Advertisement

Fair energy management with void hole avoidance in intelligent heterogeneous underwater WSNs

  • Nadeem Javaid
  • Zaheer Ahmad
  • Arshad Sher
  • Zahid Wadud
  • Zahoor Ali Khan
  • Syed Hassan Ahmed
Original Research
  • 71 Downloads

Abstract

Due to the detrimental nature of aquatic environment, the design of routing protocols for underwater wireless sensor networks (UWSNs) faces numerous challenges, such as an optimal route selection, energy efficiency, propagation delay, etc. However, the energy efficiency is considered a key parameter while designing a routing strategy for UWSNs. Therefore, the dissipation of energy needs to be efficient for the prolongation of the network lifespan. For efficient battery consumption of a node, redundant data transmissions and communication over long distances are desired to be controlled because data transmissions over long distances causes an uneven dissipation of energy resulting in void hole creation. Due to the void hole creation, the node can not forward its data towards the destination because of the unavailability of relay node(s). In order to avoid the creation of void holes, we propose a routing mechanism which detects a void hole prior to its occurrence and takes an alternative route for successful data delivery. We compute an optimal number of forwarders at each hop as back up potential forwarders nodes to reduce the probability of data loss because of void hole occurrence. In addition, forwarding communication range is logically divided in to sub-forwarding areas in order to suppress the number of duplicate transmissions. We perform simulations to show that our claims are well grounded. The results depict that the proposed work has outperformed the compared baseline schemes (WDFAD-DBR and VHGOR) in terms of packet delivery ratio (PDR), total energy tax, end to end delay and number of redundant packets.

Keywords

Energy efficiency Delay-sensitive routing Forwarding function Holding time Network lifetime Redundant packets Suppression Neighbor prediction 

