Efficient Scheduling Strategy for Data Collection in Delay-Tolerant Wireless Sensor Networks with a Mobile Sink

  • Zhansheng ChenEmail author
  • Hong Shen
  • Xiaofan Zhao
  • Tingmei Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 931)


Wireless sensor networks based on mobile sink (MS) can significantly alleviate the problem of network congestion and energy hole, but it results in large delay because of restriction of moving speed and lead to the loss of data due to the limited communication time. In this paper, a grid-based efficient scheduling and data gathering scheme (GES-DGS) is proposed for maximizing the amount of data collected and reducing energy consumption simultaneously within the delay of network tolerance. The main challenges of our scheme are how to optimize the trajectory and the sojourn times of MS and how to deliver the sensed data to MS in an energy-efficient way. To deal with the above problems, we first divide the monitoring field into multiple grids and construct the hop gradient of each grid. Second, we design a heuristic rendezvous point selection strategy to determine the trajectory of MS and devise a routing protocol based on hops and energy. With extensive simulation, we demonstrate that GES-DGS scheme not only significantly extends network lifespan compared with MS-based data gathering schemes, but also pro-actively adapts to the changes in delay in specific applications.


Wireless sensor networks Trajectory Mobile sink Data gathering Delay 



This work was supported in part by the National Natural Science Foundation of China under Grant 61170232 and 81160183, Australian Research Council Discovery Project under Grant DP150104871 and the Fundamental Research Funds for the New Teachers’ Scientific Research Fund of People’s Public Security University of China under its projects number 2018JKF609.


  1. 1.
    Chen, Z., Shen, H.: A grid-based reliable multi-hop routing protocol for energy-efficient wireless sensor networks. Int. J. Distrib. Sens. Netw. 14 (2018). Scholar
  2. 2.
    Xing, G., Wang, T., Xie, Z., et al.: Rendezvous planning in wireless sensor networks with mobile elements. IEEE Trans. Mob. Comput. 7(12), 1430–1443 (2008)CrossRefGoogle Scholar
  3. 3.
    Wen, W., Zhao, S., Shang, C., et al.: EAPC: Energy-aware path construction for data collection using mobile sink in wireless sensor networks. IEEE Sens. J. PP(99), 1 (2017)Google Scholar
  4. 4.
    Yun, Y.S., Xia, Y.: Maximizing the lifespan of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Trans. Mob. Comput. 9(9), 1308–1318 (2010)CrossRefGoogle Scholar
  5. 5.
    Cayirpunar, O., Tavli, B., Kadioglu-Urtis, E., et al.: Optimal mobility patterns of multiple base stations for wireless sensor network lifespan maximization. IEEE Sens. J. 17(21), 7177–7188 (2017)CrossRefGoogle Scholar
  6. 6.
    Cheng, C.F., Li, L.H., Wang, C.C.: Data gathering with minimum number of relay packets in wireless sensor networks. IEEE Sens. J. PP(99), 1 (2017)Google Scholar
  7. 7.
    Huynh, T.T., Dinh-Duc, A.V., Tran, C.H.: Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. J. Commun. Netw. 18(4), 580–588 (2016)CrossRefGoogle Scholar
  8. 8.
    Somasundara, A.A., Kansal, A., Jea, D.D., et al.: Controllably mobile infrastructure for low energy embedded networks. IEEE Trans. Mob. Comput. 5(8), 958–973 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zhansheng Chen
    • 1
    • 2
    Email author
  • Hong Shen
    • 3
    • 4
  • Xiaofan Zhao
    • 5
  • Tingmei Wang
    • 2
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.School of Applied Science and TechnologyBeijing Union UniversityBeijingChina
  3. 3.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  4. 4.School of Computer ScienceUniversity of AdelaideAdelaideAustralia
  5. 5.People’s Public Security University of ChinaBeijingChina

Personalised recommendations