Efficient Cooperative Inference Architecture for Reasoning Agents in Context-Aware Surveillance Networks

In this paper, we investigate the model of multicamera, multisensor surveillance networks. To accomplish context awareness in wide area surveillance, several reasoning agents are distributed to analyze and process various events. Context ontology provides a more manageable and scalable representation of surveillance data for reasoning. For cooperative reasoning, agents exchange context knowledge to draw an integrated higher inference. Integrating heterogeneous ontologies is important for inference agents utilizing multiple ontologies. In this paper, architecture based on information-centric networking is proposed for a more efficient surveillance data delivery. Increasing surveillance data across areas generates concerns regarding the cost of transferring large amounts of event-related data sets. In an information-centric network, content is delivered over content stores and caching desired data from them can save bandwidth. In the proposed scheme, delivering semantically similar content within threshold values given in interest packets further reduces traffic. Estimation of similarity incorporated with weighted ontology, which considers trust level, importance and cost, provides efficient use of cache capacity. An experimental validation of the proposed method analyzes the cost of data transmission. Simulations show that the given information-centric architecture enables high reliability and performance with low transmission costs.