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A Collaborative Energy-Aware Sensor Management System Using Team Theory

Published:23 May 2016Publication History
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Abstract

With limited battery supply, power is a scarce commodity in wireless sensor networks. Thus, to prolong the lifetime of the network, it is imperative that the sensor resources are managed effectively. This task is particularly challenging in heterogeneous sensor networks for which decisions and compromises regarding sensing strategies are to be made under time and resource constraints. In such networks, a sensor has to reason about its current state to take actions that are deemed appropriate with respect to its mission, its energy reserve, and the survivability of the overall network. Sensor Management controls and coordinates the use of the sensory suites in a manner that maximizes the success rate of the system in achieving its missions. This article focuses on formulating and developing an autonomous energy-aware sensor management system that strives to achieve network objectives while maximizing its lifetime. A team-theoretic formulation based on the Belief-Desire-Intention (BDI) model and the Joint Intention theory is proposed as a mechanism for effective and energy-aware collaborative decision-making. The proposed system models the collective behavior of the sensor nodes using the Joint Intention theory to enhance sensors’ collaboration and success rate. Moreover, the BDI modeling of the sensor operation and reasoning allows a sensor node to adapt to the environment dynamics, situation-criticality level, and availability of its own resources. The simulation scenario selected in this work is the surveillance of the Waterloo International Airport. Various experiments are conducted to investigate the effect of varying the network size, number of threats, threat agility, environment dynamism, as well as tracking quality and energy consumption, on the performance of the proposed system. The experimental results demonstrate the merits of the proposed approach compared to the state-of-the-art centralized approach adapted from Atia et al. [2011] and the localized approach in Hilal and Basir [2015] in terms of energy consumption, adaptability, and network lifetime. The results show that the proposed approach has 12 × less energy consumption than that of the popular centralized approach.

References

  1. D. Akselrod, Thomas Lang, Micheal McDonald, and Thiagalingam Kirubarajan. 2010. Markov decision process-based resource and information management for sensor networks. In Sensor Neyworks: Where Theory Meets Practice, G. Ferrari (Ed.). Springer, Berlin.Google ScholarGoogle Scholar
  2. D. Akselrod, A. Sinha, and T. Kirubarajan. 2007. Hierarchical Markov decision processes based distributed data fusion and collaborative sensor management for multitarget multisensor tracking applications. In Proceedings of the IEEE International Conference on SMC. 157--164.Google ScholarGoogle Scholar
  3. Giuseppe Anastasi, Marco Conti, Mario Di Francesco, and Andrea Passarella. 2009. Energy conservation in wireless sensor networks: A survey. Ad Hoc Network 7, 3, 537--568. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. G. Atia, V. Veeravalli, and J. Fuemmeler. 2011. Sensor scheduling for energy-efficient target tracking in sensor networks. IEEE Transactions on Signal Processing PP, 99, 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Michael Bratman. 1987. Intention, Plans, and Practical Reason. Center for the Study of Language and Information.Google ScholarGoogle Scholar
  6. Christopher H. Brooks and Edmund H. Durfee. 2003. Congregation formation in multiagent systems. Autonomous Agents and Multi-Agent Systems 7, 1, 145--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Marco Conti and Silvia Giordano. 2014. Mobile ad hoc networking: Milestones, challenges, and new research directions. IEEE Communications Magazine 52, 1, 85--96.Google ScholarGoogle ScholarCross RefCross Ref
  8. R. Cortez, X. Papageorgiou, H. Tanner, A. Klimenko, K. Borozdin, R. Lumia, and W. Priedhorsky. 2008. Smart radiation sensor management. IEEE Robotics Automation Magazine 15, 3, 85--93.Google ScholarGoogle ScholarCross RefCross Ref
