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A self-adaptive fault-tolerant systems for a dependable Wireless Sensor Networks

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Abstract

As the size and complexity of Wireless Sensor Networks (WSN) continue to grow, there is a need to develop techniques capable of achieving a level of service with successful operations upon which users can depend on. The routing protocol plays an important role in a multihop WSN as it manages and controls the delivery of the data packets. The function of a WSN can be affected by radio anomalies that may degrade the performance of the network. Unreliable and irregular link qualities, due to interference, are common in WSN as the nodes use the same frequency range as the other radio devices. A self-adaptive fault-tolerant network is required that has ability to maintain the level of service even in the presence of faults. Each node needs to monitor and adapt its routing protocols according to the operating environment. Due to resources constraint in the node, it must be carried out in an energy-efficient way and must be dependable. In this paper, we propose an immune-inspired algorithm that provides a level of “self-healing” in the network, through a combined process of self-detection, self-diagnosis and self-recovery and the Immune-inspired Detection and Recovery Systems (IDRS) is presented. In order to evaluate the performance of IDRS, a trace-based simulation, using traces from the hardware, is proposed to analyse the robustness and scalability. The Systematic Protocol Evaluation Technique (SPET) is applied to measure the dependability of the routing protocol. The proposed solution immune-inspired solution using multi-modal mechanism has achieved a higher dependability than existing reactive routing approaches and can adapt to the current operating environment to achieve the level of service required. Both the hardware and simulation results have validated the accuracy and the performance of the proposed systems. The simulated results have demonstrated that the IDRS can be scaled to a larger networks.

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Notes

  1. Downloadable at http://rtslab.wikispaces.com/file/view/idrs.tar.

Abbreviations

WSN::

Wireless Sensor Network

MRP::

Multimodal Routing Protocol

RSSI::

Radio Signal Strength Indicator

PSR::

Packet Sending Ratio

PRR::

Packet Reception Rate

RDM::

RDA Diagnostic Module

NST::

Not-So-Tiny AODV

TPC::

Transmission Power Control

PDR::

Packet Delivery Rate

RT::

Retransmission

GD::

Global Discovery

SPET::

Systematic Protocol Evaluation Technique

RDA::

Receptor Density Algorithm

FFT::

Fast Fourier Transform

TCR::

T-Cell Receptor

MDM::

MRP Detection Module

RIRM::

Radio Interference Response Module

MRP::

Multimodal Routing Protocol

MTPC::

MRP Transmission Power Control

TO::

Transmission Overhead

LD::

Local Discovery

RFI::

Radio Frequency Interference

References

  1. Balakrishnan H, Padmanabhan V, Seshan S, Katz R (1997) A comparison of mechanisms for improving tcp performance over wireless links. IEEE/ACM Trans Netw 5(6):756–769

    Article  Google Scholar 

  2. Boers NM, Nikolaidis I, Gburzynski P (2010) Patterns in the RSSI traces from an indoor urban environment. In: Proceedings of international workshop on computer aided modeling, analysis and design of communication links and networks. IEEE Press, New York, pp 61–65

    Google Scholar 

  3. Botta A, Dainotti A, Pescapé A (2010) Do you trust your software-based traffic generator? IEEE Commun Mag 48(9):158–165

    Article  Google Scholar 

  4. Candea G, Cutler J, Fox A (2004) Improving availability with recursive microreboots: a soft-state system case study. Perform Eval 56(1):213–248

    Article  Google Scholar 

  5. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58

    Article  Google Scholar 

  6. Chipara O, Lu C, Bailey T, Roman G (2010) Reliable clinical monitoring using wireless sensor networks: experiences in a step-down hospital unit. In: Proceedings of the 8th conference on embedded networked sensor systems. ACM, New York, pp 155–168

    Google Scholar 

  7. Davoudani D, Hart E, Paechter B (2007) An immune-inspired approach to speckled computing. In: Castro L, Zuben F, Knidel H (eds) Artificial immune systems. Lecture notes in computer science, vol 4628. Springer, Berlin Heidelberg, pp 288–299

    Chapter  Google Scholar 

  8. Gnawali O, Fonseca R, Jamieson K, Moss D, Levis P (2009) Collection tree protocol. In: Proceedings of the 7th conference on embedded networked sensor systems. ACM, New York, pp 1–14

