scholarly journals Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shahzad Ashraf ◽  
Omar Alfandi ◽  
Arshad Ahmad ◽  
Asad Masood Khattak ◽  
Bashir Hayat ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Computation Time ◽  
Fruit Fly ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Area ◽  
Pulse Emission ◽  
Fruit Fly Optimization

Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

scholarly journals Wireless Sensor Network Coverage Optimization Based on Fruit Fly Algorithm

2018 ◽  
Vol 14 (06) ◽  
pp. 58 ◽  
Author(s):  
Ren Song ◽  
Zhichao Xu ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fruit Fly ◽  
Effective Radius ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Network Coverage ◽  
Coverage Optimization ◽  
Improved Algorithm

<p class="0abstract"><span lang="EN-US">To solve the defect of traditional node deployment strategy, the improved <a name="_Hlk502130691"></a>fruit fly algorithm was combined with wireless sensor network. The optimization of network coverage was implemented. </span><span lang="EN-US">Based on a new type of intelligent algorithm, the change step of fruit fly optimization algorithm (CSFOA)</span><span lang="EN-US">was proposed. At the same time, the mathematical modeling of two network models was carried out respectively. The grid coverage model was used. The network coverage and redundancy were transformed into corresponding mathematical variables by means of grid partition.</span><span lang="EN-US">Among them, the maximum effective radius of sensor nodes was fixed in mobile node wireless sensor network. The location of nodes was randomly cast. The location of sensor nodes was placed in fixed position nodes. The effective radius of nodes can be changed dynamically.</span><span lang="EN-US">Finally, combined with the corresponding network model, the improved algorithm was applied to wireless sensor network.</span><span lang="EN-US">The combination of the optimal solution of the node position and the perceptual radius was found through the algorithm. The maximum network coverage was achieved.</span><span lang="EN-US">The two models were simulated and verified. The results showed that the improved algorithm was effective and superior to the coverage optimization of wireless sensor networks.</span></p>

Proficient Clustering algorithm for Wireless Sensor Networks

2020 ◽  
pp. 249-261
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

Intelligent Technique Based on Enhanced Metaheuristic for Optimization Problem in Internet of Things and Wireless Sensor Network

2020 ◽  
Vol 12 (3) ◽  
pp. 17-42 ◽  
Author(s):  
Miloud Mihoubi ◽  
Abdellatif Rahmoun ◽  
Meriem Zerkouk ◽  
Pascal Lorenz ◽  
Lotfi Baidar
Keyword(s):  
Internet Of Things ◽  
Optimization Problem ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Node Localization ◽  
Fruit Fly Optimization ◽  
Intelligent Technique ◽  
The Internet Of Things ◽  
Strong Capacity

For the last decade, there has been an intensive research development in the area of wireless sensor networks (WSN). This is mainly due to their growing interest in several applications of the Internet of Things (IoT). Several issues are thus discussed such as node localization, a capability that is highly desirable for performance evaluation in monitoring applications. The localization aim is to look for precise geographical positions of sensors. Recently, swarm intelligence techniques are suggested to deal with localization challenge and localization is seen as an optimization problem. In this article, an Enhanced Fruit Fly Optimization Algorithm (EFFOA) is proposed to solve the localization. EFFOA has a strong capacity to calculate the position of the unknown nodes and converges iteratively to the best solution. Distributing and exploiting nodes is a chief challenge to testing the scalability performance. the EFFOA is simulated under variant studies and scenarios. in addition, a comparative experimental study proves that EFFOA outperforms some of the well-known optimization algorithms.

Proficient Clustering algorithm for Wireless Sensor Networks

2017 ◽  
pp. 345-357
Author(s):  
Nivetha Gopal ◽  
Venkatalakshmi Krishnan
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Fruit Fly ◽  
Wireless Sensor ◽  
Fruit Fly Optimization Algorithm ◽  
Matlab Simulation ◽  
Fruit Fly Optimization

Enhancing the energy efficiency and maximizing the networking lifetime are the major challenges in Wireless Sensor Networks (WSN).Swarm Intelligence based algorithms are very efficient in solving nonlinear design problems with real-world applications.In this paper a Swarm based Fruit Fly Optimization Algorithm (FFOA) with the concept of K-Medoid clustering and swapping is implemented to increase the energy efficiency and lifetime of WSN. A comparative analysis is performed in terms of cluster compactness,cluster error and convergence. MATLAB Simulation results show that K-Medoid Swapping and Bunching Fruit Fly optimization (KMSB-FFOA) outperforms FFOA and K-Medoid Fruit Fly Optimization Algorithm (KM-FFOA).

scholarly journals Underwater Self Deployment of Wireless Sensors for Maximum Coverage and Connectivity using K-Means Clustering

2018 ◽  
Vol 7 (2.11) ◽  
pp. 5
Author(s):  
Gurjaspreet Kaur ◽  
Surinder Singh ◽  
Ramanpreet Kaur ◽  
Gaurav Garg
Keyword(s):  
Wireless Sensors ◽  
Euclidean Distance ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Area ◽  
Maximum Coverage ◽  
3D Space ◽  
Main Emphasis

By virtue of abundant research being executed in terrestrial wireless sensor networks, we get enlightened about the different advantages of using wireless sensors and implemented these wireless sensors in underwater to probe the area below the sea for its various applications. The most crucial task in underwater 3D space is the node deployment such that sensors can cover the maximum area while simultaneously maintaining the connectivity with the base station. Due to the advantages of self deployment where no pre calculations and no human interventions are needed, we proposed a distributed move restricted self deployment underwater wireless sensor network. The main emphasis is on maximizing the coverage area by the sensor nodes and simultaneously maintaining the connectivity with the base station. Firstly, the maximum coverage has achieved by removing the interference between the sensor nodes which minimize the coverage overlap and thus more area can be covered which improves the network coverage. Then by using K-Means clustering, all the sensors nodes were divided into clusters, having one centroid for each cluster. Further all these centroids have been interconnected and then by finding the centroid which is nearest to the sink by Euclidean distance formula, we connect it to the sink.  

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