Residual energy aware mobile data gathering in wireless sensor networks
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  • 作者:Xuan Rao ; Hongyu Huang ; Jiqiang Tang ; Huan Zhao
  • 关键词:Residual energy awareness ; Spectral clustering ; Data relay tree ; Path scheduling
  • 刊名:Telecommunication Systems
  • 出版年:2016
  • 出版时间:May 2016
  • 年:2016
  • 卷:62
  • 期:1
  • 页码:31-41
  • 全文大小:1,019 KB
  • 参考文献:1.Vaish, G.D. (2012) Routing rotocols in wireless sensor networks: A survey, 2012 Second International Conference on Advanced Computing & Communication Technologies(ACCT), 7–8 Jan, pp. 474–480.
    2.Incel, O. D., Ghosh, A., Krishnamachari, B., & Chintalapudi, K. (2012). Fast data collection in tree-based wireless sensor networks. IEEE Transctions on Mobile Computing, 11(1), 86–99.CrossRef
    3.Zhang, D., Zhou, J., Guo, M., & Cao, J. (2011). TASA: Tag-free activity sensing using RFID tag arrays. IEEE Transactions on Parallel and Distributed Systems (TPDS), 22, 558–570.CrossRef
    4.Zhang, Yin, Chen, Min, Mao, S., Hu, L., & Leung, V. (2014). CAP: Crowd activity prediction based on big data analysis. IEEE Network, 28(4), 52–57.CrossRef
    5.Ma, M., Yang, Y., & M, Zhao. (2013). Tour planning for mobile data-gathering mechanisms in wireless sensor networks. IEEE Transactions on Vehicular Technology, 62, 1472–1483.CrossRef
    6.Zhao, M., & Yang, Y. (2012). Bounded relay hop mobile data gathering in wireless sensor networks. IEEE Transactions on Computers, 61(2), 265–277.CrossRef
    7.Elbhiri, B., Fkihi, S. E., Saadane, R., Lasaad, N., Jorio, A., Aboutajdine, D. (2013). A new spectral classfication for robust clustering in wireless sensor networks. IFIP WMNC’.
    8.Zhang, Z., Ma, M., & Yuan, Y. (2008). Energy-efficien multihop polling in clusters of two-layerd heterogeneous sensor networks. IEEE Transactions On Computer, 57(2), 231–245.CrossRef
    9.Heinzelman, W.R, Chandrakasan, A., Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless micro-sensor networks, Proceedings Hawaii International Conference on System Sciences(HICS).
    10.Younis, O., & Fahmy, S. (2004). Distributed clustering in ad-hoc sensor networks: A hybird, energy-efficient approachy: IEEE INFOCOM: Proc.
    11.Zhang, D., Zhang, D., Xiong, H., Hsu, C., & Vasilakos, A. (2014). BASA: Building mobile ad-hoc social networks on top of android. IEEE Networks, 28(1), 4–9.CrossRef
    12.Manjeshwar, A., and Agrawal, D.P. (2001). Teen: A routing protocol for enhanced efficiency in wireless sensor networks, Proc. IEEE International Parallel and Distributed Processing Symp. (IPDPS), Apr.
    13.Ma, M., & Yang, Y. (2007). MDC: An energy-efficient data gathering mechanism for larg-scale multihop sensor networks. IEEE Transaction on Parallel and Distributed System, 18, 1476–1488.CrossRef
    14.Zhao, M., Ma, M., & Yang, Y. (2011). Efficient data gathering with mobile collectors and space-division multiple access technique in wireless sensor networks. IEEE Transactions on Computers, 60(3), 400–417.CrossRef
    15.Zhao, M., Ma, M., & Yang, Y. (2011). Data gathering in wireless sensor networks with mulitiple mobile collectors and SDMA technique sensor nerworks. IEEE Transactions On Computers, 60, 3.CrossRef
    16.Zhao, M., & Yang, Y. (2012). Optimization-based distributed algorithms for mobile data gathering in wireless sensor networks. IEEE Transactions on Computers, 11(10), 1464–1477.CrossRef
    17.Xing, G., Wang, T., Xie, Z., & Jia, W. (2008). Rendezvous planning in wireless sensor networks with mobile elements. IEEE Transactions on Mobile Computing, 7(12), 1430–1443.CrossRef
    18.Di Francesco, M., & Das, S. K. (2011). Data collection in wireless sensor networks with mobile elements: A survey. ACM Transactions on Sensor Networks, 8(1), 7.CrossRef
    19.Zhao, M., Gong, D., Yang, Y. (2010). A cost minimization algorithm for mobile data gathering in wireless sensor networks, IEEE 7th International Conference on Mobile Adhoc and Sensor Systems (MASS), CA.
    20.Somasundara, A. A., Ramamoorthy, A., & Srivastava, M. B. (2007). Mobile element scheduling with dynamic deadlines. IEEE Transaction Mobile Computing, 6(4), 395–410.CrossRef
    21.Zhang, D., Chen, M., Guizani, M., Xiong, H., & Zhang, D. (2014). Mobility prediction in telecom cloud using mobile calls. IEEE Wireless Communications, 21(1), 26–32.CrossRef
    22.Guo, S., and Yang, Y. (2012). A distributed optimal framework for mobile data gathering with concurrent data uploading in wireless sensor networks, Proceedings IEEE INFOCOM.
    23.Zhao, M., and Yang, Y. (2011). A framework for mobile data gathering with load balanced clustering and MIMO uploading, Proceedings IEEE INFOCOM.
    24.Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395–416.CrossRef
    25.Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems, 2, 849–856.
    26.Heinzelman, W.R., Chandrakasan, A., and Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensro networks, In Proceedings of the 33rd Hawaii International Conference on System Sciences(HICSS-33), January (2000).
  • 作者单位:Xuan Rao (1) (2)
    Hongyu Huang (1) (2)
    Jiqiang Tang (1) (2)
    Huan Zhao (1) (2)

    1. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing, China
    2. College of Computer Science, Chongqing University, Chongqing, China
  • 刊物类别:Business and Economics
  • 刊物主题:Economics
    Business Information Systems
    Computer Communication Networks
    Artificial Intelligence and Robotics
    Probability Theory and Stochastic Processes
  • 出版者:Springer Netherlands
  • ISSN:1572-9451
文摘
The intrinsic characteristic of wireless sensor networks is the power limitation of sensor nodes. The most difficult challenge is how to save energy of sensor nodes so that the lifetime of a sensor network will be prolonged. A mobile data collector (MDC) is introduced to achieve this goal. We suppose that all sensor nodes are kept static once deployed, and a single MDC traverses the network to reduce the communication of relaying data among sensors. In general, we need to consider two factors when designing a traveling path of a MDC, i.e., the data overflow on a sensor node and the timeliness of each data. In this paper, we aim to prolong lifetime of a sensor network by designing heuristic traveling paths of the MDC under these two constraints. It is obviously that a fixed MDC path leads to a quicker energy consumption of the nodes near that path. So we propose an iterative scheme which determines the traveling path of the MDC before each round of the data gathering. For each data gathering round, our scheme consists of four steps. First we iteratively partition the network into clusters by spectral clustering, and then select a cluster head as the polling point which is a special position for collecting data depended on residual energy. Following that, we construct a balanced data relay tree in each cluster. Last, we design a shortest path for the MDC. Since the paths of MDC are different in each round, the lifetime of the sensor network can be prolonged. Simulations reveal that our method is better than the existing methods and prolong the lifetime of wireless sensor network.

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