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WECRR: Weighted Energy-Efficient Clustering with Robust Routing for Wireless Sensor Networks

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Abstract

Usually, Wireless Sensor Network operates in a wide range of untrustworthy environments that are deployed in an ad-hoc approach. Although different cluster-based schemes have been proposed for improving network lifetime, however, most of the existing solutions incur probabilistic methods, which result in non-uniform energy consumption and imbalanced load distribution. In addition, end-to-end route discovery is non-optimized in terms of the limited resources of sensor nodes, which leads to frequent route discoveries and network overheads. In this research paper, we present Weighted Energy-Efficient Clustering with Robust Routing (WECRR) protocol that maintains balanced energy consumption and improves network-wide data delivery performance. The contributions of our proposed WECRR protocol are: Firstly, WECRR initiates a deterministic approach to avoid the uncertainties in Cluster Heads election mechanism and performs bounded clustering mechanism. Secondly, it provides multi-level optimized routing decision by making use of multi-facet attributes. At the end, it provides a route maintenance strategy, upon encounters any faulty or exhausted nodes on primary route, which results in reducing re-transmissions and route breakages. Simulation results reveal improved performance of WECRR protocol than compared to existing work.

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References

  1. Abbasi, A. Z., Islam, N., & Shaikh, Z. A. (2014). A review of wireless sensors and networks’ applications in agriculture. Computer Standards & Interfaces, 36(2), 263–270.

    Article  Google Scholar 

  2. Alamri, A., et al. (2013). A survey on sensor-cloud: Architecture, applications, and approaches. International Journal of Distributed Sensor Networks, 2013, 1–19.

    Google Scholar 

  3. Lee, S. H., et al. (2009).Wireless sensor network design for tactical military applications: remote large-scale environments. In Military communications conference, 2009. MILCOM 2009, IEEE. Boston, MA: IEEE.

  4. Ahmed, A., et al. (2015). A survey on trust based detection and isolation of malicious nodes in ad-hoc and sensor networks. Frontiers of Computer Science, 9(2), 280–296.

    Article  MathSciNet  Google Scholar 

  5. Sha, K., Gehlot, J., & Greve, R. (2013). Multipath routing techniques in wireless sensor networks: A survey. Wireless Personal Communications, 70(2), 807–829.

    Article  Google Scholar 

  6. Lazarescu, M. T. (2013). Design of a WSN platform for long-term environmental monitoring for IoT applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 3(1), 45–54.

    Article  Google Scholar 

  7. Lou, C., & Zhuang, W. (2016). Energy-efficient routing over coordinated sleep scheduling in wireless ad hoc networks. Peer-to-Peer Networking and Applications, 9(2), 384–396.

    Article  Google Scholar 

  8. Vasilakos, A. V., et al. (2015). Information centric network: Research challenges and opportunities. Journal of Network and Computer Applications, 52, 1–10.

    Article  Google Scholar 

  9. Liu, Z., et al. (2010). An effective scheduling scheme for multi-hop multicast in wireless mesh networks. Frontiers of Computer Science in China, 4(1), 135–142.

    Article  Google Scholar 

  10. Batra, P. K., & Kant, K. (2016). LEACH-MAC: A new cluster head selection algorithm for wireless sensor networks. Wireless Networks, 22(1), 49–60.

    Article  Google Scholar 

  11. Lee, I., Shaw, W., & Park, J. H. (2010). On prolonging the lifetime for wireless video sensor networks. Mobile Networks and Applications, 15(4), 575–588.

    Article  Google Scholar 

  12. Liu, L., Hu, B., & Li, L. (2010). Algorithms for energy efficient mobile object tracking in wireless sensor networks. Cluster Computing, 13(2), 181–197.

    Article  Google Scholar 

  13. Manap, Z., et al. (2013). A review on hierarchical routing protocols for wireless sensor networks. Wireless Personal Communications, 72(2), 1077–1104.

    Article  Google Scholar 

  14. Nam, C.-S., Jeong, H.-J., & Shin, D.-R. (2008). The adaptive cluster head selection in wireless sensor networks. In Semantic computing and applications, 2008. IWSCA’08. IEEE international workshop. Incheon: IEEE.

  15. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences. Maui: IEEE.

  16. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  17. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Aerospace conference proceedings, IEEE. IEEE.

  18. Park, G. Y., et al. (2013). A novel cluster head selection method based on k-means algorithm for energy efficient wireless sensor network. In 2013 27th international conference on advanced information networking and applications workshops (WAINA). Barcelona: IEEE.

  19. Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189–199.

