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Lifetime Improvement of Wireless Sensor Networks Using Tree-Based Routing Protocol

  • Sushaptha Rajagopal
  • R. Vani
  • J. C. Kavitha
  • R. Saravanan
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Wireless sensor networks (WSNs) are used for handling a large amount of data despite their serious limitations such as delay and energy consumption. The constraints become a serious issue when we deploy many nodes for the purpose of data handling. WSNs can be made energy efficient by means of Energy-Efficient Low Duty Cycle (ELDC) protocol which is an artificial neural network-based energy-efficient and robust routing scheme used in wireless sensor networks. ELDC is an extension of energy-efficient unequal clustering and energy-efficient multiple distance-aware clustering protocols. It is a dynamic group-based routing protocol which makes multi-hop strategy for communication. In ELDC there is a large amount of packet loss and load balance is not provided which reduces the lifetime of the network. Therefore, we suggest the use of General Self-Organization Tree-Based Energy Balance (GSTEB) protocol to provide load balance. It is a dynamic tree-based routing protocol which minimizes the energy consumption and has a minimum or no packet loss and data compression is also provided to improve the performance. It also prevents the entry of unauthorized node containing malicious data and the packet delivery ratio is also high. The proposed system greatly improves the lifetime of the network. The applications of our paper include environmental monitoring, air traffic control, surveillance, etc.

Keywords

Tree-based routing Packet loss Energy consumption Load balance Data compression Lifetime 

References

  1. 1.
    R. Nigel Horspool, Improving LZW, in Data Compression Conference, 1991, pp. 332–341Google Scholar
  2. 2.
    M. Rangchi, H. Bakhshi, A new energy efficient routing algorithm based on load balancing for wireless sensor networks, in Seventh International Symposium on Telecommunications, 2014, pp. 1201–1205Google Scholar
  3. 3.
    F. Bajaber, I. Awan, Centralized dynamic clustering for wireless sensor network, in International Conference on Advanced Information Networking and Applications Workshops, 2013, pp. 193–198Google Scholar
  4. 4.
    B. Tang, D. Wang, H. Zhang, A centralized clustering geographic energy aware routing for wireless sensor networks, in IEEE International Conference on Systems, Man and Cybernetics, 2013, pp. 1–6Google Scholar
  5. 5.
    M. Baniata, J. Hong, Energy-efficient unequal chain length clustering for wireless sensor networks in smart cities. Wirel. Commun. Mob. Comput. 2017, 1–9 (2017)CrossRefGoogle Scholar
  6. 6.
    N. Gautam, J.Y. Pyun, Distance aware intelligent clustering protocol for wireless sensor networks. J. Commun. Networks 12(2), 122–129 (2010)CrossRefGoogle Scholar
  7. 7.
    H. Xia, R.H. Zhang, J. Yu, Z.K. Pan, Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks. Int. J. Wireless Inf. Networks 23(2), 141–150 (2016)CrossRefGoogle Scholar
  8. 8.
    A. Rahmanian, H. Omranpour, M. Akbari, K. Raahemifar, A novel genetic algorithm in LEACH-C routing protocol for sensor networks, in IEEE in Electrical and Computer Engineering, 2011Google Scholar
  9. 9.
    L. David, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    R. Zhang, L. Ju, Z. Jia, X. Li, Energy efficient routing algorithm for WSNs via unequal clustering, in International Conference on Embedded Software and Systems, 2012, pp. 1226–1231Google Scholar
  11. 11.
    M. Vijayalakshmi, D.S. Rao, Energy-Aware Multicast Clustering (EAMC) with increased Quality of Service (QoS) in MANETs, in Applied and Theoretical Computing and Communication Technology, 2016, pp. 793–798Google Scholar
  12. 12.
    A. Mehmood, Z. Lv, J. Lloret, M.M. Umar, ELDC: an artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs. IEEE Trans. Emerg. Top. Comput. 99, 1 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sushaptha Rajagopal
    • 1
  • R. Vani
    • 2
  • J. C. Kavitha
    • 3
  • R. Saravanan
    • 1
  1. 1.Department of Electronics and Communication EngineeringMeenakshi College of EngineeringChennaiIndia
  2. 2.Department of Electronics and Communication EngineeringSRM UniversityChennaiIndia
  3. 3.Department of Information TechnologyRMD Engineering CollegeChennaiIndia

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