A Distributed Round-Based Prediction Model for Hierarchical Large-Scale Sensor Networks

• Published in: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) pp. 1 - 6
• Main Authors: Saad, Ghina, Harb, Hassan, Jaoude, Chady Abou, Jaber, Ali
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: Computational modeling; Data collection; Data communication; Data models; Data Prediction; Energy Saving; IoT; LSTM; Prediction algorithms; Predictive models; Sleep Mode; Wireless sensor networks; WSN
• ISSN: 2160-4894
• DOI: 10.1109/WiMOB.2019.8923312

Nowadays, the technology surrounds every corner of our lives and produces a huge amount of data about people and things' behaviours. This leads to a new sector of data analytics and decision making known as Big Data analytics era. In that era, the Internet of things (IoT) and the wireless sensor networks (WSNs) play a vital role and allow to monitor a wide number of applications, zones and environments. However, the limited resources of devices along with the redundancy among collected data makes big data collection is a major challenge for such networks. In this paper, we propose a distributed round-based prediction model dedicated to hierarchical large-scale sensor networks. First, we divide the network lifetime into a set of rounds where each round consists of several periods. At each round, each sensor collects data for some periods of the round then it sends them to the next node then, it enters into sleep mode for the other periods of the round. Upon receiving the data from each sensor, the sink uses a prediction model based on the long short-term memory (LSTM) time series in order to expect sensor data during the sleeping mode. We applied our approach on real sensor data while the obtained results show its relevance in terms of reducing data transmission and saving network lifetime.