Adaptive Routing Protocol for Lifetime Maximization in Multi-Constraint Wireless Sensor Networks

  • Fouad El Hajji
  • Cherkaoui Leghris
  • Khadija Douzi
Research paper
  • 58 Downloads

Abstract

Routing in wireless sensor networks plays a crucial role in deploying and managing an efficient and adaptive network. Ensuring efficient routing entails an ever-increasing necessity for optimized energy consumption and reliable resource management of both the sensor nodes and the overall sensor network. An efficient routing solution is characterized by its ability to increase network lifetime, enhance efficiency, and ensure the appropriate quality of service. Therefore, the routing protocols need to be designed with an ultimate objective by considering and efficiently managing many characteristics and requirements, such as fault tolerance, scalability, production costs, and others.

Unfortunately, many of the existing solutions lead to higher power consumption and communication control overheads, which not only increase network congestion but also decrease network lifetime. In addition, most of these protocols consider a limited number of criteria, in contrast to the complexity and the evolution of WSNs. This paper presents a new adaptive and dynamic multi-criteria routing protocol. Our protocol operates in multi-constraint environments, where most of the current solutions fail to monitor successive and continuous changes in network state and user preferences. This approach provides a routing mechanism, which creates a routing tree based on the evaluation of many criteria. These criteria can cover the topological metrics of neighboring nodes (the role of the nodes in intracommunications, connections between different parts of the network, etc.), the estimated power consumption to reach each direct neighbor, the path length (number of hops to the sink), the remaining energy of individual sensor nodes, and others. These criteria are controlled and supervised dynamically through a detection scheme. In addition, a dynamic selection mechanism, based on multi-attribute decision-making methods, is implemented to build and update the routing tree. In response to changes in the network state, user preferences, and application-defined goals, the election mechanism provides the best routing neighbor between each node and the sink.

Keywords

wireless sensor networks centrality measures multi-criteria routing protocol network lifetime 