  9. Eric W. Frew, Cory Dixon, Jack Elston, and Maciej Stachura. 2009. Active sensing by unmanned aircraft systems in realistic communication environments. In IFAC Workshop on Networked Robotics.Google ScholarGoogle ScholarCross RefCross Ref
  10. J. A. Fuemmeler, G. K. Atia, and V. V. Veeravalli. 2011. Sleep control for tracking in sensor networks. IEEE Transactions on Signal Processing 59, 9, 4354--4366. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jason A. Fuemmeler and Venugopal V. Veeravalli. 2010. Energy efficient multi-object tracking in sensor networks. IEEE Transactions on Signal Processing 58, 7, 3742--3750. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Charu Gandhi and Vivek Arya. 2014. A survey of energy-aware routing protocols and mechanisms for mobile ad hoc networks. Intelligent Computing, Networking, and Informatics: Proceedings of the International Conference on Advanced Computing, Networking, and Informatics, Durga Prasad Mohapatra and Srikanta Patnaik (Eds.). Vol. 243. Springer India, New Delhi, 111--117. http://dx.doi.org/10.1007/978-81-322-1665-0_11.Google ScholarGoogle Scholar
  13. Tian He, Sudha Krishnamurthy, Liqian Luo, Ting Yan, Lin Gu, Radu Stoleru, Gang Zhou, Qing Cao, Pascal Vicaire, John A. Stankovic, Tarek F. Abdelzaher, Jonathan Hui, and Bruce Krogh. 2006. VigilNet: An integrated sensor network system for energy-efficient surveillance. ACM Transactions on Sensor Networks 2, 1, 1--38 Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Allaa Hilal. 2013. An Intelligent Sensor Management Framework for Pervasive Surveillance. Ph.D. Dissertation. University of Waterloo, Waterloo, ON.Google ScholarGoogle Scholar
  15. A. R. Hilal and O. A. Basir. 2015. A scalable sensor management architecture using BDI model for pervasive surveillance. IEEE Systems Journal 9, 2, 529--541.Google ScholarGoogle ScholarCross RefCross Ref
  16. Allaa R. Hilal and Otman A. Basir. 2013. Pervasive Surveillance System Management. CRC Press, Boca Raton, FL, 199--228.Google ScholarGoogle Scholar
  17. Allaa R. Hilal, A. El-Nahas, Ahmed Bashandy, and Samir Shahin. 2008. Traffic differentiating queue for enhancing AODV performance in real-time interactive applications. In IEEE International Performance, Computing and Communications Conference (IPCCC’08). IEEE, 378--383.Google ScholarGoogle Scholar
  18. Allaa R. Hilal, Alaa Khamis, and Otman Basir. 2011a. HASM: A hybrid architecture for sensor management in a distributed surveillance context. In IEEE ICNSC. 492--497.Google ScholarGoogle Scholar
  19. Allaa R. Hilal, A. Khamis, and O. Basir. 2011b. A holonic federated sensor management framework for pervasive surveillance systems. In Proceedings of the IEEE International Systems Conference. 361--366.Google ScholarGoogle Scholar
  20. Allaa R. Hilal, Alaa Khamis, and Otman Basir. 2011c. A service-oriented architecture suite for sensor management in distributed surveillance systems. In International Conference on Computer and Management (CAMAN’11). IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  21. Infineon. 2015. Datasheet of Infineon TDA5250. Retrieved April 19, 2016 from http://www.infineon.com/cms/ en/product/rf-and-wireless-control/wireless-control/transceiver/TDA5250/productType.html?productType=db3a304318f3fe2901190a03ce4a2a9d.Google ScholarGoogle Scholar
  22. K. L. Jenkins and D. A. Castanon. 2011. Information-based adaptive sensor management for sensor networks. In Proceedings of the American Control Conference. 4934--4940.Google ScholarGoogle Scholar
  23. N. R. Jennings. 1995. Controlling cooperative problem solving in industrial multi-agent systems using joint intentions. Artificial Intelligence 75, 2, 195--240. Retrieved April 19, 2016 from http://www.sciencedirect.com/science/article/pii/0004370294000202. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jie Jia, Jian Chen, Guiran Chang, Yingyou Wen, and Jingping Song. 2009. Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Computers & Mathematics with Applications 57, 11, 1767--1775. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K. Kastella. 1997. Discrimination gain to optimize detection and classification. 27, 112--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M. P. Kolba, W. R. Scott, and L. M. Collins. 2011. A framework for information-based sensor management for the detection of static targets. IEEE Transactions on SMC-Part A: Systems 41, 1, 105--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Mark P. Kolba and Leslie M. Collins. 2007a. Information-based sensor management in the presence of uncertainty. IEEE Transactions on Signal Processing 55, 6, 2731--2735. Google ScholarGoogle ScholarCross RefCross Ref
