Detecting users in an indoor environment based on Wi-Fi signal strength has a wide domain of applications. This can be used for objectives like locating users in smart home systems, locating criminals in bounded regions, obtaining the count of users on an access point etc. The paper develops an optimized model that could be deployed in monitoring and tracking devices used for locating users based on the Wi-Fi signal strength they receive in their personal devices. Here, we procure data of signal strengths from various routers, map them to the user’s location and consider this mapping as a classification problem. We train a neural network using the weights obtained by the proposed fuzzy hybrid of Particle Swarm Optimization & Gravitational Search Algorithm (FPSOGSA), an optimization strategy that results in better accuracy of the model. © Springer Nature Singapore Pte Ltd. 2017.
|Journal||Data powered by TypesetAdvances in Intelligent Systems and Computing Proceedings of Sixth International Conference on Soft Computing for Problem Solving|
|Publisher||Data powered by TypesetSpringer Singapore|