References

  1. Jiang J, Han G, Guo H, Shu L, Rodrigues JJ (2016) Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks. J Netw Comput Appl 59:4–13CrossRefGoogle Scholar
  2. Yu H, Yao N, Wang T, Li G, Gao Z, Tan G (2016) WDFAD-DBR: weighting depth and forwarding area division DBR routing protocol for UASNs. Ad Hoc Netw 37:256–282CrossRefGoogle Scholar
  3. Javaid N, Shah M, Ahmad A, Imran M, Khan MI, Vasilakos AV (2016) An enhanced energy balanced data transmission protocol for underwater acoustic sensor networks. Sensors 16(4):487CrossRefGoogle Scholar
  4. Ayaz M, Abdullah A, Faye I (2010) Hop-by-hop reliable data deliveries for underwater wireless sensor networks. In Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on IEEE, pp 363–368Google Scholar
  5. Coutinho RW, Boukerche A, Vieira LF, Loureiro AA, (2014) GEDAR: geographic and opportunistic routing protocol with depth adjustment for mobile underwater sensor networks. In Communications (ICC), 2014 IEEE International Conference on IEEE, pp 251–256Google Scholar
  6. Kanthimathi N (2017) Void handling using Geo-Opportunistic Routing in underwater wireless sensor networks. Computers and Electrical EngineeringGoogle Scholar
  7. Yan H, Shi ZJ, Cui JH (2008) DBR: depth-based routing for underwater sensor networks. In International conference on research in networking, pp 72–86. Springer, BerlinGoogle Scholar
  8. Wahid A, Lee S, Jeong HJ, Kim D (2011) Eedbr: energy-efficient depth-based routing protocol for underwater wireless sensor networks. In Advanced Computer Science and Information Technology, pp 223–234. Springer, BerlinGoogle Scholar
  9. Han G, Jiang J, Bao N, Wan L, Guizani M (2015) Routing protocols for underwater wireless sensor networks. IEEE Commun Mag 53(11):72–78CrossRefGoogle Scholar
  10. Darehshoorzadeh A, Boukerche A (2015) Underwater sensor networks: a new challenge for opportunistic routing protocols. IEEE Commun Mag 53(11):98–107CrossRefGoogle Scholar
  11. Ayaz M, Abdullah A (2009) Hop-by-hop dynamic addressing based (H2-DAB) routing protocol for underwater wireless sensor networks. In Information and Multimedia Technology, 2009. ICIMT’09. International Conference on IEEE, pp 436–441Google Scholar
  12. Gopi S, Govindan K, Chander D, Desai UB, Merchant SN (2010) E-PULRP: energy optimized path unaware layered routing protocol for underwater sensor networks. IEEE Trans Wireless Commun 9(11):3391–3401CrossRefGoogle Scholar
  13. Chen J, Wu X, Chen G (2008) REBAR: a reliable and energy balanced routing algorithm for UWSNs. In Grid and Cooperative Computing, 2008. GCC’08. Seventh International Conference on IEEE, pp 349–355Google Scholar
  14. Ali T, Jung LT, Ameer S (2012) Flooding control by using angle based cone for UWSNs. In Telecommunication Technologies (ISTT), 2012 International Symposium on IEEE, pp 112–117Google Scholar
  15. Sher A, Javaid N, Azam I, Ahmad H, Abdul W, Ghouzali S, Niaz IA, Khan FA (2017) Monitoring square and circular fields with sensors using energy-efficient cluster-based routing for underwater wireless sensor networks. Int J Distrib Sensor Netw 13(7):1550147717717189CrossRefGoogle Scholar
  16. Domingo MC (2011) A distributed energy-aware routing protocol for underwater wireless sensor networks. Wireless Personal Commun 57(4):607–627CrossRefGoogle Scholar
  17. Wang P, Li C, Zheng J (2007) Distributed minimum-cost clustering protocol for underwater sensor networks (UWSNs). In Communications, 2007. ICC’07. IEEE International Conference on IEEE, pp 3510–3515Google Scholar
  18. Anupama, K.R., Sasidharan, A. and Vadlamani, S., 2008, August. A location-based clustering algorithm for data gathering in 3D underwater wireless sensor networks. In Telecommunications, 2008. IST 2008. International Symposium on IEEE, pp 343–348Google Scholar
  19. Wang C, Liu G (2011) LUM-HEED: a location unaware, multi-hop routing protocol for underwater acoustic sensor networks. In Computer Science and Network Technology (ICCSNT), 2011 International Conference on IEEE 4:2336–2340Google Scholar
  20. Liu G, Wei C (2011) A new multi-path routing protocol based on cluster for underwater acoustic sensor networks. In Multimedia Technology (ICMT), 2011 International Conference on IEEE, pp 91–94Google Scholar
  21. Umar A, Javaid N, Ahmad A, Khan ZA, Qasim U, Alrajeh N, Hayat A (2015) DEADS: depth and Energy Aware Dominating Set Based Algorithm for Cooperative Routing along with Sink Mobility in Underwater WSNs. Sensors 5(6):14458–14486Google Scholar
  22. Zhou Z, Peng Z, Cui JH, Shi Z (2011) Efficient multipath communication for time-critical applications in underwater acoustic sensor networks. IEEE/ACM Trans Netw 19(1):28–41CrossRefGoogle Scholar
  23. Javaid N, Jafri MR, Khan ZA, Qasim U, Alghamdi TA, Ali M (2014) Iamctd: improved adaptive mobility of courier nodes in threshold-optimized dbr protocol for underwater wireless sensor networks. International Journal of Distributed Sensor NetworksGoogle Scholar
  24. Hwang D, Kim D (2008) DFR: directional flooding-based routing protocol for underwater sensor networks. In OCEANS 2008 pp 1–7Google Scholar
  25. Xie P, Cui JH, Lao L (2006) VBF: vector-based forwarding protocol for underwater sensor networks. In International Conference on Research in Networking, pp 1216–1221. Springer, BerlinGoogle Scholar
  26. Nicolaou N, See A, Xie P, Cui JH, Maggiorini D (2007) Improving the robustness of location-based routing for underwater sensor networks. In OCEANS 2007-Europe, pp 1–6Google Scholar
  27. Yu H, Yao N, Liu J (2015) An adaptive routing protocol in underwater sparse acoustic sensor networks. Ad Hoc Networks 34:121–143Google Scholar
  28. Kleerekoper A, Filer N (2012) Revisiting blacklisting and justifying the unit disk graph model for energy-efficient position-based routing in wireless sensor networks.’ In Wireless Days (WD), 2012 IFIP, pp 1–3Google Scholar
  29. Chang X, Yu YL, Yang Y, Xing EP (2017) Semantic pooling for complex event analysis in untrimmed videos. IEEE transactions on pattern analysis and machine intelligence 39(8):1617–1632Google Scholar
  30. Chang X, Ma Z, Lin M, Yang Y, Hauptmann AG (2017) Feature interaction augmented sparse learning for fast kinect motion detection. IEEE Trans Image Process 26(8):3911–3920MathSciNetCrossRefGoogle Scholar
  31. Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2017) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern 47(5):1180–1197CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information Technology44000Pakistan
  2. 2.University of Engineering and Technology PeshawarPeshawarPakistan
  3. 3.CIS, Higher Colleges of TechnologyFujairahUnited Arab Emirates
  4. 4.University of Central FloridaOrlandoUSA

Personalised recommendations