An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks

Song, Peng; Xiong, Yan

doi:10.3390/s19051192

Cited by 23 publications

(16 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The particle position of i -th particle is represented as βi, and its velocity is represented as εi=()εi1,εi2,…,εin, that is, the change difference of the sensing directions. Then, the particles are manipulated according to the following two equations [29]:εid()t+1=ω·εid()t+k1·b1·()Pbest-βid()t+k2·b2·()Gbest-βid()t βid()t+1=βid()t+εid()t+1 where k1 and k20≤…”

Section: Two-phase Spatial-temporal Coverage-enhancing Methodsmentioning

confidence: 99%

“…The constants k1 and k2 determine the speed that a particle would accelerate towards the personal best value and the global best value. Usually, k1 and k2 are equal to 2 [29], but other values can also be taken . Generally speaking, k1=k2, and the range is between 0 and 4 [30].…”

Section: Two-phase Spatial-temporal Coverage-enhancing Methodsmentioning

confidence: 99%

“…The sensor node scheduling problem has been proved as NP-hard [29], but the existing methods focus on a finite number of solution spaces and cannot truly achieve global optimization.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

Xiong

2019

Sensors

Self Cite

View full text Add to dashboard Cite

Coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to meet both coverage and network lifetime requirements. Therefore, in order to satisfy the network lifetime constraint, sometimes the coverage needs to be traded for network lifetime. In this paper, we study how to schedule sensor nodes to maximize the spatial-temporal coverage of the critical locations under the constraint of network lifetime. First, we analyze the sensor node scheduling problem for the spatial-temporal coverage of the critical locations and establish a mathematical model of the node scheduling. Next, by analyzing the characteristics of the model, we propose a Two-phase Spatial-temporal Coverage-enhancing Method (TSCM). In phase one, a Particle Swarm Optimization (PSO) algorithm is employed to organize the directions of sensor nodes to maximize the number of covered critical locations. In the second phase, we apply a Genetic Algorithm (GA) to get the optimal working time sequence of each sensor node. New coding and decoding strategies are devised to make GA suitable for this scheduling problem. Finally, simulations are conducted and the results show that TSCM has better performance than other approaches.

show abstract

Section: Two-phase Spatial-temporal Coverage-enhancing Methodsmentioning

confidence: 99%

Section: Two-phase Spatial-temporal Coverage-enhancing Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

Xiong

2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…19 Let Rs be 1 and Rc be 3. In addition, by comprehensively comparing the data obtained from multiple simulation experiments and combining with the Van Der Wals Force 15,20,21 the parameters are RTH = 2.3, RB = 1.4, and k = 12 in formula (3) and formula (4) when the deployment is relatively ideal.…”

Section: Experimental Settingsmentioning

confidence: 99%

A self-adaptive deployment model of UAV cluster for emergency communication network

Fang

Cheng²,

Kun-peng

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Equipped with micro wireless sensor nodes, a unmanned aerial vehicle) cluster can form an emergency communication network, which can have several applications such as environmental monitoring, disaster relief, military operations and so on. However, situations where there is excessive aggregation and small amount of dispersion of the unmanned aerial vehicle cluster may occur when the network is formed. To mitigate these, a solution based on a 3D virtual force driven by self-adaptive deployment (named as 3DVFSD) is proposed. As a result, the three virtual forces of central gravity, uniform force, and boundary constraint force are combined to act on each node of the communication network. By coordinating the distance between the nodes, especially the threshold of the distance between the boundary node and the boundary, the centralized nodes can be relatively dispersed. Meanwhile, the nodes can be prevented from being too scattered by constraining the distance from the boundary node to the end. The simulation results show that the 3DVFSD algorithm is superior to the traditional virtual force-driven deployment strategy in terms of convergence speed, coverage, and uniformity.

show abstract

“…In reference [ 23 ], an Improved Adaptive Particle Swarm Optimization (IAPSO) algorithm was proposed to solve the problem of the blind area and overlapping coverage caused by the random deployment of directional sensor nodes. The area that cannot be covered by any sensors is called a blind area and if an area was covered by multiple sensors at the same time is called overlapping.The multi-objective optimization model was established to improve the coverage rate and reduce the redundancy rate.…”

Section: Related Workmentioning

confidence: 99%

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

Cheng

Huangfu

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks

Cited by 23 publications

References 34 publications

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network

A self-adaptive deployment model of UAV cluster for emergency communication network

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

Contact Info

Product

Resources

About