access icon free Semi-myopic algorithm for resource allocation in wireless body area networks

© The Institution of Engineering and Technology
Received 15/05/2017, Accepted 12/09/2017, Revised 21/08/2017, Published 12/09/2017

The key features of wireless body area networks are limited energy resources of the sensor nodes and the need for highly reliable packet transmission. Therefore, designing a suitable algorithm that schedules transmission of the nodes is very important. Although an optimal algorithm has been previously reported based on a partially observable Markov decision process (POMDP), its complexity is high. In this study, the authors propose a suboptimal algorithm for scheduling the transmissions, i.e. a semi-myopic algorithm, with a performance close to the optimal, albeit with much lower complexity. To this end, they modify the structure of the cost function of the optimal algorithm such that the scheduling decision at each superframe is made regarding its effect on only a few upcoming superframes. The authors' proposed algorithm is considered in two different incoming packet scenarios, deterministic and random arrivals. Simulation results show that for both types of arrivals, there is a negligible difference between their proposed algorithm and the optimal one, i.e. POMDP, in terms of energy consumption and reliability.

Inspec keywords: energy consumption; telecommunication scheduling; Markov processes; resource allocation; computational complexity; telecommunication power management; body area networks

Other keywords: semi-MYOPIC algorithm; sensor nodes; limited energy resources; resource allocation; wireless body area network; transmission scheduling; highly reliable packet transmission; computational complexity reduction; partially observable Markov decision process; POMDP; energy consumption

Subjects: Radio links and equipment; Markov processes; Telecommunication systems (energy utilisation); Probability theory, stochastic processes, and statistics

