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Energy-aware medium access control for energy-harvesting machine-to-machine networks

Published:08 April 2019Publication History

ABSTRACT

Energy-harvesting is being actively researched for the Machine-to-Machine networks. Without replacement of battery, energy-harvesting enables nodes (or machines) to perform their work permanently by recharging energy store periodically from an external source. After performing given tasks, in many applications, each energy-harvesting node transmits data to the gateway node. Here, the difference in harvested/consumed energy could lead to sub-optimal communication due to depletion of energy. In this paper, we design an energy-aware medium access control scheme for energy-harvesting machine-to-machine networks. The proposed algorithm controls delivery error rate due to energy depletion through limited contention among energy-exhausting nodes, and maximize slot efficiency to minimize overall communication duration. Maximizing slot efficiency is implemented in two ways: utility-based and learning-based. Simulation studies have shown that the proposed schemes effectively minimize delivery error rate and communication period, outperforming the existing strategies in the literature.

References

  1. Luigi Atzori, Antonio Iera, and Giacomo Morabito. The internet of things: A survey. Computer networks, 54(15):2787--2805, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Carsten Bormann, Mehmet Ersue, and Ari Keranen. Terminology for constrained-node networks. Technical report, 2014.Google ScholarGoogle Scholar
  3. Jaewoo Kim, Jaiyong Lee, Jaeho Kim, and Jaeseok Yun. M2m service platforms: Survey, issues, and enabling technologies. IEEE Communications Surveys and Tutorials, 16(1):61--76, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  4. Sujesha Sudevalayam and Purushottam Kulkarni. Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys & Tutorials, 13(3):443--461, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  5. Chin Keong Ho, Pham Dang Khoa, and Pang Chin Ming. Markovian models for harvested energy in wireless communications. In Communication Systems (ICCS), 2010 IEEE International Conference on, pages 311--315. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  6. Francisco Vázquez-Gallego, Charalampos Kalalas, Luis Alonso, and Jesus Alonso-Zarate. Contention tree-based access for wireless machine-to-machine networks with energy harvesting. IEEE Transactions on Green Communications and Networking, 1(2):223--234, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  7. Fabio Iannello, Osvaldo Simeone, Petar Popovski, and Umberto Spagnolini. Energy group-based dynamic framed aloha for wireless networks with energy harvesting. In Information Sciences and Systems (CISS), 2012 46th Annual Conference on, pages 1--6. IEEE, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  8. Fabio Iannello, Osvaldo Simeone, and Umberto Spagnolini. Medium access control protocols for wireless sensor networks with energy harvesting. IEEE Transactions on Communications, 60(5):1381--1389, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  9. Christopher JCH Watkins and Peter Dayan. Q-learning. Machine learning, 8(3-4):279--292, 1992.Google ScholarGoogle Scholar
  10. Tarek AlSkaif, Boris Bellalta, Manel Guerrero Zapata, and Jose M. Barcelo Ordinas. Energy efficiency of mac protocols in low data rate wireless multimedia sensor networks: A comparative study. Ad Hoc Networks, 56:141 -- 157, 2017.Google ScholarGoogle Scholar
  11. Wei Ye, John Heidemann, and Deborah Estrin. An energy-efficient mac protocol for wireless sensor networks. In INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 3, pages 1567--1576. IEEE, 2002.Google ScholarGoogle Scholar
  12. Wei Ye, John Heidemann, and Deborah Estrin. Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Transactions on Networking (ToN), 12(3):493--506, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Joseph Polastre, Jason Hill, and David Culler. Versatile low power media access for wireless sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 95--107. ACM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Wei Ye, Fabio Silva, and John Heidemann. Ultra-low duty cycle mac with scheduled channel polling. In Proceedings of the 4th international conference on Embedded networked sensor systems, pages 321--334. ACM, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. Tang, Y. Sun, O. Gurewitz, and D. B. Johnson. Pw-mac: An energy-efficient predictive-wakeup mac protocol for wireless sensor networks. In 2011 Proceedings IEEE INFOCOM, pages 1305--1313, April 2011.Google ScholarGoogle ScholarCross RefCross Ref
  16. Wen-Zhan Song, Renjie Huang, Behrooz Shirazi, and Richard LaHusen. Treemac: Localized tdma mac protocol for real-time high-data-rate sensor networks. Pervasive and Mobile Computing, 5(6):750 -- 765, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Lichun Bao and JJ Garcia-Luna-Aceves. A new approach to channel access scheduling for ad hoc networks. In Proceedings of the 7th annual international conference on Mobile computing and networking, pages 210--221. ACM, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Venkatesh Rajendran, Katia Obraczka, and Jose Joaquin Garcia-Luna-Aceves. Energy-efficient, collision-free medium access control for wireless sensor networks. Wireless networks, 12(1):63--78, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Ieee standard for information technology-telecommunications and information exchange between systems local and metropolitan area networks-specific requirements - part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications. IEEE Std 802.11-2016 (Revision of IEEE Std 802.11-2012), pages 1--3534, Dec 2016.Google ScholarGoogle Scholar
  20. Gang Lu, Bhaskar Krishnamachari, and Cauligi S Raghavendra. An adaptive energy-efficient and low-latency mac for data gathering in wireless sensor networks. In Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International, page 224. IEEE, 2004.Google ScholarGoogle Scholar
  21. Enrico Casini, Riccardo De Gaudenzi, and Oscar Del Rio Herrero. Contention resolution diversity slotted aloha (crdsa): An enhanced random access scheme for satellite access packet networks. IEEE Transactions on Wireless Communications, 6(4), 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Gianluigi Liva. Graph-based analysis and optimization of contention resolution diversity slotted aloha. IEEE Transactions on Communications, 59(2):477--487, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  23. Francisco Vazquez-Gallego, Marc Rietti, Joan Bas, Jesus Alonso-Zarate, and Luis Alonso. Performance evaluation of frame slotted-aloha with succesive interference cancellation in machine-to-machine networks. In European Wireless 2014; 20th European Wireless Conference; Proceedings of, pages 1--6. VDE, 2014.Google ScholarGoogle Scholar
  24. Leonard Kleinrock and Fouad Tobagi. Packet switching in radio channels: Part i-carrier sense multiple-access modes and their throughput-delay characteristics. IEEE transactions on Communications, 23(12):1400--1416, 1975.Google ScholarGoogle Scholar
  25. Lawrence G Roberts. Aloha packet system with and without slots and capture. ACM SIGCOMM Computer Communication Review, 5(2):28--42, 1975. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. A. George and T. G. Venkatesh. Performance analysis of m2m data collection networks using dynamic frame-slotted aloha. IEEE Transactions on Green Communications and Networking, 2(2):493--505, June 2018.Google ScholarGoogle ScholarCross RefCross Ref
  27. Ajinkya Rajandekar and Biplab Sikdar. A survey of mac layer issues and protocols for machine-to-machine communications. IEEE Internet of Things Journal, 2(2):175--186, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  28. Orazio Briante, Anna Maria Mandalari, Antonella Molinaro, Giuseppe Ruggeri, Jesus Alonso-Zarate, and Francisco Vazquez-Gallego. Duty-cycle optimization for machine-to-machine area networks based on frame slotted-aloha with energy harvesting capabilities. In European Wireless 2014; 20th European Wireless Conference; Proceedings of, pages 1--6. VDE, 2014.Google ScholarGoogle Scholar
  29. F Vázquez Gallego, Jesus Alonso-Zarate, and Luis Alonso. Energy and delay analysis of contention resolution mechanisms for machine-to-machine networks based on low-power wifi. In Communications (ICC), 2013 IEEE International Conference on, pages 2235--2240. IEEE, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  30. Frits Schoute. Dynamic frame length aloha. IEEE Transactions on communications, 31(4):565--568, 1983.Google ScholarGoogle ScholarCross RefCross Ref
  31. Chun-Yi Wang and Chi-Chung Lee. A grouping-based dynamic framed slotted aloha anti-collision method with fine groups in rfid systems. In Future Information Technology (FutureTech), 2010 5th International Conference on, pages 1--5. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  32. Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction, volume 1. MIT press Cambridge, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing
          April 2019
          2682 pages
          ISBN:9781450359337
          DOI:10.1145/3297280

          Copyright © 2019 ACM

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          Publication History

          • Published: 8 April 2019

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