arXiv subjects

Sigmund Akselsen

Publications and source records attributed to Sigmund Akselsen.

2 recordsLinked to original sources

arXiv2026

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

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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arXiv2021

Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks

Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. The emergence of wireless sensor networks in locations without available reference data makes calibrating such networks without the aid of true values a key area of research. While deep learning (DL) has proved successful on numerous other tasks, it is under-researched in the context of blind WSN calibration, particularly in scenarios with networks that mix static and mobile sensors. In this paper we investigate the use of DL architectures for such scenarios, including the effects of weather in both drifting and sensor measurement. New models are proposed and compared against a baseline, based on a previous proposed model and extended to include mobile sensors and weather data. Also, a procedure for generating simulated air quality data is presented, including the emission, dispersion and measurement of the two most common particulate matter pollutants: PM 2.5 and PM 10 . Results show that our models reduce the calibration error with an order of magnitude compared to the baseline, showing that DL is a suitable method for WSN calibration and that these networks can be remotely calibrated with minimal cost for the deployer.

cs.NI↗