    Article  Google Scholar 

  20. Arumugam, G. S., & Ponnuchamy, T. (2015). EE-LEACH: Development of energy-efficient LEACH Protocol for data gathering in WSN. EURASIP Journal on Wireless Communications and Networking, 2015(1), 1–9.

    Article  Google Scholar 

  21. Jiang, D., Xu, Z., & Lv, Z. (2016). A multicast delivery approach with minimum energy consumption for wireless multi-hop networks. Telecommunication Systems, 62(4), 771–782.

    Article  Google Scholar 

  22. Mamalis, B., et al. (2009) Clustering in wireless sensor networks. In Zhang/RFID and sensor networks (pp. 324–350).

  23. Dahnil, D. P., Singh, Y. P., & Ho, C. K. (2012). Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks. IET Wireless Sensor Systems, 2(4), 318–327.

    Article  Google Scholar 

  24. Ruan, F., et al. (2013). A distance clustering routing algorithm considering energy for wireless sensor networks. International Journal of Future Generation Communication and Networking, 6(5), 73–80.

    Article  Google Scholar 

  25. Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  26. Venkanna, U., & Velusamy, R. L. (2016). TEA-CBRP: Distributed cluster head election in MANET by using AHP. Peer-to-Peer Networking and Applications, 9(1), 159–170.

    Article  Google Scholar 

  27. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Article  Google Scholar 

  28. Weng, C.-E., & Lai, T.-W. (2013). An energy-efficient routing algorithm based on relative identification and direction for wireless sensor networks. Wireless Personal Communications, 69(1), 1–16.

    Article  Google Scholar 

  29. Zhao, L., Chen, Z., & Sun, G. (2014). Dynamic cluster-based routing for wireless sensor networks. Journal of Networks, 9(11), 2951–2956.

    Google Scholar 

  30. Ortiz, A. M., et al. (2013). Fuzzy-logic based routing for dense wireless sensor networks. Telecommunication Systems, 52(4), 2687–2697.

    Article  Google Scholar 

  31. Jiang, H., Sun, Y., Sun, R., & Xu, H. (2013). Fuzzy-logic-based energy optimized routing for wireless sensor networks. International Journal of Distributed Sensor Networks, 2013, 216561.

    Article  Google Scholar 

  32. Fersi, G., Louati, W., & Jemaa, M. B. (2013). Distributed Hash table-based routing and data management in wireless sensor networks: A survey. Wireless Networks, 19(2), 219–236.

    Article  Google Scholar 

  33. Damdinsuren, C., et al. (2013). Lifetime extension based on residual energy for receiver-driven multi-hop wireless network. Cluster Computing, 16(3), 469–480.

    Article  Google Scholar 

  34. Naeimi, S., et al. (2012). A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors, 12(6), 7350–7409.

    Article  Google Scholar 

  35. Venkateswarlu Kumaramangalam, M., Adiyapatham, K., & Kandasamy, C. (2014). Zone-based routing protocol for wireless sensor networks. International Scholarly Research Notices, 2014, 1–9.

    Article  Google Scholar 

  36. Amgoth, T., & Jana, P. K. (2015). Energy-aware routing algorithm for wireless sensor networks. Computers & Electrical Engineering, 41, 357–367.

    Article  Google Scholar 

  37. Kuila, P., & Jana, P. K. (2014). Approximation schemes for load balanced clustering in wireless sensor networks. The Journal of Supercomputing, 68(1), 87–105.

    Article  Google Scholar 

  38. Das, S. K., Tripathi, S., & Burnwal, A. (2015). Intelligent energy competency multipath routing in wanet. In Information systems design and intelligent applications (pp. 535–543). Springer.

  39. Wu, D., et al. (2014). Joint multi-radio multi-channel assignment, scheduling, and routing in wireless mesh networks. Wireless Networks, 20(1), 11–24.

    Article  Google Scholar 

  40. Thakkar, A., & Kotecha, K. (2014). Cluster head election for energy and delay constraint applications of wireless sensor network. IEEE Sensors Journal, 14(8), 2658–2664.

    Article  Google Scholar 

  41. Zhang, D.-G., et al. (2015). A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Computing, 19(7), 1817–1827.

    Article  Google Scholar 

  42. Jin, R.-C., et al. (2013). Passive cluster-based multipath routing protocol for wireless sensor networks. Wireless Networks, 19(8), 1851–1866.

    Article  Google Scholar 

  43. Tsai, C.-H., & Tseng, Y.-C. (2012). A path-connected-cluster wireless sensor network and its formation, addressing, and routing protocols. IEEE Sensors Journal, 12(6), 2135–2144.

