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Data Dissemination Protocol for Mobile Sink in Wireless Sensor Networks

DOI: 10.1155/2014/560675

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Abstract:

In wireless sensor networks, the sensor nodes find the route towards the sink to transmit the data. The sensor node transmits the data directly to the sink, or it relays the data through neighbor nodes. The nodes near to the sink transmit more data than other nodes. It results in the small lifetime of the network. To prolong the lifetime of the network, we use the mobile sink approach. The mobile sink makes the network dynamic. It is a challenging task to find the route in the dynamic network. In this paper, we have proposed a distributed tree-based data dissemination (TEDD) protocol with mobile sink. The protocol is validated through simulation and compared with the existing protocols using some metrics such as energy consumption, average end-to-end delay, and throughput. The experiment results show that the proposed protocol outperforms the existing protocols. 1. Introduction Sensor network is a multihop network which consists of hundreds of sensor nodes. The main resource constraint of the sensor node is the energy. Generally, sensor networks are deployed in the unattended and hostile environment such as wildlife detection, continuous environment monitoring, and military. So it is impossible to replace or recharge the battery. The main goal of the proposed paper is to develop the energy-efficient protocol to prolong the lifetime of the network. In the sensor network, the work of the sensor node is not only to sense environmental data, but also to relay those data to the sink. Sink is a resource-rich node, whose responsibility is to collect the sensed data from the sensor nodes and send it to the user via the Internet. Sensor node has constraints of limited communication range, which does not allow direct communication between source and sink. It relays the data to the sink in the multihop manner. The sensor nodes close to the sink transmit more data than the other nodes in the network. That is why they depleted their energy and died. This may result in the partition of the network. This situation is called “crowded center effect” [1] or “energy hole problem” [2]. The energy hole problem can be overcome by using the mobile sink in the network. The mobile sink moves across the network and collects the data from the sensor nodes. The movement of the sink may be random, controlled, or predefined. The mobile sink makes the network dynamic. So the data dissemination protocols for network with static sink are unsuitable for the network with the mobile sink. It is a challenge to develop energy-efficient data dissemination protocols for the mobile sink. In

