OPTIMIZACIÓN DEL POSICIONAMIENTO DE SENSORES MEDIANTE EL RECOCIDO SIMULADO

Julio Trujillo-González

doi:10.48204/semillaeste.v4n1.4445

Submitted November 13, 2023

Published 2023-11-17

Artículos

Vol. 4 No. 1 (2023): Revista Semilla del Este

OPTIMIZING THE POSITIONING OF SENSORS THROUGH SIMULATED ANNEALING

Julio Trujillo-González⁺⁻

DOI https://doi.org/10.48204/semillaeste.v4n1.4445

PDF (Español (España))

References

DOI: 10.48204/semillaeste.v4n1.4445

Published: 2023-11-17

How to Cite

Trujillo-González, J. (2023). OPTIMIZING THE POSITIONING OF SENSORS THROUGH SIMULATED ANNEALING. Revista Semilla Del Este, 4(1), 154–165. https://doi.org/10.48204/semillaeste.v4n1.4445

Abstract

This study addresses the challenge of optimizing sensor placement in a crop field irrigated by canals, with the aim of maximizing the coverage of points of interest using the least number of sensors. This location problem resembles issues of maximum coverage and the well-known knapsack and set cover problems. To overcome this challenge, the use of the Simulated Annealing algorithm is proposed, a local search-based optimization technique. The algorithm was implemented on a rectangular surface of 2000 x 2000 m with 75 points of interest and 30 sensors with a coverage radius of 100 units each. The results showed that the algorithm was able to cover 54 out of the 75 points of interest. Despite the stochastic nature of Simulated Annealing, it was concluded that repeated execution of the algorithm can provide consistently optimal solutions.

References

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications magazine, 40(8), 102-114.
Chmielewski, D. J., Palmer, T., & Manousiouthakis, V. (2002). On the theory of optimal sensor placement. AIChE journal, 48(5), 1001-1012.
Eldeeb, H., Arafa, M., & Saidahmed, M. T. F. (2017). Optimal placement of access points for indoor positioning using a genetic algorithm. En 2017 12th international conference on computer engineering and systems (ICCES) (pp. 306-313). IEEE.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (pp. 10-pp). IEEE.
Ingber, L. (1993). Simulated annealing: Practice versus theory. Mathematical and computer modelling, 18(11), 29-57.
Mainwaring, A., Culler, D., Polastre, J., Szewczyk, R., & Anderson, J. (2002, September). Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (pp. 88-97).
Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680.
Pisinger, D. (2005). Where are the hard knapsack problems?. Computers & Operations Research, 32(9), 2271-2284.
Reina, D. G., Marin, S. T., Bessis, N., Barrero, F., & Asimakopoulou, E. (2013). An evolutionary computation approach for optimizing connectivity in disaster response scenarios. Applied Soft Computing, 13(2), 833-845.
Zhang, Y., Cao, L., Yue, Y., Cai, Y., & Hang, B. (2021). A novel coverage optimization strategy based on grey wolf algorithm optimized by simulated annealing for wireless sensor networks. Computational Intelligence and Neuroscience, 2021, 1-14.