IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
권호 표지
DOI QR코드

DOI QR Code

Fair Bit Allocation in Spatially Correlated Sensor Fields Using Shapley Value

공간 상관성을 갖는 센서장에서 섀플리 값을 이용한 공정한 비트 할당

  • Received : 2023.05.02
  • Accepted : 2023.06.08
  • Published : 2023.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The degree of contribution each sensor makes towards the total information gathered by all sensors is not uniform in spatially correlated sensor fields. Considering bit allocation problem in such a spatially correlated sensor field, the number of bits to be allocated to each sensor should be proportional to the degree of contribution the sensor makes. In this paper, we deploy Shapley value, a representative solution concept in cooperative game theory, and utilize it in order to quantify the degree of contribution each sensor makes. Shapley value is a system that determines the contribution of an individual player when two or more players work in collaboration with each other. To this end, we cast the bit allocation problem into a cooperative game called bit allocation game where sensors are regarded as the players, and a payoff function is given in the criteria of mutual information. We show that the Shapley value fairly quantifies an individual sensor's contribution to the total payoff achieved by all sensors following its desirable properties. By numerical experiments, we confirm that sensor that needs more bits to cover its area has larger Shapley value in spatially correlated sensor fields.

Keywords

Acknowledgement

이 논문은 2020년도 부산가톨릭대학교 교내연구비에 의하여 연구되었음.

References

  1. M. Kaur, A. Munjal, "Data Aggregation Algorithms for Wireless Sensor Network: A Review," Ad Hoc Networks, Vol. 100, No. 102083, 2020.
  2. J. Wen, J. Yang, T. Wang, Y. Li, Z. Lv, "Energy Efficient Task Allocation for Reliable Parallel Computation of Cluster-Based Wireless Sensor Network in Edge Computing," Digital Communications and Networks, Article in Press, https://doi.org/10.1016/j.dcan.2022.06.014.
  3. S. Li, C. He, Y. Wang, Y. Zhang, J. Liu, T. Huang, "A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks," Sensors, Vol. 17, No. 563, pp. 1-13, 2017. https://doi.org/10.1109/JSEN.2016.2633501
  4. E. Masazade, R. Niu, P. K. Varshney, "Dynamic Bit Allocation for Object Tracking in Wireless Sensor Networks," IEEE Transactions on Signal Processing, Vol. 60, No. 10, pp. 5048-5063, 2012. https://doi.org/10.1109/TSP.2012.2204257
  5. J. Luo, Y. Han, X. He, "Optimal Bit Allocation for Maneuvering Target Tracking in UWSNs with Additive and Mutplicative Noise," Signal Processing, Vol. 164, No. C, pp. 125-135, 2019. https://doi.org/10.1016/j.sigpro.2019.06.005
  6. Y. Wu, W. Liu, K. Li, "Power Allocation and Relay Selection for Energy Efficient Cooperation in Wireless Sensor Networks with Energy Harvesting," EURASIP Journal on Wireless Communications and Networking, Vol. 2017, No. 26, pp. 1-11, 2017. https://doi.org/10.1186/s13638-016-0795-x
  7. A. Ponti, A. Candelieri, F. Archetti, "A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks," Water, Vol. 13, No. 1625, pp. 1-18, 2021.
  8. M. Ghasemzadeh, A. Kefal, "Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method," Sensors, Vol. 22, No. 9252, pp. 1-29, 2022. https://doi.org/10.1109/JSEN.2021.3136033
  9. V. AKbarzadeh, J-C. Levesque, C. Gagne, M. Parizeau, "Efficient Sensor Placement Optimization Using Gradient Descent and Probabilistic Coverage," Sensors, Vol. 14, pp. 15525-15552, 2014. https://doi.org/10.3390/s140815525
  10. R. Soman, P. Malinowski, "A Real-Valued Genetic Algorithm for Optimization of Sensor Placement for Guided Wave-Based Structural Health Monitoring," Journal of Sensors, No. 961430, pp. 1-10, 2019.
  11. C. Guestrin, A. Krause, A. Singh, "Near-Optimal Sensor Placement in Gaussian Processes," Proceedings of ACM ICML, 2005.
  12. D. Jung, Y. Dong, E. Frisk, M. Krysander, G. Biswas, "Sensor Selection for Fault Diagnosis in Uncertain Systems," International Journal of Control, No. 1484171, pp. 1-11, 2018.
  13. L. Zhao, S. Qu, Y. Yi, "A Modified Cluster-Head Selection Algorithm in Wireless Sensor Networks Based on LEACH," EURASIP Journal of Wireless Communications and Networking, Vol. 2018, No. 287, pp. 1-8, 2018. https://doi.org/10.1186/s13638-017-1011-3
  14. A. Kulkarni, J. Terpenny, V. Prabhu, "Sensor Selection Framework for Designing Fault Diagnostic System," Sensors, Vol. 21, No. 6470, pp. 1-17, 2021. https://doi.org/10.1109/JSEN.2020.3039123
  15. B. Peleg, P. Sudholter, "Introduction to the Theory of Cooperative Games 2nd ed.," Springer, 2007.
  16. E. Winter, "The Shapley Value," Handbook of Game Theory with Economic Applications, Vol. 3, pp. 2025-2054, 2002. https://doi.org/10.1016/S1574-0005(02)03016-3
  17. C. Trudeau, "Network Flow Problems and Permutationally Concave Games," Mathematical Social Science, Vol. 58, No. 1, pp. 121-131, 2009. https://doi.org/10.1016/j.mathsocsci.2009.01.002
  18. T. M. Cover, J. A. Thomas, "Elements of Information Theory 2nd ed.," Wiley-Interscience, 2006.
  19. J. Castro, D. Gomez, J. Tejada, "Polynomial calculation of the Shapley Value Based on Sampling," Computers and Operations Research, Vol. 36, No. 5, pp. 1726-1730, 2009. https://doi.org/10.1016/j.cor.2008.04.004