and placing a suitable cluster head in all clusters. Choosing the right cluster head, significantly reduces energy consumption in the network and increases network lifetime. Objective: The purpose of this paper is to increase network lifetime by using the efficient clustering algorithm, which is used in Meta-heuristic bee colony to select the cluster head. Simulation of this paper is performed by MATLB software and the proposed method is compared with LEACH and GACR approaches. Conclusion: The simulation findings in this study show that the intended study has remarkably increased the length of the network lifetime by LEACH and GACR algorithms. Due to the limitation of energy in the wireless sensor network such solutions and using Meta-heuristic algorithms can give rise a remarkable increasing in network lifetime.

scholarly journals Routing and Clustering of Sensor Nodes in the Honeycomb Architecture

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Amine Rais ◽  
Khalid Bouragba ◽  
Mohammed Ouzzif
Keyword(s):  
Energy Consumption ◽  
Routing Protocol ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Delivery ◽  
Hybrid Routing ◽  
Hybrid Routing Protocol ◽  
Hierarchical Protocols

Energy is the most valuable resource in wireless sensor networks; this resource is limited and much in demand during routing and communication between sensor nodes. Hierarchy structuring of the network into clusters allows reducing the energy consumption by using small distance transmissions within clusters in a multihop manner. In this article, we choose to use a hybrid routing protocol named Efficient Honeycomb Clustering Algorithm (EHCA), which is at the same time hierarchical and geographical protocol by using honeycomb clustering. This kind of clustering guarantees the balancing of the energy consumption through changing in each round the location of the cluster head, which is in a given vertex of the honeycomb cluster. The combination of geographical and hierarchical routing with the use of honeycomb clustering has proved its efficiency; the performances of our protocol outperform the existing protocols in terms of the number of nodes alive, the latency of data delivery, and the percentage of successful data delivery to the sinks. The simulations testify the superiority of our protocol against the existing geographical and hierarchical protocols.

A Cluster Algorithm for Wireless Sensor Networks Based on Energy Efficiency

2015 ◽  
Vol 785 ◽  
pp. 744-750
Author(s):  
Lei Gao ◽  
Qun Chen
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Selection Criterion ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Head Node

In order to solve the energy limited problem of sensor nodes in the wireless sensor networks (WSN), a fast clustering algorithm based on energy efficiency for wire1ess sensor networks is presented in this paper. In the system initialization phase, the deployment region is divided into several clusters rapidly. The energy consumption ratio and degree of the node are chosen as the selection criterion for the cluster head. Re-election of the cluster head node at this time became a local trigger behavior. Because of the range of the re-election is within the cluster, which greatly reduces the complexity and computational load to re-elect the cluster head node. Theoretical analysis indicates that the timing complexity of the clustering algorithm is O(1), which shows that the algorithm overhead is small and has nothing to do with the network size n. Simulation results show that clustering algorithm based on energy efficiency can provide better load balancing of cluster heads and less protocol overhead. Clustering algorithm based on energy efficiency can reduce energy consumption and prolong the network lifetime compared with LEACH protocol.

scholarly journals Data Clustering Method in Wireless Sensor Networks Based on Residual Energy Perception

2018 ◽  
Vol 14 (06) ◽  
pp. 85 ◽  
Author(s):  
Xudong Yang
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Data Gathering ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Forwarding ◽  
Cluster Data ◽  
Head Node

<p class="0abstract"><span lang="EN-US">To prolong the survival time of wireless sensor network, an iterative scheme was proposed. First of all, spectrum clustering algorithm iteratively segmented the network into clusters, and cluster head nodes in each sub cluster were determined depending on the size of residual energy of sensor nodes. Then, a data forwarding balance tree was constructed in each sub cluster. Data forwarding path of each non-cluster head node was defined, and the moving path of a mobile data collector was determined, which used the residual energy as the basis for the network optimization. Finally, this scheme was simulated, and two traditional data gathering algorithms were compared. The results showed that the algorithm designed in this experiment could effectively balance energy consumption among all WSN nodes and had great performance improvement compared with the traditional data collection algorithm. To sum up, this algorithm can significantly reduce the energy consumption of the network and improve the lifetime of the network. </span></p>

scholarly journals A clustering algorithm based on nonuniform partition for WSNs

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 1154-1160
Author(s):  
Chunling Tang
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Fire Detection ◽  
Base Station ◽  
Sensor Nodes ◽  
Energy Efficient Routing ◽  
Distance Information ◽  
Battery Capacity

