Dual-Network Task Scheduling inCyber–Physical Systems: A Cooptimization Approach

A Cyber-physical system (CPS) is an engineering system made of computational components, i.e. cyber elements, and physical elements, that are connected by a communication network. CPSs have emerged as the contemporarily leading technology in major industry sectors such as manufacture, aerospace, automotive, etc. Nowadays CPS is almost the synonym of control systems for large and complex engineering systems. In addition, CPSs have inevitably interweaved with new technologies like Internet of Things, cloud computing, ubiquitous computing, and big data processing. Taming the complexity has been the key challenge in CPS design. A novel declarative computing based platform was proposed in our previous paper to unify modeling and design of both cyber and physical components in CPSs. In this paper, the concepts and principles of the proposed declarative platform are depicted in details. In addition, modeling techniques of declarative networking and declarative control are showcased with concrete simulation examples.

This paper addresses the problem of devising an effective information fusion architecture for a task scheduling algorithm which facilitates data processing of a Cyber Physical System (CPS) under bounded latency for bursty or lossy traffic. Task scheduling traditionally caters to real-time systems where a feedback loop does not exist allowing the serviced application to be independent of the inputs from the server. However, owing to the nature of a near real-time CPS, such liberties cannot be entertained. Additionally, the advent of big data in CPS has necessitated the use of Cloud Computing as a scalable and cost effective alternative. Task scheduling in such CPSs, where inputs from the Cloud complete the feedback loop is a major research issue. Therefore, in this paper, we propose a multi-layered information fusion architecture which integrates such a task scheduling mechanism by accommodating both traffic bursts and packet losses. Our scheduling algorithm ensures that the overall latency always remains under an acceptable upper bound as required by the CPS application.

With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

Since routers in IP network are connected with ATM virtual path (VP) permanent virtual connection (PVC) and each VP contains bundles of VCs, a VP layer network can serve as a server layer and each IP layer network can be a client layer. Therefore the effective Internet service provisioning completely depends on the reliability of VP server layer network from the perspective of the optimality and the network survivability. This paper proposes the distributed ATM VP PVC restoration framework with the dynamic rerouting schemes in hierarchical transport network. The rerouting algorithm aims to provide rapid ATM VP PVC restoration in the case of fault or performance degradation in terms of maximization of network resource utilization and satisfaction of the end user's QoS requirement. It guarantees the IP network survivability in spite of ATM network faults or performance degradations. In addition, this paper evaluates the rerouting performance for a real network, the High Speed Information Network (HSIN).

Parallel manipulators with less than six degrees of freedom (DOF) have been increasingly used in high-speed hybrid machine tools. The structural features of parallel manipulators are dynamic, a characteristic that is particularly significant when these manipulators are used in high-speed machine tools. However, normal kinematic control method cannot satisfy the requirements of the control system. Many researchers use model-based dynamic control methods, such as the dynamic feedforward control method. However, these methods are rarely used in hybrid machine tools because of the complex dynamic model of the parallel manipulator. In order to study the dynamic control method of parallel manipulators, the dynamic feedforward control method is used in the dynamic control system of a 3-PSP (prismatic-spherical-prismatic) 3-DOF spatial parallel manipulator used as a spindle head in a high-speed hybrid machine tool. Using kinematic analysis as basis and the Newton-Euler method, we derive the dynamic model of the parallel manipulator. Furthermore, a model-based dynamic feedforward control system consisting of both kinematic control and dynamic control subsystems is established. The dynamic control subsystem consists of two modules. One is used to eliminate the influence of the dynamic characteristics of high-speed movement, and the other is used to eliminate the dynamic disturbances in the milling process. Finally, the simulation model of the dynamic feedforward control system of the 3-PSP parallel manipulator is constructed in Matlab/Simulink. The simulations of the control system eliminating the influence of the dynamic characteristics and dynamic disturbances are conducted. A comparative study between the simulations and the normal kinematic control method is also presented.The simulations prove that the dynamic feedforward control method effectively eliminates the influence of the dynamic disturbances and dynamic characteristics of the parallel manipulator on high-speed machine tools, and significantly improves the trajectory accuracy. This is the first attempt to introduce the dynamic feedfordward control method into the 3-PSP spatial parallel manipulator whose dynamic model is complex and provides a study basis for the real-time dynamic control of the high-speed hybrid machine tools.

