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Towards efficient and energy-aware query processing for industrial internet of things

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Abstract

With the rapid growth of connected devices, Internet of Things (IoT) has been gradually adopted in many fields such as industry. The Industrial IoT (IIoT) devices are typically deployed in noisy environments for supporting critical applications, which require flexibility and efficiency of network management, i.e., timely collection and processing of environmental data. Focused on this issue, in this work, we take wireless sensor networks (WSNs) as an instance in IIoT, and propose an efficient algorithm called Trust and Energy-aware based Holistic optimizing Algorithm for Spatial window query (TEHAS), which can improve the efficiency of query processing, reduce the energy consumptions and avoid the loss of critical information. In addition to a theoretical analysis, our experimental results demonstrate that our approach could perform better than most existing algorithms regarding efficiency, communication security and energy consumption.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No.(61402225, 61373015, 41301407), the National Natural Science Foundation of Jiangsu Province under Grant No. BK20140832, the Jiangsu Postdoctoral Science Foundation under Grant No. 1301020C, the China Postdoctoral Science Foundation under Grant No. 2013 M540447, State Key Laboratory for smart grid protection and operation control Foundation, Science and Technology Funds from National Electric Net Ltd. (The Research on Key Technologies of Distributed Parallel Database Storage and Processing based on Big Data), the Foundation of Graduate Innovation Center in NUAA under Grant No. kfjj20181608.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Liang Liu, Yiting Wang, Zhe Xu & Zuchao Ma

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

  3. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Wenzhao Gao

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  1. Liang Liu
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Correspondence to Weizhi Meng.

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This article is part of the Toipical Collection: Special Issue on Convergence of Edge Computing and Next Generation Networking

Guest Editors: Deze Zeng, Geyong Min, Qiang He, and Song Guo

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Liu, L., Wang, Y., Meng, W. et al. Towards efficient and energy-aware query processing for industrial internet of things. Peer-to-Peer Netw. Appl. 14, 3895–3914 (2021). https://doi.org/10.1007/s12083-021-01163-w

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