More Web Proxy on the site http://driver.im/

research-article

Aggregated multi-attribute query processing in edge computing for industrial IoT applications

Authors:

Zhangbing Zhou,

Shangguang Wang,

Junsheng ZhangAuthors Info & Claims

Volume 151, Issue C

Pages 114 - 123

https://doi.org/10.1016/j.comnet.2019.01.022

Published: 14 March 2019 Publication History

Abstract

The popularity of smart things constructs sensing networks for the Internet of Things (IoT), and promotes intelligent decision-makings to support industrial IoT applications, where multi-attribute query processing is an essential ingredient. Considering the huge number of smart things and large-scale of the network, traditional query processing mechanisms may not be applicable, since they mostly depend on a centralized index tree structure. To remedy this issue, this article proposes a multi-attribute aggregation query mechanism in the context of edge computing, where an energy-aware IR-tree is constructed to process query processing in single edge networks, while an edge node routing graph is established to facilitate query processing for marginal smart things contained in contiguous edge networks. This decentralized and localized strategy has shown its efficiency and applicability of query processing in IoT sensing networks. Experimental evaluation results demonstrate that this technique performs better than the rivals in reducing the traffic and energy consumption of the network.

References

[1]

W. Feng, Y. Qin, S. Zhao, D. Feng, AAoT: lightweight attestation and authentication of low-resource things in IoT and CPS, Comput. Netw. 134 (2018) 167–182.

[2]

W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu, Edge computing: vision and challenges, IEEE Internet Things J. 3 (5) (2016) 637–646.

[3]

S. Wang, A. Zhou, M. Yang, L. Sun, C.H. Hsu, F. Yang, Service composition in cyber-physical-social systems, IEEE Transactions on Emerging Topics in Computing PP (99), 2017.

[4]

Y. Liu, C. Xu, Y. Zhan, Z. Liu, J. Guan, H. Zhang, Incentive mechanism for computation offloading using edge computing: a stackelberg game approach, Comput. Netw. 129 (2017) 399–409.

[5]

K. Kaur, S. Garg, G.S. Aujla, N. Kumar, J.J.P.C. Rodrigues, M. Guizani, Edge computing in the industrial internet of things environment: software-defined-networks-based edge-cloud interplay, IEEE Commun. Mag. 56 (2) (2018) 44–51.

Digital Library

[6]

D. Zhang, J. Wan, C.H. Hsu, A. Rayes, Industrial technologies and applications for the internet of things, Comput. Netw. 101 (2016) 1–4.

[7]

S. Xiong, Q. Ni, X. Wang, Y. Su, A connectivity enhancement scheme based on link transformation in IoT sensing networks, IEEE Internet Things J. 4 (6) (2017) 2297–2308.

[8]

Y. Zhou, X. Xie, C. Wang, Y. Gong, W.Y. Ma, Hybrid index structures for location-based web search, ACM Int. Conf. Inf. Knowl. Manage. (2005) 155–162.

[9]

D. Harman, R. Baeza-Yates, E. Fox, W. Lee, Inverted files, Information retrieval, 1992, pp. 28–43.

[10]

G. Cong, C.S. Jensen, D. Wu, Efficient retrieval of the top-k most relevant spatial web objects, Proceedings of the VLDB Endowment, 2009, pp. 337–348.

[11]

Z. Li, K.C.K. Lee, B. Zheng, W. Lee, D.L. Lee, X. Wang, IR-tree: an efficient index for geographic document search, IEEE Trans. Knowl. Data Eng. 23 (4) (2011) 585–599.

[12]

D. Wu, G. Cong, C.S. Jensen, A framework for efficient spatial web object retrieval, Int. J. Very Large Data Bases 21 (2012) 797–822.

[13]

D. Zhang, Y.M. Chee, A. Mondal, A.K.H. Tung, M. Kitsuregawa, Keyword search in spatial databases: towards searching by document, IEEE International Conference on Data Engineering, 2009, pp. 688–699.

