{'fraction_non_alphanumeric': 0.02228612508986341, 'fraction_numerical': 0.0, 'mean_word_length': 5.1866666666666665, '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}}
Sensor Data Prediction techniques for nodes in IoT (poster)

As many Internet of Things devices rely on batteries, reducing energy consumption where battery replacement may be a problem may extend their lifetime and possible applications. This problem may be hindering the expansion of IoT devices usage in some applications. In this paper, we propose three data prediction and aggregation techniques to be applied to sensor readings and avoid unnecessary communication between a sensor node and Sink, so as to save energy and reduce network traffic. Since we have limited memory and processing capacity within sensors, this proposed approach collects data, compare the readings with expected value from one of the three techniques, and if the monitored data is within an ex-ante defined and acceptable range, no data transmission is necessary - the Sink would infer the actual reading within a uncertainty margin. Otherwise, last reading and estimated trend is transmitted and both sensor node and Sink are synchronized. The proposed techniques are (i) constant; (ii) linear; and (iii) using machine learning (Weightless Neural Network). We discuss pros and cons of each technique depending on the characteristics of the monitored data, taking into consideration the number of times the sensor reading had to be sent to Sink, and the average difference between estimated and actual reading.