Meta-Routing Paradigm for Robotic Ad-hoc Networks

Mustafa Ayad; Richard Voyles

doi:doi:10.11648/j.ajset.20230802.12

| Peer-Reviewed

Meta-Routing Paradigm for Robotic Ad-hoc Networks

Mustafa Ayad, Richard Voyles

Published in American Journal of Science, Engineering and Technology (Volume 8, Issue 2)

Received: 14 March 2023 Accepted: 4 April 2023 Published: 15 April 2023

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.

Published in	American Journal of Science, Engineering and Technology (Volume 8, Issue 2)
DOI	10.11648/j.ajset.20230802.12
Page(s)	81-96
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Link Connectivity Maintenance, Gradient, RF Mapping Recognition, Nod Control Movement

References

[1]	Wu Y., Ren X., Zhou H., Wang Y., and Yi X. (2020). A Survey on Multi-Robot Coordination in Electromagnetic Adversarial Environment: Challenges and Techniques in IEEE Access, vol. 8, pp. 53484-53497.
[2]	Devi, Munisha, and Nasib Singh Gill. (2019). Mobile ad hoc networks and routing protocols in IoT enabled. Journal of Engineering and Applied Sciences 14. 3: 802-811.
[3]	Voyles, R. M., Bae, J., Larson, A. and Ayad, M. (2009). Wireless video sensor network for sparse, resource-constrained, multi-robot teams. In Journal of Intelligent Service Robots, vol. 2, no. 4, pp. 235-246.
[4]	Nabati, Mohammad, Mohsen Maadani, and Mohammad Ali Pourmina. (2022). AGEN-AODV: an intelligent energy-aware routing protocol for heterogeneous mobile ad-hoc networks. Mobile Networks and Applications 27. 2: 576-587.
[5]	Kumar, G. Hemanth, and G. P. Ramesh. (2022). Node localization algorithm for detecting malicious nodes to prevent connection failures and improve end-to-end delay. Computer Communications 190: 37-47.
[6]	Queralta, J. P., Taipalmaa, J., Pullinen, B. C., Sarker, V. K., Gia, T. N., Tenhunen, H.,... & Westerlund, T. (2020). Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision. Ieee Access, 8, 191617-191643.
[7]	Tripathy, B. K., Jena, S. K., Bera, P., & Das, S. (2020). An adaptive secure and efficient routing protocol for mobile ad hoc networks. Wireless Personal Communications, 114 (2), 1339-1370.
[8]	Ayad, M., and Voyles, R. (2019). Physical Link Maintenance and Logical Message Routing Integration for Robotic Network Connectivity. Vehicular Technology Conference, IEEE. Hawaii.
[9]	Thrane, J., Sliwa, B., Wietfeld, C., & Christiansen, H. L. (2020, December). Deep learning-based signal strength prediction using geographical images and expert knowledge. In GLOBECOM 2020-2020 IEEE Global Communications Conference (pp. 1-6). IEEE.
[10]	Cardona, G. A., & Calderon, J. M. (2019). Robot swarm navigation and victim detection using rendezvous consensus in search and rescue operations. Applied Sciences, 9 (8), 1702.
[11]	Ayad, M., Voyles, R., & Ayad, M. (2022). Robotic Ad-hoc Networks Connectivity Maintenance based on RF Signal Strength Mapping. International Journal of Advanced Computer Science and Applications, 13 (2).
[12]	Thiagarajan, R., & Moorthi, M. (2017, February). Efficient routing protocols for mobile ad hoc networks. In 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication, and Bio-Informatics (AEEICB) (pp. 427-431). IEEE.
[13]	Malar, A. C. J., Kowsigan, M., Krishnamoorthy, N., Karthick, S., Prabhu, E., & Venkatachalam, K. (2021). Multi-constraints applied energy-efficient routing technique based on ant colony optimization for disaster-resilient location detection in the mobile ad-hoc network. Journal of Ambient Intelligence and Humanized Computing, 12, 4007-4017.
[14]	Zhang, D. G., Zhao, P. Z., Cui, Y. Y., Chen, L., Zhang, T., & Wu, H. (2019). A new method of mobile ad hoc network routing based on greed forwarding improvement strategy. IEEE Access, 7, 158514-158524.
[15]	Younis, Z. A., Abdulazeez, A. M., Zeebaree, S. R., Zebari, R. R., & Zeebaree, D. Q. (2021). Mobile Ad Hoc Network in Disaster Area Network Scenario: A Review on Routing Protocols. International Journal of Online & Biomedical Engineering, 17 (3).
