{'fraction_non_alphanumeric': 0.021739130434782608, 'fraction_numerical': 0.001358695652173913, 'mean_word_length': 5.464912280701754, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Edge Computing Empowered Indoor Positioning: A Brief Introduction

In the indoor environment, the accurate of the positioning cannot be guaranteed by the Global Navigation Satellite System (GNSS). Specially, for the power applications, such as large substations and power plant, which have high demand of positioning accuracy, it brings significant challenges. In this paper, we summarize the indoor positioning methods and discuss the advantages and disadvantages of each methods. Finally, we focus on the indoor positioning assisted with edge computing. We propose a weighted adaptive KNN algorithm based on the received 5G signal to improve the accuracy of the indoor positioning. Simulations show the effectiveness of the algorithm.