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Minimum cost event driven WSN with spatial differentiated QoS requirements

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Abstract

In wireless sensor networks applications like rare-event detection, maximizing lifetime, minimizing end-to-end delay, and minimizing the network cost, are some of the most important quality of service requirements. In applications like disastrous or fire event detection, if an event is detected very close to the center facility, the event information should reach to the base-station much faster than an event detected far away. In this work, we are interested to find a minimum cost network for such applications. A stochastic approach is used to find the minimum cost network for given lifetime requirement and spatial differentiated delay constraints. We use Monte-Carlo simulations for validating our analysis. In order to show the effectiveness of our approach, we use network simulator-2 simulations.

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Notes

  1. The e2e delay of a node is defined as the average time a packet takes from the node to reach the base-station. Moreover, the e2e delay of the network is defined as the maximum e2e delay encountered by any node in the network.

  2. In this paper we assume that solving the problem of minimum cost network is as same as finding the critical sensor density because the number of nodes is directly proportional to the overall deplyment cost of the network.

  3. This kind of deployment can be justified when the sensor nodes are air-dropped from a plane in a hostile environment.

  4. Note that, a sensor node may not directly communicate to base-station, but multi-hop communication can be used to send the data-packet to he base-station.

  5. Note that, \(t_D\) denotes the average transmission delay without accounting the randomness involved in wireless channel.

  6. The corresponding expected e2e delay associated with a sensor density is estimated using the method described Sect. 3.1.

  7. The CSD is the average number of sensors present in each \({\mathrm{m}}^2\) area.

References

  1. AlSkaif, T., Bellalta, B., Zapata, M. G., & Ordinas, J. M. B. (2017). Energy efficiency of mac protocols in low data rate wireless multimedia sensor networks: A comparative study. Ad Hoc Networks, 56, 141–157.

    Article  Google Scholar 

  2. Biswas, S., & Morris, R. (2005). Exor: Opportunistic multi-hop routing for wireless networks. ACM SIGCOMM Computer Communication Review, 35(4), 133–144.

    Article  Google Scholar 

  3. Cai, H., Jia, X., & Sha, M. (2011). Critical sensor density for partial connectivity in large area wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 7(4), 35.

    Article  Google Scholar 

  4. Chen, X., Ma, M., & Liu, A. (2017). Dynamic power management and adaptive packet size selection for iot in e-healthcare. Computers and Electrical Engineering, 65, 357–375.

    Article  Google Scholar 

  5. Chen, Z., Ma, M., Liu, X., Liu, A., & Zhao, M. (2017). Reliability improved cooperative communication over wireless sensor networks. Symmetry, 9(10), 209.

    Article  Google Scholar 

  6. Dorling, K., Messier, G. G., Valentin, S., & Magierowski, S. (2015). Minimizing the net present cost of deploying and operating wireless sensor networks. IEEE Transactions on Network and Service Management, 12(3), 511–525.

    Article  Google Scholar 

  7. Ghaffarzadeh, H., & Doustmohammadi, A. (2014). Two-phase data traffic optimization of wireless sensor networks for prolonging network lifetime. Wireless Networks, 20(4), 671–679.

    Article  Google Scholar 

  8. Hammoudeh, M., Al-Fayez, F., Lloyd, H., Newman, R., Adebisi, B., Bounceur, A., et al. (2017). A wireless sensor network border monitoring system: Deployment issues and routing protocols. IEEE Sensors Journal, 17(8), 2572–2582.

    Article  Google Scholar 

  9. Huang, M., Liu, A., Wang, T., & Huang, C. (2018). Green data gathering under delay differentiated services constraint for internet of things. Wireless Communications and Mobile Computing

  10. Jang, B., Lim, J. B., & Sichitiu, M. L. (2008). As-mac: An asynchronous scheduled mac protocol for wireless sensor networks. In Proceedings of the 5th IEEE international conference on mobile ad hoc and sensor systems (pp. 434–441). IEEE.

  11. Jang, U., Lee, S., & Yoo, S. (2012). Optimal wake-up scheduling of data gathering trees for wireless sensor networks. Journal of Parallel and Distributed Computing, 72(4), 536–546.

    Article  Google Scholar 

  12. Kim, J., Lin, X., Shroff, N., & Sinha, P. (2010). Minimizing delay and maximizing lifetime for wireless sensor networks with anycast. IEEE/ACM Transactions on Networking, 18(2), 515–528.

    Article  Google Scholar 

  13. Kim, J., Lin, X., & Shroff, N. (2011). Optimal anycast technique for delay-sensitive energy-constrained asynchronous sensor networks. IEEE/ACM Transactions on Networking, 19(2), 484–497.

