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Data Uploading Mechanism for Internet of Things with Energy Harvesting

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10874)

Abstract

To facilitate uploading of sensing data with a tremendous and still growing number of devices in Internet of Things (IoT), is one of the most pressing tasks today. It is not sensible to equip each IoT device with cellular or other wide range access technologies, thus the network access management and sensing data fusion are necessary to solve the paradox of spectrum drain and endless user experience. In this framework, we considered a practical scenario where exist heterogeneous IoT devices with energy harvesting and limited short range access technologies, e.g. WiFi, BLE4.0, and their data can be uploaded through an access point (AP) in given area. The AP needs to schedule the data uploading of heterogeneous IoT devices, and conservatively satisfy the quality of experience (QoE) requirements, e.g. delay, sensing interval, data rate. First, we modeled the energy harvesting and sensing data of IoT devices by Markov chain and probability transfer matrix, and derived the expression of urgency function which can clearly distinguish the urgency of data transmission among devices. Secondly, an auction-based IoT devices data uploading mechanism is proposed, which satisfies the expected economic robustness with low communication overhead. Finally, we performed extensive simulations to verify the proposed data uploading scheme and algorithm. The simulation results indicate that the proposed scheme works well.

Notes

Acknowledgment

The authors would like to thank the support from the Natural Science Foundation of China (61602062, 61702056), Educational Commission of JiangSu Province (17KJB520001), the Natural Science Foundation of JiangSu Province (BK20160410), the Provincial Key Laboratory for Computer Information Processing Technology, Soochow University (KJS1521).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringChangshu Institute of TechnologySuzhouChina
  2. 2.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina

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