research-article

Communication-Topology-preserving Motion Planning: Enabling Static Routing in UAV Networks

Authors:

Xueyong XuAuthors Info & Claims

ACM Transactions on Sensor Networks, Volume 20, Issue 1

Article No.: 24, Pages 1 - 39

https://doi.org/10.1145/3631530

Published: 07 December 2023 Publication History

Abstract

Unmanned Aerial Vehicle (UAV) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes. This might lead to failures of time-critical tasks. One mitigation solution is to keep the physical topology unchanged, thus maintaining a fixed communication topology and enabling static routing. However, keeping physical topology unchanged may sacrifice the coverage. In this article, we propose to maintain a fixed communication topology among UAVs, which allows certain changes in physical topology, so that to maximize the coverage. We develop a distributed motion planning algorithm for the online multi-target coverage problem with the constraint of keeping communication topology intact. As the communication topology needs to be timely updated when UAVs leave or arrive at the swarm, we further design a topology-management protocol. Experimental results from the ns-3 simulator show that under our algorithms, UAV swarms of different sizes achieve significantly improved delay and loss ratio, efficient coverage, and rapid topology update.

References

[1]

Muhammad Morshed Alam and Sangman Moh. 2022. Joint topology control and routing in a UAV swarm for crowd surveillance. J. Netw. Comput. Appl. 204 (2022), 103427. https://www.sciencedirect.com/science/article/pii/S1084804522000844

Digital Library

[2]

Ebtehal Turki Alotaibi, Shahad Saleh Alqefari, and Anis Koubaa. 2019. LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access 7 (2019), 55817–55832.

[3]

Baruch Awerbuch. 1987. Optimal distributed algorithms for minimum weight spanning tree, counting, leader election, and related problems. In Proceedings of the 19th Annual ACM Symposium on Theory of Computing. 230–240.

[4]

Tauã M. Cabreira, Lisane B. Brisolara, and Paulo R. Ferreira Jr. 2019. Survey on coverage path planning with unmanned aerial vehicles. Drones 3, 1 (2019), 4.

[5]

Christelle Caillouet, Frédéric Giroire, and Tahiry Razafindralambo. 2018. Optimization of mobile sensor coverage with UAVs. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS’18). IEEE, 622–627.

[6]

Tristan Charrier, Arthur Queffelec, Ocan Sankur, and François Schwarzentruber. 2019. Reachability and coverage planning for connected agents. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 1874–1876.

[7]

Ziyan Chen, Nan Cheng, Zhisheng Yin, Jingchao He, and Ning Lu. 2022. Service-oriented topology reconfiguration of UAV networks with deep reinforcement learning. In Proceedings of the 14th International Conference on Wireless Communications and Signal Processing (WCSP’22). IEEE, 753–758.

[8]

Thomas Clausen, Philippe Jacquet, Cédric Adjih, Anis Laouiti, Pascale Minet, Paul Muhlethaler, Amir Qayyum, and Laurent Viennot. 2003. Optimized link state routing protocol (OLSR), Technical Report. IETF. 75 pages.

[9]

Chen Dai, Kun Zhu, and Ekram Hossain. 2022. Multi-agent deep reinforcement learning for joint decoupled user association and trajectory design in full-duplex multi-UAV networks. IEEE Trans. Mobile Comput. (2022).

[10]

Tulio Dapper e Silva, Carlos F. Emygdio de Melo, Pedro Cumino, Denis Rosario, Eduardo Cerqueira, and Edison Pignaton De Freitas. 2019. STFANET: SDN-based topology management for flying ad hoc network. IEEE Access 7 (2019), 173499–173514.

[11]

Milan Erdelj, Tahiry Razafindralambo, and David Simplot-Ryl. 2012. Covering points of interest with mobile sensors. IEEE Trans. Parallel Distrib. Syst. 24, 1 (2012), 32–43.

Digital Library

[12]

Milan Erdelj, Osamah Saif, Enrico Natalizio, and Isabelle Fantoni. 2019. UAVs that fly forever: Uninterrupted structural inspection through automatic UAV replacement. Ad Hoc Netw. 94 (2019), 101612.

