Distributed Query Processing in the Edge-Assisted IoT Data Monitoring System
Zhipeng Cai, Tuo Shi
Abstract
The massive amount of data generated by the Internet-of-Things (IoT) devices places enormous pressure on sensory data query processing. Due to the limitations of computation and data transmission capabilities in traditional wireless sensor networks (WSNs), the current query processing methods are no longer effective. Furthermore, processing vast amount of sensory data also overloads the cloud. To address these problems, we investigate query processing in an edge-assisted IoT data monitoring system (EDMS). Multiaccess edge computing (MEC) is an emerging topic in IoTs. Unlike WSNs, the edge servers in an EDMS can deploy the computation and storage resources to nearby IoT devices and offer data processing services. Therefore, queries toward massive sensory data can be processed in an EDMS in a distributed manner and the edge servers can handle the sensory data in a distributed manner, reducing the workload of the cloud. In this article, we define a query processing problem in an EDMS, which aims to derive a distributed query plan with the minimum query response latency. We prove that this problem is NP-Hard and propose a corresponding approximation algorithm. The performance of the proposed algorithm is bounded. Furthermore, we evaluate the performance of the proposed algorithm through extensive simulations.