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.

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.

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 SHRP - Secure Hybrid Routing Protocol over Hierarchical Wireless Sensor Networks

2017 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Balaubramanian Muthusenthil ◽  
Hyunsung Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Cluster Head ◽  
Secure Routing ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Hybrid Routing ◽  
Security Issues ◽  
Greedy Forwarding

A data collection via secure routing in wireless sensor networks (WSNs)has given attention to one of security issues. WSNs pose unique security challengesdue to their inherent limitations in communication and computing, which makes vulnerableto various attacks. Thus, how to gather data securely and efficiently based onrouting protocol is an important issue of WSNs. In this paper, we propose a securehybrid routing protocol, denoted by SHRP, which combines the geographic basedscheme and hierarchical scheme. First of all, SHRP differentiates sensor nodes intotwo categories, nodes with GPS (NG) and nodes with antennas (NA), to put differentroles. After proposing a new clustering scheme, which uses a new weight factor toselect cluster head efficiently by using energy level, center weight and mobility afterforming cluster, we propose routing scheme based on greedy forwarding. The packetsin SHRP are protected based on symmetric and asymmetric cryptosystem, which providesconfidentiality, integrity and authenticity. The performance analyses are doneby using NS2 and show that SHRP could get better results of packet loss rate, deliveryratio, end to end delay and network lifetime compared to the well known previousschemes.

Minimizing Energy Consumption in Wireless Sensor Networks by Partition

2016 ◽  
Vol 850 ◽  
pp. 23-29
Author(s):  
Wen Zhi Zhu ◽  
Feng Xu
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clustering Methods ◽  
Improve Energy Efficiency

In wireless sensor networks, clustering class routing protocol is an important protocol type. Different clustering methods, and cluster head selection method directly affects the energy consumption of the entire network communication. This paper studies the effect of different partition methods of the network energy consumption, and to study the partitioning methods under the conditions of uneven distribution of nodes. We believe that energy efficiency clustering method should adapt the distributed of sensor nodes in order to improve energy efficiency. And according to the partition method we propose a low-power adaptive clustering routing protocol based on node distribution to partition. The protocol can effectively extend the lifetime of a wireless sensor network. Simulation results show that the proposed protocol can effectively prolong the network lifetime.

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 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 Data Transmission for WSNs with Data-Location Integration

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1499
Author(s):  
Shuiyan Wu ◽  
Xiaofei Min ◽  
Jing Li
Keyword(s):  
Energy Consumption ◽  
Data Transmission ◽  
Clustering Algorithm ◽  
Optimal Number ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Average Delay ◽  
Hybrid Routing ◽  
Cluster Heads ◽  
Data Location

Wireless sensor networks (WSNs) have good performance for data transmission, and the data transmission of sensor nodes has the function of symmetry. However, the wireless sensor nodes are facing great pressure in data transmission due to the increasing amount and types of data that easily cause premature energy consumption of some nodes and, thus, affects data transmission. Clustering algorithm is a common method to balance energy consumption, but the existing algorithms fail to balance the network oad effectively for big data transmission. Therefore, an optimal data transmission with data-location integration (ODTD-LI) is proposed for WSNs in this paper. For optimal data transmission, we update the network topology once for one round. In the proposed algorithm, we perform calculations of the optimal cluster heads, clustering and data transmission routing through three steps. We first deploy N homogeneous and symmetry nodes in a square area randomly and calculate the optimal number of cluster heads according to the node ocations. then, the optimal number of cluster heads, energy consumption, the distances and degrees of the nodes are taken into consideration during the clustering phase. Direct communication is carried out within a cluster, and the member nodes of the cluster pass the information directly to the cluster head. Lastly, an optimal hybrid routing from each cluster node to Sink is constructed for data transmission after clustering. The simulations verify the good performance of the proposed algorithm in view of the ifetime, average delay, coverage rate (CR) and oad balance of the network compared with the existing algorithms. Through the research conducted in this paper, we find that our work has good performance for selecting the hybrid routing in the network with the nodes randomly arranged.

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.

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