An energy balanced-virtual force algorithm for Mobile-WSNs

Li, Yaobing; Zhang, Baihai; Chai, Senchun

doi:10.1109/icma.2015.7237755

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…Tongxin Shu [16] proposed a coverage optimization algorithm combining Voronoi Diagram and geometric center to maximize area coverage and minimize energy consumption under the constraints of communication range. Yaobing [10] proposed to adjust virtual force parameters based on the energy of neighbor nodes, and measure the uniformity as an experimental indicator. In addition, biologically heuristic intelligent optimization algorithms have also received extensive attention in this field.…”

Section: Related Workmentioning

confidence: 99%

“…This paper focuses on the coverage optimization of hybrid WSN redeployment, which aims to improve area coverage rate and reduce the energy consumption deviation of nodes. Reference [10] knows that the energy consumption of a node movement is directly related to its distance. Therefore, the problem of moving energy consumption deviation can be converted into a moving distance deviation.…”

Section: Network Modelmentioning

confidence: 99%

“…MTPSO firstly initializes the position of the particles (that is the randomly distributed node deployment plan), speed and other necessary parameter settings (see 1). Then it utilizes a two-stage iterative optimization within the Maxiter iterations constraint, of which the first stage is updating the speed and position, providing candidate target deployment (see [4][5][6]; the second stage is adjusting the relative position among nodes according to the virtual force mechanism to optimize the candidate target position (see [8][9][10]. Finally, it updates the global optimal deployment plan of the current particle swarm, according to the value of f itness (see 11).…”

Section: Algorithm Implementation Algorithm 1 Two-stage Coverage Optimization Algorithmmentioning

confidence: 99%

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WSN Coverage Optimization Based on Two-Stage PSO

Fan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wireless Sensor Networks (WSN) coverage perception is an important basis for communication between the cyber world and the physical world in Cyber-Physical Systems (CPS). To address the coverage redundancy, hole caused by initial random deployment and the energy constraint in redeployment, this paper proposes a multi-objective two-stage particle swarm optimization algorithm (MTPSO) based on coverage rate and moving distance deviation to improve coverage efficiency. This algorithm establishes a multi-objective optimization model for above problems, and determines the candidate deployment scheme by reducing its local convergence probability through improved inertia weight, and then introduces virtual force mechanism to adjust the relative position between nodes. This paper mainly analyzes the influence of different initial deployment category and mobile nodes proportion on multi-objective optimization performance, and gives the corresponding algorithm implement. Simulation experiments show that compared with MVFA, SPSO and OPSO algorithms, MTPSO algorithm has a better redeployment coverage performance, which fully demonstrates its effectiveness.

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Section: Related Workmentioning

confidence: 99%

Section: Network Modelmentioning

confidence: 99%

Section: Algorithm Implementation Algorithm 1 Two-stage Coverage Optimization Algorithmmentioning

confidence: 99%

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WSN Coverage Optimization Based on Two-Stage PSO

Fan

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…The Virtual Force Algorithm (VFA) was originally an algorithm that allowed mobile robots to evade obstacles in an unknown environment [19,20,21]. The VFA has been applied to WSN coverage optimization, and can quickly disperse sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization

Wang

Yang

Wang

et al. 2019

Sensors

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The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.

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“…The relocation of the SNs at the appropriate movement distances reduced the network energy consumption and maximized the coverage level. Conversely, studies have proposed that each MSN is placed at the position where the energy consumption between each MSN is balanced [17,18]. By applying energy costs, such as energy consumption and the standard deviation of energy consumption between MSN to coverage to MSN deployment model, the overall energy of WSNs is balanced.…”

Section: Introductionmentioning

confidence: 99%

Mobile Sensor Node Deployment Strategy by using Graph Structure based on Estimation of Communication Connectivity and Movement Path

Kawabata¹,

Suzuki²

2020

IJACSA

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We propose a multiple-mobile sensor node (MSN) deployment strategy that considers wireless communication quality and operation time of underground wireless sensor networks. After an underground disaster, it is difficult to perform a rescue operation because the internal situation cannot be confirmed. Hence, gathering information using a teleoperated robot has been widely discussed. However, wireless communication is unstable and the corresponding wireless infrastructure to operate the teleoperated robot is unavailable underground. Therefore, we studied the disaster informationgathering support system using wireless sensor networks and a rescue robot. In this study, the movement path information of the teleoperated robot is fed to MSNs in a graph structure. MSNs are deployed in the underground environment by adding an evaluation of communication quality and operation status to a given graph structure. The simulation was evaluated in an assumed underground environment. The results confirmed that the wireless communication quality between each MSN was maintained and energy consumption was balanced during the deployment. Keywords-Wireless sensor networks; deployment strategy; communication connectivityFrom studies based on past accident analysis, researchers have recently focused on a disaster information-gathering method using a wireless sensor network (WSN) and a rescue robot in closed areas. The WSN consists of spatially distributed sensor nodes (SNs) to cooperatively monitor environmental conditions such as temperature, sound, vibration, pressure, motion, etc. The WSN is then enabled to provide wireless communication without the existing infrastructure. In a closed area, it is constructed using a rescue robot. Therefore, an information-gathering method by constructing the communication infrastructure in a disaster area using a WSN has been discussed [6,7]. However, the scope of application is limited to outside the disaster area, and studies assuming a closed space have not been reported.Information gathering by rescue robots and disaster rescue support systems is effective. The use of robot technology can reduce the activity burden on rescue workers. Rescue robots are often remotely controlled by considering the impact on the disaster area and work safety. When the robot is operated remotely, it is possible to support rescue operations in spaces where people cannot easily enter, such as closed spaces, narrow spaces, and underwater. The connectivity and stability of communication are very important during remotely controlled operations. Owing to the closed environment, wireless communication is often unstable underground, as compared to the outdoors. Hence, degradation of wireless communication due to disturbances such as fading and shadowing is more likely to occur underground than outdoors.Therefore, we studied the information-gathering system using teleoperated robots and WSNs, as observed in Fig. 1 [8 -10]. In this system, a WSN is constructed using a mobile sensor node (MSN). This system responds quickly to ...

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An energy balanced-virtual force algorithm for Mobile-WSNs

Cited by 5 publications

References 15 publications

WSN Coverage Optimization Based on Two-Stage PSO

WSN Coverage Optimization Based on Two-Stage PSO

A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization

Mobile Sensor Node Deployment Strategy by using Graph Structure based on Estimation of Communication Connectivity and Movement Path

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