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In the past few years, unmanned aerial vehicles (UAVs) have significantly gained attention and popularity from industry, government, and academia. With their rapid development and deployment into the civilian airspace, UAVs play an important role in different applications, including goods delivery, search‐and‐rescue, and traffic monitoring. Therefore, providing secure communication through authentication models for UAVs is necessary for a successful and reliable flight mission. To satisfy such requirements, numerous authentication mechanisms have been proposed in the literature. However, the literature lacks a comprehensive study evaluating the security and performance of these solutions. In this article, we analyze the security and performance of 27 recent UAV authentication works by considering ten different key metrics. First, in the performance analysis, we show that the majority of UAV authentication schemes are lightweight in their communication cost. However, the storage overhead or the energy consumption is not reported by many authentication studies. Then, we reveal in the security analysis the widely employed formal models (i.e., abstract description of an authentication protocol through a mathematical model), while most of the studies lack coverage of many attacks that can target UAV systems. Afterwards, we highlight the challenges that need to be addressed in order to design and implement secure and reliable UAV authentication schemes. Finally, we summarize the lessons learned on the authentication strategies for UAVs to motivate promising direction for further research. [ABSTRACT FROM AUTHOR]

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications. [ABSTRACT FROM AUTHOR]