[14]

A. Skovsgaard, C.S. Jensen, Finding top-k relevant groups of spatial web objects, Int. J. Very Large Data Bases 24 (2015) 537–555.

[15]

X. Cao, G. Cong, C.S. Jensen, B.C. Ooi, Collective spatial keyword querying, ACM SIGMOD International Conference on Management of Data, 2011, pp. 373–384.

[16]

L. Zhang, X. Sun, Z. Hai, Density-based spatial keyword querying, Future Gener. Comput. Syst. 32 (1) (2014) 211–221.

[17]

G. Li, J. Feng, J. Xu, Desks: direction-aware spatial keyword search, IEEE International Conference on Data Engineering, 2012, pp. 474–485.

[18]

N.K. Tran, Q.Z. Sheng, M.A. Babar, L. Yao, Searching the web of things: state of the art, challenges, and solutions, ACM Comput. Surv. (CSUR) 50 (4) (2017) 55.

[19]

Y. Zhou, S. De, W. Wang, K. Moessner, Search techniques for the web of things: a taxonomy and survey, Sensors 16 (5) (2016) 600.

[20]

G.A. Akpakwu, B.J. Silva, G.P. Hancke, A.M. Abu-Mahfouz, A survey on 5g networks for the internet of things: communication technologies and challenges, IEEE Access 5 (12) (2017) 3619–3647.

[21]

C.T. Cheng, N. Ganganath, K.Y. Fok, Concurrent data collection trees for IoTapplications, IEEE Trans. Ind. Inf. 13 (2) (2017) 793–799.

[22]

O. Diallo, J.J.P.C. Rodrigues, M. Sene, Real-time data management on wireless sensor networks: a survey, J. Netw. Comput. Appl. 35 (3) (2012) 1013–1021.

[23]

F. Ren, J. Zhang, Y. Wu, T. He, C. Chen, C. Lin, Attribute-aware data aggregation using potential-based dynamic routing in wireless sensor networks, IEEE Trans. Parallel Distrib. Syst. 24 (5) (2013) 881–892.

[24]

H. Wang, H. Xu, L. Huang, J. Wang, X. Yang, Load-balancing routing in software defined networks with multiple controllers, Comput. Netw. 141 (2018) 82–91.

[25]

Z. Fadlullah, M. Fouda, N. Kato, A. Takeuchi, Toward intelligent machine-to-machine communications in smart grid, IEEE Commun. Mag. 49 (4) (2011) 60–65.

[26]

B. Guo, J. Yu, B. Liao, D. Yang, L. Lu, A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing, J. Netw. Comput. Appl. 84 (2017) 118–130.

[27]

W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, IEEE Comput. Soc. 18 (2000) 8020.

[28]

Z. Zhou, D. Zhao, L. Shu, H.C. Chao, Efficient multi-attribute query processing in heterogeneous wireless sensor networks, J. Internet Technol. 15 (5) (2014) 699–712.

[29]

F. Shang, Y. Lei, An energy-balanced clustering routing algorithm for wireless sensor network, J. Comput. Theor. Nanosci. (2010) 777–783.

[30]

A. Guttman, R-trees: a dynamic index structure for spatial searching, ACM SIGMOD Int. Conf. Manage. Data 14 (2) (2016) 47–57.

[31]

R. Hariharan, B. Hore, C. Li, S. Mehrotra, Processing spatial-keyword (SK) queries in geographic information retrieval (GIR) systems, International Conference on Scientific and Statistical Database Management, 2007.

[32]

C. Zhu, H. Zhou, V.C.M. Leung, K. Wang, Y. Zhang, L.T. Yang, Toward big data in green city, IEEE Commun. Mag. 55 (11) (2017) 14–18.

[33]

J. Tang, B. Zhang, Y. Zhou, L. Wang, An energy-aware spatial index tree for multi-region attribute query aggregation processing in wireless sensor networks, IEEE Access 5 (99) (2017) 2080–2095.

