A Diversity-Feedback-Regulated Particle Swarm Optimization for Coverage Enhancing Problem in Directional Sensor Network

2021 ◽  
pp. 1416-1424
Author(s):  
Meng Tian ◽  
Qing Liu ◽  
Yaling Lu ◽  
Junliang Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Directional Sensor Network ◽  
Directional Sensor

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

scholarly journals Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization

2018 ◽  
Vol 10 (3) ◽  
pp. 1287
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Packet Size ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid & Force Based sensor deployment algorithms based on interval and packet size. From the results of Combination of Grid & Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.

scholarly journals Random, PSO and MDBPSO based Sensor Deployment in Wireless Sensor Network

2018 ◽  
Vol 10 (3) ◽  
pp. 1278 ◽  
Author(s):  
Aparna Pradeep Laturkar ◽  
Sridharan Bhavani ◽  
DeepaliParag Adhyapak
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Wide Range ◽  
Battery Capacity

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling and data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. This paper discusses sensor deployment using Random; Particle Swarm Optimization (PSO) and grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization) methods. This paper analyzes the performance of Random, PSO based and MDBPSO based sensor deployment methods by varying different grid sizes and the region of interest (ROI). PSO and MDBPSO based sensor deployment methods are analyzed based on number of iterations. From the simulation results; it can be concluded that MDBPSO performs better than other two methods.

Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization

2021 ◽  
Vol 11 (12) ◽  
pp. 3054-3061
Author(s):  
S. Sureshu ◽  
R. Vijayabhasker
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Network ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Cloud Services ◽  
Swarm Optimization ◽  
Body Sensor Network ◽  
Modified Particle Swarm Optimization ◽  
Network Platform

Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented resource scheduling approach might be utilized to enhance the viability of cloud resources.

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