scholarly journals Estimation of Optimum Rendezvous Point for Mobile Sink (ORP-MS) in WSN

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1322 ◽  
Author(s):  
Vrince Vimal ◽  
Madhav J Nigam
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
High Throughput ◽  
Mobile Sink ◽  
Base Station ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Rendezvous Point ◽  
Coverage Holes

Clustering of the sensors in wireless sensor network is done to achieve energy efficiency. The nodes, which are unable to join any cluster, are referred to as isolated nodes and tend to transfer information straight to the base station. It is palpable that isolated nodes and cluster heads communicate with the base station and tend to exhaust their energy leaving behind coverage holes. In this paper, we propose the innovative clustering scheme using mobile sink approach to extend networks lifetime. The proposed (ORP-MS) algorithm is implemented in MATLAB 2017a and the results revealed that the proposed algorithm outdid the existing algorithms in terms networks lifetime and energy efficiency simultaneously achieved high throughput.  

scholarly journals An Enhanced Clustering Method for Extending Sensing Lifetime of Wireless Sensor Network

2021 ◽  
pp. 67-82
Author(s):  
Yakubu Abdul-Wahab Nawusu ◽  
Alhassan Abdul-Barik ◽  
Salifu Abdul-Mumin
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Performance ◽  
Rank Order ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Heterogeneous Wireless Sensor Network

Extending the lifetime of a wireless sensor network is vital in ensuring continuous monitoring functions in a target environment. Many techniques have appeared that seek to achieve such prolonged sensing gains. Clustering and improved selection of cluster heads play essential roles in the performance of sensor network functions. Cluster head in a hierarchical arrangement is responsible for transmitting aggregated data from member nodes to a base station for further user-specific data processing and analysis. Minimising the quick dissipation of cluster heads energy requires a careful choice of network factors when selecting a cluster head to prolong the lifetime of a wireless sensor network. In this work, we propose a multi-criteria cluster head selection technique to extend the sensing lifetime of a heterogeneous wireless sensor network. The proposed protocol incorporates residual energy, distance, and node density in selecting a cluster head. Each factor is assigned a weight using the Rank Order Centroid based on its relative importance. Several simulation tests using MATLAB 7.5.0 (R2007b) reveal improved network lifetime and other network performance indicators, including stability and throughput, compared with popular protocols such as LEACH and the SEP. The proposed scheme will be beneficial in applications requiring reliable and stable data sensing and transmission functions.

Energy Consumption Analysis of Secure and Clustered Wireless Sensor Network

2014 ◽  
Vol 5 (1) ◽  
pp. 15-36 ◽  
Author(s):  
Ashim Pokharel ◽  
Ethiopia Nigussie
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Aggregation ◽  
Secure Communication ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Design Strategies

Due to limited energy resources, different design strategies have been proposed in order to achieve better energy efficiency in wireless sensor networks, and organizing sensor nodes into clusters and data aggregation are among such solutions. In this work, secure communication protocol is added to clustered wireless sensor network. Security is a very important requirement that keeps the overall system usable and reliable by protecting the information in the network from attackers. The proposed and implemented AES block cipher provides confidentiality to the communication between nodes and base station. The energy efficiency of LEACH clustered network and with added security is analyzed in detail. In LEACH clustering along with the implemented data aggregation technique 48% energy has been saved compared to not clustered and no aggregation network. The energy consumption overhead of the AES-based security is 9.14%. The implementation is done in Contiki and the simulation is carried out in Cooja emulator using sky motes.

Cluster-based optimal wireless sensor deployment for structural health monitoring

2017 ◽  
Vol 17 (2) ◽  
pp. 266-278 ◽  
Author(s):  
Kun Fang ◽  
Chengyin Liu ◽  
Jun Teng
Keyword(s):  
Wireless Sensor Network ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Data Communication ◽  
Base Station ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Structural Health ◽  
Cluster Heads

A well-designed wireless sensor deployment method not only directly influences the number of deployed sensors and data accuracy, but also influences on network topology. As most of the energy cost comes from the transmission and receiving of data packets, clustering optimization in wireless sensor network becomes an important issue for energy-efficient coordination among the densely deployed nodes for data communication. In a typical hierarchical wireless sensor network, total intra-cluster communication distance and total distance of cluster heads to base station depend on number of cluster heads. This work presents a novel approach by selecting the number of clusters in hierarchical wireless sensor network. We analyze and demonstrate the validity of the cluster optimization for wireless sensor deployment using an example of a numerically simulated simply supported truss, in terms of efficient use of the constrained wireless sensor network resources. Followed by a cluster-based optimization framework, we show how to adopt our approach to achieve scalable and efficient deployment, through a comprehensive optimization study of a realistic wireless structural health monitoring system. Finally, we suggest optimal deployment scheme based on the comparative performance evaluation results in the case study.

