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Fuzzy Logic Based Distance and Energy-Aware Routing Protocol in Delay-Tolerant Mobile Sensor Networks

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Abstract

In challenged networks such as Wireless Sensor Networks, limitations such as nodes mobility, short radio range and sparse network density can prevent communications among nodes. Consequently, it can result in long delays in exchanging messages among nodes. Designing Delay-Tolerant Networks is considered to be an approach for dealing with lengthy breakdown of communication between nodes. Using multi-replica methods seems rational for these networks. However, a majority of these methods inject a large amount of replications of a message in the network so as to enhance message delivery probability which consequently leads to the loss of energy and reduction of network efficiency. Two major issues should be considered to achieve data delivery in such challenging networking environments: a routing strategy for the network and a buffer management policy. This study proposes a new routing protocol called Fuzzy-Logic based Distance and Energy Aware Routing protocol (FLDEAR) in delay tolerant mobile sensor network. A FLDEAR is a distance and energy aware protocol that reduces the number of message replications and uses two fuzzy inference systems in routing and buffer management. The results of conducted simulations indicated that this routing algorithm can be used for enhancing data packet delivery ratios and reducing data transmission overhead than several current Delay-Tolerant Mobile Sensor Networks routing protocols.

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References

  1. Saha, D., & Mukherjee, A. (2003). Pervasive computing: A paradigm for the 21st century. Computer (Long. Beach. Calif), 36(3), 25–31.

    Google Scholar 

  2. Ghaffari, A. (2015). Congestion control mechanisms in wireless sensor networks: A survey. Journal of Network and Computer Applications, 52, 101–115.

    Article  Google Scholar 

  3. Mohsenifard, E., & Ghaffari, A. (2016). Data aggregation tree structure in wireless sensor networks using cuckoo optimization algorithm. Information System Telecommunication, 4, 182.

    Google Scholar 

  4. Liu, N., Liu, M., Zhu, J., & Gong, H. (2009). A community-based event delivery protocol in publish/subscribe systems for delay tolerant sensor networks. Sensors, 9(10), 7580–7594.

    Article  Google Scholar 

  5. Song, G., Zhou, Y., Ding, F., & Song, A. (2008). A mobile sensor network system for monitoring of unfriendly environments. Sensors, 8, 7259–7274.

    Article  Google Scholar 

  6. Wang, Y., Lin, F. & Wu, H. (2005). Efficient data transmission in delay fault tolerant mobile sensor networks (DFT-MSN). In Proceedings of IEEE international conference on network protocols (ICNP’05).

  7. McDonald, P., Geraghty, D., Humphreys, I., Farrell, S., & Cahill, V. (2007). Sensor network with delay tolerance (SeNDT). In Proceedings of 16th international conference on computer communications and networks, 2007. ICCCN 2007 (pp. 1333–1338).

  8. Mottaghinia, Z., & Ghaffari, A. (2016). A unicast tree-based data gathering protocol for delay tolerant mobile sensor networks. Information System Telecommunication, 59, 1–12.

    Google Scholar 

  9. Li, Y., Jin, D., Hui, P., & Chen, S. (2016). Contact-aware data replication in roadside unit aided vehicular delay tolerant networks. IEEE Transactions on Mobile Computing, 15(2), 306–321.

    Article  Google Scholar 

  10. Lu, Y., Wang, W., Chen, L., Zhang, Z., & Huang, A. (2016). Distance-based energy-efficient opportunistic broadcast forwarding in mobile delay-tolerant networks. IEEE Transactions on Vehicular Technology, 65(7), 5512–5524.

    Article  Google Scholar 

  11. Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications (pp. 27–34). ACM.

  12. Burleigh, S., et al. (2003). Delay-tolerant networking: An approach to interplanetary internet. IEEE Communications Magazine, 41(6), 128–136.