  28. Mark P. Kolba and Leslie M. Collins. 2007b. Managing landmine detection sensors: Results from application to AMDS data. In Proceedings of the SPIE International Society for Optical Engineering. 1--11.Google ScholarGoogle Scholar
  29. Mark P. Kolba and Leslie M. Collins. 2009. Sensor management using a new framework for observation modeling. In Proceedings of the SPIE, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets. 1--12.Google ScholarGoogle Scholar
  30. Chris Kreucher, Doron Blatt, Alfred Hero, and Keith Kastella. 2006. Adaptive multi-modality sensor scheduling for detection and tracking of smart targets. Digital Signal Processing 16, 5, 546--567. Special Issue on DASP 2005.Google ScholarGoogle ScholarCross RefCross Ref
  31. C. Kreucher and A. Hero. 2005. Non-myopic approaches to scheduling agile sensors for multitarget detection, tracking, and identification. In Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing, Vol. V. 885--888.Google ScholarGoogle Scholar
  32. C. M. Kreucher, A. O. Hero, K. D. Kastella, and M. R. Morelande. 2007. An information-based approach to sensor management in large dynamic networks. Proceedings of the IEEE 95, 5, 978--999.Google ScholarGoogle ScholarCross RefCross Ref
  33. C. Kreucher, K. Kastella, and A. Hero. 2005. Multiplatform information-based sensor management. In Proceedings of the SPIE Defense Transformation and Network-Centric Systems Symposium, Vol. 5820. 141--151.Google ScholarGoogle Scholar
  34. V. Krishnamurthy and D. V. Djonin. 2007. Structured threshold policies for dynamic sensor scheduling - a partially observed Markov decision process approach. IEEE Transactions on Signal Processing 55, 10, 4938--4957. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. N. Lechevin, C. A. Rabbath, and M. Lauzon. 2009. A decision policy for the routing and munitions management of multiformations of unmanned combat vehicles in adversarial urban environments. IEEE Transactions on Control Systems Technology 17, 3, 505--519.Google ScholarGoogle ScholarCross RefCross Ref
  36. Ming Li, D. Ganesan, and P. Shenoy. 2009. PRESTO: Feedback-driven data management in sensor networks. IEEE/ACM Transactions on Networking 17, 4, 1256--1269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Y. Li, L. W. Krakow, E. K. P. Chong, and K. N. Groom. 2009. Approximate stochastic dynamic programming for sensor scheduling to track multiple targets. Digital Signal Processing 19, 6, 978--989. DASP’06 - Defense Applications of Signal Processing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Wenhui Liao, Qiang Ji, and W. A. Wallace. 2009. Approximate nonmyopic sensor selection via submodularity and partitioning. IEEE Transactions on SMC, Part A: Systems and Humans 39, 4, 782--794. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Scott A. Miller, Zachary A. Harris, and Edwin K. P. Chong. 2009. A POMDP framework for coordinated guidance of autonomous UAVs for multitarget tracking. EURASIP Journal of Advances in Signal Processing 1--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. S. Misra, S. V. Rohith Mohan, and R. Choudhuri. 2010. A probabilistic approach to minimize the conjunctive costs of node replacement and performance loss in the management of wireless sensor networks. IEEE Transactions on Network and Service Management 7, 2, 107--117. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Babar Nazir and Halabi Hasbullah. 2011. Dynamic sleep scheduling for minimizing delay in wireless sensor network. In Saudi International Electronics, Communnications and Photonics Conference (SIECPC’11). IEEE, 1--5.Google ScholarGoogle Scholar
  42. Alexander Pokahr, Lars Braubach, and Winfried Lamersdorf. 2005. A goal deliberation strategy for BDI agent systems. Multiagent System Technologies 82--93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Alexander Pokahr, Lars Braubach, and Winfried Lamersdorf. 2008. The Jadex research project. Retrieved April 19, 2016 from http://jadex.informatik.uni-hamburg.de/bin/view/About/Overview.Google ScholarGoogle Scholar
