Papers for

healthcare iot developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Machine learning models adapt online for edge cloud IoT systems

Online Continual Learning in Edge-Cloud Applications

Abstract: Deployed machine learning models in real-world IoT and edge/cloud systems face a critical reliability bottleneck: the requirement for online adaptation to non-stationary data streams while operating without access to the full original training dataset. Performance is severely degraded when models encounter Out-of-Distribution (OOD) inputs or suffer from catastrophic forgetting. Addressing these challenges requires adaptation mechanisms capable of learning from evolving data streams while preserving previously acquired knowledge. This paper proposes an edge-cloud methodology for integrating Continual Learning (CL) with Out-of-Distribution (OOD) Detection and Concept Drift Detection in IoT applications. The methodology incorporates ODIN and ECDD as alternative mechanisms for identifying informative samples for annotation and triggering rehearsal-based model updates. Experimental results across automotive, healthcare, and agricultural datasets demonstrate the applicability of the proposed methodology and its potential to support model adaptation while mitigating catastrophic forgetting.

Fri 23 OctMachine LearningComputer Vision and Pattern Recognition
The gist
Machine learning models used in Internet of Things devices and cloud systems need to keep learning from new data while avoiding forgetting what they learned before. The problem is that these models often cannot access the original training data again and struggle when the new data looks very different. The authors present a method combining smart detection of unusual data with continual learning techniques that update models without erasing past knowledge. Their approach works across different fields like cars, healthcare, and farming, helping models adapt reliably over time.
Open → 2610.32472v1

Semantic communications prioritize meaning over raw data transmission

Semantic Communications: Principles, Architectures and Applications

Abstract: Conventional communication systems are designed to transmit data reliably, focusing on metrics such as bit error rate, throughput, and channel capacity. However, these traditional measures do not evaluate whether the received information is actually useful for a specific downstream task. To address this limitation, semantic communication has emerged as a promising paradigm that prioritizes the meaning, context, and task relevance of transmitted information rather than raw bit level reconstruction. This paper presents a comprehensive review of the theoretical foundations, system architectures, learning techniques, and evaluation practices in semantic communications. We classify existing state-of-the-art algorithms ranging from deep joint source-channel coding (Deep JSCC) and task-oriented feature transmission to generative diffusion models and contrast their operational trade offs against conventional baselines. Furthermore, we examine key application domains, including remote sensing, autonomous vehicles, and healthcare IoT, while addressing open challenges in security, privacy, robustness, and standardized evaluation.

Tue 20 OctNetworking and Internet Architecture
The gist
Traditional communication systems focus on sending data accurately as bits, but they don’t check if the information is actually useful for what you want to do with it. This paper reviews a new approach called semantic communication, which cares more about the meaning and relevance of the data for specific tasks instead of just perfect copying. The authors categorize different techniques like deep learning methods and explain how they compare to classic approaches. They also discuss how this approach applies in areas like health devices, self-driving cars, and remote sensing, while pointing out challenges in security and evaluation.
Open → 2610.27544v1