    Google Scholar 

  9. Gomez C, Salvatella P, Alonso O, Paradells J (2006) Adapting AODV for IEEE 802.15.4 mesh sensor networks: theoretical discussion and performance evaluation in a real environment. In: Proceedings of the international symposium on world of wireless, mobile and multimedia networks. IEEE Press, New York, pp 159–170

    Google Scholar 

  10. Gutierrez J, Naeve M, Callaway E, Bourgeois M, Mitter V, Heile B (2001) IEEE 802.15.4: a developing standard for low-power low-cost wireless personal area networks. Networks 15(5):12–19

    Google Scholar 

  11. Hart E, Timmis J (2008) Application areas of AIS: the past, the present and the future. Appl Softw Comput 8(1):191–201

    Article  Google Scholar 

  12. Hilder J, Owens N, Neal M, Hickey P, Cairns S, Kilgour D, Timmis J, Tyrrell A (2012) Chemical detection using the receptor density algorithm. IEEE Trans Syst Man Cybern, Part C, Appl Rev 42(6):1730–1741

    Article  Google Scholar 

  13. Hsu L, King C, Banerjee A (2007) On broadcasting in wireless sensor networks with irregular and dynamic radio coverage. In: International conference on parallel processing. IEEE Press, New York, pp 55

    Google Scholar 

  14. IEEE: Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs). http://standards.ieee.org/getieee802/download/802.15.4-2006.pdf (2006) [Online; accessed 1-March-2013]

  15. Karlof C, Wagner D (2003) Secure routing in wireless sensor networks: attacks and countermeasures. In: Proceedings of the 1st international workshop on sensor network protocols and applications. IEEE Press, New York, pp 113–127

    Google Scholar 

  16. Ko J, Terzis A (2010) Power control for mobile sensor networks: an experimental approach. In: Proceedings of the 7th annual communications society conference on sensor mesh and ad hoc communications and networks. IEEE Press, New York

    Google Scholar 

  17. Lau H, Bate I, Cairns P, Timmis J (2011) Adaptive data-driven error detection in swarm robotics with statistical classifiers. Robot Auton Syst 59(12):1021–1035

    Article  Google Scholar 

  18. Levis P, Madden S, Polastre J, Szewczyk R, Whitehouse K, Woo A, Gay D, Hill J, Welsh M, Brewer E, Culler D (2005) TinyOS: an operating system for sensor networks ambient intelligence. In: Weber W, Rabaey JM, Aarts E (eds) Ambient intelligence, Chap. 7. Springer, Berlin Heidelberg, pp 115–148

    Chapter  Google Scholar 

  19. Lim TH, Bate I, Timmis J (2011) Multi-modal routing to tolerate failures. In: Proceedings of the 7th international conference on intelligent sensors, sensor networks and information processing. IEEE Press, New York, pp 211–216

    Google Scholar 

  20. Lim TH, Bate I, Timmis J (2012) Validation of performance data using experimental verification process in wireless sensor network. In: Proceedings of the 16th conference on emerging technologies factory automation. IEEE Press, New York

    Google Scholar 

  21. Lin S, Zhang J, Zhou G, Gu L, Stankovic J, He T (2006) ATPC: adaptive transmission power control for wireless sensor networks. In: The 4th international conference on embedded networked sensor systems, pp 223–236

    Chapter  Google Scholar 

  22. Lin S, Zhou G, Whitehouse K, Wu Y, Stankovic J, He T (2009) Towards stable network performance in wireless sensor networks. In: Proceedings of the 30th real-time systems symposium. IEEE Press, New York, pp 227–237

    Google Scholar 

  23. Liu H, Li J, Xie Z, Lin S, Whitehouse K, Stankovic JA, Siu D (2010) Automatic and robust breadcrumb system deployment for indoor firefighter applications. In: Proceedings of the 8th international conference on mobile systems, applications, and services. IEEE Press, New York, pp 21–34

    Google Scholar 

  24. Marchiori A, Guo L, Thomas J, Han Q (2010) Realistic performance analysis of wsn protocols through trace based simulation. In: Proceedings of the 7th ACM workshop on performance evaluation of wireless ad hoc, sensor, and ubiquitous networks. ACM, New York, pp 87–94

