Explainable Cooperative Edge Sensing for Human-Centric Digital Ecosystems: Architectures, Intelligence, and Future Directions
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Updated time：2026-07-27 12:58:05 Views：7
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Abstract
Abstract—In today's digital era, cooperative sensing between wearable devices, home devices, mobile devices and a range of edge computing devices are vital for delivering reliable and context-aware services in human-centric digital systems. Though collaborative sensing helps to better cover the area, strengthen the accuracy, and enhance the resilience of the system, decisions derived from collaborations are often not transparent and are consequently hard for the users to interpret and trust. The paper gives an overview of explainable cooperative edge sensing – an emerging research area, which brings together cooperative sensing, explanatory artificial intelligence, and trust management in a single research concept. A six-layer architecture is proposed to show the whole sensing pipeline ranging from human-centric data acquisition, cooperative edge processing, explainable decision fusion, trust evaluation, edge–cloud intelligence to delivered intelligent applications. Original mathematical models for adaptive fusion weighting, dynamic updating of trust and human-centric utility evaluation are added to improve the framework. The proposed scheme is further analyzed and compared with existing schemes in terms of trust level, response time, and energy efficiency, to show the usefulness of the proposed scheme over the existing schemes. Moreover, the work identifies some of the research dilemmas such as scalability, privacy preservation, interoperability and real-time explainability and the future research directions to create trustworthy and transparent cooperative edge sensing systems for next-generation intelligent environments.
Keywords
Cooperative edge sensing, explainable artificial intelligence, human-centric computing, trust management, sensor fusion, edge–cloud intelligence.
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