Image compression and encryption based on integer wavelet transform and hybrid hyperchaotic system

Compression and encryption of images are emerging as recent topics in the area of research to improve the performance of data security. A joint lossless image compression and encryption algorithm based on Integer Wavelet Transform (IWT) and the Hybrid Hyperchaotic system is proposed to enhance the security of data transmission. Initially, IWT is used to compress the digital images and then the encryption is accomplished using the Hybrid Hyperchaotic system. A Hybrid Hyperchaotic system; Fractional Order Hyperchaotic Cellular Neural Network (FOHCNN) and Fractional Order Four-Dimensional Modified Chua’s Circuit (FOFDMCC) is used to generate the pseudorandom sequences. The pixel substitution and scrambling are realized simultaneously using Global Bit Scrambling (GBS) that improves the cipher unpredictability and efficiency. In this study, Deoxyribonucleic Acid (DNA) sequence is adopted instead of a binary operation, which provides high resistance to the cipher image against crop attack and salt-and-pepper noise. It was observed from the simulation outcome that the proposed Hybrid Hyperchaotic system with IWT demonstrated more effective performance in image compression and encryption compared with the existing models in terms of parameters such as unified averaged changed intensity, a number of changing pixels rate, and correlation coefficient.

Novel energy-aware approach to resource allocation in cloud computing

In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged. Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines (VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model can produce promising results compared to other works of literature. This model also presents good scalability to improve the learning, allowing, thus, to achieve our objectives.

A saliency model-oriented convolution neural network for cloud detection in remote sensing images

Remote sensing is an indispensable technical way for monitoring earth resources and environmental changes. However, optical remote sensing images often contain a large number of cloud, especially in tropical rain forest areas, make it difficult to obtain completely cloud-free remote sensing images. Therefore, accurate cloud detection is of great research value for optical remote sensing applications. In this paper, we propose a saliency model-oriented convolution neural network for cloud detection in remote sensing images. Firstly, we adopt Kernel Principal Component Analysis (KCPA) to unsupervised pre-training the network. Secondly, small labeled samples are used to fine-tune the network structure. And, remote sensing images are performed with super-pixel approach before cloud detection to eliminate the irrelevant backgrounds and non-clouds object. Thirdly, the image blocks are input into the trained convolutional neural network (CNN) for cloud detection. Meanwhile, the segmented image will be recovered. Fourth, we fuse the detected result with the saliency map of raw image to further improve the accuracy of detection result. Experiments show that the proposed method can accurately detect cloud. Compared to other state-of-the-art cloud detection method, the new method has better robustness.

Urban street scene analysis using lightweight multi-level multi-path feature aggregation network

Urban street scene analysis is an important problem in computer vision with many off-line models achieving outstanding semantic segmentation results. However, it is an ongoing challenge for the research community to develop and optimize the deep neural architecture with real-time low computing requirements whilst maintaining good performance. Balancing between model complexity and performance has been a major hurdle with many models dropping too much accuracy for a slight reduction in model size and unable to handle high-resolution input images. The study aims to address this issue with a novel model, named M2FANet, that provides a much better balance between model’s efficiency and accuracy for scene segmentation than other alternatives. The proposed optimised backbone helps to increase model’s efficiency whereas, suggested Multi-level Multi-path (M2) feature aggregation approach enhances model’s performance in the real-time environment. By exploiting multi-feature scaling technique, M2FANet produces state-of-the-art results in resource-constrained situations by handling full input resolution. On the Cityscapes benchmark data set, the proposed model produces 68.5% and 68.3% class accuracy on validation and test sets respectively, whilst having only 1.3 million parameters. Compared with all real-time models of less than 5 million parameters, the proposed model is the most competitive in both performance and real-time capability.

Adaptive replication strategy based on popular content in cloud computing

The cloud is an infrastructure that provides decentralized on-demand services. It allows consumers to pay only for the services they use. The consumer is the important entity in the cloud. The violation of the SLA contract between the consumer and the provider often leads to consequences because the service provider has to pay penalties. Data replication is emerging as an ideal solution to meet the new challenges of the cloud. This paper proposes a new replication strategy based on the popularity of data. This strategy adaptively selects the files to be replicated to improve the overall availability of data in the system, minimize query response time, and achieve the required quality of service. In addition, it dynamically determines the number of replicas to add and the best locations to store them. Experimental results show the effectiveness of the proposed strategy.

