access icon free Optimal hops for minimal route power under SINR constraints in wireless sensor networks

© The Institution of Engineering and Technology
Received 30/06/2017, Accepted 21/03/2018, Revised 27/02/2018, Published 27/03/2018

This study attempts to deduce the optimal hops in a multi-hop wireless sensor network (WSN) ensuring power-efficient communication under signal-to-interference noise ratio (SINR) constraints. The authors model the total route power consumption under wireless channel quality constraints. WSNs usually employ a multiple-source, single-sink structure for information transfer, necessitating data aggregation. The authors calculate optimal hops for three different data aggregation schemes. They further propose a robust hybrid data aggregation strategy that demonstrates an improved performance with respect to minimal power consumption when compared with employing individual data aggregation schemes. The data aggregation scheme employing optimal hops has been implemented and results have been verified for an illustrative application in water supply and drainage monitoring system. The authors have formulated the problem as a standard convex optimisation problem. They employ Karush–Kuhn–Tucker optimality conditions to derive the analytical expressions of the globally optimal hops, which incorporate the influence of route power, receiver noise, and SINR thresholds. The transmit power and hop count are varied simultaneously until an optimised hop count is achieved. The relation between the node transmit power and the number of hops in the route is derived and the condition for feasible optimal hops is presented.

Inspec keywords: radiofrequency interference; wireless channels; telecommunication network routing; power consumption; data aggregation; wireless sensor networks; convex programming

Other keywords: multiple-source single-sink structure; SINR thresholds; WSN; standard convex optimisation problem; wireless channel quality constraints; receiver noise; total route power consumption; SINR constraints; node transmit power; analytical expressions; signal-to-interference noise ratio constraints; power-efficient communication; drainage monitoring system; multihop wireless sensor network; water supply; robust hybrid data aggregation strategy; global optimal hops; Karush–Kuhn–Tucker optimality conditions; minimal route power; hop count; information transfer

Subjects: Optimisation techniques; Communication network design, planning and routing; Electromagnetic compatibility and interference; Wireless sensor networks

