With the advent of Internet of Things (IoT) applications, smart IoT devices are ubiquitous. Executing these devices on cloud data centers can lead to network congestion and transmission delay that causes failure instances in cloud architecture. Therefore, fog computing is derived to satisfy the low latency, location awareness, and mobility requirements of massive IoT applications near end users. Concerned with the high requirements of compute-intensive business applications, fog computing structure becomes complex due to limited computing capacity, and therefore these applications are offloaded to the cloud. Therefore, to overcome the offloading problem of workflow tasks in this work, a novel method named: Improved Chemical Reaction Optimization for Workflow Scheduling in a Hybrid Environment (ICRO-WSHE) has been proposed. The proposed algorithm aims at minimizing the execution cost of workflow applications under the defined deadline constraints. The algorithm is based on CRO, which has fewer parameters and the fastest convergence speed. However, the existing CRO has drawbacks in terms of getting stuck in the local optimum and having difficulty obtaining real optimal solutions. Therefore, the proposed mechanism modifies the existing CRO algorithm and includes the suitable features of PSO (Particle Swarm Optimization) and FQR (Fitness-based Quasi Reflection) methods to prevent the shortcomings of the CRO method. Further, a double-point synthesis operation is incorporated to increase the exploration rate and improve the working of the proposed algorithm. The result of the simulation experiment based on different types of workflows shows that the proposed method outperforms existing algorithms and proves to be a practical approach.

Abstract Wireless sensor networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high‐noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision‐making processes in the base station. Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier‐detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global‐collaborative detection. Hence, the research presented in this article is intended to design and implement a global outlier‐detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time‐series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research Laboratory has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.

Real-time encryption and decryption of digital images stored on end-user devices is a challenging task due to the inherent features of the images. Traditional software encryption applications generally suffered from the expense of user convenience, performance efficiency, and the level of security provided. To overcome these limitations, the concept of transparent encryption has been proposed. This type of encryption mechanism can be implemented most efficiently with kernel file systems. However, this approach has some disadvantages since developing a new file system and attaching it in the kernel level requires a deep understanding of the kernel internal data structure. A filesystem in userspace (FUSE) can be used to bridge the gap. Nevertheless, current implementations of cryptographic FUSE-based file systems suffered from several weaknesses that make them less than ideal for deployment. This paper describes the design and implementation of ImgFS, a fully transparent cryptographic file system that resides on user space. ImgFS can provide a sophisticated way to access, manage, and monitor all encryption and key management operations for image files stored on the local disk without any interaction from the user. The development of ImgFS has managed to solve weaknesses that have been identified on cryptographic FUSE-based implementations. Experiments were carried out to measure the performance of ImgFS over image files’ read and write against the cryptographic service, and the results indicated that while ImgFS has managed to provide higher level of security and transparency, its performance was competitive with other established cryptographic FUSE-based schemes of high performance.

Securing digital images is becoming an important concern in today’s information security due to the extensive use of secure images that are either transmitted over a network or stored on disks. Image encryption is the most effective way to fulfil confidentiality and protect the privacy of images. Nevertheless, owing to the large size and complex structure of digital images, the computational overhead and processing time needed to carry out full image encryption prove to be limiting factors that inhibit it of being used more heavily in real time. To solve this problem, many recent studies use the selective encryption approach to encrypt significant parts of images with a hope to reduce the encryption overhead. However, it is necessary to realistically evaluate its performance compared to full encryption. In this paper, we study the performance and efficiency of image segmentation methods used in the selective encryption approach, such as edges and face detection methods, in determining the most important parts of visual images. Experiments were performed to analyse the computational results obtained by selective image encryption compared to full image encryption using symmetric encryption algorithms. Experiment results have proven that the selective encryption approach based on edge and face detection can significantly reduce the time of encrypting still visual images as compared to full encryption. Thus, this approach can be considered a good alternative in the implementation of real-time applications that require adequate security levels.

Providing fault tolerance for message passing parallel application on a distributed environment is a rule rather than an exception. A node failure can cause the whole computation to stop and has to be restarted from the beginning if no fault tolerance is available. However, introducing fault tolerance has some overhead on speedup that can be achieved. In this paper, we introduce a new technique called replication with cross-over packets for reliability and to increase fault tolerance over Very Large Scale Grids (VLSG). This technique has two pronged effect of avoiding single point of failure and single link of failure. We incorporate this new technique into the L-BSP model and show the possible speedup of parallel process. We also derive the achievable speedup for some fundamental parallel algorithms using this technique.