Coverage control study of mobile uwasns nodes based on particle swarm optimization algorithm

Dong, Chaoping; Guo, Longxiang; Yin, Jingwei

doi:10.1145/2999504.3001084

Cited by 3 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Maximum connectivity and coverage (MCC) self‐deployment algorithm based on ACO (MCC‐ACO) was developed to achieve energy efficient coverage of target events. PSO algorithm for mobile UASN‐based deployment was developed by Chaoping Dong et al 22 This algorithm aims to solve the continuous variable optimization problem in UASN. Sangeeta Kumari and Govind P. Gupta 23 proposed a Cuckoo Search Optimization (CSO) based placement strategy in UASN.…”

Section: Related Workmentioning

confidence: 99%

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Gola

Chaurasia²,

Gupta

et al. 2021

Int J Communication

View full text Add to dashboard Cite

Summary In the ocean, huge number of sensor nodes (SNs) are located to transfer the information between other nodes using the Underwater Acoustic Sensor Network (UASN) framework. An underwater acoustic communication technique is utilized by this UASN to exchange the information. Because of environmental conditions and adverse channel, the SNs in UASN may have link breakages. Likewise, maximum target coverage rate for SN deployment is considered as another issue. So it is very essential to create a strong communication system in underwater together with the different kind of variations in ocean environment. As a result, the system will perform better data transmission with the severely fluctuating underwater communication conditions. In this paper, a latest optimization algorithm named as Sea Lion Optimization (SLO) procedure is proposed to discover the optimal location for SN in underwater communication. This algorithm optimally places the acoustic SNs based on the maximum connectivity rate by finding the targeted optimal position. The Matlab tool is utilized for implementation purpose, and the different kinds of parameters like connectivity rate, coverage rate, and delay are taken to evaluate the performance of proposed methodology. Moreover, the existing methods like deployment scheme, Connected Dominating set based depth computation Approach (CDA) approach, and distributive approach are taken to contrast the performance of proposed methodology. When compared to the previous algorithms, our proposed methodology achieves 95% connectivity ratio for varying number of acoustic SNs.

show abstract

Section: Related Workmentioning

confidence: 99%

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Gola

Chaurasia²,

Gupta

et al. 2021

Int J Communication

View full text Add to dashboard Cite

show abstract

“…The technology to improve connectivity and reliability of sensor networks is a hot topic nowadays. There are several kinds of methods aiming to improve connectivity and reliability to meet the requirement of applications, such as the algorithms for the routing layer and MAC layer [ 12 , 13 , 14 , 15 , 16 ], the nodes complement about dynamic nodes and static nodes [ 17 , 18 , 19 ] and so on. In this section, we provide a review on research works on this topic.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, Ref. [ 19 ] makes use of the virtual force algorithm to make the nodes cover the network connection holes. The algorithm can reduce the node redundancy, but requires a higher computational capability and self-mobility capability and the driving force of node movement also generates a large energy expenditure.…”

Section: Related Workmentioning

confidence: 99%

A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks

Jin

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20–58% for a typical network’s setting.

show abstract

Mobile sink path planning research for underwater heterogeneous sensor network

Zheng

Liu

et al. 2018

2018 Chinese Control and Decision Conference (CCDC)

View full text Add to dashboard Cite

Coverage control study of mobile uwasns nodes based on particle swarm optimization algorithm

Cited by 3 publications

References 1 publication

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks

Mobile sink path planning research for underwater heterogeneous sensor network

Contact Info

Product

Resources

About