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Goodput optimization via dynamic frame length and charging time adaptation for backscatter communication

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Abstract

Computational radio frequency identification (CRFID) sensors present a new frontier for pervasive sensing and computing. They exploit ambient light or radio frequency (RF) for energy and use backscatter communication with an RFID reader for data transfer. Unlike conventional RFID tags that only transmit identifiers, CRFID sensors need to transfer potentially large amounts of data to a reader during each contact. Existing EPC Gen2 protocol is inefficient in dealing with a small number of CRFID sensors transferring a large amount of buffered data to the RFID reader and it has no specific design for adaptation to dynamic energy harvesting and channel conditions. In this article, we propose to adopt dynamic frame length and charging time for CRFID backscatter communication, aiming to adapt to the changing energy harvesting and channel conditions and improve the system goodput. First, optimal frame length and charging time that maximizes the goodput are obtained by solving the formulated goodput optimization problem. Then we propose a dynamic frame length and charging time adaptation scheme (DFCA) that increase or decrease the frame length and charging time at runtime based on the goodput measurement. Simulations show that our proposed DFCA scheme outperforms current fixed-frame-length approach and can converge to theoretically optimal under different energy harvesting and channel conditions.

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References

  1. Buettner M, Wetherall D (2008) An empirical study of UHF RFID performance. In: Proceedings of the 14th ACM international conference on mobile computing and networking (MobiCom). ACM, pp 223–234

  2. Buettner M, Prasad R, Philipose M, Wetherall D (2009) Recognizing daily activities with RFID-based sensors. In: Proceedings of the 11th international conference on ubiquitous computing (UbiComp). ACM, pp 51–60

  3. Chebrolu K, Raman B, Mishra N, Valiveti P K, Kumar R (2008) Brimon: a sensor network system for railway bridge monitoring. In: Proceedings of the 6th international conference on mobile systems, applications, and services (MobiSys). ACM, pp 2–14

  4. Dong W, Chen C, Liu X, He Y, Liu Y, Bu J, Xu X (2014) Dynamic packet length control in wireless sensor networks. IEEE Trans Wirel Commun 13(3):1172–1181

    Article  Google Scholar 

  5. Fu L, Cheng P, Gu Y, Chen J, He T (2016) Optimal charging in wireless rechargeable sensor networks. IEEE rans Veh Technol 65(1):278–291

    Article  Google Scholar 

  6. GS1 EPCglobal Inc (2015) EPC UHF Gen2 Air Interface Protocol v2.0.1. http://www.gs1.org/sites/default/files/docs/epc/Gen2_Protocol_Standard.pdf

  7. Gummeson J, Zhang P, Ganesan D (2012) Flit: a bulk transmission protocol for RFID-scale sensors. ACM, pp 71–84

  8. Kellogg B, Talla V, Gollakota S, Smith JR (2016) Passive wi-fi: bringing low power to wi-fi transmissions. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI), pp 151–164

  9. Kim B S, Fang Y, Wong TF, Kwon Y (2005) Throughput enhancement through dynamic fragmentation in wireless LANs. IEEE Trans Veh Technol 54(4):1415–1425

    Article  Google Scholar 

  10. Kim S, Vyas R, Bito J, Niotaki K, Collado A, Georgiadis A, Tentzeris MM (2014) Ambient RF energy-harvesting technologies for self-sustainable standalone wireless sensor platforms. Proc IEEE 102(11):1649–1666

    Article  Google Scholar 

  11. Lee Y, Bang S, Lee I, Kim Y, Kim G, Ghaed MH, Pannuto P, Dutta P, Sylvester D, Blaauw D (2013) A modular 1 mm 3 die-stacked sensing platform with low power i 2C inter-die communication and multi-modal energy harvesting. IEEE J Solid State Circ 48(1):229–243

    Article  Google Scholar 

  12. Ou J, Li M, Zheng Y (2015). In: Proceedings of the 21st International Conference on Mobile Computing and Networking (MobiCom), pp 500–511

  13. Quan CH, Hong WK, Kim HC (2006) Performance analysis of tag anti-collision algorithms for rfid systems. In: Proceedings of international conference on embedded and ubiquitous computing. Springer, pp 382–391

  14. Sample AP, Yeager DJ, Powledge PS, Mamishev AV, Smith JR (2008) Design of an RFID-based battery-free programmable sensing platform. IEEE Trans Instrum Meas 57(11):2608– 2615

    Article  Google Scholar 

  15. Simon M, Divsalar D (2006) Some interesting observations for certain line codes with application to RFID. IEEE Trans Commun 54(4):583–586

