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A general approach is proposed to determine the common sensors that shall be used to estimate and classify the approximate number of people (within a range) in a room. The range is dynamic and depends on the maximum occupancy met in a training data set for instance. Means to estimate occupancy include motion detection, power consumption, CO 2 concentration sensors, microphone or door/window positions. The proposed approach is inspired by machine learning. It starts by determining the most useful measurements in calculating information gains. Then, estimation algorithms are proposed: they rely on decision tree learning algorithms because these yield decision rules readable by humans, which correspond to nested if-then-else rules, where thresholds can be adjusted depending on the living areas considered. In addition, the decision tree depth is limited in order to simplify the analysis of the tree rules. Finally, an economic analysis is carried out to evaluate the cost and the most relevant sensor sets, with cost and accuracy comparison for the estimation of occupancy. C45 and random forest algorithms have been applied to an office setting, with average estimation error of 0.19-0.18. Over-fitting issues and best sensor sets are discussed.

A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO2 concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort.

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With the increasing use of resource-constrained IoT devices, the number of IoT Botnets has exploded with many variations and ways of penetration. Nowadays, studies based on machine learning and deep learning have focused on dealing with IoT Botnet with many successes, and these studies have required relevant data during malware execution. For this, the sandbox environment and behavior collection tools play an essential role. However, the existing sandboxes do not provide adequate behavior data of IoT botnet such as the C&C server communication, shared libraries requirements. Moreover, these sandboxes do not support a wide range of CPU architectures, data is not exhaustively collected during executable file runtime. In this paper, we present a new practical sandbox, named V-Sandbox, for dynamic analysis of the IoT Botnet. This sandbox is an ideal environment for IoT Botnet samples that exhibit all of their malicious behavior. It supports the C&C servers connection, shared libraries for dynamic files, and a wide range of CPU architectures. Experimental results on the 6141 IoT Botnet samples in our dataset have demonstrated the effectiveness of the proposed sandbox, compared to existing ones. The contribution of this paper is specific to the development of a usable, efficient sandbox for dynamic analysis of resource-constrained IoT devices.

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