References

    1. 1)
      • 14. Liu, Z., Liu, B., Chen, C.W.: ‘Transmission-rate-adaption assisted energy-efficient resource allocation with QoS support in WBANs’, IEEE Sens. J., 2017, 17, (17), pp. 5767–5780.
    2. 2)
      • 3. Movassaghi, S., Abolhasan, M., Lipman, J., et al: ‘Wireless body area networks: a survey’, IEEE Commun. Surv. Tutor., 2014, 16, (3), pp. 1658–1686.
    3. 3)
      • 10. Ouyang, Y., Teneketzis, D.: ‘On the optimality of myopic sensing in multi-state channels’, IEEE Trans. Inf. Theory, 2014, 60, (1), pp. 681–696.
    4. 4)
      • 26. Puterman, M.L.: ‘Markov decision processes: discrete stochastic dynamic programming’ (John Wiley & Sons Press, 2014).
    5. 5)
      • 18. Pan, R., Chua, D., Pathmasuntharam, J.S., et al: ‘An opportunistic relay protocol with dynamic scheduling in wireless body area sensor network’, IEEE Sens. J., 2015, 15, (7), pp. 3743–3750.
    6. 6)
      • 20. Liu, B., Yan, Z., Chen, C.W.: ‘Medium access control for wireless body area networks with QoS provisioning and energy efficient design’, IEEE Trans. Mob. Comput., 2017, 16, (2), pp. 422–434.
    7. 7)
      • 25. Tachtatzis, C., Di Franco, F., Tracey, D.C., et al: ‘An energy analysis of IEEE 802.15.6 scheduled access modes’. Proc. IEEE in GLOBECOM Workshops (GC Wkshps), 2010, pp. 1270–1275.
    8. 8)
      • 9. Ngo, M.H., Krishnamurthy, V.: ‘Monotonicity of constrained optimal transmission policies in correlated fading channels with ARQ’, IEEE Trans. Signal Process., 2010, 58, (1), pp. 438–451.
    9. 9)
      • 11. Zhao, Q., Krishnamachari, B., Liu, K.: ‘On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance’, IEEE Trans. Wirel. Commun., 2008, 7, (12), pp. 5431–5440.
    10. 10)
      • 23. Timmons, N.F., Scanlon, W.G.: ‘Improving the ultra-low-power performance of IEEE 802.15.6 by adaptive synchronisation’, IET Wirel. Sens. Syst., 2011, 1, (3), pp. 161–170.
    11. 11)
      • 22. Smith, D.B., Lamahewa, T., Hanlen, L.W., et al: ‘Simple prediction based power control for the on-body area communications channel’. Proc. IEEE Int. Conf. in Communications (ICC), 2011, pp. 1–5.
    12. 12)
      • 7. Ngo, M.H., Krishnamurthy, V.: ‘Optimality of threshold policies for transmission scheduling in correlated fading channels’, IEEE Trans. Commun., 2009, 57, (8), pp. 2474–2483.
    13. 13)
      • 2. Otto, C., Milenković, A., Sanders, C., et al: ‘System architecture of a wireless body area sensor network for ubiquitous health monitoring’, J. Mob. Multimedia, 2006, 1, (4), pp. 307–326.
    14. 14)
      • 17. Argyriou, A., Breva, A.C., Aoun, M.: ‘Optimizing data forwarding from body area networks in the presence of body shadowing with dual wireless technology nodes’, IEEE Trans. Mob. Comput., 2015, 14, (3), pp. 632–645.
    15. 15)
      • 19. Arghavani, A., Zhang, H., Huang, Z.: ‘LPA: Learning-based power aware communication protocol in WBANs’. Proc. IEEE 14th Int. Conf. in Dependable, Autonomic and Secure Computing, 14th Int. Conf. Pervasive Intelligence and Computing, 2nd Int. Conf. Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2016, pp. 512–519.
    16. 16)
      • 16. Tseng, H.W., Wu, R.Y., Wu, Y.Z.: ‘An efficient cross-layer reliable retransmission scheme for the human body shadowing in ieee 802.15.6-based wireless body area networks’, IEEE Sens. J., 2016, 16, (9), pp. 3282–3292.
    17. 17)
      • 27. Roberts, N.E., Oh, S., Wentzloff, D.D.: ‘Exploiting channel periodicity in body sensor networks’, IEEE J. Emerg. Sel. Top. Circuits Syst., 2012, 2, (1), pp. 4–13.
    18. 18)
      • 24. Alam, M.M., Berder, O., Menard, D., et al: ‘TAD-MAC: traffic-aware dynamic MAC protocol for wireless body area sensor networks’, IEEE J. Emerg. Sel. Top. Circuits Syst., 2012, 2, (1), pp. 109–119.
    19. 19)
      • 21. Rashwand, S., Mišić, J., Khazaei, H.: ‘IEEE 802.15.6 under saturation: some problems to be expected’, J. Commun. Netw., 2011, 13, (2), pp. 142–148.
    20. 20)
      • 1. Milenković, A., Otto, C., Jovanov, E.: ‘Wireless sensor networks for personal health monitoring: issues and an implementation’, Comput. Commun., 2006, 29, (13), pp. 2521–2533.
    21. 21)
      • 12. Ahmad, S.H.A., Liu, M., Javidi, T., et al: ‘Optimality of myopic sensing in multichannel opportunistic access’, IEEE Trans. Inf. Theory, 2009, 55, (9), pp. 4040–4050.
    22. 22)
      • 13. Ouyang, Y., Teneketzis, D.: ‘On the optimality of a myopic policy in multi-state channel probing’. Proc. 50th Annual Allerton Conf. in Communication, Control, and Computing (Allerton), 2012, pp. 342–349.
    23. 23)
      • 15. Davaslioglu, K., Liu, Y., Gitlin, R.D.: ‘CLOEE-Cross-Layer optimization for energy efficiency of IEEE 802.15. 6 IR-UWB WBANs’. Proc. IEEE Global Communications Conf. (GLOBECOM), 2016, pp. 1–7.
    24. 24)
      • 6. Omidvar, H., Ashtiani, F., Javidi, T., et al: ‘An energy-efficient multi-sensor scheduling mechanism with QoS support for WBANs’. Proc. IEEE Wireless Communications and Networking Conf. (WCNC), 2014, pp. 1703–1708.
    25. 25)
      • 8. Bai, X., Shami, A., Primak, S.: ‘Power efficient scheduling over fading channel for cross-layer optimization’, Wirel. Commun. Mob. Comput., 2012, 12, (13), pp. 1215–1224.
    26. 26)
      • 5. Sukor, M., Ariffin, S., Fisal, N., et al: ‘Performance study of wireless body area network in medical environment’. Proc. Asia Int. Conf. Modelling & Simulation, 2008, pp. 202–206.
    27. 27)
      • 4. IEEE Standards Association.: ‘IEEE standard for local and metropolitan area networks part 15.6: wireless body area networks’, IEEE Stand. Inf. Technol., 2012, 802, (6), pp. 1–271.
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