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arXiv · 2607.03993

Evaluating 5G-connected IoT for Power Line Temperature Prediction: Real-World Latency and Cost Trade-offs Between MEC and Cloud

Aakash Sharma·Sigmund Akselsen·Anders Andersen·Lars Ailo Bongo·Arne Munch-Ellingsen
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Abstract

One of the key promises of Mobile Edge Computing (MEC) is its low latency. Current large-scale IoT deployments rely on cloud for their reliability, low cost, and ease of use. For outdoor IoT deployments, 5G cellular networks offer significantly enhanced bandwidth and dramatically reduced latency compared to previous generations, enabling real-time data processing and control. Therefore, leveraging 5G connectivity is crucial for outdoor IoT applications requiring responsiveness and complex data handling. Combining MEC with 5G has the potential to provide the ease of cloud computing alongside low latency. We investigate the latency performance on a 5G cellular network with an experimental MEC setup. In our proof-of-concept, we demonstrate the benefits of using an edge-based compute server for real-time power transmission line analytics. We compare our solution with state-of-the-art multi-region cloud deployments and discuss the advantages of mobile edge computing (MEC). Our real-world evaluation demonstrates a low latency of 44.62 ms for MEC compared to cloud regions; however, the gap is narrowing. While such low latencies can benefit real-world deployments, they remain insufficient to meet the stringent requirements of smart power grid operations (~8 ms).

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Aakash Sharma, Sigmund Akselsen, Anders Andersen, Lars Ailo Bongo, Arne Munch-Ellingsen. 2026-07-04. Evaluating 5G-connected IoT for Power Line Temperature Prediction: Real-World Latency and Cost Trade-offs Between MEC and Cloud. https://doi.org/10.1007/978-3-032-23347-9_16

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