References

    1. 1)
      • 22. CAMTECH: ‘Handbook for leakage treatment in buildings’ (Government of India, Ministry of Railways, Maharajpur, Gwalior, India, 2006).
    2. 2)
      • 10. Holland, M., Wang, T., Tavli, B., et al: ‘Optimizing physical layer parameters for wireless sensor networks’, ACM Trans. Sensor Netw. (TOSN), 2011, 7, (4), pp. 28.1–28.20.
    3. 3)
      • 7. Luo, Z.-Q., Yu, W.: ‘An introduction to convex optimization for communications and signal processing’, IEEE J. Sel. Areas Commun., 2006, 24, (8), pp. 1426–1438.
    4. 4)
      • 28. IS: 12183 (Part I): ‘Indian standard: code of practice for plumbing in multi-storeyed buildings’, 1987, Reaffirmed 2009.
    5. 5)
      • 19. Sarangapani, J.: ‘Wireless ad hoc and sensor networks: protocols, performance and control’ (CRC Press, Florida, United States, 2007).
    6. 6)
      • 6. Wong, H., Agoulmine, N., Ma, M., et al: ‘Network lifetime optimization in wireless sensor networks’, IEEE J. Sel. Areas Commun., 2010, 28, (7), pp. 1127–1137.
    7. 7)
      • 20. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, Cambridge, United Kingdom, 2009).
    8. 8)
      • 16. Hoang, D.C., Kumar, R., Panda, S.K.: ‘Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm’, IET Wirel. Sensor Syst., 2012, 2, (3), pp. 282–292.
    9. 9)
      • 2. Grindvoll, H., Vermesan, O., Crosbie, T., et al: ‘A wireless sensor network for intelligent building energy management based on multi-communication standards – a case study’, J. Inf. Technol. Constr., 2012, 17, pp. 43–62.
    10. 10)
      • 25. Seybold, J.S.: ‘Introduction to RF propagation’ (Wiley Inderscience, Olney, Bucks, United Kingdom, 2005).
    11. 11)
      • 18. Tse, D., Viswanath, P.: ‘Fundamentals of wireless communication’ (Cambridge University Press, Cambridge, United Kingdom, 2005).
    12. 12)
      • 26. Grant, M.C., Boyd, S.P.: ‘The CVX users’ guide’ (CVX Research Inc., Stanford, United States, Release 2.1, 2014).
    13. 13)
      • 23. Simonen, F.A.: ‘Review of failure probability calculations for HFIR primary coolant system piping’ (US Department of Energy, Richland, WA, United States, Pacific Northwest National Laboratory, 2001).
    14. 14)
      • 13. Lambor, S.M., Joshi, S.: ‘Critical hops calculation for energy conservation in a multi-hop wireless sensor network’. Sixth IEEE Int. Conf. Wireless Communication and Sensor Networks (WCSN-2010), Allahabad, India, December 2010.
    15. 15)
      • 1. Martinez, K., Hart, J.K., Ong, R.: ‘Sensor network applications: environmental sensor networks’, IEEE Comput. Soc., 2004, 37, (8), pp. 50–56.
    16. 16)
      • 4. Tychogiorgos, G., Gkelias, A., Leung, K.K.: ‘A non-convex distributed optimization framework and its application to wireless ad-hoc networks’, IEEE Trans. Wirel. Commun., 2013, 12, (9), pp. 4286–4296.
    17. 17)
      • 15. Doug, Q., Yu, L., Lu, H., et al: ‘Design of building monitoring systems based on wireless sensor networks’, Sci. Res. Wirel. Sensor Netw., 2010, 2, pp. 703–709.
    18. 18)
      • 24. Fadaee, M.J., Tabatabaei, R.: ‘Estimation of failure probability in water pipes network using statistical model’, World Appl. Sci. J., 2010, 11, pp. 1157–1163.
    19. 19)
      • 21. White Paper: ‘Energy management and energy optimization in the process industry’ (Siemens, Sector Industry, IA AS S MP 7, 2011).
    20. 20)
      • 5. Sultan, A.: ‘Sensing and transmit energy optimization for an energy harvesting cognitive radio’, IEEE Wirel. Commun. Lett., 2012, 1, (5), pp. 500–503.
    21. 21)
      • 8. Okazaki, A.M., Frӧhlich, A.A.: ‘Ant based dynamic hop optimization protocol: a routing algorithm for mobile wireless sensor networks’. IEEE GLOBECOM Workshops, Houstan, TX, USA, May 2011, pp. 1139–1143.
    22. 22)
      • 27. ‘Council on tall buildings and urban habitats’. Available at http://www.ctbuh.org/TallBuildings/HeightStatistics/tabid/1735/language/en-US/Default.aspx, accessed October 2016.
    23. 23)
      • 11. Farooq, M.O., Kunz, T.: ‘Impact of route length on the performance of routing and flow admission control algorithms in wireless sensor networks’, IET Wirel. Sensor Syst., 2016, 6, (1), pp. 10–16.
    24. 24)
      • 17. Mehrjoo, S., Khunjush, F.: ‘Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics’, Wiley Comput. Intell., 2017(Accepted for publication), 33, pp. 1–19.
    25. 25)
      • 9. Chen, X., Wang, X., Chen, X.: ‘Energy-efficient optimization for wireless information and power transfer in large-scale MIMO systems employing energy beamforming’, IEEE Wirel. Commun. Lett., 2013, 2, (6), pp. 667–670.
    26. 26)
      • 3. Mainwaring, A., Polastre, J., Szewczyk, R., et al: ‘Wireless sensor networks for habitat monitoring’. Proc. ACM Int. Workshop Wireless Sensor Networks and Applications, Atlanta, USA, September 2002, pp. 88–97.
    27. 27)
      • 14. Kheireddine, M., Abdellatif, R.: ‘Analysis of hops length in wireless sensor networks’, Sci. Res. Wirel. Sensor Netw., 2014, 6, pp. 109–117.
    28. 28)
      • 12. Kheireddine, M., Abdellatif, R.: ‘Short-hops vs. long-hops – energy efficiency analysis in wireless sensor networks’, in Amine, A., Mohamed, O., Benatallah, B. (Eds.): ‘Network security technologies: design and application’ (IGI Global, Hershey, PA, 2014), pp. 74–83.
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