Lightweight Trustworthy Distributed Clustering
This addresses data trustworthiness for edge computing applications like autonomous systems and IoT, but appears incremental as it adapts existing methods to a specific environment.
The paper tackles the challenge of ensuring data trustworthiness in resource-constrained edge computing systems by presenting a lightweight, fully distributed k-means clustering algorithm that uses additive secret sharing for secure updates, achieving unspecified but implied accuracy improvements.
Ensuring data trustworthiness within individual edge nodes while facilitating collaborative data processing poses a critical challenge in edge computing systems (ECS), particularly in resource-constrained scenarios such as autonomous systems sensor networks, industrial IoT, and smart cities. This paper presents a lightweight, fully distributed k-means clustering algorithm specifically adapted for edge environments, leveraging a distributed averaging approach with additive secret sharing, a secure multiparty computation technique, during the cluster center update phase to ensure the accuracy and trustworthiness of data across nodes.