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                <term xml:lang="en">Sleep Mode</term>
                <term xml:lang="en">Wireless Sensor Nodes</term>
                <term xml:lang="en">Prediction Model</term>
                <term xml:lang="en">Training Dataset</term>
                <term xml:lang="en">Artificial Neural Network</term>
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                <term xml:lang="en">Sampling Period</term>
                <term xml:lang="en">Energy Efficiency</term>
                <term xml:lang="en">Convolutional Neural Network Model</term>
                <term xml:lang="en">Least Mean Square</term>
                <term xml:lang="en">Mean Absolute Percentage Error</term>
                <term xml:lang="en">Prediction Error</term>
                <term xml:lang="en">Reduce Energy Consumption</term>
                <term xml:lang="en">Number Of Sensor Nodes</term>
                <term xml:lang="en">Sleep Schedule</term>
                <term xml:lang="en">Electronic Board</term>
                <term xml:lang="en">Memory Space</term>
                <term xml:lang="en">Error Metrics</term>
                <term xml:lang="en">Neural Network</term>
                <term xml:lang="en">data prediction</term>
                <term xml:lang="en">embedded artificial neural network</term>
                <term xml:lang="en">wireless sensors networks</term>
                <term xml:lang="en">energy consumption</term>
                <term xml:lang="en">lifetime</term>
                <term xml:lang="en">Wireless Sensor</term>
                <term xml:lang="en">Wireless Sensor Networks</term>
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                <term xml:lang="en">Convolutional Neural Network</term>
                <term xml:lang="en">Energy Conservation</term>
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              <p>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 wirelesssensor 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 structures 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.</p>
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