Dynamic Task Prioritization for Edge AI in Smart Cities: Balancing Latency and Energy Efficiency
DOI:
https://doi.org/10.70393/6a696574.343034ARK:
https://n2t.net/ark:/40704/JIET.v1n1a07Disciplines:
Intelligent SystemsSubjects:
OtherReferences:
22Keywords:
Edge Computing, Microservice Orchestration, Quantum Inspired Algorithms, Directed Acyclic Graph Scheduling, Latency Energy BalanceAbstract
The growth of latency sensitive smart city applications is rapid nowadays. Therefore deploying microservice architecture in the heterogeneous edge cloud continuum has become a mainstream choice. However a structural challenge arises in orchestrating these coupled services modeled as Directed Acyclic Graphs. Exact algorithms like Branch and Bound struggle with computation in high concurrency scenarios. Meanwhile Deep Reinforcement Learning methods face the challenges of excessive training overhead and a lack of zero shot adaptation capability for topology changes. Recently popular quantum inspired algorithms often fail to satisfy strict predecessor constraints. This makes them unsuitable for use in dependent workflows. To alleviate this dilemma this paper proposes a Dependency Aware Quantum Inspired Scheduler. This framework utilizes a topological quantum coding scheme and dynamic dependency masks to integrate DAG constraints into the quantum search process. It also introduces an entropy weighted evolutionary rotation mechanism to accelerate the convergence of critical paths. After conducting extensive simulation experiments in city level environments we found that the scheduling success rate of DAQ Scheduler is 100% while the success rate of standard quantum inspired algorithms is only 58.1%. Compared with leading multi objective DRL baselines this method reduces the average makespan by 9.9%. This method provides near optimal scheduling solutions with millisecond level inference latency. It builds an efficient and lightweight paradigm for real time edge intelligence and balances theoretical optimality with engineering feasibility.
References
[1] Anand, J., & Karthikeyan, B. (2026). Adaptive and intelligent customized deep Q-network for energy-efficient task offloading in mobile edge computing environments. Scientific reports, 16(1), 5456.
[2] Luo, M., Du, B., Zhang, W., Song, T., Li, K., Zhu, H., ... & Wen, H. (2023). Fleet rebalancing for expanding shared e-mobility systems: A multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3868-3881.
[3] Zhu, H., Luo, Y., Liu, Q., Fan, H., Song, T., Yu, C. W., & Du, B. (2019). Multistep flow prediction on car-sharing systems: A multi-graph convolutional neural network with attention mechanism. International Journal of Software Engineering and Knowledge Engineering, 29(11n12), 1727-1740.
[4] Luo, M., Zhang, W., Song, T., Li, K., Zhu, H., Du, B., & Wen, H. (2021, January). Rebalancing expanding EV sharing systems with deep reinforcement learning. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 1338-1344).
[5] Liu, W. (2025). Few-Shot and Domain Adaptation Modeling for Evaluating Growth Strategies in Long-Tail Small and Medium-sized Enterprises. Journal of Industrial Engineering and Applied Science, 3(6), 30–35.
[6] Yu, C., Wang, H., Chen, J., Wang, Z., Deng, B., Hao, Z., ... & Song, Y. (2026). When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms. arXiv preprint arXiv:2601.11634.
[7] Wang, H., Li, Q., & Liu, Y. (2023). Adaptive supervised learning on data streams in reproducing kernel Hilbert spaces with data sparsity constraint. Stat, 12(1), e514.
[8] Liu, W. (2025). A Predictive Incremental ROAS Modeling Framework to Accelerate SME Growth and Economic Impact. Journal of Economic Theory and Business Management, 2(6), 25–30.
[9] Liu, W. (2025). Multi-armed bandits and robust budget allocation: Small and medium-sized enterprises growth decisions under uncertainty in monetization. European Journal of AI, Computing & Informatics, 1(4), 89–97.
[10] Wang, H., Li, Q., & Liu, Y. (2022). Regularized Buckley–James method for right‐censored outcomes with block‐missing multimodal covariates. Stat, 11(1), e515.
[11] Wang, H., Li, Q., & Liu, Y. (2024). Multi-response Regression for Block-missing Multi-modal Data without Imputation. Statistica Sinica, 34(2), 527.
[12] Yu, C., Wu, H., Ding, J., Deng, B., & Xiong, H. (2025, September). Unified Survey Modeling to Limit Negative User Experiences in Recommendation Systems. In Proceedings of the Nineteenth ACM Conference on Recommender Systems (pp. 1104-1107).
[13] Li, K., Chen, X., Song, T., Zhou, C., Liu, Z., Zhang, Z., ... & Shan, Q. (2025). Solving situation puzzles with large language model and external reformulation. arXiv preprint arXiv:2503.18394.
[14] Mindil, A., Hamed, A. Y., Hassan, M. R., & Elnahary, M. K. (2026). A novel approach for dynamic task scheduling for IOT in fog-cloud environment. Scientific reports, 16(1), 5501.
[15] Madiyev, A., Bulegenov, D., Karzhaubayev, A., Murzabulatov, M., & Bui, D. M. (2025). Energy-efficient offloading framework for mobile edge/cloud computing based on convex optimization and Deep Q-Network: A. Madiyev et al. The Journal of Supercomputing, 81(11), 1182.
[16] Yu, C., Li, P., Wu, H., Wen, Y., Deng, B., & Xiong, H. (2024). USM: Unbiased Survey Modeling for Limiting Negative User Experiences in Recommendation Systems. arXiv preprint arXiv:2412.10674.
[17] Wang, J., Kudagama, B. J., Perera, U. S., Li, S., & Zhang, X. (2025). Framework for generating high-resolution Hong Kong local climate projections to support building energy simulations. Physics of Fluids, 37(3).
[18] Liu, Z., Jin, C., Li, S., Li, W., & Wang, J. (2024). Improvement for modeling the damping of the wake oscillator based on the Van der Pol scheme. Physics of Fluids, 36(7).
[19] Wang, C. (2026). A Study on Data-Driven Budget Optimization for US Enterprises’ Cross-Border Marketing. Frontiers in Management Science, 5(1), 41-46.
[20] Wu, Y. (2026). Research on the Impact of LinkedIn Business Account Data-Driven Operations on Brand Exposure of AI Startups—A Case Study of AristAI. International Academic Journal of Social Science, 2, 27-37.
[21] Lin, A. (2025). Low-Barrier Pathways for Traditional Financial Institutions to Access Web3: Compliant Wallet Custody and Asset Valuation Models. Frontiers in Management Science, 4(6), 80-86.
[22] Wang, C. (2025). Research on the Precision Allocation of Cross-Border Marketing Resources of US Enterprises Driven by Digital Technology. Innovation in Science and Technology, 4(11), 7-13.
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