WN-LEACH: A Weighted Residual Energy Based Cluster-Heads Selection Algorithm for Wireless Sensor Network

2013 ◽  
Vol 765-767 ◽  
pp. 980-984
Author(s):  
Xi Rong Bao ◽  
Jia Hua Xie ◽  
Shuang Long Li
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Cumulative Number ◽  
Cluster Heads

This article focused on the energy limit property of Wireless Sensor Network, and proposed a residual energy based algorithm WN-LEACH, with the classic network mode of LEACH routing algorithm. The algorithm combines the proportion of residual energy in the total energy with the cumulative number of the normal nodes supported by the cluster heads as a cluster selection reference. In order to balance the energy consumption of each cluster-head, the algorithm took both the different positions of the base station and the initial energy of the network into consideration, and weighted the two factors to balance the energy consumption between transmitting the signals and data fusion. Simulation results show that the algorithm can promote the lifetime of the uneven energy network and does not impair the effects of the LEACH algorithm.

scholarly journals A REVIEW ON A HYBRID APPROACH USING MOBILE SINK AND FUZZY LOGIC FOR REGION BASED CLUSTERING IN WSN

2017 ◽  
Vol 16 (2) ◽  
pp. 7586-7590
Author(s):  
Amneet Kaur ◽  
Harpreet Kaur
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Hybrid Approach ◽  
Mobile Sink ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wide Range ◽  
Energy Constrained

A Wireless Sensor Network or WSN is supposed to be made up of a large number of sensors and at least one base station. The sensors are autonomous small devices with several constraints like the battery power, computation capacity, communication range and memory. They also are supplied with transceivers to gather information from its environment and pass it on up to a certain base station, where the measured parameters can be stored and available for the end user. In most cases, the sensors forming these networks are deployed randomly and left unattended to and are expected to perform their mission properly and efficiently. As a result of this random deployment, the WSN has usually varying degrees of node density along its area. Sensor networks are also energy constrained since the individual sensors, which the network is formed with, are extremely energy-constrained as well. Wireless sensor networks have become increasingly popular due to their wide range of application. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization.

scholarly journals EODC: An Energy Optimized Dynamic Clustering Protocol for Wireless Sensor Network using PSO approach

2019 ◽  
Vol 14 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Jothi Kumar C ◽  
Revathi Venkataraman
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fitness Function ◽  
Cluster Head ◽  
Base Station ◽  
Link Quality ◽  
Wireless Sensor ◽  
Manhattan Distance ◽  
Dynamic Clustering ◽  
Cluster Heads

Wireless Sensor Network comprises of a number of small wireless nodes whose role is to sense, gather, process and communicate. One of the primary concerns of the network is to optimize the energy consumption and extend the network lifespan. Sensor nodes can be clustered to increase the network lifespan. This is done by selecting the cluster head for every cluster and by performing data fusion on the cluster head. The proposed system is using an energy efficient hierarchical routing protocol named Energy Optimized Dynamic Clustering (EODC) for clustering large ad-hoc WSN and route the data towards the sink. The sink receives the data collected from the set of cluster heads after every round. The cluster head was selected using Particle Swarm Optimization (PSO) approach and the cluster members are allocated based on Manhattan distance. The metrics used to find the fitness function are location, link quality, energy of active node and energy of inactive node. The system employs shortest path approach to communicate between the cluster heads till it reaches the base station. By this, we have increased the energy efficiency and lifetime of the network. The analysis and outcomes show that the EODC was found to outperform the existing protocol which compares with this algorithm.

scholarly journals Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Santosh Soni ◽  
Manish Shrivastava
Keyword(s):  
Reinforcement Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Research Study ◽  
Hot Spot ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Heads ◽  
Hot Spot Problem

Generally, wireless sensor network is a group of sensor nodes which is used to continuously monitor and record the various physical, environmental, and critical real time application data. Data traffic received by sink in WSN decreases the energy of nearby sensor nodes as compared to other sensor nodes. This problem is known as hot spot problem in wireless sensor network. In this research study, two novel algorithms are proposed based upon reinforcement learning to solve hot spot problem in wireless sensor network. The first proposed algorithm RLBCA, created cluster heads to reduce the energy consumption and save about 40% of battery power. In the second proposed algorithm ODMST, mobile sink is used to collect the data from cluster heads as per the demand/request generated from cluster heads. Here mobile sink is used to keep record of incoming request from cluster heads in a routing table and visits accordingly. These algorithms did not create the extra overhead on mobile sink and save the energy as well. Finally, the proposed algorithms are compared with existing algorithms like CLIQUE, TTDD, DBRkM, EPMS, RLLO, and RL-CRC to better prove this research study.

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