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Swarm intelligence-based energy-efficient data delivery in WSAN to virtualise IoT in smart cities

Swarm intelligence-based energy-efficient data delivery in WSAN to virtualise IoT in smart cities

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With the rapid implementation and expansion of the Internet of things (IoT) technologies in smart cities, network congestion control and energy-efficient routing in wireless sensor and actuator networks (WSANs) for virtualising IoT have emerged as a crucial area of research in recent years. This study implements the particle swarm optimisation (PSO) algorithm to realise network congestion control and energy-efficient routing in the transport layer of WSANs. This algorithm is based on the flocking behaviour of birds. The simulation results show that the proposed PSO-based approach provides better performance in terms of network lifetime and packet drop ratio compared with the ant colony optimisation and the artificial bee colony algorithm.

References

    1. 1)
      • 1. Sarkar, C.: ‘Virtualizing the Internet of things’. Available at https://repository.tudelft.nl, accessed 15 September 2017.
    2. 2)
      • 2. Matthew, H., Margaret, R.: ‘WSAN (wireless sensor and actuator network)’. Available at http://internetofthingsagenda.Techtarget.com/definition/WSAN-wireless-sensor-and-actuator-network, accessed 15 September 2017.
    3. 3)
      • 3. Stoica, I., Shenker, S., Zhang, H.: ‘Core-stateless fair queueing: achieving approximately fair bandwidth allocations in high speed networks’, IEEE/ACM Trans. Netw., 2003, 11, (1), pp. 3346.
    4. 4)
      • 4. Tsukamoto, K., Fukuda, Y., Hori, Y., et al: ‘New TCP congestion control schemes for multimodal mobile hosts’. Proc. 57th IEEE Semi-Annual Vehicular Technology Conf. (VTC) Spring 2003, Jeju, South Korea, April 2003, vol. 3, pp. 17201724.
    5. 5)
      • 5. Mirza, W.H., Sanjay, J., Majid, Z.: ‘Intelligent congestion control algorithm in a network: possible alternatives’, Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2003, 5, (1), pp. 282284.
    6. 6)
      • 6. Priya, T.S., Ananthanarayanan, N.R.: ‘A study on TCP congestion control using RED algorithm with SNR ratio’, IPASJ Int. J. Comput. Sci. (IIJCS), 2014, 2, (8), pp. 2834.
    7. 7)
      • 7. Prakul, S., Anamika, Y.: ‘A survey on congestion control at transport layer in wireless sensor network’, Open Trans. Wirel. Commun., 2014, 1, (1), pp. 1728.
    8. 8)
      • 8. Gungar, V.C., Vuran, M.C., Akan, O.B.: ‘On the cross layer interactions between congestion and intention in wireless sensor and actor networks’, Ad Hoc Netw., 2007, 5, pp. 897909.
    9. 9)
      • 9. Chen, J., Lu, Q.: ‘A fair congestion control algorithm for fast TCP’. Proc. 2006 Int. Conf. Communications, Circuits and Systems, Guilin, China, June 2006, pp. 16651667.
    10. 10)
      • 10. Wang, C., Sohraby, K., Hu, Y., et al: ‘Issues of transport control protocols for wireless sensor networks’. Proc. 2005 Int. Conf. Communications, Circuits and Systems, Hong Kong, China, May 2005, pp. 422426.
    11. 11)
      • 11. Sun, W., Shen, K.G.: ‘TCP performance under aggregate fair queering’. Proc. IEEE GLOBECOM 2004, TX, USA, November 2004, pp. 13081313.
    12. 12)
      • 12. Wang, C., Sohraby, K., Li, B.: ‘SenTCP: a hop by hop congestion control protocol for wireless sensor networks’. Proc. IEEE INFOCOM 2005, Miami, FL, USA, March 2005.
    13. 13)
      • 13. Garetlo, M., Cigno, R.L., Meo, M., et al: ‘Closed queueing network models of interacting long-lived TCP flows’, IEEE/ACM Trans. Netw., 2004, 12, (2), pp. 300311.
    14. 14)
      • 14. Al-Turjman, F., Alturjman, S.: ‘Context-sensitive access in industrial Internet of things (IIoT) healthcare applications’, IEEE Trans. Ind. Inf., 2018, 14, pp. 27362744, 10.1109/TII.2018.2808190.
    15. 15)
      • 15. Al-Turjman, F., Ever, Y., Ever, E., et al: ‘Seamless key agreement framework for mobile-sink in IoT based cloud-centric secure public safety networks’, IEEE. Access, 2017, 5, (1), pp. 2461724631.
    16. 16)
      • 16. Al-Turjman, F.: ‘5G-enabled devices and smart-spaces in social-IoT: an overview’, Elsevier Future Gener. Comput. Syst., 2017, 10.1016/j.future.2017.11.035.
    17. 17)
      • 17. Alabady, S., Fadzli, M., Salleh, M., et al: ‘LCPC error correction code for Internet of things applications’, Elsevier Sustain. Cities Soc., 2018, 42, pp. 663673, 10.1016/j.scs.2018.01.036.
    18. 18)
      • 18. Eberhart, R.C., Kennedy, J.A.: ‘New optimizer using particle swarm theory’. Proc. Sixth Int. Symp. Micromachine and Human Science, Nagoya, Japan, October 1995, pp. 3943.
    19. 19)