Abstract Wireless sensor networks (WSNs) have great application potential in partition parameter observation, such as forest fire detection. Due to the limited battery capacity of sensor nodes, how to reduce energy consumption is an important technical challenge. In this paper, we propose an energy efficient routing algorithm of adaptive double cluster head (CH) based on nonuniform partition for WSN. Firstly, according to the distance information from the base station (BS) to every sensor node, the network is divided into several uneven partitions. Secondly, CH is selected for each partition as the primary cluster head (PCH). Because of the cluster-level routing, the CHs close to the BS need to forward more data than the CHs in other areas, which consumes more energy. Therefore, an adaptive double CH method can be used to generate a secondary cluster head (SCH) in the cluster near the BS according to the parameters. Finally, the PCH is responsible for data collection, data integration, and data transmission. while the SCH is in charge of data routing. Simulation results show that the proposed algorithm can reduce the energy consumption and extend the life of the WSNs, compared with LEACH protocol and the HEED protocol.

scholarly journals Local Load Balancing for Globally Efficient Routing in Wireless Sensor Networks

2005 ◽  
Vol 1 (2) ◽  
pp. 163-185 ◽  
Author(s):  
Ioan Raicu ◽  
Loren Schwiebert ◽  
Scott Fowler ◽  
Sandeep K.S. Gupta
Keyword(s):  
Clustering Algorithm ◽  
Routing Algorithm ◽  
Energy Resource ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Setup Costs ◽  
Energy Dissipated ◽  
The Cost ◽  
Distribution Cost

One of the limitations of wireless sensor nodes is their inherent limited energy resource. Besides maximizing the lifetime of the sensor node, it is preferable to distribute the energy dissipated throughout the wireless sensor network in order to minimize maintenance and maximize overall system performance. Any communication protocol that involves synchronization of peer nodes incurs some overhead for setting up the communication. We introduce a new algorithm, e3D (energy-efficient Distributed Dynamic Diffusion routing algorithm), and compare it to two other algorithms, namely directed, and random clustering communication. We take into account the setup costs and analyze the energy-efficiency and the useful lifetime of the system. In order to better understand the characteristics of each algorithm and how well e3D really performs, we also compare e3D with its optimum counterpart and an optimum clustering algorithm. The benefit of introducing these ideal algorithms is to show the upper bound on performance at the cost of astronomical prohibitive synchronization costs. We compare the algorithms in terms of system lifetime, power dissipation distribution, cost of synchronization, and simplicity of the algorithm. Our simulation results show that e3D performs comparable to its optimal counterpart while having significantly less overhead.

scholarly journals TRACKING OF MOVING OBJECT IN WIRELESS SENSOR NETWORK

2015 ◽  
pp. 42-47
Author(s):  
D. CHARANYA ◽  
G. V. UMA
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Base Station ◽  
Moving Object ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Current Location

A Wireless Sensor Network is a collection of sensor nodes distributed into a network to monitor the environmental conditions and send the sensed data to the Base Station. Wireless Sensor Network is one of the rapidly developing area in which energy consumption is the most important aspect to be considered while tracking, monitoring, reporting and visualization of data. An Energy Efficient Prediction-based Clustering algorithm is proposed to track the moving object in wireless sensor network. This algorithm reduces the number of hops between transmitter and receiver nodes and also the number of transmitted packets. In this method, the sensor nodes are statically placed and clustered using LEACH-R algorithm. The Prediction based clustering algorithm is applied where few nodes are selected for tracking which uses the prediction mechanism to predict the next location of the moving object. The Current Location of the target is found using Trilateration algorithm. The Current Location or Predicted Location is sent to active Cluster Head from the leader node or the other node. Based on which node send the message to the Cluster Head, the Predicted or Current Location will be sent to the base station. In real time, the proposed work is applicable in traffic tracking and vehicle tracking. The experiment is carried out using Network Stimulator-2 environment. Simulation result shows that the proposed algorithm gives a better performance and reduces the energy consumption.

