Cognitive-Node Architecture and a Deployment Strategy for the Future WSNs

Article

Abstract

The advent of sensing and communication technologies represents the next step in the evolution of wireless sensor networks (WSNs) and future applications. Future WSNs systems demand that connected devices could be able to work autonomously, while surfing on-line generated data and process them for self-decision making. Accordingly, we propose a cognitive Information-Centric Sensor Network (ICSN) framework. The fundamentals of cognition in ICSN can be recognized as a promising direction in addressing opportunities and challenges posed by the needs of WSNs. Nevertheless, these fundamental concepts could be difficult to implement due to the wide spectrum of tasks to be covered from the perspective of each device in the WSN. Consequently, we propose the use of Cognitive Nodes (CNs) in typical sensor networks to provide intelligent information processing and knowledge-based services to the end-users. The CNs act on queries from the end-user, containing information about the nature of the request, without the need to modify and/or change any component at the end nodes from where data is collected. We present the detailed architecture and deployment strategy of the cognitive inspired framework that learns and exploits an expanded network information from relays and clustered sensors. Extensive simulation results show that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to ensure that end-user’s Quality of Information (QoI) requirements are met, even under heavy traffic conditions and extensive packets’ payloads. This shows the potential use of ICSN framework in producing the future cognitive applications, while catering to user-desired information quality.

Keywords

Information-centric sensor networks Large-scale applications Cognitive node Deployment strategy 