    Article  Google Scholar 

  13. Leguay, J., Friedman, T. & Conan, V. (2005). DTN routing in a mobility pattern space. In Proceedings of the 2005 ACM SIGCOMM workshop on delay-tolerant networking (pp. 276–283).

  14. Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.

    Article  Google Scholar 

  15. Warthman, F. (2010). Delay-tolerant networks (DTNs); DTN research group: March 2003.

  16. Neely, M. J., & Modiano, E. (2005). Capacity and delay tradeoffs for ad hoc mobile networks. IEEE Transactions on Information Theory, 51(6), 1917–1937.

    Article  MathSciNet  MATH  Google Scholar 

  17. Partan, J., Kurose, J., & Levine, B. N. (2007). A survey of practical issues in underwater networks. ACM SIGMOBILE Mobile Computing and Communications Review, 11(4), 23–33.

    Article  Google Scholar 

  18. Heidemann, J., Ye, W., Wills, J., Syed, A. & Li, Y. (2006). Research challenges and applications for underwater sensor networking. In Wireless communications and networking conference, 2006. WCNC 2006 (Vol. 1, pp. 228–235). IEEE.

  19. Lu, Z. & Fan, J. (2010). Delay/disruption tolerant network and its application in military communications. In 2010 international conference on computer design and applications (ICCDA) (Vol. 5, pp. V5-231).

  20. Pentland, A., Fletcher, R., & Hasson, A. (2004). Daknet: Rethinking connectivity in developing nations. Computer (Long. Beach. Calif), 37(1), 78–83.

    Google Scholar 

  21. Seth, A., Kroeker, D., Zaharia, M., Guo, S. & Keshav, S. (2006). Low-cost communication for rural internet kiosks using mechanical backhaul. In Proceedings of the 12th annual international conference on mobile computing and networking (pp. 334–345).

  22. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. S., & Rubenstein, D. (2002). Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet. ACM SIGARCH Computer Architecture News, 30(5), 96–107.

    Article  Google Scholar 

  23. Vahdat, A., & Becker, D. (2000). Epidemic routing for partially connected ad hoc networks. Technical report, Univ. of California, San Diego, 2000.

  24. Burgess, J., Gallagher, B., Jensen, D. D., & Levine, B. N. (2006). MaxProp: Routing for vehicle-based disruption-tolerant networks. In Proceedings of the IEEE INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE Press, March 2006.

  25. Ramanathan, R., Hansen, R., Basu, P., Rosales-Hain, R. & Krishnan, R. (2007). Prioritized epidemic routing for opportunistic networks. In Proceedings of the 1st international MobiSys workshop on mobile opportunistic networking (pp. 62–66).

  26. Balasubramanian, A., Levine, B., & Venkataramani, A. (2007). DTN routing as a resource allocation problem. ACM SIGCOMM Computer Communication Review, 37(4), 373–384.

    Article  Google Scholar 

  27. Spyropoulos, T., Psounis, K. & Raghavendra, C. S. (2005). Spray and wait: An efficient routing scheme for intermittently connected mobile networks. In Proceedings of the 2005 ACM SIGCOMM workshop on delay-tolerant networking (pp. 252–259).

  28. Ghaffari, A. (2017). Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms. Wireless Networks, 23, 703–714.

    Article  Google Scholar 

  29. Perkins, C. E., & Bhagwat, P. (1994). Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. ACM SIGCOMM Computer Communication Review, 24(4), 234–244.

    Article  Google Scholar 

  30. Perkins, C., Belding-Royer, E., & Das, S. (2003). Ad hoc on-demand distance vector (AODV) routing. IETF RFC 3561, July 2003.

  31. Pushpa Lakshmi, R., & Vincent Antony Kumar, A. (2014). A fuzzy based secure QoS routing protocol using ant colony optimization for mobile ad hoc network. Journal of Intelligent Fuzzy Systems, 27(1), 317–329.