    Article  Google Scholar 

  44. Cota-Ruiz, J., et al. (2016). A recursive shortest path routing algorithm with application for wireless sensor network localization. IEEE Sensors Journal, 16(11), 4631–4637.

    Article  Google Scholar 

  45. Lin, K., et al. (2012). Energy efficiency routing with node compromised resistance in wireless sensor networks. Mobile Networks and Applications, 17(1), 75–89.

    Article  Google Scholar 

  46. Bajaber, F., & Awan, I. (2009). Base-station controlled dynamic clustering protocol. In Proceedings of international conference on advanced information networking and applications. Bradford, UK.

  47. Jannatul Ferdous, M., Ferdous, J., & Dey T. (2009). Central base-station controlled density aware clustering protocol for wireless sensor networks In 12th international conference on computers and information technology, 2009. ICCIT’09. Dhaka: IEEE.

  48. Xinhua, W., & Sheng, W. (2010). Performance comparison of LEACH and LEACH-C protocols by NS2. In 2010 ninth international symposium on distributed computing and applications to business engineering and science (DCABES). Hong Kong: IEEE.

  49. Zhao, F., Xu, Y., & Li, R. (2012). Improved LEACH routing communication protocol for a wireless sensor network. International Journal of Distributed Sensor Networks, 2012(2012), 1–5.

    Google Scholar 

  50. Sasikumar, P., & Khara, S. (2012). K-means clustering in wireless sensor networks. In 2012 fourth international conference on computational intelligence and communication networks (CICN). IEEE.

  51. Peng, W., & Edwards, D. J. (2010). K-means like minimum mean distance algorithm for wireless sensor networks. In 2010 2nd international conference on computer engineering and technology (ICCET). IEEE.

  52. Yu, J., et al. (2012). A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU-International Journal of Electronics and Communications, 66(1), 54–61.

    Article  Google Scholar 

  53. Ever, E., et al. (2012). UHEED-an unequal clustering algorithm for wireless sensor networks (pp. 1–9). Rome.

  54. Xuhui, C., Zhiming, Y., & Huiyan, C. (2009). Unequal clustering mechanism of leach protocol for wireless sensor networks. In Computer science and information engineering, 2009 WRI world congress. Los Angeles, CA: IEEE.

  55. Chen, G., et al. (2009). An unequal cluster-based routing protocol in wireless sensor networks. Wireless Networks, 15(2), 193–207.

    Article  Google Scholar 

  56. Mao, S., et al. (2013). An improved fuzzy unequal clustering algorithm for wireless sensor network. Mobile Networks and Applications, 18(2), 206–214.

    Article  Google Scholar 

  57. Nguyen, T. T., Shieh, C. S., Horng, M. F., Ngo, T.-G., & Dao, T.-K. (2015). Unequal clustering formation based on bat algorithm for wireless sensor networks. Knowledge and Systems Engineering. doi:10.1007/978-3-319-11680-8_53.

    Google Scholar 

  58. Gong, B., & Jiang, T. (2011). A tree-based routing protocol in wireless sensor networks. In 2011 International conference on electrical and control engineering (ICECE). IEEE.

  59. Zhou, Z., et al. (2014). EGF-tree: An energy-efficient index tree for facilitating multi-region query aggregation in the internet of things. Personal and Ubiquitous Computing, 18(4), 951–966.

    Article  Google Scholar 

  60. Azharuddin, M., Kuila, P., & Jana, P. K. (2015). Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Computers & Electrical Engineering, 41, 177–190.

    Article  Google Scholar 

  61. Samanta, M., & Banerjee, I. (2014). Optimal load distribution of cluster head in fault-tolerant wireless sensor network. In 2014 IEEE students’ conference electrical, electronics and computer science (SCEECS). Bhopal: IEEE.

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Authors and Affiliations

  1. Department of Computer Science, Islamia College (Chartered University), Peshawar, Pakistan

    Khalid Haseeb

  2. Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Kamalrulnizam Abu Bakar & Tasneem Darwish

  3. Department of Information Technology, Quaid-e-Awam, University of Engineering, Science and Technology, Nawabshah, Pakistan

    Adnan Ahmed

  4. Institute of Management Sciences, Peshawar, Pakistan

    Imran Ahmed

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  1. Khalid Haseeb
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Correspondence to Khalid Haseeb.

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Haseeb, K., Bakar, K.A., Ahmed, A. et al. WECRR: Weighted Energy-Efficient Clustering with Robust Routing for Wireless Sensor Networks. Wireless Pers Commun 97, 695–721 (2017). https://doi.org/10.1007/s11277-017-4532-5

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