A Context-based Sensed Data Search on Edge Computing for Finding Moving People

This paper proposes a novel context-based search method on edge computing for finding data of moving people. As the Internet of Things (IoT) spreads, many sensors are going to be connected to a wide area network. To utilize sensed data for various services, they first need to be searched for. Different from term-based web content search, sensed data require context to be derived by heavy load processing such as image analysis from enormous data. Therefore, it incurs a huge amount of time and cost. Time and cost can be reduced by estimating the edge server that stores the required data. This paper proposes a novel context-based search method that selects data to be analyzed on the basis of the existence probability of context in accordance with the installation area of edge servers and the data generation time. Also, it selects a server with low load to reduce response time. Since the proposed method incorporates not only low layer information such as computer load but also high layer information such as the existence probability of context in the data, the method enables context-based search for sensed data quickly with low cost. Simulations showed that the proposed method reduces the response time by 95% compared with selecting data without using the existence probability of context.