Digital Library

[13]

Jose Falcon, Mehrube Mehrubeoglu, and Pablo Rangel. 2022. Real-time image processing in a multi-UAV system for structural surveillance through IoT platform. In Real-Time Image Processing and Deep Learning 2022, Vol. 12102. SPIE, 139–151.

[14]

Malintha Fernando, Ransalu Senanayake, and Martin Swany. 2022. CoCo games: Graphical game-theoretic swarm control for communication-aware coverage. IEEE Robot. Autom. Lett. 7, 3 (2022), 5966–5973.

[15]

Andy Ham, Delchyne Similien, Stanley Baek, and George York. 2022. Unmanned aerial vehicles (UAVs): Persistent surveillance for a military scenario. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS’22). IEEE, 1411–1417.

[16]

Erez Hartuv, Noa Agmon, and Sarit Kraus. 2018. Scheduling spare drones for persistent task performance under energy constraints. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 532–540.

[17]

Ji Hong, Zhi Gao, Tiancan Mei, Yifan Li, and Chunyang Zhao. 2019. UAV-based traffic flow estimation and analysis. In Proceedings of the IEEE 15th International Conference on Control and Automation (ICCA’19). IEEE, 812–817.

[18]

Abhishek Joshi, Sarang Dhongdi, Mihir Dharmadhikari, Ojit Mehta, and K. R. Anupama. 2022. Enclosing and monitoring of disaster area boundary using multi-UAV network. J. Ambient Intell. Human. Comput. (2022), 1–19.

[19]

Yiannis Kantaros and Michael M. Zavlanos. 2016. Distributed communication-aware coverage control by mobile sensor networks. Automatica 63 (2016), 209–220.

Digital Library

[20]

Adil Khan, Jinling Zhang, Shabeer Ahmad, Saifullah Memon, Haroon Akhtar Qureshi, and Muhammad Ishfaq. 2022. Dynamic positioning and energy-efficient path planning for disaster scenarios in 5G-Assisted multi-UAV environments. Electronics 11, 14 (2022), 2197.

[21]

Karthik Soma, Koresh Khateri, Mahdi Pourgholi, Mohsen Montazeri, Lorenzo Sabattini, and Giovanni Beltrame. 2023. A complete set of connectivity-aware local topology manipulation operations for robot swarms. In 2023 IEEE International Conference on Robotics and Automation (ICRA), 5522–5529. DOI:

[22]

Do-Yup Kim and Jang-Won Lee. 2019. Joint mission assignment and topology management in the mission-critical FANET. IEEE IoT J. 7, 3 (2019), 2368–2385.

[23]

Junguk L. Kim and Geneva G. Belford. 1996. A distributed election protocol for unreliable networks. J. Parallel Distrib. Comput. 35, 1 (1996), 35–42.

Digital Library

[24]

Demeke Shumeye Lakew, Umar Sa’ad, Nhu-Ngoc Dao, Woongsoo Na, and Sungrae Cho. 2020. Routing in flying ad hoc networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 22, 2 (2020), 1071–1120.

[25]

Zhuofan Liao, Jianxin Wang, Shigeng Zhang, Jiannong Cao, and Geyong Min. 2014. Minimizing movement for target coverage and network connectivity in mobile sensor networks. IEEE Trans. Parallel Distrib. Syst. 26, 7 (2014), 1971–1983.

Digital Library

[26]

Daniel Bonilla Licea, Moises Bonilla, Mounir Ghogho, Samson Lasaulce, and Vineeth S. Varma. 2019. Communication-aware energy efficient trajectory planning with limited channel knowledge. IEEE Trans. Robot. 36, 2 (2019), 431–442.

[27]

Chao Liu and Zhongshan Zhang. 2022. Towards a robust FANET: Distributed node importance estimation-based connectivity maintenance for UAV swarms. Ad Hoc Netw. 125 (2022), 102734.

Digital Library

[28]

Chao Liu, Zhongshan Zhang, and Qian Zeng. 2021. Distributed connectivity maintenance for Flying Ad-hoc Networks considering bridging links. Phys. Commun. 48 (2021), 101409.

