Skip to main content

Advertisement

SpringerLink
Log in

Big Data Oriented Energy Aware Routing for Wireless Sensor Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Shah RC, Rabaey JM. Energy Aware Routing for Low Energy Ad Hoc Sensor Networks In technical support F29601–99–1-0169

  2. Rabaey JM et al. (2000) Pico Radio supports ad hoc ultra-low power wireless networking. In IEEE Computer, pp. 42–48

  3. Toh CK (2001) Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc networks In IEEE Comm. Mag., 138–147

  4. Jain R, Puri A, Sengupta R (2001) Geographical routing for wireless adhoc networks using partial information In IEEE Personal Comm

  5. Perkins CE, Bhagwat P (1994) Highly dynamic destination sequenced distance vector routing (DSDV) for mobile computers In Comp. Commun. Rev., pp. 234–244

  6. Jacquet P et al. Optimized link state routing in Internet draft, http://www.ietf.org/internet-drafts/draft-ietf-manet-oslr-04.txt

  7. Perkins C, Royer E (1999) Ad hoc on demand distance vector routing In Proc.2nd IEEE Wksp Mobile Comp. sys and Apps

  8. Johnson DB, Maltz DA (1996) Dynamic source routing in ad hoc wireless networks In Mobile Computing, Kluwer, pp. 153–181

  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. 56–67

  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 21–24

  11. Vali D, Paskalis S, Kaloxylos A, Merakos L (2004) A survey of internet QoS Signalling. In IEEE Commun Surv Tutorials 6:32–12

    Article  Google Scholar 

  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 21–25

  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 11–15

  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):1496–1506

    Article  Google Scholar 

  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). IEEE

  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 127:53–65

    Article  Google Scholar 

  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 Science

  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):388–397

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engg, B N M Institute of Technology, Bengaluru, India

    J. Reshma

  2. Department of Computer Science & Engg, Global Academy of Technology, Bengaluru, India

    T. Satish Kumar

  3. Department of Information Science & Engg, Sambhram Institute of Technology, Bengaluru, India

    B. A. Vani

  4. Department of Computer Science & Engg, Sona College of Technology, Salem, India

    S. Sakthivel

Authors
  1. J. Reshma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Satish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. A. Vani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Sakthivel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Satish Kumar.

Appendix

Appendix

1.1 Proof of theorem 1

Proof: this proof is established through contradiction. Suppose, \( {b}_j< mbw-{\alpha}_{a_r} \), then, all the nodes which provide bandwidth lesser than \( mbw-{\alpha}_{a_r} \) will be selected for routing. This implies that, even if some node that provides zero bandwidth will also be selected. In this case, such nodes will not be able to transmit any packets, and hence, the whole routing mechanisms comes to standstill. Hence, \( {b}_j< mbw-{\alpha}_{a_r} \) is not a valid condition. Suppose, \( mbw\le {\alpha}_{a_r} \). Then, all the nodes which provide zero or negative bandwidth will be eligible. Clearly, this is not a valid condition. Hence, the parameters \( mbw\ and\ {\alpha}_{a_r} \) should not be set with values which satisfy the condition \( mbw\le {\alpha}_{a_r} \).

Suppose, (\( md+{\beta}_{a_r}\Big)-{d}_j<0 \), then, (\( md+{\beta}_{a_r}\Big)<{d}_j \). This implies that, the nodes which offer delay that is greater than (\( md+{\beta}_{a_r}\Big) \) will be selected for routing. This implies that, a node which offers infinite delay will also be selected. In this case, such nodes will infinitely delay the packets and the packets will never be transmitted. Hence, (\( md+{\beta}_{a_r}\Big)-{d}_j<0 \) is not a valid design.

1.2 Proof of theorem 2

Proof: There are two situations in which the theorem statement can be violated. Consider the first situation, suppose some node Ni has been selected for data packet routing of highest priority application, but, it does not satisfy the QoS constraints. This situation can never occur, because the node selection mechanism represented in Equations 12 and 13 ensure that, node Ni is selected only if it provides the requested QoS of highest priority application.

Consider the second situation, wherein, node Ni was earlier providing the requested QoS for the highest priority application, but, due to continuous usage of node resources, currently, it is unable to satisfy the QoS constraints. This situation cannot occur, because the route maintenance stage ensures that, if node Ni cannot satisfy QoS constraints of the highest priority application, then, RMP are generated to perform fresh route discovery, which will not utilize node Ni.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reshma, J., Satish Kumar, T., Vani, B.A. et al. Big Data Oriented Energy Aware Routing for Wireless Sensor Networks. Mobile Netw Appl 24, 298–306 (2019). https://doi.org/10.1007/s11036-018-1042-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1042-y

Keywords

Search

Navigation