Asynchronous event detection for context inconsistency in pervasive computing

Event detection for context inconsistency is challenging in pervasive computing environments, where contexts are often noisy owing to fragile connectivity, node frequent movement and resource constraints. As a recent scheme, CEDA — Concurrent Event Detection for Asynchronous inconsistency checking (CEDA) — concurrently detects context inconsistency by exploring the happened-before relation among events. Nevertheless, CEDA suffers from several problems — unscalable from partial centralized detection manner, heavy computation complexity and false negative. To address these challenges, we propose in this paper the SECA scheme — asynchronous event detection for context inconsistency in pervasive computing. It puts forward a new type logical clock — snapshot timestamp — to check event relations, which enables it to be efficient in the scenarios where CEDA fails to. Meanwhile, SECA comes up with a lightweight update mechanism for the snapshot clock, which considerably reduces time and space complexity. Extensive experiments have been conducted and results show that SECA surmounts CEDA with respect to detection accuracy and scalability.