Enabling Predictable Wireless Data Collection in Severe Energy Harvesting Environments

Zheng Dong, Yu Gu, Jiming Chen, Shaojie Tang, Tian He, Cong Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Micro-powered wireless embedded devices are widely used in many application domains. Their efficiency in practice, however, is significantly constrained by the dual limitations of low harvesting rates and tiny energy buffer. Recent research presents a network stack that efficiently fragments a large packet into many smaller packets that can fit within the available energy in the energy buffer of limited size. While this fragmentation technique represents a major step forward in solving the minuscule energy budget problem, it also introduces a tremendous practical challenge where potentially many fragmented packets belonging to different devices may contend for the communication channel. Designing purely heuristic-based packet transmission protocol is undesirable because the resulting per-packet and end-to-end transmission delay are unknown, thus causing unpredictable system performance which is unacceptable for many applications with real-time constraints. In this paper, we first formulate this packet transmission scheduling problem considering physical properties of the charging and transmission processes. We then develop a novel packet prioritization and transmission protocol NERF that yields tight and predictable delay bounds for transmitting packets from multiple micropowered devices to a charger. We have implemented our protoco on top of the WISP 4.1 platform and the SPEEDWAY RFID READER, and conducted validation experiments. Our experiments validate the correctness of our implementation and show that NERF can reduce the total collection delay by 40% when compared to an existing protocol ALOHA. We have also performed extensive data trace-driven simulations. Simulation results demonstrate the effectiveness of our proposed protocol. On average, our protocol yields an over 30%improvement in terms of runtime transmission delay compared to existing methods, while being able to guarantee tight and provable response time bounds.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE Real-Time Systems Symposium, RTSS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages157-166
Number of pages10
ISBN (Electronic)9781509053025
DOIs
StatePublished - Jul 2 2016
Externally publishedYes
Event2016 IEEE Real-Time Systems Symposium, RTSS 2016 - Porto, Portugal
Duration: Nov 29 2016Dec 2 2016

Publication series

NameProceedings - Real-Time Systems Symposium
Volume0
ISSN (Print)1052-8725

Other

Other2016 IEEE Real-Time Systems Symposium, RTSS 2016
CountryPortugal
CityPorto
Period11/29/1612/2/16

Keywords

  • predictable system performance
  • rechargeable sensors
  • response time bound
  • scheduling

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  • Cite this

    Dong, Z., Gu, Y., Chen, J., Tang, S., He, T., & Liu, C. (2016). Enabling Predictable Wireless Data Collection in Severe Energy Harvesting Environments. In Proceedings - 2016 IEEE Real-Time Systems Symposium, RTSS 2016 (pp. 157-166). [7809852] (Proceedings - Real-Time Systems Symposium; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTSS.2016.024