    MathSciNet  MATH  Google Scholar 

  32. Ghaffari, A. & Rahmani, A. (2008). Fault tolerant model for data dissemination in wireless sensor networks. In International symposium on information technology, 2008. ITSim 2008 (pp. 1–8).

  33. Azari, L., & Ghaffari, A. (2015). Proposing a novel method based on network-coding for optimizing error recovery in wireless sensor networks. Indian Journal of Science and Technology, 8, 859–867.

    Article  Google Scholar 

  34. Youssef, M., Ibrahim, M., Latif, M. A., Chen, L., & Vasilakos, A. V. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  Google Scholar 

  35. Spyropoulos, T., Psounis, K. & Raghavendra, C. S. (2007). Spray and focus: Efficient mobility-assisted routing for heterogeneous and correlated mobility. In Fifth annual IEEE international conference on pervasive computing and communications workshops, 2007. PerCom Workshops’ 07 (pp. 79–85).

  36. Wang, Y. & Wu, H. (2006). Replication-based efficient data delivery scheme (RED) for delay/fault-tolerant mobile sensor network (DFT-MSN). In Fourth annual IEEE international conference on pervasive computing and communications workshops, 2006. PerCom Workshops 2006 (p. 5–pp).

  37. Wang, Y., & Wu, H. (2007). Delay/fault-tolerant mobile sensor network (DFT-MSN): A new paradigm for pervasive information gathering. IEEE Transactions on Mobile Computing, 6(9), 1021–1034.

    Article  MathSciNet  Google Scholar 

  38. Feng, Y., Gong, H., Fan, M., Liu, M., & Wang, X. (2011). A distance-aware replica adaptive data gathering protocol for delay tolerant mobile sensor networks. Sensors, 11(4), 4104–4117.

    Article  Google Scholar 

  39. Singh, A. K., Purohit, N., & Varma, S. (2013). Fuzzy logic based clustering in wireless sensor networks: A survey. International Journal of Electronics, 100(1), 126–141.

    Article  Google Scholar 

  40. Jiang, H., Sun, Y., Sun, R., & Xu, H. (2013). Fuzzy-logic-based energy optimized routing for wireless sensor networks. International Journal of Distributed Sensor Networks, 9(8), 216561.

    Article  Google Scholar 

  41. Otal, B., Verikoukis, C. & Alonso, L. (2009). Fuzzy-logic scheduling for highly reliable and energy-efficient medical body sensor networks. In IEEE international conference on communications workshops, 2009. ICC Workshops 2009 (pp. 1–5).

  42. Hung, H., & Wen, J. (2012). Reduce-complexity fuzzy-inference-based iterative multiuser detection for wireless communication systems. International Journal of Communication Systems, 25(4), 478–490.

    Article  Google Scholar 

  43. Makhlouta, J., Harkous, H., Hutayt, F. & Artail, H. (2011). Adaptive fuzzy spray and wait: Efficient routing for opportunistic networks. In 2011 international conference on selected topics in mobile and wireless networking (iCOST) (pp. 64–69).

  44. Mathurapoj, A., Pornavalai, C. & Chakraborty, G. (2009). Fuzzy-spray: Efficient routing in delay tolerant ad-hoc network based on fuzzy decision mechanism. In IEEE international conference on fuzzy systems, 2009. FUZZ-IEEE 2009 (pp. 104–109).

  45. Ma, Y., Kibria, M. R. & Jamalipour, A. (2008). A fuzzy logic-based delivery framework for optimized routing in mobile ad hoc networks. In Wireless communications and mobile computing conference, 2008. IWCMC’08. International (pp. 801–806).

  46. Lo, S.-C., Chiang, M.-H., Liou, J.-H. & Gao, J.-S. (2011). Routing and buffering strategies in delay-tolerant networks: Survey and evaluation. In 2011 40th international conference on parallel processing workshops (ICPPW) (pp. 91–100).