    Article  Google Scholar 

  14. Lazos, L., & Poovendran, R. (2006). Stochastic coverage in heterogeneous sensor networks. ACM Transactions on Sensor Networks (TOSN), 2(3), 325–358.

    Article  Google Scholar 

  15. Liu, A., Chen, Z., & Xiong, N. N. (2018a). An adaptive virtual relaying set scheme for loss-and-delay sensitive wsns. Information Sciences, 424, 118–136.

    Article  MathSciNet  Google Scholar 

  16. Liu, S., Fan, K. W., & Sinha, P. (2009). Cmac: An energy-efficient mac layer protocol using convergent packet forwarding for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(4), 29.

    Article  Google Scholar 

  17. Liu, Y., Ota, K., Zhang, K., Ma, M., Xiong, N., Liu, A., et al. (2018b). Qtsac: An energy-efficient mac protocol for delay minimization in wireless sensor networks. IEEE Access, 6, 8273–8291.

    Article  Google Scholar 

  18. Luo, H., Wu, K., Guo, Z., Gu, L., & Ni, L. M. (2012). Ship detection with wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(7), 1336–1343.

    Article  Google Scholar 

  19. Mitton, N., Simplot-Ryl, D., & Stojmenovic, I. (2009). Guaranteed delivery for geographical anycasting in wireless multi-sink sensor and sensor-actor networks. In Proceedings of IEEE INFOCOM 2009 (pp. 2691–2695). IEEE.

  20. Paillard, G., Ravelomananana, V. (2008). Limit theorems for degree of coverage and lifetime in large sensor networks. In Proceedings of the 27th conference on computer communications. IEEE.

  21. Rossi, M., & Zorzi, M. (2007). Integrated cost-based mac and routing techniques for hop count forwarding in wireless sensor networks. IEEE Transactions on Mobile Computing, 6(4), 434–448.

    Article  Google Scholar 

  22. Rossi, M., Zorzi, M., & Rao, R. R. (2008). Statistically assisted routing algorithms (sara) for hop count based forwarding in wireless sensor networks. Wireless Networks, 14(1), 55–70.

    Article  Google Scholar 

  23. Salim, A., Osamy, W., & Khedr, A. M. (2014). Ibleach: Intra-balanced leach protocol for wireless sensor networks. Wireless Networks, 20(6), 1515–1525.

    Article  Google Scholar 

  24. Sun, Z., Wang, P., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., & Akyildiz, I. F. (2011). Bordersense: Border patrol through advanced wireless sensor networks. Ad Hoc Networks, 9(3), 468–477.

    Article  Google Scholar 

  25. Tang, J., Liu, A., Zhao, M., & Wang, T. (2018). An aggregate signature based trust routing for data gathering in sensor networks. Security and Communication Networks

  26. Yadav, S., & Yadav, R. S. (2016). A review on energy efficient protocols in wireless sensor networks. Wireless Networks, 22(1), 335–350.

    Article  Google Scholar 

  27. Yang, T., Mu, D., Hu, W., & Zhang, H. (2014). Energy-efficient border intrusion detection using wireless sensors network. EURASIP Journal on Wireless Communications and Networking, 1, 46.

    Article  Google Scholar 

  28. Yen, L., Yu, C., & Cheng, Y. (2006). Expected k-coverage in wireless sensor networks. Ad Hoc Networks, 4(5), 636–650.

    Article  Google Scholar 

  29. Zhang, H., & Hou, J. (2004). On deriving the upper bound of alpha-lifetime for large sensor networks. In Proceedings of the 5th ACM international symposium on mobile ad hoc networking and computing (pp. 121–132). ACM.

  30. Zhang, H., & Hou, J. (2006). Is deterministic deployment worse than random deployment for wireless sensor network? In Proceedings of IEEE INFOCOM (pp. 1–13). IEEE.

  31. Zhao, Y., Wu, J., Li, F., & Lu, S. (2010). VBS: Maximum lifetime sleep scheduling for wireless sensor networks using virtual backbones. In Proceedings of IEEE INFOCOM (pp. 1–5). IEEE.

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Acknowledgements

We would like to thank department of Computer Science and Engineering, Indian Institute of Technology Guwahati for providing us all the facilities to carry out the research. We would also acknowledge MHRD for their funding for the work.

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Authors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    Debanjan Sadhukhan & Seela Veerabhadreswara Rao

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  1. Debanjan Sadhukhan
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  2. Seela Veerabhadreswara Rao
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Correspondence to Debanjan Sadhukhan.

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Sadhukhan, D., Rao, S.V. Minimum cost event driven WSN with spatial differentiated QoS requirements. Wireless Netw 25, 3899–3915 (2019). https://doi.org/10.1007/s11276-018-01926-z

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