Wireless Edge Computing with Latency and Reliability Guarantees
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Abstract (arXiv)
Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 May 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 3; 2025: 1.
6 papers.
- 2024Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selectioncited by 15
- 2021Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffscited by 3
- 2023LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devicescited by 3
- 2024A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networkscited by 0
- 2024SafeTail: Efficient Tail Latency Optimization in Edge Service Scheduling via Computational Redundancy Managementcited by 0
- 2025Mitigating Resolution-Drift in Federated Learning: Case of Keypoint Detectioncited by 0
- 2025Mitigating Resolution-Drift in Federated Learning: Case of Keypoint Detection31 Jul 2025
- 2024Adaptive Learn-then-Test: Statistically Valid and Efficient Hyperparameter Selection24 Sep 2024
- 2024SafeTail: Efficient Tail Latency Optimization in Edge Service Scheduling via Computational Redundancy Management30 Aug 2024
- 2024A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks11 May 2024
- 2023LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices1 Dec 2023
- 2021Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs4 Oct 2021