This paper compares the quality and execution times of several algorithms for scheduling service based workflow applications with changeable service availability and parameters. A workflow is defined as an acyclic directed graph with nodes corresponding to tasks and edges to dependencies between tasks. For each task, one out of several available services needs to be chosen and scheduled to minimize the workflow execution time and keep the cost of service within the budget. During the execution of a workflow, some services may become unavailable, new ones may appear, and costs and execution times may change with a certain probability. Rescheduling is needed to obtain a better schedule. A solution is proposed on how integer linear programming can be used to solve this problem to obtain optimal solutions for smaller problems or suboptimal solutions for larger ones. It is compared side-by-side with GAIN, divide-and-conquer, and genetic algorithms for various probabilities of service unavailability or change in service parameters. The algorithms are implemented and subsequently tested in a real BeesyCluster environment.

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.

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.

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli (SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

X-ray pulsar navigation (XPNAV) is an attractive method for autonomous navigation of deep space in the future. Currently, techniques for estimating the phase of X-ray pulsar radiation involve the maximization of the general non-convex object functions based on the average profile from the epoch folding method. This results in the suppression of useful information and highly complex computation. In this paper, a new maximum likelihood (ML) phase estimation method that directly utilizes the measured time of arrivals (TOAs) is presented. The X-ray pulsar radiation will be treated as a cyclo-stationary process and the TOAs of the photons in a period will be redefined as a new process, whose probability distribution function is the normalized standard profile of the pulsar. We demonstrate that the new process is equivalent to the generally used Poisson model. Then, the phase estimation problem is recast as a cyclic shift parameter estimation under the ML estimation, and we also put forward a parallel ML estimation method to improve the ML solution. Numerical simulation results show that the estimator described here presents a higher precision and reduces the computational complexity compared with currently used estimators.

The use of IEEE 802.15.4 standard based application systems has been rapidly increasing, for example, in medical services, sensor networks, public safety systems, and home automation systems. However, issues arise from the fact that IEEE 802.15.4 standard based low rate wireless personal area networks (LR-WPANs) use the same frequency bands as wireless local area networks (WLANs), and they interfere with each other. Based on past research on this issue, the interference has a more serious impact on LR-WPANs’ performance than on WLANs’ performance. In this paper we propose a method to improve LR-WPANs’ performance while coexisting with WLANs, which is called the reliable beacon transmission based medium access control (MAC) protocol. Since the reliability of a beacon frame is important, in this method, only the beacon frame is transmitted in interference-free channels, and the data packets are transmitted in interfered channels instead of abandoning the channels altogether. This method increases the reliability of beacon frames as well as overall channel utilizations. The effectiveness of the proposed method was evaluated through extensive simulations, and this paper proves that this method improves the performance of IEEE 802.15.4 based wireless sensor networks (WSNs) over WLANs’ interferences.

We present how residual intensity modulation (RIM) affects the performance of a resonator fiber optic gyro (R-FOG) through a sinusoidal wave phase modulation technique. The expression for the R-FOG system’s demodulation curve under RIM is obtained. Through numerical simulation with different RIM coefficients and modulation frequencies, we find that a zero deviation is induced by the RIM effect on the demodulation curve, and this zero deviation varies with the RIM coefficient and modulation frequency. The expression for the system error due to this zero deviation is derived. Simulation results show that the RIM-induced error varies with the RIM coefficient and modulation frequency. There also exists optimum values for the RIM coefficient and modulation frequency to totally eliminate the RIM-induced error, and the error increases as the RIM coefficient or modulation frequency deviates from its optimum value; however, in practical situations, these two parameters would not be exactly fixed but fluctuate from their respective optimum values, and a large system error is induced even if there exists a very small deviation of these two critical parameters from their optimum values. Simulation results indicate that the RIM-induced error should be considered when designing and evaluating an R-FOG system.