Broker-based optimization of SLA negotiations in cloud computing

Cloud computing provisions and allocates resources, in advance or real-time, to dynamic applications planned for execution. This is a challenging task as the Cloud-Service-Providers (CSPs) may not have sufficient resources at all times to satisfy the resource requests of the Cloud-Service-Users (CSUs). Further, the CSPs and CSUs have conflicting interests and may have different utilities. Service-Level-Agreement (SLA) negotiations among CSPs and CSUs can address these limitations. User Agents (UAs) negotiate for resources on behalf of the CSUs and help reduce the overall costs for the CSUs and enhance the resource utilization for the CSPs. This research proposes a broker-based mediation framework to optimize the SLA negotiation strategies between UAs and CSPs in Cloud environment. The impact of the proposed framework on utility, negotiation time, and request satisfaction are evaluated. The empirical results show that these strategies favor cooperative negotiation and achieve significantly higher utilities, higher satisfaction, and faster negotiation speed for all the entities involved in the negotiation.

Energy trading and control of islanded DC microgrid using multi-agent systems

The energy sector is experiencing a revolution that is fuelled by a multitude of factors. Among them are the aging grid system, the need for cleaner energy and the increasing demands on energy sector. The demand-response program is an advanced feature in smart grid that strives to match suppliers to their demands using price-based and incentive programs. The objective of the work is to analyse the performance of the load shedding technique using dynamic pricing algorithm. The system was designed using multi-agent system (MAS) for a DC microgrid capable of real-time monitoring and controlling of power using price-based demand-response program. As a proof of concept, the system was implemented using intelligent physical agents, Java Agent Development Framework (JADE), and agent simulation platform (REPAST) with two residential houses (non-critical loads) and one hospital (critical load). The architecture has been implemented using embedded devices, relays, and sensors to control the operations of load shedding and energy trading in residential areas that have no access to electricity. The measured results show that the system can shed the load with the latency of less than 600 ms, and energy cost saving with an individual houses by 80% of the total cost with 2USD per day. The outcome of the studies demonstrates the effectiveness of the proposed multi-agent approach for real-time operation of a microgrid and the implementation of demand-response program.

Agent-based access control framework for enterprise content management

Telework is an important alternative to work that seeks to enhance employees’ safety and well-being while reducing the company costs. Employees can work anytime, any where and under high mobility conditions using new devices. Therefore, the access control of remote exchanges of Enterprise Content Management systems (ECM) have to take into consideration the diversity of users’ devices and context conditions in a telework open network. Different access control models were proposed in the literature to deal with the dynamic nature of users’ context and devices. However, most access control models rely on a centralized management of permissions by an authorization entity which can reduce its performance with the increase of number of users and requests in an open network. Moreover, they often depend on the administrator’s intervention to add new devices’ authorization and to set permissions on resources. In this paper, we suggest a distributed management of access control for telework open networks that focuses on an agent-based access control framework. The framework uses a multi-level rule engine to dynamically generate policies. We conducted a usability test and an experiment to evaluate the security performance of the proposed framework. The result of the experiment shows that the ability to resist deny of service attacks over time increased in the proposed distributed access control management compared with the centralized approach.

A new popularity-based data replication strategy in cloud systems

Data-intensive cloud computing systems are growing year by year due to the increasing volume of data. In this context, data replication technique is frequently used to ensure a Quality of service, e.g., performance. However, most of the existing data replication strategies just reproduce the same number of replicas on some nodes, which is certainly not enough for more accurate results. To solve these problems, we propose a new data Replication and Placement strategy based on popularity of User Requests Group (RPURG). It aims to reduce the tenant response time and maximize benefit for the cloud provider while satisfying the Service Level Agreement (SLA). We demonstrate the validity of our strategy in a performance evaluation study. The result of experimentation shown robustness of RPURG.

Parallelism exploration in sequential algorithms via animation tool

Different high performance techniques, such as profiling, tracing, and instrumentation, have been used to tune and enhance the performance of parallel applications. However, these techniques do not show how to explore the potential of parallelism in a given application. Animating and visualizing the execution process of a sequential algorithm provide a thorough understanding of its usage and functionality. In this work, an interactive web-based educational animation tool was developed to assist users in analyzing sequential algorithms to detect parallel regions regardless of the used parallel programming model. The tool simplifies algorithms’ learning, and helps students to analyze programs efficiently. Our statistical t-test study on a sample of students showed a significant improvement in their perception of the mechanism and parallelism of applications and an increase in their willingness to learn algorithms and parallel programming.