Energiaoptimalizálás WSN hálózatokon belül rajintelligenciával és megerősítéses tanulással
Energy Optimization in WSNs Using Swarm Intelligence and Reinforcement Learning
Kulcsszavak:
Wireless Sensor Networks, Reinforcement Learning, Swarm Intelligence, Energy Optimization, Clustering, /, vezeték nélküli szenzorhálózatok, megerősítéses tanulás, rajintelligencia, energiaoptimalizálás, klaszterezésAbsztrakt
Wireless sensor networks play an important role in numerous application areas. Sensor nodes typical-ly have limited energy resources; therefore, optimizing their energy consumption is essential. This study presents
a hybrid approach in which a reinforcement learning agent estimates an appropriate number of clus-ter heads, while a swarm intelligence algorithm selects the most suitable nodes for this purpose in or-der to extend the lifetime of the network.
Kivonat
A vezeték nélküli szenzorhálózatok napjainkban is fontos szerepet töltenek be számos alkalmazási terü-leten.
A szenzorcsomópontok jellemzően korlátozott energiakészlettel rendelkeznek, ezért energiafogyasztá-suk optimalizálása elengedhetetlen. A tanulmány egy hibrid megközelítést mutat be, amelyben egy megerősítéses tanulási ágens megbecsüli a megfelelő klaszterfejszámot, míg egy rajintelligencia-algoritmus kiválasztja az erre a célra legalkalmasabb csomópontokat a hálózat élettartamának növe-lése érdekében.
Hivatkozások
[1] Alsattar, H. A.; Zaidan, A. A.; Zaidan, B. B.: Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 2020, 53, 2237–2264. DOI: 10.1007/s10462-019-09732-5.
[2] Chakraborty, A.; Kar, A. K.: Swarm Intelligence: A Review of Algorithms. In: Nature-Inspired Computing and Optimization: Theory and Applications. Springer, 2017, 475–494. DOI: 10.1007/978-3-319-50920-4_19.
[3] Heinzelman, W. R.; Chandrakasan, A.; Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Maui, HI, USA, 2000. DOI: 10.1109/HICSS.2000.926982.
[4] Kumar, S.; Agrawal, R.: A comprehensive survey on meta-heuristic-based energy minimization routing techniques for wireless sensor network: classification and challenges. Journal of Supercomputing, 2022, 78, 6612–6663. DOI: 10.1007/s11227-021-04128-1.
[5] Mann, H. B.; Whitney, D. R.: On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, Institute of Mathematical Statistics, 1947, 18(1), 50–60. DOI: 10.1214/aoms/1177730491.
[6] Mavrovouniotis, M.; Li, C.; Yang, S.: A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 2017, 33, 1–17. DOI: 10.1016/j.swevo.2016.12.005.
[7] Miranda, J.; Abrishambaf, R.; Gomes, T.; Gonçalves, P.; Cabral, J.; Tavares, A.; Monteiro, J.: Path loss exponent analysis in Wireless Sensor Networks: Experimental evaluation. 2013 11th IEEE International Conference on Industrial Informatics (INDIN), Bochum, Germany, 2013, 54–58. DOI: 10.1109/INDIN.2013.6622857.
[8] Morales, E. F.; Escalante, H. J.: A brief introduction to supervised, unsupervised, and reinforcement learning. In: Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press, 2022, 111–129.
[9] Srivastava, A.; Mishra, P. K.: A Survey on WSN Issues with its Heuristics and Meta-Heuristics Solutions. Wireless Personal Communications, 2021, 121, 745–814. DOI: 10.1007/s11277-021-08659-x.
[10] Suk, H. I.: An introduction to neural networks and deep learning. In: Deep Learning for Medical Image Analysis. Academic Press, 2017, 3–24.
[11] Tang, J.; Liu, G.; Pan, Q.: A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends. IEEE/CAA Journal of Automatica Sinica, 2021, 8(10), 1627–1643.
[12] Wang, Y.; He, H.; Tan, X.: Truly proximal policy optimization. In: Uncertainty in Artificial Intelligence. PMLR, 2020, 113–122.