  47. Prodhan, A. T., Das, R., Kabir, H., & Shoja, G. C. (2011). TTL based routing in opportunistic networks. Journal of Network and Computer Applications, 34(5), 1660–1670.

    Article  Google Scholar 

  48. Jain, S., Chawla, M., Soares, V. N. G. J., & Rodrigues, J. J. (2016). Enhanced fuzzy logic-based spray and wait routing protocol for delay tolerant networks. International Journal of Communication Systems, 29(12), 1820–1843.

    Article  Google Scholar 

  49. Kong, X., Lin, C., Jiang, Y., Yan, W., & Chu, X. (2011). Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction. Journal of Network and Computer Applications, 34(4), 1068–1077.

    Article  Google Scholar 

  50. Khan, S. A., Daachi, B., & Djouani, K. (2012). Application of fuzzy inference systems to detection of faults in wireless sensor networks. Neurocomputing, 94, 111–120.

    Article  Google Scholar 

  51. Pedrycz, W., & Gomide, F. (1998). An introduction to fuzzy sets: Analysis and design. Cambridge: MIT Press.

    MATH  Google Scholar 

  52. Abadeh, M. S., Habibi, J., & Lucas, C. (2007). Intrusion detection using a fuzzy genetics-based learning algorithm. Journal of Network and Computer Applications, 30(1), 414–428.

    Article  Google Scholar 

  53. Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010). Limits of predictability in human mobility. Science, 327(5968), 1018–1021.

    Article  MathSciNet  MATH  Google Scholar 

  54. Shin, K., & Kim, S. (2011). Enhanced buffer management policy that utilises message properties for delay-tolerant networks. IET Communications, 5(6), 753–759.

    Article  MathSciNet  Google Scholar 

  55. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2008). Efficient routing in intermittently connected mobile networks: The single-copy case. IEEE/ACM Transactions on Networking, 16(1), 63–76.

    Article  Google Scholar 

  56. Guo, Z., Wang, B. & Cui, J.-H. (2010). Prediction assisted single-copy routing in underwater delay tolerant networks. In Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE (pp. 1–6).

  57. Ren, Z., Peng, S., Chen, H., Fang, J., & Chen, Q. (2015). Epidemic routing based on adaptive compression of vectors: Efficient low-delay routing for opportunistic networks based on adaptive compression of vectors. International Journal of Communication Systems, 28(3), 560–573.

    Article  Google Scholar 

  58. Hui, P., Crowcroft, J., & Yoneki, E. (2011). Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing, 10(11), 1576–1589.

    Article  Google Scholar 

  59. Daly, E. M. & Haahr, M. (2007). Social network analysis for routing in disconnected delay-tolerant manets. In Proceedings of the 8th ACM international symposium on mobile ad hoc networking and computing (pp. 32–40).

  60. Yang, K., Cheng, X., Hu, L., & Zhang, J. (2012). Mobile social networks: State-of-the-art and a new vision. International Journal of Communication Systems, 25(10), 1245–1259.

    Article  Google Scholar 

  61. Cheng, X., Thaeler, A., Xue, G. & Chen, D. (2004). TPS: A time-based positioning scheme for outdoor wireless sensor networks. In Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004 (Vol. 4, pp. 2685–2696).

  62. Thaeler, A., Ding, M., & Cheng, X. (2005). iTPS: An improved location discovery scheme for sensor networks with long-range beacons. Journal of Parallel Distributed Computing, 65(2), 98–106.

    Article  Google Scholar 

  63. Ari, K. & Ott, J. (2007). Increasing reality for dtn protocol simulations. Technical Report, Helsinki University of Technology.

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Mottaghinia, Z., Ghaffari, A. Fuzzy Logic Based Distance and Energy-Aware Routing Protocol in Delay-Tolerant Mobile Sensor Networks. Wireless Pers Commun 100, 957–976 (2018). https://doi.org/10.1007/s11277-018-5360-y

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