scholarly journals Optimal clustering algorithm for wireless sensor networks using combined turbid ant swarm and improved myopic algorithm for maximizing life of sensors

2021 ◽  
Author(s):  
Muthukumar S ◽  
D. Hevin Rajesh
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Control Factor ◽  
Network Simulator ◽  
Adaptive Clustering ◽  
Save Energy

Abstract Wireless sensor network (WSNs) consistsof a variety of sensor nodes to sense the environmentalparameters and communicate to the sink knot. The control factor is that controlling the power of the sensor nodes and charging or replacing the battery is an expensive and complicated process, which affects the sensor node lifetime as well as network lifetime. Clustering is one of the schemes that save energy by reducing the amount of intra-cluster communication cost. In this paper, an optimal clustering (OC) algorithm proposed to maximizes the network lifetime at data transmission without compromising energy expenditure. In OC algorithm, first we propose the turbid ant swarm(TAS) algorithm to form the clusters, which reduces much amount of energy consumption. Then, an improved myopic (IM) algorithm proposed to determines the cluster head (CH) of cluster, which minimizes re-clustering frequency and intra-communication charge. The proposed OC-TAS-IM algorithm is concentrate to get better the energy efficiency and extend the network life span. Moreover, the planned algorithm is practical to the low-energy adaptive clustering hierarchy (LEACH) to perform the entire routing. The completion and imitation experiment with Network Simulator (NS2) are obtainable in order to authenticate our planned OC-TAS-IM algorithm. Imitation outcome illustrate that OC-TAS-IM algorithm is stable in terms of energy consumption and network lifetime because of optimal clustering.

Performance Analysis of RNC Clustering Protocol in Wireless Sensor Network

2019 ◽  
Vol 10 ◽  
Author(s):  
Piyush Rawat ◽  
Siddhartha Chauhan
Keyword(s):  
Energy Consumption ◽  
Network Performance ◽  
Energy Levels ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Threshold Function ◽  
Efficient Manner ◽  
Clustering Protocol

Background and Objective: The functionalities of wireless sensor networks (WSN) are growing in various areas, so to handle the energy consumption of network in an efficient manner is a challenging task. The sensor nodes in the WSN are equipped with limited battery power, so there is a need to utilize the sensor power in an efficient way. The clustering of nodes in the network is one of the ways to handle the limited energy of nodes to enhance the lifetime of the network for its longer working without failure. Methods: The proposed approach is based on forming a cluster of various sensor nodes and then selecting a sensor as cluster head (CH). The heterogeneous sensor nodes are used in the proposed approach in which sensors are provided with different energy levels. The selection of an efficient node as CH can help in enhancing the network lifetime. The threshold function and random function are used for selecting the cluster head among various sensors for selecting the efficient node as CH. Various performance parameters such as network lifespan, packets transferred to the base station (BS) and energy consumption are used to perform the comparison between the proposed technique and previous approaches. Results and Discussion: To validate the working of the proposed technique the simulation is performed in MATLAB simulator. The proposed approach has enhanced the lifetime of the network as compared to the existing approaches. The proposed algorithm is compared with various existing techniques to measure its performance and effectiveness. The sensor nodes are randomly deployed in a 100m*100m area. Conclusion: The simulation results showed that the proposed technique has enhanced the lifespan of the network by utilizing the node’s energy in an efficient manner and reduced the consumption of energy for better network performance.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

Sign in / Sign up

Export Citation Format

Share Document