Cloud computing is an Internet‐based computing where the information technology resources are provided to end users following their request. With this technology, users and businesses can access programs, storage, and application development platforms through the Internet and via the services offered by the cloud service providers (CSPs). One of the biggest obstructions in the cloud computing environment is data security. Actually, the data are dispersed across multiple machines and storage devices such as servers, computers, and various mobile devices. The uncontrolled access to these resources and data leads to many important data security risks for the end users. In this way, and in order to ensure the reliability of the cloud and the trust of the users regarding this environment, controlling access to data and resources as well as protecting and ensuring their security becomes a critical task for CSPs. In this work, we present a comprehensive review of existing access control mechanisms used in the cloud computing environment. The advantages and disadvantages of each of these models are discussed and presented along with their analysis. Also, we study the cloud requirements of these models, and we evaluate existing control mechanisms against these requirements.

In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.

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International audienceOn-line data stream analysis is an important challenge today because of the always-increasing rates of the streams issued from multiple heterogeneous sources, in many application domains. To reduce the amount of the data stream, several sampling methods were designed by the data stream research community. We focus in this paper, on the chain sampling algorithm proposed by Babcock et al. The aim of this algorithm is to select randomly and at any time, a given fixed proportion from the most recent items of the stream contained in the last sliding window. This algorithm is well adapted to the stream context, as only one pass over the data is performed. Moreover it uses a small memory, as it does not store all the items of the current sliding window. We show in this paper that the chain sampling algorithm suffers from some collision or redundancy problems. The collision occurs when the same item is selected as a sample more than once during the execution of the algorithm. We propose two approaches to overcome this weakness and improve the chain sampling algorithm. The first one is called “inverting the selection for a high sampling rate” and the second one is inspired from the “divide to conquer strategy”. Different experimentations are performed to show the efficiency of these two improvements, in particular their impact on the execution time of the algorithm

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The convergence of AI, IoT, and blockchain as the next disruptive technology is inevitable. Studying the ethics of these converged technologies is crucial for the steady development and adoption of the applications. Even though this topic is crucial, it is still not yet touched in the literature. This work is the first attempt to tackle this issue by developing a conceptual ethics framework for converged AI, IoT, and blockchain. The framework is based on a systematic literature review and categorizes the ethical issues into three layers namely micro (ethical issue involving the technology itself), meso (ethical issue involving the applications), and macro (ethical issue involving the society and institutions).

International audienceAbstract:Data streams are large data sets generated continuously and at a fast tempo. Their arrival rate is large compared to the treatment and storage capacities. Thus, these streams cannot be entirely stored. That is why we need to treat them in a single pass, without storing them exhaustively. However, for a particular stream, it is not always possible to predict in advance all of the processing to be performed. It is therefore necessary to save some of this data for future treatments. These stored data then build “summaries”. Several ways exist for the construction of the summary, among them, the sampling algorithms. We propose in this paper an in-depth study of sampling methods used for the construction of data stream summaries. This paper includes two main parts. First, we introduce the basic concepts of data stream: Windowing models over data stream as well as data stream applications. Then we describe the different sampling algorithms used in stream environments. We particularly focus on their advantages and drawbacks. Finally, we compare the performance of the Simple Random Sampling to the chain sampling algorithm and we discuss the relevant research challenges for data stream sampling