期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 16, 页码 12679-12693出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3026988
关键词
Query processing; Wireless sensor networks; Distributed databases; Monitoring; Cloud computing; Servers; Internet of Things; Data monitoring; multiaccess edge computing (MEC); query processing
资金
- National Science Foundation [2011845, 1741277, 1912753, 1704287]
- Direct For Education and Human Resources
- Division Of Graduate Education [1912753] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1741277] Funding Source: National Science Foundation
This article investigates query processing in an edge-assisted IoT data monitoring system (EDMS), proposing an approximation algorithm and evaluating its performance through extensive simulations.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据