Mission-Critical Search and Rescue Networking Based on Multi-agent Cooperative Communication

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


Improving the existing maritime search and rescue network and carrying out a fast and effective search and rescue activities have become a research hotspot. Intelligent devices such as an unmanned surface vehicle (USV) and unmanned aerial vehicle (UAV) can be used to build multi-layer networks. Their fast maneuverability and distributed computing performance provide us with new research directions. In this chapter, a new search and rescue system is designed which applies the ant colony optimization (ACO) and particle swarm optimization (PSO) for search and rescue decision-making and network scheduling. Each device in the system carries out information sharing, edge calculation, and realizes autonomous synchronous search and rescue. We use ACO to plan search path and PSO to schedule data packet forwarding to build a complete maritime search communication network. This network adopts the distributed cluster control mode, reduces the calculation consumption of the control center, and improves the search and rescue efficiency.


  1. 1.
    Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T.: Exploring the shortest path in PSO communication network. In: Proceedings 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, pp. 1–6 (2017)Google Scholar
  2. 2.
    Ruan, L., et al.: Energy-efficient multi-UAV coverage deployment in UAV networks: a game-theoretic framework. China Commun. 15(10), 194–209 (2018)CrossRefGoogle Scholar
  3. 3.
    Kim, G., Mahmud, I., Cho, I.: Self-recovery scheme using neighbor information for multi-drone ad hoc networks. In: Proceedings 2017 23rd Asia-Pacific Conference on Communications (APCC), Perth, WA, pp. 1–5 (2017)Google Scholar
  4. 4.
    Wang, T., Miao, Y., Ma, Q.: GMDSS automatic evaluation system. In: Proceedings 2010 International Conference on Logistics Engineering and Intelligent Transportation Systems, Wuhan, pp. 1–4 (2010)Google Scholar
  5. 5.
    Xu, H., Xu, W., Yang, Z., Shi, J., Chen, M.: Pilot reuse among D2D users in D2D underlaid massive MIMO systems. IEEE Trans. Veh. Technol. 67(1), 467–482 (2018). Jan.CrossRefGoogle Scholar
  6. 6.
    Nousiainen, J., Virtamo, J., Lassila, P.: Impact of multidirectional forwarding on the capacity of large wireless networks. IEEE Commun. Lett. 18(2), 372–375 (2014). FebruaryCrossRefGoogle Scholar
  7. 7.
    Fourmann, J. et al.: Wireless pressure measurement in air blast using PVDF sensors. In Proceedings: IEEE Sensors. Orlando, FL, pp. 1–3 (2016)Google Scholar
  8. 8.
    Xiao, Y., Hao, L., Ma, Z., Ding, Z., Zhang, Z., Fan, P.: Forwarding Strategy selection in Dual-Hop NOMA relaying systems. IEEE Commun. Lett. 22(8), 1644–1647 (2018). Aug.CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Zhao, X., He, X.: A minimum resource neural network framework for solving multiconstraint shortest path problems. IEEE Trans. Neural Netw. Learn. Syst. 25(8), 1566–1582 (2014). Aug.CrossRefGoogle Scholar
  10. 10.
    Yanjie, C., Qiang, W.: A network specific information search system based on mobile agent. In Proceedings: Third Global Congress on Intelligent Systems. Wuhan, pp. 302–304 (2012)Google Scholar
  11. 11.
    Meng, X., Li, H., Cui, J.: Different strategies for differentially private histogram publication. J. Commun. Inf. Netw. 2(3), 68C77 (2017)Google Scholar
  12. 12.
    Su, X., Hui, B., Chang, K.: Multi-hop clock synchronization based on robust reference node selection for ship ad-hoc network. J. Commun. Netw. 18(1), 65–74 (2016). Feb.CrossRefGoogle Scholar
  13. 13.
    Lin, C., Bi, Y., Zhao, H., Wang, Z., Wang, J.: Scheduling algorithms for time-constrained big-file transfers in the Internet of Vehicles. J. Commun. Inf. Netw. 2(2), 126–135 (2017). Jun.CrossRefGoogle Scholar
  14. 14.
    Zheng, C., Shan, Q., Zhang, H., Wang, Z.: On stabilization of stochastic cohen-grossberg neural networks with mode-dependent mixed time-delays and markovian switching. IEEE Trans. Neural Netw. Learn. Syst. 24(5), 800–811 (2013). MayCrossRefGoogle Scholar
  15. 15.
    Wen, G., Zhang, Q., Wang, H., Tian, Q., Tao, Y.: An ant colony algorithm based on cross-layer design for routing and wavelength assignment in optical satellite networks. China Commun. 14(8), 63–75 (2017). Aug.CrossRefGoogle Scholar
  16. 16.
    Liu, X., Wei, Z.: Distributed computing system based on microprocessor cluster for wearable devices. In: Proceedings 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi’an, pp. 66–71 (2017)Google Scholar
  17. 17.
    Du, W., Zhong, W., Tang, Y., Du, W., Jin, Y.: High-dimensional robust multi-objective optimization for order scheduling: a decision variable classification approach. IEEE Trans. Ind. Inf. 15(1), 293–304 (2019). Jan.CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electrical Engineering and IntelligentizationDongguan University of TechnologyDongguanChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

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