Deep Learning-Based Context-Aware Resource Allocation for Networked Mobile Computing Environments
Abstract
This paper proposes a deep learning-based, context-aware resource allocation framework for networked mobile computing environments, addressing dynamic challenges like fluctuating networks, user mobility, device heterogeneity, and energy constraints. The approach utilizes a multidimensional context vector encompassing device status (CPU, memory, battery), network conditions (bandwidth, latency), user mobility patterns, and application requirements (task size, complexity, deadlines) to dynamically decide between local task execution and offloading to edge/cloud servers. A fully-connected deep neural network (DNN) with 3-4 hidden layers (128-16 neurons, ReLU activations) and sigmoid output approximates the multi-objective optimization problem of minimizing latency and energy consumption, trained via supervised learning on 10,000 simulated scenarios using binary cross-entropy loss. NS-3 simulations demonstrate superior performance: 4-11% latency reduction over DRL/LSTM baselines, 43% energy savings versus context-unaware methods, and up to 50% higher throughput across 50-200 devices, with ablation studies confirming network context's criticality. The framework's novelty lies in its comprehensive context integration, low-overhead inference (5 ms), scalability, and detailed architecture, outperforming rule-based, DRL, and LSTM approaches for real-world mobile applications.
References
S. Yu and X. Wang, “Computation offloading for mobile edge computing: A deep learning approach,” In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Oct. 2017
L. Huang, S. Bi, and Y. J. Zhang, “Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks,” IEEE Transactions on Mobile Computing, vol. 19, no. 11, pp. 1–1, 2019
Z. Ning et al., “Deep Reinforcement Learning for Intelligent Internet of Vehicles: An Energy-Efficient Computational Offloading Scheme,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 1060–1072, Jul. 2019
C. Wang, C. Liang, F. R. Yu, Q. Chen, and L. Tang, “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, Aug. 2017
W. Hou, H. Wen, H. Song, W. Lei, and W. Zhang, “Multiagent Deep Reinforcement Learning for Task Offloading and Resource Allocation in Cybertwin-Based Networks,” IEEE Internet of Things Journal, vol. 8, no. 22, pp. 16256–16268, Nov. 2021
N. Din, H. Chen, and D. Khan, “Mobility-Aware Resource Allocation in Multi-Access Edge Computing Using Deep Reinforcement Learning,” 2019 IEEE International Confrence on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Dec. 2019
Y. Xu, Z. Alam, T. Wang, X. Zhou, and A. K. Singh, “C-FDRL: Context-Aware Privacy-Preserving Offloading Through Federated Deep Reinforcement Learning in Cloud-Enabled IoT,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1155–1164, Feb. 2022
K. Tokuda, T. Sato, And E. Oki, “Resource Allocation for Mobile Edge Computing System Considering User Mobility with Deep Reinforcement Learning,” IEICE Transactions on Communications, vol. E107.B, no. 1, pp. 173–184, Jan. 2024
S. Chen, H. Chen, J. Ruan, and Z. Wang, “Context-Aware Online Offloading Strategy with Mobility Prediction for Mobile Edge Computing,” 2021 International Conference on Computer Communications and Networks (ICCCN), Jul. 2021
Z. Chen, B. Xiong, X. Chen, G. Min, and J. Li, “Joint Computation Offloading and Resource Allocation in Multi-edge Smart Communities with Personalized Federated Deep Reinforcement Learning,” IEEE Transactions on Mobile Computing, pp. 1–16, Jan. 2024
I. Batool, M. M. Fouda, M. I. Ibrahem, M. Ismail, and Z. M. Fadlullah, “Adaptive Resource Allocation in Emerging High-mobility Networks Using Hybrid Deep Learning Models,” ICC 2025 - IEEE International Conference on Communications, pp. 3503–3508, Jun. 2025
P. Sharma, T. Sakthivel, and D. Arora, “Deep Q-Learning Network-Based Energy and Network-Aware Optimization Model for Resources in Mobile Cloud Computing,” International Journal of Computer Networks and Applications, vol. 9, no. 3, p. 361, Jun. 2022
B. Huang et al., “Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–17, Jul. 2020
M. Idrees, “Intelligent Resource Management Framework Using Context Aware Statistics for B5G Network,” DSpace RepositoryIntelligent Resource Management Framework Using Context Aware Statistics for B5G Network, 2025.