[34]

R. Sowmya, K.R. Suneetha, Data mining with big data, International Conference on Intelligent Systems and Control, 2017, pp. 246–250.

[35]

M. Gohar, S.H. Ahmed, M. Khan, N. Guizani, A. Ahmed, A.U. Rahman, A big data analytics architecture for the internet of small things, IEEE Commun. Mag. 56 (2) (2018) 128–133.

[36]

C. Sarkar, U.N.S.N. Akshay, R.V. Prasad, A. Rahim, R. Neisse, G. Baldini, DIAT: a scalable distributed architecture for IoT, IEEE Internet Things J. 2 (3) (2017) 230–239.

[37]

N. Sheneela, R.N.B. Rais, P.A. Shah, S. Yasmin, A. Qayyum, S. Rho, Y. Nam, A dynamic caching strategy for CCN-based MANETs, Comput. Netw. 142 (2018) 93–107.

[38]

L. Ni, J. Zhang, C. Jiang, C. Yan, K. Yu, Resource allocation strategy in fog computing based on priced timed petri nets, IEEE Internet Things J. 4 (5) (2017) 1216–1228.

[39]

X. Xue, S. Wang, L. Zhang, Z. Feng, Y. Guo, Social learning evolution (SLE): computational experiment-based modeling framework of social manufacturing, IEEE Transactions on Industrial Informatics PP (99), 2018.

[40]

X. Xue, S. Wang, L.j. Zhang, Z.y. Feng, Evaluating of dynamic service matching strategy for social manufacturing in cloud environment, Future Gener. Comput. Syst. 91 (2019) 311–326.

Cited By

Li HYi LTang BLu HJensen C(2022)Efficient and error-bounded spatiotemporal quantile monitoring in edge computing environmentsProceedings of the VLDB Endowment10.14778/3538598.353860015:9(1753-1765)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.14778/3538598.3538600
Dong MYu HSun ZWu MZhang LSui YYu GHan TZhao R(2022)Design of IoT Gateway for Crop Growth Environmental Monitoring Based on Edge-Computing TechnologyComputational Intelligence and Neuroscience10.1155/2022/83270062022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8327006

Index Terms

Aggregated multi-attribute query processing in edge computing for industrial IoT applications
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
  2. Information retrieval
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

Index terms have been assigned to the content through auto-classification.

Recommendations

Energy-Aware Marginal Multi-attribute Federated Query in IoT Networks
Green, Pervasive, and Cloud Computing
Abstract
The popularity of smart things constructs sensing networks for the Internet of Things (IoT), and promotes intelligent decision-makings in industrial IoT applications, where multi-attribute query processing is an essential ingredient. Considering ...
Cybersecurity Challenges and Opportunities in the New "Edge Computing + IoT" World
SDN-NFV Sec'18: Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization

The paradigm shift to the Internet of Things (IoT) and the emergence of the edge computing concept have brought huge potentials for various future IoT application scenes such as smart home, smart transportation, smart health, smart grids, and smart ...
Right-Provisioned IoT Edge Computing: An Overview
GLSVLSI '19: Proceedings of the 2019 Great Lakes Symposium on VLSI

Edge computing on the Internet of Things (IoT) is an increasingly popular paradigm in which computation is moved closer to the data source (i.e., edge devices). Edge computing mitigates the overheads of cloud-based computing arising from increased ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 151, Issue C

Mar 2019

260 pages

ISSN:1389-1286

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 14 March 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li HYi LTang BLu HJensen C(2022)Efficient and error-bounded spatiotemporal quantile monitoring in edge computing environmentsProceedings of the VLDB Endowment10.14778/3538598.353860015:9(1753-1765)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.14778/3538598.3538600
Dong MYu HSun ZWu MZhang LSui YYu GHan TZhao R(2022)Design of IoT Gateway for Crop Growth Environmental Monitoring Based on Edge-Computing TechnologyComputational Intelligence and Neuroscience10.1155/2022/83270062022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8327006

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents