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κ-FSOM: Fair Link Scheduling Optimization for Energy-Aware Data Collection in Mobile Sensor Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8354)

Abstract

We consider the problem of data collection from a continental-scale network of mobile sensors, specifically applied to wildlife tracking. Our application constraints favor a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with a network of powered base stations. Individual nodes move freely in the environment, resulting in low-quality radio links and hot-spot arrival patterns with the available data exceeding the radio link capacity. We propose a novel scheduling algorithm, κ-Fair Scheduling Optimization Model (κ-FSOM), that maximizes the amount of collected data under the constraints of radio link quality and energy, while ensuring a fair access to the radio channel. We show the problem is NP-complete and propose a heuristic to approximate the optimal scheduling solution in polynomial time. We use empirical link quality data to evaluate the κ-FSOM heuristic in a realistic setting and compare its performance to other heuristics. We show that κ-FSOM heuristic achieves high data reception rates, under different fairness and node lifetime constraints.

Keywords

Link scheduling Optimization Fairness Energy Mobile Sensor Network 

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References

  1. 1.
    Wood, R., Nagpal, R., Wei, G.Y.: Flight of the robobees. Scientific American 308, 60–65 (2013)CrossRefGoogle Scholar
  2. 2.
    Dantu, K., Kate, B., Waterman, J., Bailis, P., Welsh, M.: Programming micro-aerial vehicle swarms with karma. In: ACM SenSys, pp. 121–134 (2011)Google Scholar
  3. 3.
    Sinha, A., Tsourdos, A., White, B.: Multi uav coordination for tracking the dispersion of a contaminant cloud in an urban region. European Journal of Control 15, 441–448 (2009)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Israel, M.: A uav-based roe deer fawn detection system. In: Eisenbeiss, H., Kunz, M., Ingensand, H. (eds.) Proceedings of the International Conference on Unmanned Aerial Vehicle in Geomatics (UAV-g), vol. 38, pp. 1–5 (2011)Google Scholar
  5. 5.
    Dyo, V., Ellwood, S.A., Macdonald, D.W., Markham, A., Mascolo, C., Pásztor, B., Scellato, S., Trigoni, N., Wohlers, R., Yousef, K.: Evolution and sustainability of a wildlife monitoring sensor network. In: ACM SenSys, pp. 127–140 (2010)Google Scholar
  6. 6.
    Corke, P., Wark, T., Jurdak, R., Hu, W., Valencia, P., Moore, D.: Environmental wireless sensor networks. Proceedings of the IEEE 98, 1903–1917 (2010)CrossRefGoogle Scholar
  7. 7.
    Group, I.W., et al.: Standard for part 15.4: Wireless medium access control (mac) and physical layer (phy) specifications for low rate wireless personal area networks (lr-wpans). ANSI/IEEE 802 15, 4 (2003)Google Scholar
  8. 8.
    Shilton, L.A., Latch, P.J., Mckeown, A., Pert, P., Westcott, D.A.: Landscape-scale redistribution of a highly mobile threatened species, pteropus conspicillatus (chiroptera, pteropodidae), in response to tropical cyclone larry. Austral Ecology 33(4), 549–561 (2008)CrossRefGoogle Scholar
  9. 9.
    Jurdak, R., Sommer, P., Kusy, B., Kottege, N., Crossman, C., Mckeown, A., Westcott, D.: Camazotz: multimodal activity-based gps sampling. In: ACM IPSN, pp. 67–78 (2013)Google Scholar
  10. 10.
    Schulman, A., Navda, V., Ramjee, R., Spring, N., Deshpande, P., Grunewald, C., Jain, K., Padmanabhan, V.N.: Bartendr: a practical approach to energy-aware cellular data scheduling. In: Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, pp. 85–96. ACM (2010)Google Scholar
  11. 11.
    Low, T.P., Pun, M.O., Hong, Y.W., Kuo, C.C.: Optimized opportunistic multicast scheduling (oms) over wireless cellular networks. IEEE Transactions on Wireless Communications 9(2), 791–801 (2010)CrossRefGoogle Scholar
  12. 12.
    Wu, D., Negi, R.: Downlink scheduling in a cellular network for quality-of-service assurance. IEEE Transactions on Vehicular Technology 53(5), 1547–1557 (2004)CrossRefGoogle Scholar
  13. 13.
    Lin, Y., Yu, W.: Fair scheduling and resource allocation for wireless cellular network with shared relays. IEEE Journal on Selected Areas in Communications 30(8), 1530–1540 (2012)CrossRefGoogle Scholar
  14. 14.
    Zhou, Y., Li, X.Y., Liu, M., Li, Z., Tang, S., Mao, X., Huang, Q.: Distributed link scheduling for throughput maximization under physical interference model. In: IEEE INFOCOM, pp. 2691–2695 (2012)Google Scholar
  15. 15.
    Leconte, M., Ni, J., Srikant, R.: Improved bounds on the throughput efficiency of greedy maximal scheduling in wireless networks. IEEE/ACM Transactions on Networking 19(3), 709–720 (2011)CrossRefGoogle Scholar
  16. 16.
    Papadaki, K., Friderikos, V.: Approximate dynamic programming for link scheduling in wireless mesh networks. International Journal of Computers and Operations Research 35(12), 3848–3859 (2008)CrossRefzbMATHGoogle Scholar
  17. 17.
    Neely, M.J.: Delay-based network utility maximization. In: IEEE INFOCOM, pp. 1–9 (2010)Google Scholar
  18. 18.
    Neely: Opportunistic scheduling with worst case delay guarantees in single and multi-hop networks. In: IEEE INFOCOM, pp. 1728–1736 (2011)Google Scholar
  19. 19.
    Tang, S., Yang, L.: Morello: A quality-of-monitoring oriented sensing scheduling protocol in sensor networks. In: IEEE INFOCOM, pp. 2676–2680 (2012)Google Scholar
  20. 20.
    Nabar, S., Walling, J., Poovendran, R.: Minimizing energy consumption in body sensor networks via convex optimization. In: International Conference on Body Sensor Networks (BSN), pp. 62–67 (2010)Google Scholar
  21. 21.
    Ergen, S.C.: Zigbee/ieee 802.15. 4 summary, UC Berkeley, September 10 (2004)Google Scholar
  22. 22.
    Martello, S., Toth, P.: Knapsack problems: algorithms and computer implementations. John Wiley and Sons, Inc. (1990)Google Scholar
  23. 23.
    TexasInstruments: Cc430f613: Msp430 soc with rf core (2013)Google Scholar
  24. 24.
    Yupho, D., Kabara, J.: The effect of physical topology on wireless sensor network lifetime. Journal of Networks 2(5), 14–23 (2007)CrossRefGoogle Scholar
  25. 25.
    Srinivasa, K., Levis, P.: Rssi is under appreciated. The Third Workshop on Embedded Networked Sensors, EmNets (2006)Google Scholar
  26. 26.
    Willkomm, D., Machiraju, S., Bolot, J., Wolisz, A.: Primary user behavior in cellular networks and implications for dynamic spectrum access. IEEE Communications Magazine 47(3), 88–95 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.Autonomous Systems LabCSIRO ICT CentreBrisbaneAustralia

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