Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6): 364-374.doi: 10.11896/jsjkx.230300185

• Computer Network • Previous Articles     Next Articles

Adaptive Sparse Sensor Network Target Coverage Algorithm Based on Edge Computing

LI Jie, WANG Yao, CHEN Kansong, XU Lijun   

  1. School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
  • Received:2023-03-23 Revised:2023-09-25 Online:2024-06-15 Published:2024-06-05
  • About author:LI Jie,born in 1988,Ph.D,master supervisor,lecturer.Her main research interests include future Internet technology and ad hoc network.
    XU Lijun,born in 1991,Ph.D,master supervisor.Her main research interests include image processing,digital twins and artificial intelligence.
  • Supported by:
    Young Programof the Natural Science Foundation Hubei Province,China(2023AFB313),Key Research and Development Program of Hubei Province of China(2021BAA184,2022BAA045),Department of Education Young Talents Program of Hubei Province of China(202211901301002) and Knowledge Innovation Project of Wuhan－“Dawn” Program(202211901251327).

PDF (PC)

483

Abstract: Ocean exploration is the key to ocean development,and how to quickly and efficiently achieve underwater target detection is a problem that must be solved for ocean exploration.Based on this,an adaptive sparse sensing network target coverage optimization algorithm based on edge computing is proposed to efficiently accomplish underwater target detection with fewer sen-sing nodes.Firstly,the energy balance of the sensing network is optimized by adding an energy factor to protect the nodes with lower energy during the node movement through the Ad Hoc mobile energy optimization strategy mechanism.Secondly,an Ad Hoc greedy detection mechanism is proposed to achieve the detection of unknown areas with minimum cost and fast target cove-rage.Finally,using the virtual force-based adaptive connectivity mechanism,the connectivity of the sparse self-organized network is ensured by increasing the virtual gravitational range to solve the disconnection problem during the node movement.Simulation results show that the proposed algorithm is able to provide fast and durable target detection coverage with a smaller number of mobile sensors,with better performance compared to the comparison algorithms.

Key words: Underwater Ad-Hoc network, Energy consumption, Edge computing, Target detection, Connectivity

CLC Number: 