Digital Library

[29]

Zhiyu Liu, Bo Wu, Jin Dai, and Hai Lin. 2020. Distributed communication-aware motion planning for networked mobile robots under formal specifications. IEEE Trans. Contr. Netw. Syst. 7, 4 (2020), 1801–1811.

[30]

Ruyu Luo, Hui Tian, and Wanli Ni. 2021. Communication-aware path design for indoor robots exploiting federated deep reinforcement learning. In Proceedings of the IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’21). IEEE, 1197–1202.

[31]

Aastha Maheshwari and Narottam Chand. 2019. A survey on wireless sensor networks coverage problems. In Proceedings of 2nd International Conference on Communication, Computing and Networking. Springer, 153–164.

[32]

Ajith Anil Meera, Marija Popović, Alexander Millane, and Roland Siegwart. 2019. Obstacle-aware adaptive informative path planning for uav-based target search. In Proceedings of the International Conference on Robotics and Automation (ICRA’19). IEEE, 718–724.

[33]

Nimrod Megiddo. 1990. On the complexity of some geometric problems in unbounded dimension. J. Symbol. Comput. 10, 3-4 (1990), 327–334.

Digital Library

[34]

Syed Agha Hassnain Mohsan, Muhammad Asghar Khan, Fazal Noor, Insaf Ullah, and Mohammed H. Alsharif. 2022. Towards the unmanned aerial vehicles (UAVs): A comprehensive review. Drones 6, 6 (2022), 147.

[35]

Arjun Muralidharan and Yasamin Mostofi. 2021. Communication-aware robotics: Exploiting motion for communication. Annu. Rev. Contr. Robot. Auton. Syst. 4, 1 (2021).

[36]

Sajjad Nematzadeh, Mahsa Torkamanian-Afshar, Amir Seyyedabbasi, and Farzad Kiani. 2022. Maximizing coverage and maintaining connectivity in WSN and decentralized IoT: An efficient metaheuristic-based method for environment-aware node deployment. Neural Comput. Appl. (2022), 1–31.

[37]

Tu N. Nguyen, Bing-Hong Liu, and Shih-Yuan Wang. 2019. On new approaches of maximum weighted target coverage and sensor connectivity: Hardness and approximation. IEEE Trans. Netw. Sci. Eng. 7, 3 (2019), 1736–1751.

[38]

Jacopo Panerati, Luca Gianoli, Carlo Pinciroli, Abdo Shabah, Gabriela Nicolescu, and Giovanni Beltrame. 2018. From swarms to stars: Task coverage in robot swarms with connectivity constraints. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’18). IEEE, 7674–7681.

[39]

Faezeh Pasandideh, Tulio Dapper e Silva, Antonio Arlis Santos da Silva, and Edison Pignaton de Freitas. 2022. Topology management for flying ad hoc networks based on particle swarm optimization and software-defined networking. Wireless Netw. (2022), 1–16.

[40]

Guangyu Pei and Tom Henderson. 2009. Validation of ns-3 802.11 b PHY model. Retrieved from http://www.nsnam.org/~pei/80211b.pdf

[41]

Charles Perkins, Elizabeth Belding-Royer, and Samir Das. 2003. RFC3561: Ad hoc on-demand Distance Vector (AODV) Routing, Technical Report. IETF. 35 pages.

[42]

Chiara Piacentini, Sara Bernardini, and J Christopher Beck. 2019. Autonomous target search with multiple coordinated UAVs. J. Artif. Intell. Res. 65 (2019), 519–568.

Digital Library

[43]

Arthur Queffelec. 2021. Connected Multi-agent Path Finding: How Robots Get Wway with Texting and Driving. Ph. D. Dissertation. Université Rennes 1.

[44]

Shirin Rahmanpour and Reza Mahboobi Esfanjani. 2018. Planning of communication and motion strategies for networked mobile robots. In Proceedings of the 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium. IEEE, 46–53.

[45]

Shirin Rahmanpour and Reza Mahboobi Esfanjani. 2019. Energy-aware planning of motion and communication strategies for networked mobile robots. Inf. Sci. 497 (2019), 149–164.

Digital Library

[46]

George F. Riley and Thomas R. Henderson. 2010. The ns-3 network simulator. In Modeling and Tools for Network Simulation. Springer, 15–34.

