Abstract
Nowadays, WiFi infrastructures and WiFi-enabled mobile devices have been ubiquitous in our daily lives, and are promising to provide both network services and indoor positioning and navigation services due to its simplicity and low costs. But, it is evident that AP placement is critical to both localization and network coverage, so that it is helpful to find the optimal AP placement scheme in terms of both localization and coverage. This paper tackles this problem by leveraging the widely used Cramer-Rao lower bound (CRLB) and heuristic genetic algorithm to develop an efficient AP optimization method. To be specific, the CRLB is used as the metric for localization and a multiple degree criterion is defined as the metric for coverage, which is incorporated into the fitness function in the genetic algorithm. Furthermore, instead of using the idea log distance path loss (LDPL) model, the more practical Motley-keenan model is adopted to reflect the influences of obstacles which are widespread in indoor environments. Finally, extensive simulations are conducted, and comparisons between the proposed method and the other three popular methods confirm the efficiency and effectiveness of the proposed method.
Supported by the National Natural Science Foundation of China under Grants 61461037, 41761086 and 61761035, the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant No. 2016YFB0502102, the Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grant 2017JQ09, and the Grassland Elite Project of the Inner Mongolia Autonomous Region under Grant CYYC5016.
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Tian, Y., Huang, B., Jia, B., Zhao, L. (2018). Optimizing WiFi AP Placement for Both Localization and Coverage. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_37
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DOI: https://doi.org/10.1007/978-3-030-05057-3_37
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