{'fraction_non_alphanumeric': 0.019642857142857142, 'fraction_numerical': 0.0, 'mean_word_length': 5.15934065934066, '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}}
Real-Time Implementation of Fault Detection in Wireless Sensor Networks Using Neural Networks

This paper presents the real-time implementation of a neural network-based fault detection for wireless sensor networks (WSNs). The method is implemented on TinyOS operating system. A collection tree network is formed and multi-hoping data is sent to the base station root. Nodes take environmental measurements every N seconds while neighboring nodes overhear the measurement as it is being forwarded to the base station and record it. After nodes complete M and receive/store M measurements from each neighboring node, recurrent neural networks (RNNs) are used to model the sensor node, the node's dynamics, and interconnections with neighboring nodes. The physical measurement is compared against the predicted value and a given threshold of error to determine sensor fault. By simply overhearing network traffic, this implementation uses no extra bandwidth or radio broadcast power. The only cost of the approach is battery power required to power the receiver to overhear packets and MCU processor time to train the RNN.