    Article  Google Scholar 

  16. Zhang P, Ganesan D (2014) Enabling bit-by-bit backscatter communication in severe energy harvesting environments. In: Proceedings of the 11th USENIX symposium on networked systems design and implementation (NSDI), pp 345–357

  17. Zhang P, Gummeson J, Ganesan D (2012) BLINK: a high throughput link layer for backscatter communication. In: Proceedings of the 10th international conference on mobile systems, applications, and services (MobiSys), pp 99–112

  18. Fu L, He L, Cheng P, Gu Y, Pan J, Chen J (2015) ESync: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks. IEEE Transactions on Vehicular Technology

  19. He L, Kong L , Gu Y, Pan J, Zhu T (2015) Evaluating the on-demand mobile charging in wireless sensor networks. IEEE Transactions on Mobile Computing 14(9):1861–1875

    Article  Google Scholar 

  20. He S, Chen J, Li X, Shen X, Sun Y (2014) Mobility and Intruder Prior Information Improving the Barrier Coverage of Sparse Sensor Networks. IEEE Transactions on Mobile Computing 13(6):1268–1282

    Article  Google Scholar 

  21. Liu Xiao, Dong M , Ota K , Hung P, Liu A (2016) Service Pricing Decision in Cyber Physical Systems: Insights from Game Theory. IEEE Transactions on Services Computing 9(2):186–198

    Article  Google Scholar 

  22. Wu J, Shi L, Xie L , Johansson KH (2015) An improved stability condition for Kalman filtering with bounded Markovian packet losses. Automatica 62:32–38

    Article  MathSciNet  MATH  Google Scholar 

  23. You P, Zhang Y, Yang Z, Low S (2015) Optimal Charging Schedule for a Battery Switching Station Serving Electric Buses. IEEE Transactions on Power Systems:1–11

  24. Zhang H, Cheng P, Shi L, Chen J (2016) Optimal DoS Attack Scheduling in Wireless Networked Control System. IEEE Transactions on Control System Technology 24(3):843–852

    Article  Google Scholar 

  25. Hu Y (2016) Improvement the quality of mobile target detection through portion of node with fully duty cycle in WSNs. Computer Systems Science and Engineering 31(1):5–17

    Google Scholar 

  26. He Liang, Yang Z , Pan J , Cai L , Xu J , Gu YJ (2014) Evaluating Service Disciplines for On-Demand Mobile Data Collectionin Sensor Networks. IEEE Transactions on Mobile Computing 13(4):797–810

    Article  Google Scholar 

  27. He Liang, Kong L, Lin S, Ying S, Gu Y J, He T, Liu C (2016) RAC: Reconfiguration-Assisted Charging in Large-Scale Lithium-Ion Battery Systems. IEEE Transactions on Smart Grid 7(3):1420–1429

    Article  Google Scholar 

  28. Kim S, Vyas R , Bito J , Niotaki K , Collado A, Georgiadis A, Tentzeris M M (2014) Ambient RF Energy-Harvesting Technologies for Self-Sustainable Standalone Wireless Sensor Platforms. Proceedings of IEEE 102(11):1649–1666

    Article  Google Scholar 

  29. Li Y, Fu L, Chen M, Chi K, Zhu Yh (2015) RF-Based charger placement for duty cycle guarantee in battery-free sensor networks. IEEE Commun Lett 19(10):1802–1805

    Article  Google Scholar 

  30. Wu J, Meng Z, Yang T, Shi G, Johansson K H (2015) Sampled-Data Consensus over Random Networks. IEEE Transactions on Signal Processing

  31. You P, Yang Z, Chow M-Y, Sun Y (2015) Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles. IEEE Transactions on Power Systems 31(4):2946–2956

    Article  Google Scholar 

  32. Yu Q, Chen J , Fan Y, Shen X (2010) Multi-channel assignment in wireless sensor networks: a game theoretic approach. IEEE INFOCOM:1–9

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Acknowledgments

This work was supported in part by China NSF grants (No. 61432015 and 61472367). This work was supported in part by China NSF grants (No. 61432015 and 61472367) and Zhejiang Provincial NSF grant (No. LY15F020026).

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Correspondence to Lingkun Fu.

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Li, Y., Fu, L., Ying, Y. et al. Goodput optimization via dynamic frame length and charging time adaptation for backscatter communication. Peer-to-Peer Netw. Appl. 10, 440–452 (2017). https://doi.org/10.1007/s12083-016-0480-1

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  • DOI: https://doi.org/10.1007/s12083-016-0480-1

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