Wireless Sensor Network Localization using Artificial Intelligence and Simulated Annealing Optimization
EL Abkari Safae1, Jilbab Abdelilah2, EL Mhamdi Jamal3
1Safae EL Abkari, Département of Electrical Engineering, Ecole Normale Supérieure de l’Enseignement Technique, Rabat, Morocco.
2Abdelilah Jilbab, Département of Electrical Engineering, Ecole Normale Supérieure de l’Enseignement Technique, Rabat, Morocco.
3Jamal EL Mhamdi, Département of Electrical Engineering, Ecole Normale Supérieure de l’Enseignement Technique, Rabat, Morocco.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3388-3491 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8911038620/2020©BEIESP | DOI: 10.35940/ijrte.F8911.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent years localization of nodes in wireless sensor networks (WSNs) has become one the main features of applications. In fact, this issue has been widely explored by the scientific community that proposed many approaches in order to localize network nodes. However, artificial neural network (ANN) can be used as an operating method. Therefore, we aim in this paper to select the best suited structure of ANN to localize in WSN using a meta-heuristic technique. To optimize this procedure, we use the Simulated Annealing (SA) algorithm. We constituted a network of ESP8266 modules to create our WSN topology as well as the training and the testing data to evaluate the performances.
Keywords: Localization, WSN, Artificial Neural Network, Simulated Annealing, Wireless
Scope of the Article: Energy Harvesting And Transfer For Wireless Sensor Networks.