Skip to main content

Advertisement

Log in

A local decision making technique for reliable service discovery using D2D communications in disaster recovery networks

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Disaster recovery networks (DRN) are prominent is refurbishing communication in disaster prone regions without the support of fixed infrastructures. The network consists of wireless devices that temporarily aid service discovery for effecting communication re-construction. Improving communication rate and non-delay tolerant request processing are challenging tasks in DRN due to the unsynchronized behavior and limited energy of the wireless devices. This manuscript introduces a reliable local decision making (RLDM) technique for improving the efficiency of DRNs. RLDM first classifies the service discovery conditions that are to be satisfied by the forwarding device followed by reliable neighbor selection. Local decision making process refines neighbor selection at the time of neighbor replacement using time-dependent function. The harmonized process of classification and neighbor selection aids to discover optimal neighbors that achieve better request handling. The experimental results prove the stability of the proposed method by achieving higher throughput and device utilization and minimizing transmission delay and overhead.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Martín-Campillo A, Crowcroft J, Yoneki E, Martí R (2013) Evaluating opportunistic networks in disaster scenarios. J Netw Comput Appl 36(2):870–880

    Article  Google Scholar 

  2. Sakanushi K, Hieda T, Shiraishi T, Ode Y, Takeuchi Y, Imai M, Higashino T, Tanaka H (2012) Electronic triage system for continuously monitoring casualties at disaster scenes. J Ambient Intell Humaniz Comput 4(5):547–558

    Article  Google Scholar 

  3. Hirose T, Nuno F, Nakatsugawa M (2015) Development of Wireless Systems for Disaster Recovery Operations. IEICE Transactions on Electronics E98.C(7):630–635

    Article  Google Scholar 

  4. Ueda Y (2014) Vehicle-mounted transportable mobile base station and backhaul link for disaster relief operation. New Breeze 26(3):1–14

    Google Scholar 

  5. Shakeel PM, Burhanuddin MA, Desa MI (2019) Lung Cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement. https://doi.org/10.1016/j.measurement.2019.05.027

  6. Sakano T, Kotabe S, Komukai T, Kumagai T, Shimizu Y, Takahara A, Ngo T, Fadlullah ZM, Nishiyama H, Kato N (2016) Bringing movable and deployable networks to disaster areas: development and field test of MDRU. IEEE Netw 30(1):86–91

    Article  Google Scholar 

  7. Mohamed Shakeel P, Baskar S, Selvakumar S (2019) Wireless Pers Commun. Retrieving Multiple Patient Information by Using the Virtual MIMO and Path Beacon in Wireless Body Area Network, pp 1–12. 10.1007/s11277-019-06525-5

  8. Morrison K (Jan.2011) Rapidly recovering from the catastrophic loss of a major telecommunications office. IEEE Commun Mag 49(1):28–35

    Article  Google Scholar 

  9. Manogaran G, Baskar S, Shakeel PM, Naveen Chilamkurti R (2019) Kumar, analytics in real time surveillance video using two-bit transform accelerative regressive frame check. Multimed Tools Appl:1–18. https://doi.org/10.1007/s11042-019-7526-3

  10. Cheng W, Zhang X, Zhang H (2016) Optimal power allocation with statistical QoS provisioning for D2D and cellular communications over Underlaying wireless networks. IEEE Journal on Selected Areas in Communications 34(1):151–162

    Article  Google Scholar 

  11. Moghaddam JZ, Usman M, Granelli F (2018) A device-to-device communication-based disaster response network. IEEE Transactions on Cognitive Communications and Networking 4(2):288–298

    Article  Google Scholar 

  12. Nakayama Y, Maruta K, Tsutsumi T, Sezaki K (2018) Wired and wireless network cooperation for wide-area quick disaster recovery. IEEE Access 6:2410–2424

    Article  Google Scholar 

  13. Hayajneh A, Zaidi S, Mclernon D, Ghogho M (2017) Performance analysis of UAV enabled disaster recovery network: A stochastic geometric framework based on matern cluster processes. IET 3rd International Conference on Intelligent Signal Processing (ISP 2017)

