Optimal Schedule of Mobile Edge Computing for Internet of Things Using Partial Information
Abstract
Mobile edge computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can get complex tasks offloaded to and processed at powerful infrastructure. Scheduling is challenging due to stochastic task arrivals and wireless channels, congested air interface, and more prominently, prohibitive feedbacks from thousands of devices. In this paper, we generate asymptotically optimal schedules tolerant to out-of-date network knowledge, thereby relieving stringent requirements on feedbacks. A perturbed Lyapunov function is designed to stochastically maximize a network utility balancing throughput and fairness. A knapsack problem is solved per slot for the optimal schedule, provided up-to-date knowledge on the data and energy backlogs of all devices. The knapsack problem is relaxed to accommodate out-of-date network states. Encapsulating the optimal schedule under up-to-date network knowledge, the solution under partial out-of-date knowledge preserves asymptotic optimality, and allows devices to self-nominate for feedback. Corroborated by simulations, our approach is able to dramatically reduce feedbacks at no cost of optimality. The number of devices that need to feed back is reduced to less than 60 out of a total of 5000 IoT devices.
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Short wavelengths can lead to fast and drastically changing channel conditions of each client.
“The short wavelengths can lead to fast and drastically changing channel conditions of each client [176].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [176]
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- Authors
- Lyu, X., Ni, W., Tian, H., Liu, R. P., Wang, X., Giannakis, G. B., & Paulraj, A.
- Affiliations
- University of Technology Sydney (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Beijing University of Posts and Telecommunications (CN)
- Fudan University (CN)
- University of Minnesota (US)
- Stanford University (US)
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2017-11
- Volume
- 35
- Issue
- 11
- Pages
- 2606-2615
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2762586323
- Cited by
- 251
- References
- 30
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 18 · 2023: 17 · 2022: 36 · 2021: 39 · 2020: 53 · 2019: 45 · 2018: 27 · 2017: 1
- FWCI
- 22.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61471060, 61421061)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Age of Information Optimization
- Keywords
- mobile edge computing, Internet of Things, task offloading, scheduling, incomplete information, feedback reduction, Lyapunov optimization, knapsack problem, throughput-fairness tradeoff, stochastic task arrivals
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