[47]

Arnau Rovira-Sugranes, Abolfazl Razi, Fatemeh Afghah, and Jacob Chakareski. 2022. A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook. Ad Hoc Netw. 130 (2022), 102790.

Digital Library

[48]

Sunitha Safavat and Danda B. Rawat. 2022. OptiML: An enhanced ML approach towards design of SDN based UAV networks. In Proceedings of the IEEE International Conference on Communications. IEEE, 1–6.

[49]

Shreya Santra, Leonard B. Paet, Mickael Laine, Kazuya Yoshida, and Emanuel Staudinger. 2021. Experimental validation of deterministic radio propagation model developed for communication-aware path planning. In Proceedings of the IEEE 17th International Conference on Automation Science and Engineering (CASE’21). IEEE, 1241–1246.

[50]

Zhexiong Shang and Zhigang Shen. 2022. Flight planning for survey-grade 3D reconstruction of truss bridges. Remote Sens. 14, 13 (2022), 3200.

[51]

Akshay Shetty, Timmy Hussain, and Grace Gao. 2021. Decentralized connectivity maintenance for multi-robot systems under motion and sensing uncertainties. In Proceedings of the 34th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+’21). 2445–2458.

[52]

Luca Siligardi, Jacopo Panerati, Marcel Kaufmann, Marco Minelli, Cinara Ghedini, Giovanni Beltrame, and Lorenzo Sabattini. 2019. Robust area coverage with connectivity maintenance. In Proceedings of the International Conference on Robotics and Automation (ICRA’19). IEEE, 2202–2208.

[53]

S. Sudhakar, V. Vijayakumar, C. Sathiya Kumar, V. Priya, Logesh Ravi, and V. Subramaniyaswamy. 2020. Unmanned aerial vehicle (UAV) based forest fire detection and monitoring for reducing false alarms in forest-fires. Comput. Commun. 149 (2020), 1–16.

Digital Library

[54]

Wen Tian, Zhenzhen Jiao, Min Liu, Meng Zhang, and Dong Li. 2019. Cooperative communication based connectivity recovery for UAV networks. In Proceedings of the ACM Turing Celebration Conference-China. 1–5.

[55]

Jiaxing Wang, Lin Bai, Jianrui Chen, and Jingjing Wang. 2023. Starling flocks-inspired resource allocation for ISAC-aided green Ad Hoc networks. IEEE Trans. Green Commun. Netw. 7, 1 (2023), 444–454.

[56]

Liao Wenxing, Wu Muqing, Zhao Min, Li Peizhe, and Li Tianze. 2017. Hop count limitation analysis in wireless multi-hop networks. Int. J. Distrib. Sens. Netw. 13, 1 (2017), 1550147716683606.

[57]

Shiguang Wu, Zhiqiang Pu, Tenghai Qiu, Jianqiang Yi, and Tianle Zhang. 2022. Deep reinforcement learning based multi-target coverage with connectivity guaranteed. IEEE Trans. Industr. Inf. (2022).

[58]

Zhaoyue Xia, Jun Du, Jingjing Wang, Chunxiao Jiang, Yong Ren, Gang Li, and Zhu Han. 2021. Multi-agent reinforcement learning aided intelligent UAV swarm for target tracking. IEEE Trans. Vehic. Technol. 71, 1 (2021), 931–945.

[59]

Hao Xu, Peize Liu, Xinyi Chen, and Shaojie Shen. 2022. \(D^2\)SLAM: Decentralized and distributed collaborative visual-inertial SLAM system for aerial swarm. arXiv:2211.01538. Retrieved from https://arxiv.org/abs/2211.01538

[60]

Xianghua Xu, Zhixiang Dai, Anxing Shan, and Tao Gu. 2019. Connected target \(\epsilon\)-probability coverage in WSNs with directional probabilistic sensors. IEEE Syst. J. (2019).

[61]

Xiaoling Xu, Lixin Yang, Wei Meng, Qianqian Cai, and Minyue Fu. 2019. Multi-agent coverage search in unknown environments with obstacles: A survey. In Proceedings of the Chinese Control Conference (CCC’19). IEEE, 2317–2322.