References

[1]  L. Popa, A. Rostamizadeh, R. Karp, C. Papadimitriou, and I. Stoica, “Balancing traffic load in wireless networks with curveball routing,” in Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 170–179, September 2007.
[2]  J. Li and P. Mohapatra, “Analytical modeling and mitigation techniques for the energy hole problem in sensor networks,” Pervasive and Mobile Computing, vol. 3, no. 3, pp. 233–254, 2007.
[3]  N. M. Khan, I. Ali, Z. Khalid, G. Ahmed, A. A. Kavokin, and R. Ramer, “Quasi centralized clustering approach for an energy-efficient and vulnerability-aware routing in wireless sensor networks,” in Proceedings of the 1st ACM International Workshop on Heterogeneous Sensor and Actor Networks, HeterSanet 2008, pp. 67–72, ACM, May 2008.
[4]  R. Sudarmani and K. R. S. Kumar, “Energy-efficient clustering algorithm for heterogeneous sensor networks with mobile sink,” European Journal of Scientific Research, vol. 68, no. 1, pp. 60–71, 2012.
[5]  L. Song and D. Hatzinakos, “Dense wireless sensor networks with mobile sinks,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), pp. 677–680, IEEE, March 2005.
[6]  L. Song and D. Hatzinakos, “Architecture of wireless sensor networks with mobile sinks: sparsely deployed sensors,” IEEE Transactions on Vehicular Technology, vol. 56, no. 4, pp. 1826–1836, 2007.
[7]  D. Puthal, B. Sahoo, and S. Sharma, “Dynamic model for efficient data collection in wireless sensor networks with mobile sink,” International Journal of Computer Science and Teleology, vol. 3, no. 1, pp. 623–628, 2012.
[8]  P. Juang, H. Oki, Y. Wang, M. Martonosi, L. S. Peh, and D. Rubenstein, “Energyefficient computing for wildlife tracking: design tradeoffs and early experiences with zebranet,” SIGOPS Operation System Review, vol. 36, no. 5, pp. 96–107, 2002.
[9]  S. Farrell, V. Cahill, D. Geraghty, I. Humphreys, and P. McDonald, “When TCP breaks: delay- and disruption-tolerant networking,” IEEE Internet Computing, vol. 10, no. 4, pp. 72–78, 2006.
[10]  L. Selavo, A. Wood, Q. Cao et al., “Luster: Wireless sensor network for environmental research,” in Proceedings of the 5th ACM International Conference on Embedded Networked Sensor Systems, pp. 103–116, ACM, New York, NY, USA, November 2007.
[11]  H. Luo, F. Ye, J. Cheng, S. Lu, and L. Zhang, “TTDD: two-tier data dissemination in large-scale wireless sensor networks,” Wireless Networks, vol. 11, no. 1-2, pp. 161–175, 2005.
[12]  H. S. Kim, T. F. Abdelzaher, and W. H. Kwon, “Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks,” in Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, pp. 193–204, ACM, November 2003.
[13]  K. I. Hwang, J. In, and D. S. Eom, “Distributed dynamic shared tree for minimum energy data aggregation of multiple mobile sinks in wireless sensor networks,” Proceedings of the 3rd European conference on Wireless Sensor Networks, Springer, Berlin, Germany, vol. 3868, pp. 132–147, 2006.
[14]  K. I. Hwang and D. S. Eom, “Adaptive sink mobility management scheme for wireless sensor networks,” in Proceedings of the 3rd International Conference on Ubiquitous Intelligence and Computing, Lecture Notes in Computer Science, pp. 478–487, Springer, Berlin, Germany, 2006.
[15]  A. Carneiro Viana, T. Herault, T. Largillier, S. Peyronnet, and F. Za?di, “Supple: A flexible probabilistic data dissemination protocol for wireless sensor networks,” in Proceedings of the 13th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 385–392, ACM, October 2010.
[16]  Y. Faheem and S. Boudjit, “SN-MPR: A multi-point relay based routing protocol for wireless sensor networks,” in Proceedings of the IEEE/ACM International Conference on Green Computing and Communications & International Conference on Cyber, Physical and Social Computing, pp. 761–767, IEEE Computer Society, December 2010.
[17]  N. C. Wang, Y. F. Huang, J. S. Chen, and P. C. Yeh, “Energy-aware data aggregation for grid-based wireless sensor networks with a mobile sink,” Wireless Personal Communications, vol. 43, no. 4, pp. 1539–1551, 2007.
[18]  E. Lee, S. Park, F. Yu, Y. Choi, M. S. Jin, and S. H. Kim, “A predictable mobility-based data dissemination protocol for wireless sensor networks,” in Proceedings of the 22nd International Conference on Advanced Information Networking and Applications, pp. 741–747, IEEE Computer Society, March 2008.
[19]  G. Wang, T. Wang, W. Jia, M. Guo, and J. Li, “Adaptive location updates for mobile sinks in wireless sensor networks,” Journal of Supercomputing, vol. 47, no. 2, pp. 127–145, 2009.
[20]  A. Munari, W. Schott, and S. Krishnan, “Energy-efficient routing in mobile wireless sensor networks using mobility prediction,” in Proceedings of the IEEE 34th Conference on Local Computer Networks (LCN '09), pp. 514–521, IEEE, October 2009.
[21]  C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: a scalable and robust communication paradigm for sensor networks,” in Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MOBICOM '00), pp. 56–67, ACM, Boston, Mass, USA, August 2000.
[22]  W. Zhang, G. Cao, and T. La Porta, “Dynamic proxy tree-based data dissemination schemes for wireless sensor networks,” Wireless Networks, vol. 13, no. 5, pp. 583–595, 2007.
[23]  Crossbow Technology, I. Micaz datasheet. Technical report, San Jose, Calif, USA, http://www.openautomation.net/uploadsproductos/micazdatasheet.pdf.
[24]  B. Liang and Z. J. Haas, “Predictive distance-based mobility management for PCS networks,” in Proceedings of the 18th Annual Joint Conference of the IEEE Computer and Communications Societie, pp. 1377–1384, IEEE, March 1999.
[25]  J. Broch, D. A. Maltz, D. B. Johnson, Y. C. Hu, and J. Jetcheva, “A performance comparison of multi-hop wireless ad hoc network routing protocols.,” in Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp. 85–97, ACM, 1998.

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