{'fraction_non_alphanumeric': 0.009888751545117428, 'fraction_numerical': 0.0, 'mean_word_length': 4.912408759124087, '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}}
Estimating Environmental Variables in Smart Sensor Networks with Faulty Nodes

Estimation of missing sensor data is an important issue in control systems that are based on smart sensor networks, since it can support an adaptive functionality of the control network. The paper investigates the extension of a low cost sensor network with a smart emulator module, able to act as a virtual sensor node on the network. The embedded emulator module should allow running of several pre-trained neural networks for estimating the values of faulty sensors. Training of the neural networks is made on a PC based on the records available at the level of the gateway module interfacing the control network. The proposed approach is exemplified for the case of a distributed control network system applied to smart homes.