[16]	Alani, H., Abdelhaq, M., & Alsaqour, R. (2020). Dynamic routing discovery scheme for high mobility in mobile ad hoc wireless networks. International Journal of Electrical & Computer Engineering (2088-8708), 10 (4).
[17]	Khudayer, B. H., Anbar, M., Hanshi, S. M., & Wan, T. C. (2020). Efficient route discovery and link failure detection mechanisms for source routing protocol in mobile ad-hoc networks. IEEE Access, 8, 24019-24032.
[18]	Raj, J. S. (2020). A novel hybrid secure routing for flying ad-hoc networks. Journal of trends in Computer Science and Smart technology (TCSST), 2 (03), 155-164.
[19]	Zhu, M., Chang, L., Wang, N., & You, I. (2020). A smart, collaborative routing protocol for delay-sensitive applications in industrial IoT. IEEE Access, 8, 20413-20427.
[20]	Ludwig, L., & Gini, M. (2006). Robotic swarm dispersion using wireless intensity signals. In Distributed autonomous robotic systems 7 (pp. 135-144). Springer, Tokyo.
[21]	Ayad, M., Zhang, J. J., Voyles, R., Mahoor, M. H. (2013). Mobile robot connectivity maintenance based on RF mapping. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE.
[22]	Michael, N., Zavlanos, M. M., Kumar, V. and Pappas, G. J. (July, 2009). Maintaining connectivity in mobile robot networks. In International Symposium on Experimental Robotics. Athena, Greece.
[23]	Kanungo, Tapas., David, M., Nathan, S. Netanyahu, Christine, D., Ruth, Silverman, and Angela, Y. (July, 2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881--892. IEEE.
[24]	Rabiner, LR. (Feb, 1989). A tutorial on hidden Markov models and selected applications in speech recognition, vol. ~77, pp. 257-286.
[25]	Loscri, V., Natalizio, E. and Costanzo, C. (July, 2010). Simulations of the impact of controlled mobility for routing protocols. EURASIP Journal on Wireless Communications and Networking.
[26]	Purohit, A., Zheng, S., Mokaya, F., and Zhang, P. (2011). Sensorfly: Controlled-mobile sensing platform for indoor emergency response applications. In Information Processing in Sensor Networks (IPSN), 10th International Conference.
[27]	Fink, J. and Kumar, V. (2010). Online methods for radio signal mapping with mobile robots network Conference on Robotics and Automation (ICRA). IEEE.
[28]	Twigg, J. N., Fink, J. R., Yu, P. L. and Sadler, B. M. (May, 2012). RSS gradient-assisted frontier exploration and radio source localization. International. Conference on Robotics and Automation (ICRA). IEEE, pp. 889-895.
[29]	Sun, Yi, Jizhong Xiao, Xiaohai Li, and Cabrera-Mora, F. (2008). Adaptive source localization by a mobile robot using signal power a gradient in sensor networks. IEEE Global Telecommunications Conference, pp. 1-5.
[30]	Yuan, Yan, and Mostofi, Y. (Nov., 2011). Co-optimization of communication and motion planning of a robotic operation in fading environments. Signals, Systems, and Computers (ASILOMAR) Conference Record of the Forty Fifth Asilomar Conference, pp. 1455-1460.
[31]	Fink, J., Kumar, V., Michael, N. and Kushleyev, A. (2009). Experimental characterization of radio signal propagation in indoor environments with application to estimation and control. IEEE International Conference on Intelligent Robots and Systems.
[32]	Ani Hsieh, M., Anthony Cowley, Vijay Kumar, and Camillo, J. Taylor (2008). Maintaining network connectivity and performance in robot teams: Research articles. Journal Field Robot.
[33]	Zavlanos, M. M. and Pappas, G. J. (Dec., 2008). Distributed connectivity control of mobile networks. IEEE Transactions on Robotics, vol. 24, no. 6, pp. 1416-1428.
[34]	Spanos, D. P. and Murray, R. M. (Dec., 2004). Robust connectivity of networked vehicles. IEEE Conference on Decision and Control Conference, vol. ~3, pp. 2893- 2898 Vol. 3.
[35]	Meng, Ji and Egerstedt, M. (Aug., 2007). Distributed coordination control of multiagent systems while preserving connectedness. IEEE Transactions on Robotics, vol. 23, no. 4, pp. 693-703.
[36]	Hsieh, M. A., Cowley, A., Kumar, V., and Taylor, C. J. (May, 2006). Towards the deployment of a mobile robot network with end-to-end performance guarantees. Proceedings IEEE International Conference on Robotics and Automation (ICRA), pp. 2085-2090.

