Enhancing Wireless Sensor Networks Lifetime through On-Device Prediction

• Main Authors: Abdoulaye, Imourane, Rodriguez, Laurent, Belleudy, Cecile, Miramond, Benoit
• Format: Conference Proceeding
• Language: English
• Subjects: Data communication; data prediction; embedded artificial neural network; energy consumption; Energy resources; lifetime; Memory management; Predictive models; Reliability; Wireless communication; Wireless sensor networks; wireless sensors networks
• ISSN: 2766-3078
• DOI: 10.1109/SAS60918.2024.10636486

Wireless sensor nodes are increasingly deployed to monitor and collect data in remote and inaccessible locations. These nodes transmit the collected information to base stations for further processing. However, one of the major challenges they face is the limited energy resource, which significantly restricts their operational lifetime and, consequently, the reliability and efficiency of the network. Addressing this critical issue, this paper presents the use of convolutional neural networks in a wireless sensor network at a node-level to enhance autonomy and extend the network lifetime by replacing certain data communications with sensor data prediction. We evaluate the performance of the proposed method by examining different neural network struc-tures and their operating modes in the context of environmental monitoring. Through many simulations using real-world sensor data, we demonstrate that, the neural network is well capable of learning to predict data with an acceptable margin of error reducing the need for data transmissions by at least 50 %. This reduction in communication leads to energy savings and extends the network's operational lifetime. Moreover, the performances in terms of memory footprint, and energy consumption on board make it possible to extend the sleep mode of the sensor nodes to increase the wireless sensor network lifetime.