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AoI-aware energy control and computation offloading for industrial IoT
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Multi‐queue Approach of Energy Efficient Task Scheduling for Sensor Hubs
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2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
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Mobile Edge Computing: A Survey on Architecture and Computation Offloading
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IEEE INFOCOM 2018 — IEEE conference on computer communications workshops (INFOCOM WKSHPS)
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iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks
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Resource Allocation in Wireless Networks With Deep Reinforcement Learning: A Circumstance-Independent Approach
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A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems
inferredcites →https://doi.org/10.1109/tcomm.2022.3141786openalex-snapshot
Query Age of Information: Freshness in Pull-Based Communication
inferredcites →https://doi.org/10.1109/tii.2018.2852491openalex-snapshot
Industrial Internet of Things: Challenges, Opportunities, and Directions
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Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things
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AoI-Delay Tradeoff in Mobile Edge Caching With Freshness-Aware Content Refreshing
inferredcites →https://doi.org/10.1109/wcncw46786.2019openalex-snapshot
2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW)
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Age of Information: A New Concept, Metric, and Tool
inferredcites →https://doi.org/10.23919/jcc.fa.2021-0830.202408openalex-snapshot
Cost-efficient edge caching for NOMA-enabled IoT services
inferredcites →https://doi.org/10.26599/tst.2021.9010050openalex-snapshot
Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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