References

  1. [1]
    I. F. Akyildiz, M. C. Vuran. Wireless sensor networks [M]. Volume 4. John Wiley & Sons, 2010.Google Scholar
  2. [2]
    S. Rani, S. H. Ahmed. Multi-hop routing in wireless sensor networks: an overview, taxonomy, and research challenges [M]. Springer, 2015.Google Scholar
  3. [3]
    L. C. Freeman. Centrality in social networks conceptual clarification [J]. Social Networks, 1978, 1(3): 215–239.CrossRefGoogle Scholar
  4. [4]
    A. Jain, B. Reddy. Node centrality in wireless sensor networks: Importance, applications and advances [C]//IEEE 3rd International Advance Computing Conference (IACC), Ghaziabad, 2013: 127–131.Google Scholar
  5. [5]
    A. Sarkar, T. S. Murugan. Routing protocols for wireless sensor networks: What the literature says [J]. Alexandria Engineering Journal, 2016, 55(4): 3173–3183.CrossRefGoogle Scholar
  6. [6]
    N. A. Pantazis, S. A. Nikolidakis, D. D. Vergados. Energy-efficient routing protocols in wireless sensor networks: A survey [J]. IEEE Communications Surveys Tutorials, 2013, 15(2): 551–591.CrossRefGoogle Scholar
  7. [7]
    S. Tanwar, N. Kumar, J. J. Rodrigues. A systematic review on heterogeneous routing protocols for wireless sensor network [J]. Journal of Network and Computer Applications, 2015, 53: 39–56.CrossRefGoogle Scholar
  8. [8]
    S. Tyagi, N. Kumar. A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks [J]. Journal of Network and Computer Applications, 2013, 36(2): 623–645.CrossRefGoogle Scholar
  9. [9]
    S. Hussain, A. W Matin, O. Islam. Genetic algorithm for energy efficient clusters in wireless sensor networks [C]//IEEE Fourth International Conference on Information Technology (ITNG’07), Dubai, 2007: 147–154.CrossRefGoogle Scholar
  10. [10]
    S. A. Sert, H. Bagci, A. Yazici. Mofca: Multi-objective fuzzy clustering algorithm for wireless sensor networks [J]. Applied Soft Computing, 2015, 30: 151–165.CrossRefGoogle Scholar
  11. [11]
    B. M. Khan, R. Bilal, R. Young. Fuzzy-topsis based cluster head selection in mobile wireless sensor networks [J]. Journal of Electrical Systems and Information Technology, 2017.Google Scholar
  12. [12]
    X. Li, Z. Guan. Energy-aware routing in wireless sensor networks using local betweenness centrality [J]. International Journal of Distributed Sensor Networks, 2013, 9(5): 307038.CrossRefGoogle Scholar
  13. [13]
    J. Duan, D. Gao, C. H. Foh, et al. Tc-bac: A trust and centrality degree based access control model in wireless sensor networks [J]. Ad Hoc Networks, 2013, 11(8): 2675–2692.CrossRefGoogle Scholar
  14. [14]
    P. H Pathak, R. Dutta. Centrality-based power control for hot-spot mitigation in multi-hop wireless networks [J]. Computer Communications, 2012, 35(9): 1074–1085.CrossRefGoogle Scholar
  15. [15]
    L. Sitanayah, K.N. Brown, C. J. Sreenan. Fault-tolerant relay deployment based on length-constrained connectivity and rerouting centrality in wireless sensor networks [C]//Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN’12), Trento, 2012: 115–130.Google Scholar
  16. [16]
    W. Hwang, Y. Cho, A. Zhang, et al. Bridging centrality: identifying bridging nodes in scale-free networks [C]//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, 2006: 20–23.Google Scholar
  17. [17]
    T. Gal, T. Stewart, T. Hanne. Multicriteria decision making: advances in MCDM models, algorithms, theory, and applications [M]. Volume 21. Springer Science & Business Media, 2013.Google Scholar
  18. [18]
    T. L. Saaty. Decision making—the analytic hierarchy and network processes (AHP/ANP) [J]. Journal of Systems Science and Systems Engineering, 2004, 13(1): 1–35.CrossRefGoogle Scholar
  19. [19]
    C. Hwang, K. Yoon. Methods for multiple attribute decision making [M]. In: Multiple Attribute Decision Making, Springer, 1981, 58–191.CrossRefGoogle Scholar
  20. [20]
    Y. Yao, J. Gehrke. The cougar approach to in-network query processing in sensor networks [J]. ACM Sigmod Record, 2002, 31(3): 9–18.CrossRefGoogle Scholar
  21. [21]
    A. Sharaf, J. Beaver, A. Labrinidis, et al. Balancing energy efficiency and quality of aggregate data in sensor networks [J]. The International Journal on Very Large Data Bases, 2004, 13(4): 384–403.CrossRefGoogle Scholar
  22. [22]
    Y. H. Suh, K. T. Kim, D. R. Shin, et al. Traffic-aware energy efficient routing (TEER) using multi-criteria decision making for wireless sensor network [C]//5th International Conference on IT Convergence and Security (ICITCS), Kuala Lumpur, 2015: 1–5.Google Scholar
  23. [23]
    M. Mallinson, P. Drane, S. Hussain. Discrete radio power level consumption model in wireless sensor networks [C]//IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS 2007), Bologna, 2007: 1–6.Google Scholar
  24. [24]
    Texas Instruments. Chipcon cc2420 [EB/OL]. TEXAS INSTRUMENTS. Available: http://www.ti.com/product/cc2420.Google Scholar
  25. [25]
    I. Dietrich, F. Dressler. On the lifetime of wireless sensor networks [J]. ACM Transactions on Sensor Networks, 2009, 5(1): 5.CrossRefGoogle Scholar
  26. [26]
    V. Kumar, S. Kumar. Energy balanced position-based routing for lifetime maximization of wireless sensor networks [J]. Ad Hoc Networks, 2016, 52: 117–129.CrossRefGoogle Scholar
  27. [27]
    Y. Gu, F. Ren, Y. Ji, et al. The evolution of sink mobility management in wireless sensor networks: A survey [J]. IEEE Communications Surveys & Tutorials, 2016, 18(1): 507–524.CrossRefGoogle Scholar
  28. [28]
    A. Wichmann, T. Korkmaz. Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks [J]. Computer Communications, 2015, 72: 93–106.CrossRefGoogle Scholar
  29. [29]
    S. Kumar, D. Singhal, R. M. Garimella. Cognitive wireless sensor networks. Intelligent Sensor Networks: The Integration of Sensor Networks [J]. Signal Processing and Machine Learning, 2012: 205.CrossRefGoogle Scholar
  30. [30]
    G. P. Joshi, S. Y. Nam, S. W. Kim. Cognitive radio wireless sensor networks: applications, challenges and research trends [J]. Sensors, 2013, 13(9): 11196–11228.CrossRefGoogle Scholar
  31. [31]
    D. Jiang, X. Ying, Y. Han, et al. Collaborative multi-hop routing in cognitive wireless networks [J]. Wireless Personal Communications, 2016, 86(2): 901–923.CrossRefGoogle Scholar

Copyright information

© Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Fouad El Hajji
    • 1
  • Cherkaoui Leghris
    • 1
  • Khadija Douzi
    • 1
  1. 1.Research Team RTM, Laboratory LIM, Faculty of Sciences and TechnologiesHassan II University of CasablancaMohammediaMorocco

Personalised recommendations