  14. Kanai T, Senoo Y, Asaka K, Sugawa J, Tamai H, Saito H, Minato N, Oguri A, Sumita S, Sato T, Kikuchi N, Matsushita S-I, Tsuritani T, Okamoto S, Yamanaka N, Suzuki K-I, Otaka A (2018) Novel automatic service restoration technique by using self-reconfiguration of network resources for a disaster-struck metro-access network. J Lightwave Technol 36(8):1516–1523

    Article  Google Scholar 

  15. Su Z, Xu Q, Luo J, Pu H, Peng Y, Lu R (2018) A secure content caching scheme for disaster backup in fog computing enabled Mobile social networks. IEEE Transactions on Industrial Informatics 14(10):4579–4589

    Article  Google Scholar 

  16. Ali K, Nguyen HX, Vien Q-T, Shah P, Chu Z (2018) Disaster management using D2D communication with power transfer and clustering techniques. IEEE Access 6:14643–14654

    Article  Google Scholar 

  17. Lourenço RB, Savas SS, Tornatore M, Mukherjee B (2018) Robust hierarchical control plane for transport software-defined networks. Opt Switch Netw 30:10–22

    Article  Google Scholar 

  18. Bilbao M, Ser JD, Perfecto C, Salcedo-Sanz S, Portilla-Figueras J (2018) Cost-efficient deployment of multi-hop wireless networks over disaster areas using multi-objective meta-heuristics. Neurocomputing 271:18–27

    Article  Google Scholar 

  19. Yücel E, Salman F, Arsik I (2018) Improving post-disaster road network accessibility by strengthening links against failures. Eur J Oper Res 269(2):406–422

    Article  MathSciNet  Google Scholar 

  20. Vodák R, Bíl M, Křivánková Z (2018) A modified ant colony optimization algorithm to increase the speed of the road network recovery process after disasters. International Journal of Disaster Risk Reduction 31:1092–1106

    Article  Google Scholar 

  21. Chen W-P, Tsai A-H, Tsai C-H (2018) Smart traffic offloading with Mobile edge computing for disaster-resilient communication networks. Journal of Network and Systems Management

  22. Andrade E, Nogueira B (2018) Dependability evaluation of a disaster recovery solution for IoT infrastructures. The Journal of Supercomputing

  23. Ma C, Yang Y, Ma C (2018) Mobility-based sinknode-aided routing in disaster network under the background of big data. Cluster Computing

  24. Gomathi P, Baskar S, Shakeel MP, Dhulipala SV (2019) Numerical function optimization in brain tumor regions using reconfigured multi-objective bat optimization algorithm. Journal of Medical Imaging and Health Informatics 9(3):482–489

    Article  Google Scholar 

  25. Asadi A, Wang Q, Mancuso V (2014) A survey on device-to-device communication in cellular networks. Commun Surveys and Tutorials, IEEE 16(4):1801–1819

    Article  Google Scholar 

  26. Xie X, Ling Q, Lu P, Xu W, Zhu Z (2017) Evacuate before too late: distributed backup in inter-DC networks with progressive disasters. IEEE Transactions on Parallel and Distributed Systems PP(99):1

    Google Scholar 

  27. Panwala FC, Kumar R, Shakeel PM (2019) An analysis of bacteria separation and filtration from blood sample using passive methods. Measurement. https://doi.org/10.1016/j.measurement.2019.02.037

  28. Lin X, Andrews J, Ghosh A (2014) Spectrum sharing for device-todevice communication in cellular networks. IEEE Transactions Wireless Communicaiton 13(12):6727–6740

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lithungo Murry.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Future Networking Applications Plethora for Smart Cities

Guest Editors: Mohamed Elhoseny, Xiaohui Yuan, and Saru Kumari

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Murry, L., Kumar, R., Tuithung, T. et al. A local decision making technique for reliable service discovery using D2D communications in disaster recovery networks. Peer-to-Peer Netw. Appl. 13, 1131–1141 (2020). https://doi.org/10.1007/s12083-019-00844-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-019-00844-x

Keywords

Navigation