Cite This Article

Plain Text BibTeX RIS

APA Style

Mustafa Ayad, Richard Voyles. (2023). Meta-Routing Paradigm for Robotic Ad-hoc Networks. American Journal of Science, Engineering and Technology, 8(2), 81-96. https://doi.org/10.11648/j.ajset.20230802.12

Copy | Download

ACS Style

Mustafa Ayad; Richard Voyles. Meta-Routing Paradigm for Robotic Ad-hoc Networks. Am. J. Sci. Eng. Technol. 2023, 8(2), 81-96. doi: 10.11648/j.ajset.20230802.12

Copy | Download

AMA Style

Mustafa Ayad, Richard Voyles. Meta-Routing Paradigm for Robotic Ad-hoc Networks. Am J Sci Eng Technol. 2023;8(2):81-96. doi: 10.11648/j.ajset.20230802.12

Copy | Download

@article{10.11648/j.ajset.20230802.12,
  author = {Mustafa Ayad and Richard Voyles},
  title = {Meta-Routing Paradigm for Robotic Ad-hoc Networks},
  journal = {American Journal of Science, Engineering and Technology},
  volume = {8},
  number = {2},
  pages = {81-96},
  doi = {10.11648/j.ajset.20230802.12},
  url = {https://doi.org/10.11648/j.ajset.20230802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20230802.12},
  abstract = {With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Meta-Routing Paradigm for Robotic Ad-hoc Networks
AU  - Mustafa Ayad
AU  - Richard Voyles
Y1  - 2023/04/15
PY  - 2023
N1  - https://doi.org/10.11648/j.ajset.20230802.12
DO  - 10.11648/j.ajset.20230802.12
T2  - American Journal of Science, Engineering and Technology
JF  - American Journal of Science, Engineering and Technology
JO  - American Journal of Science, Engineering and Technology
SP  - 81
EP  - 96
PB  - Science Publishing Group
SN  - 2578-8353
UR  - https://doi.org/10.11648/j.ajset.20230802.12
AB  - With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.
VL  - 8
IS  - 2
ER  -

Copy | Download

Author Information

Mustafa Ayad

Electrical and Computer Engineering, State University of New York (SUNY) at Oswego, New York, The United States
Richard Voyles

Engineering Technology, Purdue University at West Lafayette, Indiana, The United States

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Mustafa Ayad, Richard Voyles. (2023). Meta-Routing Paradigm for Robotic Ad-hoc Networks. American Journal of Science, Engineering and Technology, 8(2), 81-96. https://doi.org/10.11648/j.ajset.20230802.12

Copy | Download

ACS Style

Mustafa Ayad; Richard Voyles. Meta-Routing Paradigm for Robotic Ad-hoc Networks. Am. J. Sci. Eng. Technol. 2023, 8(2), 81-96. doi: 10.11648/j.ajset.20230802.12

Copy | Download

AMA Style

Mustafa Ayad, Richard Voyles. Meta-Routing Paradigm for Robotic Ad-hoc Networks. Am J Sci Eng Technol. 2023;8(2):81-96. doi: 10.11648/j.ajset.20230802.12

Copy | Download

@article{10.11648/j.ajset.20230802.12,
  author = {Mustafa Ayad and Richard Voyles},
  title = {Meta-Routing Paradigm for Robotic Ad-hoc Networks},
  journal = {American Journal of Science, Engineering and Technology},
  volume = {8},
  number = {2},
  pages = {81-96},
  doi = {10.11648/j.ajset.20230802.12},
  url = {https://doi.org/10.11648/j.ajset.20230802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20230802.12},
  abstract = {With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Meta-Routing Paradigm for Robotic Ad-hoc Networks
AU  - Mustafa Ayad
AU  - Richard Voyles
Y1  - 2023/04/15
PY  - 2023
N1  - https://doi.org/10.11648/j.ajset.20230802.12
DO  - 10.11648/j.ajset.20230802.12
T2  - American Journal of Science, Engineering and Technology
JF  - American Journal of Science, Engineering and Technology
JO  - American Journal of Science, Engineering and Technology
SP  - 81
EP  - 96
PB  - Science Publishing Group
SN  - 2578-8353
UR  - https://doi.org/10.11648/j.ajset.20230802.12
AB  - With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.
VL  - 8
IS  - 2
ER  -

Copy | Download