Localization in mobile wireless sensor networks using drones

被引:7
作者
Kaushik, Abhinesh [1 ]
Lobiyal, D. K. [1 ]
机构
[1] Jawaharlal Nehru Univ, Sch Comp & Syst Sci, New Delhi 110067, India
关键词
ALGORITHM;
D O I
10.1002/ett.4213
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Localization is one of the most important aspects of wireless sensor networks. Most of the localization algorithms proposed in the literature, work for static networks. However, the performance of these protocols in the mobile environment still needs to be investigated. Localization in a mobile environment may pose challenges of accuracy and computational complexity due to mobility of the nodes. In this article, we have proposed an algorithm for localization in mobile wireless sensor networks using drones (LMWSND). A drone-based algorithm is proposed to simulate the moving trajectory of the drones. The proposed drone-based algorithm uses the technique of received signal strength between the drone and unknown sensor nodes to find the distance between them. The distance thus obtained is used for localization of the unknown nodes. A new method is used to solve the system of distance equations to restrict the propagation of error. Furthermore, the mathematical analysis of the propagation error in LMWSND proves the superiority of our proposed algorithm in comparison to other algorithms. The results obtained through simulations further strengthen our conclusion that the proposed algorithm reduces the propagation of error. We have also proposed another algorithm to choose the nearby beacon points for better utilization of the network resources and to reduce the computational efforts.
引用
收藏
页数:18
相关论文
共 41 条
[1]   Wireless sensor networks: a survey [J].
Akyildiz, IF ;
Su, W ;
Sankarasubramaniam, Y ;
Cayirci, E .
COMPUTER NETWORKS, 2002, 38 (04) :393-422
[2]   An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks [J].
Annepu, Visalakshi ;
Rajesh, A. .
NEURAL COMPUTING & APPLICATIONS, 2020, 32 (15) :11651-11663
[3]   Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks [J].
Annepu, Visalakshi ;
Anbazhagan, Rajesh .
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2020, 33 (10)
[4]   Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications [J].
Arafat, Muhammad Yeasir ;
Moh, Sangman .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (05) :8958-8976
[5]  
BOUKERCHE A., 2008, ALGORITHMS PROTOCOLS
[6]   GPS-less low-cost outdoor localization for very small devices [J].
Bulusu, N ;
Heidemann, J ;
Estrin, D .
IEEE PERSONAL COMMUNICATIONS, 2000, 7 (05) :28-34
[7]   Improved DV-Hop Localization Algorithm Based on Dynamic Anchor Node Set for Wireless Sensor Networks [J].
Cao, Yuxiao ;
Wang, Zhen .
IEEE ACCESS, 2019, 7 :124876-124890
[8]  
Capkun S., 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences, DOI 10.1109/HICSS.2001.927202
[9]   A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks [J].
Cheikhrouhou, Omar ;
Bhatti, Ghulam M. ;
Alroobaea, Roobaea .
SENSORS, 2018, 18 (05)
[10]   Localization protocols for mobile wireless sensor networks: A survey [J].
Chelouah, Leila ;
Semchedine, Fouzi ;
Bouallouche-Medjkoune, Louiza .
COMPUTERS & ELECTRICAL ENGINEERING, 2018, 71 :733-751