      • 19. Kennedy, J., Eberhart, R.C.: ‘Particle swarm optimization’. Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, December 1995, pp. 19421948.
    20. 20)
      • 20. Marco, D., Thomas, S.: ‘The ant colony optimization metaheuristic: algorithms, applications, and advances, handbook of metaheuristics’, Int. Ser. Oper. Res. Manage. Sci., 2003, 57, pp. 250285.
    21. 21)
      • 21. Marco, D., Gianni, D.C.: ‘The ant colony optimization meta-heuristic, new ideas in optimization’ (McGraw-Hill Ltd., Maidenhead, UK), pp. 1132.
    22. 22)
      • 22. Teodorović, D.: ‘Bee colony optimization (BCO)’, in Lim, C.P., Jain, L.C., Dehuri, S. (Eds.): ‘Innovations in swarm intelligence. Studies in computational intelligence’, vol. 248, (Springer, Berlin, Heidelberg, 2009), pp. 3960.
    23. 23)
      • 23. Okdem, S., Karaboga, D., Ozturk, C.: ‘An application of wireless sensor network routing based on artificial bee colony algorithm’. IEEE Congress of Evolutionary Computation (CEC), New Orleans, LA, 2011, pp. 326330, doi: 10.1109/CEC.2011.5949636.
    24. 24)
      • 24. Manshahia, M.S., Dave, M., Singh, S.B.: ‘Firefly algorithm based clustering technique for wireless sensor networks’. Proc. Int. Conf. Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, March 2016.
    25. 25)
      • 25. Manshahia, M.S., Dave, M., Singh, S.B.: ‘Congestion control in wireless sensor networks based on bioluminescent firefly behavior’, Wirel. Sens. Netw., 2015, 7, pp. 149156.
    26. 26)
      • 26. Paganini, F., Wang, F., Doyle, J.C., et al: ‘Congestion control for high performance, stability and fairness in general networks’, IEEE/ACM Trans. Netw., 2005, 13, pp. 4356.
    27. 27)
      • 27. Manshahia, M.S.: ‘Wireless sensor networks: a survey’, Int. J. Sci. Eng. Res., 2016, 7, (4), pp. 710716.
    28. 28)
      • 28. Manshahia, M.S.: ‘Water wave optimization algorithm based congestion control and quality of service improvement in wireless sensor networks’, Trans. Netw. Commun., 2017, 5, (4), pp. 3139.
    29. 29)
      • 29. Manshahia, M.S.: ‘A firefly based energy efficient routing in wireless sensor networks’, IEEE Afr. J. Comput. ICT, 2015, 8, (4), pp. 2732.
    30. 30)
      • 30. Hasan, M., Al-Turjman, F., Al-Rizzo, H.: ‘Analysis of cross-layer design of quality-of-service forward geographic wireless sensor network routing strategies in green Internet of things’, IEEE Access J., 2018, 6, (1), pp. 2037120389.
    31. 31)
      • 31. Al-Turjman, F., Al-Fagih, A., Alsalih, W., et al: ‘A delay-tolerant framework for integrated RSNs in IoT’, Elsevier Comput. Commun. J., 2013, 36, (9), pp. 9981010.
    32. 32)
      • 32. Singh, G., Al-Turjman, F.: ‘Learning data delivery paths in QoI-aware information-centric sensor networks’, IEEE Internet Things J., 2016, 3, (4), pp. 572580.
    33. 33)
      • 33. Hasan, M., Al-Turjman, F.: ‘SWARM-based data delivery in social Internet of things’, Elsevier Future Gener. Comput. Syst., 2017, doi: 10.1016/j.future.2017.10.032.
    34. 34)
      • 34. Al-Turjman, F.: ‘Cognitive routing protocol for disaster-inspired Internet of things’, Elsevier Future Gener. Comput. Syst., 2017, doi: 10.1016/j.future.2017.03.014.
    35. 35)
      • 35. Al-Turjman, F.: ‘Price-based data delivery framework for dynamic and pervasive IoT’, Elsevier Pervasive Mob. Comput. J., 2017, 42, pp. 299316, doi: 10.1016/j.pmcj.2017.05.001.
    36. 36)
      • 36. Hasan, M., Al-Rizzo, H., Al-Turjman, F.: ‘A survey on multipath routing protocols for QoS assurances in real-time multimedia wireless sensor networks’, IEEE Commun. Surv. Tutor., 2017, 19, pp. 14241456, doi: 10.1109/COMST.2017.2661201.
    37. 37)
      • 37. Al-Turjman, F.: ‘Cognitive caching for the future sensors in fog networking’, Elsevier Pervasive Mob. Comput., 2017, 42, pp. 317334.
    38. 38)
      • 38. Hasan, M., Al-Turjman, F., Al-Rizzo, H.: ‘Optimized multi-constrained quality-of-service multipath routing approach for multimedia sensor networks’, IEEE Sens. J., 2017, 17, (7), pp. 22982309.
    39. 39)
      • 39. Manshahia, M.S., Dave, M., Singh, S.B.: ‘Bio inspired congestion control mechanism for wireless sensor networks’. Proc. 2015 IEEE Int. Conf. Computational Intelligence and Computing Research (ICCIC), Madurai, India, December 2015, pp. 141146.
    40. 40)
      • 40. Manshahia, M.S., Singh, S.B., Dave, M.: ‘Congestion control in computer networks’, Int. J. Ultra Sci. Phys. Sci., 2008, 20, (2), pp. 291302.
    41. 41)
      • 41. Manshahia, M.S., Dave, M., Singh, S.B.: ‘Improved bat algorithm based energy efficient congestion control scheme for wireless sensor networks’, Wirel. Sens. Netw., 2016, 8, (11), pp. 229241.
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