{'fraction_non_alphanumeric': 0.01838235294117647, 'fraction_numerical': 0.001838235294117647, 'mean_word_length': 5.1525423728813555, '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}}
An Adaptive Framework Using Machine Learning in Wireless Sensor Network

A wireless sensor network (WSN) is a system with functionalities of sensing its environment, processing data, and communicating each other node. These functionalities require the energy or power, commonly from a battery, to work in realtime. Much research has provided the methods in power efficiency relating to the work of the WSN. This paper aims to create an adaptive framework using machine learning in WSN. This paper analyzes the effect of implementing the methods used in machine learning in wireless sensor networks on energy efficiency. Performance analysis in wireless sensor networks is related to the cluster selection and cluster head (CH) election. To see the performance of the proposed adaptive method in the wireless sensor network, the comparison related to the power efficiency of this method was compared with the low energy adaptive clustering hierarchy (LEACH) method. It has shown that the wireless sensor network using machine learning has prolonged the network lifetime by about 1.5 times.