  44. L. A. Prashanth, Avhishek Chatterjee, and Shalabh Bhatnagar. 2014. Adaptive sleep-wake control using reinforcement learning in sensor networks. In 6th International Conference on Communication Systems and Networks (COMSNETS’14). IEEE, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  45. Qi Qu, Laurence B. Milstein, and Dhadesugoor R. Vaman. 2010. Cooperative and constrained MIMO communications in wireless ad hoc/sensor networks. IEEE Transactions on Wireless Communications 9, 10, 3120--3129. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Sutharshan Rajasegarar, Alistair Shilton, Christopher Leckie, Ramamohanarao Kotagiri, and Marimuthu Palaniswami. 2010. Distributed training of multiclass conic-segmentation support vector machines on communication constrained networks. In 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP’10). IEEE, 211--216.Google ScholarGoogle ScholarCross RefCross Ref
  47. A. Rao and M. Georgeff. 1991. Modeling rational agents within a BDI architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning. 473--484.Google ScholarGoogle Scholar
  48. Tifenn Rault, Abdelmadjid Bouabdallah, and Yacine Challal. 2014. Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks 67, 104--122.Google ScholarGoogle ScholarCross RefCross Ref
  49. M. A. Razzaque and S. Dobson. 2014. Energy-efficient sensing in wireless sensor networks using compressed sensing. Sensors 14, 2, 2822--2859.Google ScholarGoogle ScholarCross RefCross Ref
  50. Allison Ryan and J. Karl Hedrick. 2010. Particle filter based information-theoretic active sensing. Robotics and Autonomous Systems 58, 5, 574--584. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Shamsan Saleh, M. Ahmed, Borhanuddin Mohd Ali, Mohd Fadlee A. Rasid, and Alyani Ismail. 2013. A survey on energy awareness mechanisms in routing protocols for wireless sensor networks using optimization methods. Transactions on Emerging TeleCommunication Technologies. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Mehdi Shajari and Ali A. Ghorbani. 2004. Application of belief-desire-intention agents in intrusion detection and response. In PST. Citeseer, 181--191.Google ScholarGoogle Scholar
  53. Kei-Chen Tung, Jonathan Lu, and Hsin-Hung Lin. 2010. A distributed sleep scheduling algorithm with range adjustment for wireless sensor networks. Computational Collective Intelligence. Technologies and Applications 387--397. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Qin Wang and Woodward Yang. 2007. Energy consumption model for power management in wireless sensor networks. In 4th Annual IEEE Communication Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’07). IEEE, 142--151.Google ScholarGoogle ScholarCross RefCross Ref
  55. J. L. Williams, J. W. Fisher, and A. S. Willsky. 2007. Approximate dynamic programming for communication-constrained sensor network management. IEEE Transactions on Signal Processing 55, 8, 4300--4311. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Michael Wooldridge and Michael Fisher. 1994. A decision procedure for a temporal belief logic. Temporal Logic 317--331. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Michael Wooldridge and Nicholas Jennings. 1996. Towards a theory of cooperative problem solving. Distributed Software Agents and Applications 40--53.Google ScholarGoogle Scholar
  58. Yingqi Xu, Julian Winter, and Wang-Chien Lee. 2004. Prediction-based strategies for energy saving in object tracking sensor networks. In Proceedings of the 2004 IEEE International Conference on Mobile Data Management. IEEE, 346--357.Google ScholarGoogle Scholar
  59. Edward N. Zalta. 1995. Basic Concepts in Modal Logic. Technical Report. Center for the Study of Language and Information, Stanford University, Stanford, CA.Google ScholarGoogle Scholar
  60. Feng Zhao and Leonidas Guibas. 2004. Wireless Sensor Networks: An Information Processing Approach (The Morgan Kaufmann Series in Networking). Morgan Kaufmann, San Francisco, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Hai-Ying Zhou, Dan-Yan Luo, Yan Gao, and De-Cheng Zuo. 2011. Modeling of node energy consumption for wireless sensor networks. Wireless Sensor Network 3, 1, 18--23.Google ScholarGoogle ScholarCross RefCross Ref

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