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Knowledge-aware Proactive Nodes Selection approach for energy management in Internet of Things
School of Information Science and Engineering, Central South University, ChangSha .
School of Information Science and Engineering, Central South University, ChangSha .
School of Information Science and Engineering, Central South University, ChangSha .
Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK .
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2017 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115Article in journal (Refereed) Epub ahead of print
Abstract [en]

Internet of Things will serve communities across the different domains of life. Tracking mobile targets is one important system engineering application in IOT, and the resource of embedded devices and objects working under IoT implementation are constrained. Thus, building a scheme to make full use of energy is key issue for mobile target tracking applications. To achieve both energy efficiency and high monitoring performance, an effective Knowledge-aware Proactive Nodes Selection (KPNS) system is proposed in this paper. The innovations of KPNS are as follows: 1) the number of proactive nodes are dynamically adjusted based on prediction accuracy of target trajectory. If the prediction accuracy is high, the number of proactive nodes in the non-main predicted area will be decreased. If prediction accuracy of moving trajectory is low, large number of proactive nodes will be selected to enhance monitoring quality. 2) KPNS takes full advantage of energy to further enhance target tracking performance by properly selecting more proactive nodes in the network. We evaluated the efficiency of KPNS with both theory analysis and simulation based experiments. The experimental results demonstrate that compared with Probability-based target Prediction and Sleep Scheduling strategy (PPSS), KPNS scheme improves the energy efficiency by 60%, and can reduce target missing rate and tracking delay to 66%, 75% respectively.

Place, publisher, year, edition, pages
Elsevier, 2017.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-65149DOI: 10.1016/j.future.2017.07.022OAI: oai:DiVA.org:ltu-65149DiVA: diva2:1133843
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2017-09-05

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CiteExportLink to record
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