Mobility -Aware Resource Allocation in Edge Networks for Smart Neighborhoods

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 8

Year of Publication : 2025

Authors : Sravya Pallantla, D. Haritha, Shanti Chilukuri

10.14445/23488549/IJECE-V12I8P117

How to Cite?

Sravya Pallantla, D. Haritha, Shanti Chilukuri, "Mobility -Aware Resource Allocation in Edge Networks for Smart Neighborhoods," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 8, pp. 188-198, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I8P117

Abstract:

Wireless Sensor Networks (WSNs) comprise sensor and actuator nodes that either generate or consume data. These end devices typically have limited memory, energy, and computational capacity, making them ill-suited for intensive processing tasks. Edge computing addresses this limitation by offloading data collection, processing, and forwarding to edge nodes with greater resources. This architecture aligns well with WSNs, but efficient allocation of edge resources remains a significant challenge, particularly under node mobility and fluctuating traffic. The resource allocation problem at the edge is NP-hard, and while static solutions exist, they often fail under dynamic network conditions. Memory management for packet queues is especially critical. The scheduling and dropping policies at edge nodes directly influence Quality of Service (QoS). Weighted Fair Queuing (WFQ), a popular scheduling method, assigns different weights to traffic classes, affecting uplink bandwidth distribution. However, bursty data, varying packet sizes, and node mobility complicate fair and efficient weight assignment. This study proposes a federated learning-based framework for dynamic queue and bandwidth allocation in mobile WSNs. The model adapts to real-time changes in traffic and topology while reducing communication overhead. Simulation outcomes demonstrate improved resource utilization and network performance, validating the effectiveness of the proposed approach in dynamic WSN environments.

Keywords:

Wireless Sensor Networks (WSN), Resource Allocation, Mobility, Federated Learning.

References:

[1] Davi Militani et al., “A Machine Learning Model to Resource Allocation Service for Access Point on Wireless Network,” 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Jihong Park et al., “Wireless Network Intelligence at the Edge,” Proceedings of the IEEE, vol. 107, no. 11, pp. 2204-2239, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Brendan McMahan, and Daniel Ramage, Federated Learning: Collaborative Machine Learning Without Centralized Training Data, Google Research Blog, 2017. [Online]. Available: https://research.google/blog/federated-learning-collaborative-machine-learning-without-centralized-training-data/
[4] Robert Chang, Mahdi Rahimi, and Vahab Pournaghshband, “Differentiated Service Queuing Disciplines in NS-3,” The Seventh IARIA International Conference on Advances in System Simulation, pp. 17-23, 2015.
[Google Scholar] [Publisher Link]
[5] Sangeetha Abdu Jyothi et al., “Measuring and Understanding Throughput of Network Topologies,” SC '16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Salt Lake City, UT, USA, pp. 761-772, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Rong Yu, and Peichun Li, “Toward Resource-Efficient Federated Learning in Mobile Edge Computing,” IEEE Network, vol. 35, no. 1, pp. 148-155, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] M. Ashour, and Tho Le-Ngoc, “Performance Analysis of Weighted Fair Queues with Variable Service Rates,” International Conference on Digital Telecommunications (ICDT'06), Cote d'Azur, France, pp. 51-55, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mark Eisen et al., “Communication-Control Co-Design in Wireless Edge Industrial Systems,” 2022 IEEE 18th International Conference on Factory Communication Systems (WFCS), Pavia, Italy, pp. 1-8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Prateesh Goyal et al., “Backpressure Flow Control,” Proceedings of the 2019 Workshop on Buffer Sizing, Palo Alto CA USA, pp. 1-3, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tobias Hoßfeld et al., “Definition of QoE fairness in Shared Systems,” IEEE Communications Letters, vol. 21, no. 1, pp. 184-187, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Chenmeng Wang et al., “Computation Offloading and Resource Allocation in Wireless Cellular Networks with Mobile Edge Computing,” IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 4924-4938, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Wei Yang Bryan Lim et al., “Federated Learning in Mobile Edge Networks: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 2031-2063, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Noor Zaman et al., “Energy Efficient Routing Protocol for Wireless Sensor Network,” 16th International Conference on Advanced Communication Technology, Pyeongchang, Korea (South), pp. 808-814, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[14] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA, vol. 2, pp. 1-10, 2000.
[CrossRef] [Google Scholar [Publisher Link]
[15] Natalie Temene et al., “A Survey on Mobility in Wireless Sensor Networks,” Ad Hoc Networks, vol. 125, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Jingjing Yan, Mengchu Zhou, and Zhijun Ding, “Recent Advances in Energy-Efficient Routing Protocols for Wireless Sensor Networks: A Review,” IEEE Access, vol. 4, pp. 5673-5686, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Yi-Han Xu et al., “Reinforcement Learning (RL)-Based Energy Efficient Resource Allocation for Energy Harvesting-Powered Wireless Body Area Network,” Sensors, vol. 20, no. 1, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Padmalaya Nayak et al., “Routing in Wireless Sensor Networks using Machine Learning Techniques: Challenges and Opportunities,” Measurement, vol. 178, pp. 1-15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Enrique Tomás Martínez Beltrán et al., “Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges,” IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2983-3013, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yunfan Ye et al., “Edgefed: Optimized Federated Learning based on Edge Computing,” IEEE Access, vol. 8, pp. 209191-209198, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Zhaohui Yang et al., “Delay Minimization for Federated Learning over Wireless Communication Networks,” arXiv Preprint, pp. 1-7, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] The ns-3 Network Simulator, 2015. [Online]. Available: http://www.nsnam.org
[23] S. Vimal et al., “Enhanced Resource Allocation in Mobile Edge Computing using Reinforcement Learning based MOACO Algorithm for IIoT,” Computer Communications, vol. 151, pp. 355-364, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Diederik P. Kingma, and Jimmy Ba, “Adam: A Method for Stochastic Optimization,” arXiv Preprint, pp. 1-15, 2014.
[CrossRef] [Google Scholar] [Publisher Link]