[62]

Evşen Yanmaz. 2022. Positioning aerial relays to maintain connectivity during drone team missions. Ad Hoc Netw. 128 (2022), 102800.

Digital Library

[63]

Jiguo Yu, Ying Chen, Liran Ma, Baogui Huang, and Xiuzhen Cheng. 2016. On connected target k-coverage in heterogeneous wireless sensor networks. Sensors 16, 1 (2016), 104.

[64]

Won Joon Yun, Soohyun Park, Joongheon Kim, MyungJae Shin, Soyi Jung, David A. Mohaisen, and Jae-Hyun Kim. 2022. Cooperative multiagent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control. IEEE Trans. Industr. Inf. 18, 10 (2022), 7086–7096.

[65]

Fan Zhang and Gangqiang Yang. 2020. A stable backup routing protocol for wireless Ad Hoc networks. Sensors 20, 23 (2020), 6743.

[66]

Ruilong Zhang, Qun Zong, Xiuyun Zhang, Liqian Dou, and Bailing Tian. 2022. Game of drones: Multi-UAV pursuit-evasion game with online motion planning by deep reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. (2022).

[67]

Xin Ming Zhang, Yue Zhang, Fan Yan, and Athanasios V. Vasilakos. 2014. Interference-based topology control algorithm for delay-constrained mobile ad hoc networks. IEEE Trans. Mobile Comput. 14, 4 (2014), 742–754.

Digital Library

[68]

Zixuan Zhang, Bo Zhang, and Yunlong Wu. 2022. Joint communication–motion planning in networked robotic systems. Appl. Sci. 12, 12 (2022), 6261.

Cited By

Routis GDagas PRoussaki I(2024)Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve CryptographyElectronics10.3390/electronics1304073013:4(730)Online publication date: 11-Feb-2024
https://doi.org/10.3390/electronics13040730
Mazaheri HGoli SNourollah A(2024)A survey of 3D Space Path-Planning Methods and AlgorithmsACM Computing Surveys10.1145/367389657:1(1-32)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3673896

Index Terms

Communication-Topology-preserving Motion Planning: Enabling Static Routing in UAV Networks

Recommendations

PAR: A Power-Aware Routing Algorithm for UAV Networks
Wireless Algorithms, Systems, and Applications
Abstract
Unmanned Aerial Vehicles (UAVs) have been widely used in both military and civilian scenarios since they are low in cost and flexible in use. They can adapt to a wide variety of dangerous scenarios and complete many tasks the Manned Aerial ...
Geographic Position based Hopless Opportunistic Routing for UAV networks
Abstract
Compared with traditional mobile Ad hoc networks (MANET) and vehicular Ad hoc networks (VANET), unmanned aerial vehicle (UAV) networks have two unique characteristics. First, the UAV network topology changes more frequently and ...
Communication-Efficient Anonymous Routing Protocol for Wireless Sensor Networks Using Single Path Tree Topology
WAINA '12: Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops

In recent years, there are anonymous routing protocols for mobile ad-hoc networks. These protocols provide anonymous communication between an arbitrary pair of nodes. However, there are also some multipoint-to-point sensor networks. In those ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 20, Issue 1

January 2024

717 pages

EISSN:1550-4867

DOI:10.1145/3618078

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 07 December 2023

Online AM: 07 November 2023

Accepted: 14 October 2023

Revised: 09 July 2023

Received: 17 December 2022

Published in TOSN Volume 20, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Key Research and Development Program of China
Science Technology and Innovation Committee of Shenzhen Municipality
National Natural Science Foundation of China
Jiangsu Natural Science Foundation of China
Key Laboratory of Computer Network and Information Integration (Southeast University)
Aeronautical Science Foundation of China
National Science Foundation
Hong Kong Research Grant Council

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
417
Total Downloads

Downloads (Last 12 months)312
Downloads (Last 6 weeks)31

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Routis GDagas PRoussaki I(2024)Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve CryptographyElectronics10.3390/electronics1304073013:4(730)Online publication date: 11-Feb-2024
https://doi.org/10.3390/electronics13040730
Mazaheri HGoli SNourollah A(2024)A survey of 3D Space Path-Planning Methods and AlgorithmsACM Computing Surveys10.1145/367389657:1(1-32)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3673896

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents