The rapid evolution of Artificial Intelligence of Things (AIoT) is accelerating the development of smart societies, where interconnected consumer electronics such as smartphones, IoT devices, smart meters, and surveillance systems play a crucial role in optimizing operational efficiency and service delivery. However, this hyper-connected digital ecosystem is increasingly vulnerable to sophisticated Android malware attacks that exploit system weaknesses, disrupt services, and compromise data privacy and integrity. These malware variants leverage advanced evasion techniques, including permission abuse, dynamic runtime manipulation, and memory-based obfuscation, rendering traditional detection methods ineffective. The key challenges in securing AIoT-driven smart societies include managing high-dimensional feature spaces, detecting dynamically evolving malware behaviours, and ensuring real-time classification performance. To address these issues, this paper proposed an AI-powered Android Malware Detection (AIMD) framework designed for AIoT-enabled smart society environments. The framework extracts multi-level features (permissions, intents, API calls, and obfuscated memory patterns) from Android APK files and employs graph embedding techniques (DeepWalk and Node2Vec) for dimensionality reduction. Feature selection is optimized using the Red Deer Algorithm (RDA), a metaheuristic approach, while classification is performed through an ensemble of machine learning models (Support Vector Machine, Decision Tree, Random Forest, Extra Trees) enhanced by bagging, boosting, stacking, and soft voting techniques. Experimental evaluations on CICInvesAndMal2019 and CICMalMem2022 datasets demonstrate the effectiveness of the proposed system, achieving malware detection accuracies of 98.78% and 99.99%, respectively. By integrating AI-driven malware detection into AIoT infrastructures, this research advances cybersecurity resilience, safeguarding smart societies against emerging threats in an increasingly connected world.

This work presents a technique for localization of a smart infrastructure node, consisting of a fisheye camera, in a prior map. These cameras can detect objects that are outside the line of sight of the autonomous vehicles (AV) and send that information to AVs using V2X technology. However, in order for this information to be of any use to the AV, the detected objects should be provided in the reference frame of the prior map that the AV uses for its own navigation. Therefore, it is important to know the accurate pose of the infrastructure camera with respect to the prior map. Here we propose to solve this localization problem in two steps, (i) we perform feature matching between perspective projection of fisheye image and bird's eye view (BEV) satellite imagery from the prior map to estimate an initial camera pose, (ii) we refine the initialization by maximizing the Mutual Information (MI) between intensity of pixel values of fisheye image and reflectivity of 3D LiDAR points in the map data. We validate our method on simulated data and also present results with real world data.

In this paper we perform an extensive experimental evaluation of three planar target based 3D-LIDAR camera calibration algorithms, on a sensor suite consisting multiple 3D-LIDARs and cameras, assessing their robustness to random initialization and by using metrics like Mean Line Re-projection Error (MLRE) and Factory Stereo Calibration Error. We briefly describe each method and provide insights into practical aspects like ease of data collection. We also show the effect of noisy sensor on the calibration result and conclude with a note on which calibration algorithm should be used under what circumstances.

This work presents an extrinsic parameter estimation algorithm between a 3D LIDAR and a Projective Camera using a marker-less planar target, by exploiting Planar Surface Point to Plane and Planar Edge Point to back-projected Plane geometric constraints. The proposed method uses the data collected by placing the planar board at different poses in the common Field of View (FoV) of the LIDAR and the Camera. The steps include, detection of the target and the edges of the target in LIDAR and Camera frames, matching the detected planes and lines across both the sensing modalities and finally solving a cost function formed by the aforementioned geometric constraints that link the features detected in both the LIDAR and the Camera using non-linear least squares. We have extensively validated our algorithm using two Basler Cameras, Velodyne VLP-32 and Ouster OS1 LIDARs.

This paper presents the OTHex platform for aerial manipulation developed at LAAS-CNRS. The OTHex is probably the first multi-directional thrust platform designed to act as Flying Assistant which can aid human operators and/or Ground Manipulators to move long bars for assembly and maintenance tasks. The work emphasis is on task-driven custom design and experimental validations. The proposed control framework is built around a low-level geometric controller, and includes an external wrench estimator, an admittance filter, and a trajectory generator. This tool gives the system the necessary compliance to resist external force disturbances arising from contact with the surrounding environment or to parameter uncertainties in the load. A set of experiments validates the real-world applicability and robustness of the overall system.