References

  1. 1.
    Hasan M Z, Al-Turjman F, Al-Rizzo H (2017) Optimized multi-constrained quality-of-service multipath routing approach for multimedia sensor networks. IEEE Sensors J 17(7):2298–2309Google Scholar
  2. 2.
    Al-Turjman F (2017) Price-based Data Delivery Framework for Dynamic and Pervasive IoT. Elsevier Pervasive Mobile Comput J. doi: 10.1016/j.pmcj.2017.05.001
  3. 3.
    Al-Turjman F, Hassanein H (2012) Towards augmented connectivity with delay constraints in WSN federation. Inderscience: Int J Ad Hoc and Ubiquitous Comput 11(2):97–108Google Scholar
  4. 4.
    Al-Fagih A, Al-Turjman F, Alsalih W, Hassanein H (2013) A priced public sensing framework for heterogeneous IoT architectures. IEEE Trans Emerg Top Comput 1(1):133–147CrossRefGoogle Scholar
  5. 5.
    Yun Z, Bai X, Xuan D, Lai TH, Jia W (2010) Optimal deployment patterns for full coverage and k-connectivity (k≤ 6) wireless sensor networks. IEEE/ACM Trans Networking (TON) 18(3):934–947CrossRefGoogle Scholar
  6. 6.
    Tufail A (2012) Reliable latency-aware routing for clustered WSNs. Int J Distrib Sens Netw 2012:681273 6CrossRefGoogle Scholar
  7. 7.
    Cheng X, Du DZ, Wang L, Xu B (2008) Relay sensor placement in wireless sensor networks. ACM/Springer J Wirel Netw 14(3):347–355CrossRefGoogle Scholar
  8. 8.
    Al-Turjman F, Hassanein H, Ibnkahla M (2011) Optimized relay placement to federate wireless sensor networks in environmental applications. In: 7th International Wireless Communications and Mobile Computing Conference (IWCMC), pp 2040–2045Google Scholar
  9. 9.
    Wang F, Wang D, Liu J (2011) Traffic-aware relay node deployment: maximizing lifetime for data collection wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(8):1415–1423CrossRefGoogle Scholar
  10. 10.
    Reddy YB, Bullmaster C (2009) Application of game theory for cross- layer design in cognitive wireless networks. In: Proc. 6th Int. Conf. Inform. Technology: New Generations, ITNG, pp 510–515Google Scholar
  11. 11.
    Bisdikian C, Kaplan LM, Srivastava MB (2013) On the quality of information in sensor networks. ACM Trans Sensor Netw 9(4):48CrossRefGoogle Scholar
  12. 12.
    Ahlgren B, Dannewitz C, Imbrenda C, Kutscher D, Ohlman B (2012) A survey of information-centric networking. IEEE Commun Mag 50(7):26–36CrossRefGoogle Scholar
  13. 13.
    Al-Turjman F, Hassanein H (2013) Enhanced data delivery framework for dynamic Information-Centric Networks (ICNs). In: Proc. of the IEEE Local Computer Networks (LCN), Sydney, Australia, pp 831–838Google Scholar
  14. 14.
    Hasan M Z, Al-Rizzo H, Al-Turjman F (2017) A survey on multipath routing protocols for QoS assurances in real-time multimedia wireless sensor networks. IEEE Commun Surv Tutorials. doi: 10.1109/COMST.2017.2661201
  15. 15.
    Thomas RW, Friend DH, Dasilva LA, Mackenzie AB (2006) Cognitive networks: adaptation and learning to achieve end-to-end performance objectives. IEEE Commun Mag 44(12):51–57CrossRefGoogle Scholar
  16. 16.
    Bdira E, Ibnkahla M (2009) Performance modeling of cognitive wireless sensor networks applied to environmental protection. In: Proc. of the IEEE Global Telecommunications Conference, pp 1–6Google Scholar
  17. 17.
    Aalamifar F, Vijay G, Abedi Khoozani P, Ibnkahla M (2011) Cognitive wireless sensor networks for highway safety. In: Proc. of the 1st ACM Int. symposium on design and analysis of intelligent vehicular networks and applications, pp 55–60Google Scholar
  18. 18.
    Aalamifar F, Khoozani P, Ibnkahla M (2012) Multiple cognitive nodes in wireless sensor networks. In: Proc. of the IEEE 26th Queen’s Biennial Symposium on Communications (QBSC), pp 194–197Google Scholar
  19. 19.
    Al-Turjman F (2016) Impact of User's habits on smartphones’ sensors: an overview. HONET-ICT International IEEE Symposium, Kyrenia, pp 70–74Google Scholar
  20. 20.
    Singh G, Al-Turjman F (2014) Cognitive routing for information-centric sensor networks in smart cities. In: Proc. of the International Wireless Communications and Mobile Computing Conference (IWCMC), Nicosia, Cyprus, pp 1124–1129Google Scholar
  21. 21.
    Al-Turjman F (2017) Information-centric sensor networks for cognitive IoT: an overview. Springer Ann Telecommun J 72(1):3–18CrossRefGoogle Scholar
  22. 22.
    Yee K-P, Fisher D, Dhamija R, Hearst M (2001) Animated exploration of dynamic graphs with Radial layout. In: Proc. Information Visualization, pp 43–50Google Scholar
  23. 23.
    Lithium Based Batteries. [Online]. Available: http://batteryuniversity.com/learn/article/lithium_based_batteries
  24. 24.
    Al-Turjman F, Hassanein H, Ibnkahla M (2015) Towards prolonged lifetime for deployed WSNs in outdoor environment monitoring. Elsevier Ad Hoc Netw J 24(A):172–185CrossRefGoogle Scholar
  25. 25.
    Al-Turjman F, Hassanein H, Ibnkahla M (2013) Quantifying connectivity in wireless sensor networks with grid-based deployments. Elsevier: J Netw Comput Appl 36(1):368–377Google Scholar
  26. 26.
    Saaty R (1987) The analytical hierarchy process-what it is and how it is used. Elsevier Math Model 9(3):161–176CrossRefMATHGoogle Scholar
  27. 27.
    Hasan MZ, Al-Turjman F, Al-Rizzo H (2016) Evaluation of a duty-cycled protocol for TDMA-based wireless sensor networks. In: Proc. of the International Wireless Communications and Mobile Computing Conference, Paphos, Cyprus, pp 964–969Google Scholar
  28. 28.
    Al-Turjman F (2016) Hybrid approach for mobile couriers election in smart-cities. In: Proc. of the IEEE local Computer networks (LCN), Dubai, UAE, pp 1–4Google Scholar
  29. 29.
    Intanagonwiwat C, Govindan R, Estrin D (2000) Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: proceedings of the 6th annual international conference on mobile computing and networking. ACMGoogle Scholar
  30. 30.
    Zayani M-H, Gauthier V, Zeghlache D (2011) A joint model for IEEE 802.15.4 physical and medium access control layers. In: Proc. of IEEE Int. Wireless Communications and Mobile Computing Conference (IWCMC)Google Scholar
  31. 31.
    Zayani M-H, Gauthier V. Usage of IEEE 802.15.4 MAC–PHY model. Online: http://www-public.it-sudparis.eu/~gauthier/Tools/802_15_4_MAC_PHY_Usage.pdf
  32. 32.
    Park P, Di Marco P, Soldati P, Fischione C, Johansson KH (2009) A generalized Markov chain model for effective analysis of slotted IEEE 802.15.4. IEEE 6th Int Conf Mob Adhoc Sens Syst 130(139):12–15Google Scholar
  33. 33.
    Al-Turjman F, Al-Fagih A, Hassanein H, Ibnkahla M (2010) Deploying fault-tolerant grid-based wireless sensor networks for environmental applications. In: 2010 I.E. 35th conference on Local Computer Networks (LCN), pp 715–722Google Scholar
  34. 34.
    Al-Turjman F, Hassanein H, Ibnkahla M (2013) Efficient deployment of wireless sensor networks targeting environment monitoring applications. Elsevier: Comput Commun J 36(2):135–148CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringMiddle East Technical UniversityMersinTurkey

Personalised recommendations