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The energy efficiency paradigm associated with Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) is a major bottleneck for the development of related technologies. To overcome this limitation, the design and development of efficient and high-performance energy harvesting systems for WSN and IoT environments are being explored.

A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data.