Mobile Networks and Applications

, Volume 24, Issue 2, pp 298–306 | Cite as

Big Data Oriented Energy Aware Routing for Wireless Sensor Networks

  • J. Reshma
  • T. Satish KumarEmail author
  • B. A. Vani
  • S. Sakthivel


Wireless Sensor Networks (WSN), require energy efficient routing protocols to address their limited node energy issue. Many protocols attempt to provide the least energy cost path to perform data routing. But, this routing solution can lead to fast node energy depletion and eventual network disconnection, if more number of packets are routed. To overcome this issue, energy aware routing protocol [1] was proposed, which achieved efficient routing data load distribution by selecting multiple low cost paths and involving these paths for data packet routing. Currently, many WSN are generating huge volumes of data/Big Data, and energy aware routing protocol is not sufficient to achieve the required load distribution for Big Data routing. In this work, energy aware routing protocol [1] is extended to address Big Data issue. Since, many Big Data applications require Quality of Service (QoS), priority levels are assigned to differentiate WSN applications. The most critical applications are provided with the best QoS. More number of nodes is involved in data packet routing compared to energy aware routing protocol [1], so that, load distribution effectiveness increase. The nodes which have richer resources to satisfy application QoS constraints and require less energy costs for data packet transmission are frequently selected through the aid of a novel probability mass function. This proposed technique is implemented in Network Simulator 3. The empirical results demonstrate orders of magnitude load distribution effectiveness and slightly increased total energy consumption of the proposed routing technique when compared to least energy cost routing protocol.


Wireless sensor networks Big Data Oriented energy aware routing Quality of service 


  1. 1.
    Shah RC, Rabaey JM. Energy Aware Routing for Low Energy Ad Hoc Sensor Networks In technical support F29601–99–1-0169Google Scholar
  2. 2.
    Rabaey JM et al. (2000) Pico Radio supports ad hoc ultra-low power wireless networking. In IEEE Computer, pp. 42–48Google Scholar
  3. 3.
    Toh CK (2001) Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc networks In IEEE Comm. Mag., 138–147Google Scholar
  4. 4.
    Jain R, Puri A, Sengupta R (2001) Geographical routing for wireless adhoc networks using partial information In IEEE Personal CommGoogle Scholar
  5. 5.
    Perkins CE, Bhagwat P (1994) Highly dynamic destination sequenced distance vector routing (DSDV) for mobile computers In Comp. Commun. Rev., pp. 234–244Google Scholar
  6. 6.
    Jacquet P et al. Optimized link state routing in Internet draft,
  7. 7.
    Perkins C, Royer E (1999) Ad hoc on demand distance vector routing In Proc.2nd IEEE Wksp Mobile Comp. sys and AppsGoogle Scholar
  8. 8.
    Johnson DB, Maltz DA (1996) Dynamic source routing in ad hoc wireless networks In Mobile Computing, Kluwer, pp. 153–181Google Scholar
  9. 9.
    Intanagonwiwat C, Govindan R, Estrin D (2000) Directed Di_usion: A scalable and robust communication paradigm for sensor networks, In IEEE ACM Mobicom, pp. 56–67Google Scholar
  10. 10.
    Chen D, Varshney PK (2004) QoS Support in Wireless Sensor Networks A survey, proceedings of the 2004 International Conference on Wireless Networks. Las Vegas, USA, June 21–24Google Scholar
  11. 11.
    Vali D, Paskalis S, Kaloxylos A, Merakos L (2004) A survey of internet QoS Signalling. In IEEE Commun Surv Tutorials 6:32–12CrossRefGoogle Scholar
  12. 12.
    Nabi M, Blagojevic M, Geilen M, Basten T (2010) MCMAC An Optimized Medium Access Control Protocol for Mobile Clusters in Wireless sensor Networks In proceedings of the 7th Annual IEEE Communications Society Conference on Sensor Mesh abd ad hoc communications and networks. Boston, USA, June 21–25Google Scholar
  13. 13.
    Zhou Y, Ngai ECH, Lyu, MR, Liu J. (2007) POWER-SPEED: A power controlled Real Time Data transport Protocol for Wireless Sensor Actuator networks in proceedings of the IEEE Wireless communications and Networking Conference, Hong Kong. March 11–15Google Scholar
  14. 14.
    Van der Linde BW, van Netten JJ, Otten B, Postema K, Geuze RH, Schoemaker MM (2015) Activities of daily living in children with developmental coordination disorder: performance, learning, and participation. Phys Ther 95(11):1496–1506CrossRefGoogle Scholar
  15. 15.
    Wang J, Wang H, Zhou Y, McDonald N 2015 Multiple kernel multivariate performance learning using cutting plane algorithm. In Systems, man, and cybernetics (SMC), 2015 I.E. international conference on (pp. 1870–1875). IEEEGoogle Scholar
  16. 16.
    Miron-Spektor E, Beenen G (2015) Motivating creativity: The effects of sequential and simultaneous learning and performance achievement goals on product novelty and usefulness. Organ Behav Hum Decis Process 127:53–65CrossRefGoogle Scholar
  17. 17.
    Jain DK, Jain N, Kumar S, Kumar A, Kumar R, Wang H (2017) An Approach for Behavior Analysis Using Correlation Spectral Embedding Method. Journal of Computational ScienceGoogle Scholar
  18. 18.
    Takaishi D, Nishiyama H, Kato N, Miur R (2014) Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks. IEEE Trans On Emerging topics in Computing 2(3):388–397CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EnggB N M Institute of TechnologyBengaluruIndia
  2. 2.Department of Computer Science & EnggGlobal Academy of TechnologyBengaluruIndia
  3. 3.Department of Information Science & EnggSambhram Institute of TechnologyBengaluruIndia
  4. 4.Department of Computer Science & EnggSona College of TechnologySalemIndia

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