  • TP393.0
[1]XIA N,WEN X Y,DU H Z,et al.Study on hunting deployment of underwater sensors based on level set method[J].Chinese Journal of Computers,2021,44(5):1024-1036.
[2]ZHOU Z,YAO B,XING R,et al.E-CARP:An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things[J].IEEE Sensors Journal,2016,16(11):4072-4082.
[3]LUO C,CAO Y,XIN G,et al.Three-Dimensional Coverage Optimization of Underwater Nodes UnderMulticonstraints Combined With Water Flow[J].IEEE Internet of Things Journal,2022,9(3):2375-2389.
[4]COBANLAR M,YILDIZ H U,AKRAM V K,et al.On theTradeoff Between Network Lifetime and k-Connectivity-Based Reliability in UWSNs[J].IEEE Internet of Things Journal,2022,9(23):24444-24452.
[5]SU R,ZHANG D,LI C,et al.Localization and Data Collection inAUV-Aided Underwater Sensor Networks:Challenges and Opportunities[J].IEEE Network,2019,33(6):86-93.
[6]LIU Z,JIN X,YANG Y,et al.Energy-Efficient Guiding-Network-Based Routing for Underwater Wireless Sensor Networks[J].IEEE Internet of Things Journal,2022,9(21):21702-21711.
[7]ZHAO X Q,CUI Y P,GAO C Y,et al.Energy-Efficient Cove-rage Enhancement Strategy for 3-D Wireless Sensor Networks Based on a Vampire Bat Optimizer[J].IEEE Internet of Things Journal,2020,7(1):325-338.
[8]YAO Y,LI Y,XIE D,et al.Coverage Enhancement Strategy for WSNs Based on Virtual Force-Directed Ant Lion Optimization Algorithm[J].IEEE Sensors Journal,2021,21(17):19611-19622.
[9]WEN Q,ZHAO X Q,CUI Y P,et al.Coverage Enhancement Algorithm for WSNs Based on Vampire Bat and Improved Virtual Force[J].IEEE Sensors Journal,2022,22(8):8245-8256.
[10]LUO C,WANG B,CAO Y,et al.A hybrid coverage control for enhancing UWSN localizability using IBSO-VFA[J].Ad Hoc Networks,2021,123:102694.
[11]YAO P,QIU L,QI J,et al.AUV path planning for coverage search of static target in ocean environment[J].Ocean Enginee-ring,2021,241:110050.
[12]LIU Y,CHIN K W,YANG C,et al.Nodes Deployment for Co-verage in Rechargeable Wireless Sensor Networks[J].IEEE Transactions on Vehicular Technology,2019,68(6):6064-6073.
[13]XIONG Y,CHEN G,LU M,et al.A Two-Phase Lifetime-Enhancing Method for Hybrid Energy-Harvesting Wireless Sensor Network[J].IEEE Sensors Journal,2020,20(4):1934-1946.
[14]GUO J,JAFARKHANI H.Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range[J].IEEE Transactions on Wireless Communications,2019,18(7):3469-3484.
[15]XU X,DAI Z,SHAN A,et al.Connected Target ε -probability Coverage in WSNs With Directional Probabilistic Sensors[J].IEEE Systems Journal,2020,14(3):3399-3409.
[16]YI J,QIAO G,YUAN F,et al.Sensor Deployment Strategies for Target Coverage Problems in Underwater Acoustic Sensor Networks[J].IEEE Communications Letters,2023,27(3):836-840.
[17]ZHU X,ZHOU M,ABUSORRAH A.Optimizing Node Deployment in Rechargeable Camera Sensor Networks for Full-View Coverage[J].IEEE Internet of Things Journal,2022,9(13):11396-11407.
[18]ZHOU M,CHEN H,SHU L,et al.UAV-Assisted Sleep Sche-duling Algorithm for Energy-Efficient Data Collection in Agricultural Internet of Things[J].IEEE Internet of Things Journal,2022,9(13):11043-11056.
[19]YANG C,CHIN K W,LIU Y,et al.Robust Targets Coverage for Energy Harvesting Wireless Sensor Networks[J].IEEE Transactions on Vehicular Technology,2019,68(6):5884-5892.
[20]ZHU X,LI J,ZHOU M.Target Coverage－Oriented Deploy-ment of Rechargeable Directional Sensor Networks with a Mobile Charger[J].IEEE Internet of Things Journal,2019,6(3):5196-5208.
[21]KATTI A.Target coverage in random wireless sensor networksusing cover sets[J].Journal of King Saud University-Computer and Information Sciences,2022,34(3):734-746.
[22]NGUYEN P L,HANH N T,KHUONG N T,et al.Node placement for connected target coverage in wireless sensor networks with dynamic sinks[J].Pervasive and Mobile Computing,2019,59:101070.