    Chapter  Google Scholar 

  25. Murphy K, Travers P, Walport M (2012) Janeway’s immunobiology, vol 7. Garland Science, New York

    Google Scholar 

  26. Ngai E, Liu J, Lyu M (2006) On the intruder detection for sinkhole attack in wireless sensor networks. In: Proceedings of the international conference on communications, vol 8. IEEE Press, New York, pp 3383–3389

    Google Scholar 

  27. NS2: The network simulator ns-2 (2002). http://www.isi.edu/nsnam/ns/ [Online; accessed 1-February-2013]

  28. Ong K, Yue S, Ling K (2010) Implementation of fast Fourier transform on body sensor networks. In: Proceeding of the international conference on body sensor networks. IEEE Press, New York, pp 197–202

    Google Scholar 

  29. Owens N, Greensted A, Timmis J, Tyrrell A (2012) The receptor density algorithm. Theoretical Computer Science

  30. Perkins C, Royer E (1999) Ad-hoc on-demand distance vector routing. In: Proceeding of the 2nd workshop on mobile computing systems and applications. IEEE Press, New York, pp 90–100

    Google Scholar 

  31. Polastre J, Szewczyk R, Culler D (2005) Telos: enabling ultra-low power wireless research. In: Proceeding of the 4th international symposium on information processing in sensor networks. IEEE Press, New York, pp 364–369

    Google Scholar 

  32. Raghunathan V, Schurgers C, Park S, Srivastava M (2002) Energy-aware wireless microsensor networks. IEEE Signal Process Mag 19(2):40–50

    Article  Google Scholar 

  33. Schaust S, Szczerbicka H (2011) Applying antigen-receptor degeneracy behavior for misbehavior response selection in wireless sensor networks. In: Proceedings of the 10th international conference on artificial immune systems. Springer, Berlin Heidelberg, pp 212–225

    Chapter  Google Scholar 

  34. Srinivasan K, Dutta P, Tavakoli A, Levis P (2010) An empirical study of low-power wireless. ACM Trans Sens Netw 6(2):1–49

    Article  Google Scholar 

  35. Szewczyk R, Polastre J, Mainwaring A, Culler D (2004) Lessons from a sensor network expedition. In: Karl H, Wolisz A, Willig A (eds) Wireless sensor networks. Lecture notes in computer science, vol 2920. Springer, Berlin Heidelberg, pp 307–322

    Chapter  Google Scholar 

  36. Trinidad M, Valle M (2009) Reliable event detectors for constrained resources wireless sensor node hardware. EURASIP J Embed Syst 2009:7

    Google Scholar 

  37. Vargha A, Delaney H (2000) A critique and improvement of the CL common language effect size statistics of McGraw and Wong. J Educ Behav Stat 25(2):101–132

    Google Scholar 

  38. Wallenta C, Kim J, Bentley P, Hailes S (2010) Detecting interest cache poisoning in sensor networks using an artificial immune algorithm. Appl Intell 32(1):1–26

    Article  Google Scholar 

  39. Wang P, Akyildiz I (2011) Spatial correlation and mobility-aware traffic modeling for wireless sensor networks. IEEE/ACM Trans Netw 19(6):1860–1873

    Article  Google Scholar 

  40. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  41. Zacharias S, Newe T, O’Keeffe S, Lewis E (2012) Identifying sources of interference in rssi traces of a single IEEE 802.15.4 channel. In: Proceeding of the 8th international conference on wireless and mobile communications, pp 408–414

    Google Scholar 

  42. Zou Y, Chakrabarty K (2007) Redundancy analysis and a distributed self-organization protocol for fault-tolerant wireless sensor networks. Int J Distrib Sens Netw 3(3):243–272

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science, Deramore Lane, University of York, Heslington, York, YO10 5GH, UK

    Tiong Hoo Lim & Iain Bate

  2. Electrical and Electronics Engineering, Institut Teknologi Brunei, Gadong, BE 1410, Negara Brunei Darussalam

    Tiong Hoo Lim

  3. Department of Electronics, University of York, Heslington, York, YO10 5DD, UK

    Jon Timmis

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Correspondence to Tiong Hoo Lim.

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Lim, T.H., Bate, I. & Timmis, J. A self-adaptive fault-tolerant systems for a dependable Wireless Sensor Networks. Des Autom Embed Syst 18, 223–250 (2014). https://doi.org/10.1007/s10617-013-9126-1

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