[23]ROUT M,ROY R.Self-Deployment of Mobile Sensors toAchieve Target Coverage in the Presence of Obstacles[J].IEEE Sensors Journal,2016,16(14):5837-5842.
[24]HOWARD A,MATARIC′ M J,SUKHATME G S.Mobile Sensor Network Deployment using Potential Fields:A Distributed,Scalable Solution to the Area Coverage Problem[C]//Distributed Autonomous Robotic Systems.Tokyo:Springer,2002:299-308.
[25]HONG C J,GAO Y,ZHANG F,et al.Reliable TransmissionStrategy for Underwater Wireless Sensor Networks[J].Computer Science,2021,48(6A):410-413.
[26]李习习,强俊,刘无纪,等.基于双主干网络的雾天交通目标检测方法研究[J].重庆工商大学学报(自然科学版),2023,40(4):25-34.
[27]WAHID A,LEE S,KIM D.An energy-efficient routing protocol for UWSNs using physical distance and residual energy[C]//OCEANS 2011 IEEE.Spain:IEEE Press,2011:1-6.
[1] XIE Guangqiang, ZHONG Biwei, LI Yang. Distributed Adaptive Multi-agent Rendezvous Control Based on Average Consensus Protocol [J]. Computer Science, 2024, 51(5): 242-249.
[2] YANG Xiuwen, CUI Yunhe, QIAN Qing, GUO Chun, SHEN Guowei. COURIER:Edge Computing Task Scheduling and Offloading Method Based on Non-preemptivePriorities Queuing and Prioritized Experience Replay DRL [J]. Computer Science, 2024, 51(5): 293-305.
[3] LIN Qiye, XIA Jianan, ZHOU Xuezhong. Study on Time-varying Brain State Based on fMRI Data－A Review [J]. Computer Science, 2024, 51(4): 182-192.
[4] WANG Zhihong, WANG Gaocai, ZHAO Qifei. Multi-objective Optimization of D2D Collaborative MEC Based on Improved NSGA-III [J]. Computer Science, 2024, 51(3): 280-288.
[5] WANG Xinlong, LIN Bing, CHEN Xing. Computation Offloading with Wardrop Routing Game in Multi-UAV-aided MEC Environment [J]. Computer Science, 2024, 51(3): 309-316.
[6] DING Shuang, CAO Muyu, HE Xin. Online Task Offloading Decision Algorithm for High-speed Vehicles [J]. Computer Science, 2024, 51(2): 286-292.
[7] ZHAO Xiaoyan, ZHAO Bin, ZHANG Junna, YUAN Peiyan. Study on Cache-oriented Dynamic Collaborative Task Migration Technology [J]. Computer Science, 2024, 51(2): 300-310.
[8] ZHANG Guodong, CHEN Zhihua, SHENG Bin. Infrared Small Target Detection Based on Dilated Convolutional Conditional GenerativeAdversarial Networks [J]. Computer Science, 2024, 51(2): 151-160.
[9] WANG Weijia, XIONG Wenzhuo, ZHU Shengjie, SONG Ce, SUN He, SONG Yulong. Method of Infrared Small Target Detection Based on Multi-depth Feature Connection [J]. Computer Science, 2024, 51(1): 175-183.
[10] LIU Xingguang, ZHOU Li, ZHANG Xiaoying, CHEN Haitao, ZHAO Haitao, WEI Jibo. Edge Intelligent Sensing Based UAV Space Trajectory Planning Method [J]. Computer Science, 2023, 50(9): 311-317.
[11] LIN Xinyu, YAO Zewei, HU Shengxi, CHEN Zheyi, CHEN Xing. Task Offloading Algorithm Based on Federated Deep Reinforcement Learning for Internet of Vehicles [J]. Computer Science, 2023, 50(9): 347-356.
[12] ZHANG Naixin, CHEN Xiaorui, LI An, YANG Leyao, WU Huaming. Edge Offloading Framework for D2D-MEC Networks Based on Deep Reinforcement Learningand Wireless Charging Technology [J]. Computer Science, 2023, 50(8): 233-242.
[13] LIU Chenwei, SUN Jian, LEI Bingbing, XU Tao, WU Zhuiwei. Task Scheduling Strategy for Energy Consumption Optimization of Cloud Data Center Based on Improved Particle Swarm Algorithm [J]. Computer Science, 2023, 50(7): 246-253.
[14] CHEN Xuzhan, LIN Bing, CHEN Xing. Stackelberg Model Based Distributed Pricing and Computation Offloading in Mobile Edge Computing [J]. Computer Science, 2023, 50(7): 278-285.
[15] QIN Jing, WANG Weibin, ZOU Qijie, WANG Zumin, JI Changqing. Review of 3D Target Detection Methods Based on LiDAR Point Clouds [J]. Computer Science, 2023, 50(6A): 220400214-7.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.