Chaotic marine predators optimization based task scheduling scheme for resource limited cyber-physical systems

In the real-time task scheduling of Cyber Physical System (CPS), the existing scheduling algorithms can’t effectively differentiate the importance level of random tasks, resulting in poor real-time scheduling of important random tasks and insufficient submission rate. This paper comprehensively considers the factors of recent available time, criticality and execution time, and then proposes a fuzzy scheduling algorithm of high priority random tasks (FSA-HPRT) for CPS. The algorithm first obtains the applicable time slice by maximizing delayed the execution time of the periodic task, and calculates the difference between the end time of the periodic task and the deadline time as the error time slice. Then use the fuzzy set to process the latest available time, criticality and execution time of the random task to decide the random task priority. Finally, the task-scheduling queue is established for the system to perform scheduling according to the priority of the random task by using the applicable time slice and the error time slice. The simulation results show that compared with the ISS algorithm, the proposed algorithm can significantly improve the submission rate of important random tasks. At the same time, high-priority random tasks can get responses preferentially.

Deterministic algorithm for VP assignment in ATM networks

A modal projection-based reduction method for transient dynamic responses of viscoelastic systems with multiple damping models

Dynamic intelligent scheduling and control of reconfigurable parallel architectures for computer vision/image processing

The Industrial Internet of Things (IIoT) is characterized by the instantaneous communication of diverse critical data across complex, resource-limited networks. To maximize the efficiency and accuracy of task scheduling in such networks, this study introduces a novel task scheduling framework based on the Adaptive Lotus Effect Optimization Algorithm (ALEOA). ALEOA perfectly combines the self-cleaning mechanism and learning function of the Lotus effect with the global search and learning capabilities of the Particle Swarm Optimization (PSO) algorithm to establish equilibrium between local and global search. ALEOA can adaptively adjust routing and task scheduling to support critical data communication and a dynamic network. Results from experiments on typical benchmarking equations and simulations of the IIoT communication environment show that the proposed ALEOA-based model yields higher task acceptance rates, lower communication delays, and improved resource utilization than traditional metaheuristic methods. Statistical analyses using the Wilcoxon Signed Rank test confirmed the effectiveness of this enhancement.

Cloud computing suffers from various challenging issues in Internet of Things (IoT) networks like real-time response, energy-efficient execution, and cost of computation. Fog is an emerging distributed computing paradigm which is useful for delay-sensitive tasks in IoT network. An offloading strategy decides where to offload the task and a task scheduling strategy chooses an appropriate fog node based on the requirements of the task while meeting the quality of services (QoS) criteria. Although the computational offloading and task scheduling problem has been widely studied, there is very limited research on delay-energy tradeoff. We propose a fog network that follows an M/M/c queue for computational offloading and a differential scale-based Best Worst Method (BWM) for computation of optimal weights in multi-objective task scheduling. The optimization problem minimizes the execution delay while meeting QoS criteria. The numerical experiments show the efficacy for the different QoS criteria.

Recent developments in computer and communication technologies are leading to an increasingly networked and wireless world. This raises new challenging questions in the context of control for cyber-physical systems (CPS), especially when the computation, communication, energy and actuation resources (for control) of the system are limited and/or shared by multiple control tasks. These limitations obstruct the use of classical design techniques for feedback control algorithms and call for new resource-aware control paradigms. These new resource-aware control systems typically have to take both discrete decisions (which task is allowed to use the resource) and continuous decisions (which continuous control input is generated for the task). In this talk two approaches are presented to address this hybrid co-design problem. Both approaches result in control algorithms that exploit real-time measurement information available on the state of the CPS and decide dynamically on the actions to take. This leads to the situation that individual control tasks are no longer executed in classical periodic time-triggered patterns, but in aperiodic patterns with varying inter-execution times. By abandoning the periodic scheduling of control tasks, the aim is to realise better trade-offs between the overall performance of the CPS and the required resource utilisation. The approaches are illustrated by various applications. interesting challenges for the future are discussed as well.

Reversible lanes play an important role in alleviating urban traffic congestion and improving the utilization of road resources. However, most of the reversible lanes currently implemented are timed and fixed, and there is little research on the safety of reversible lanes without isolation facilities. In view of the existing problems of reversible lanes, this paper proposed a real-time dynamic reversible lane safety control model. The influencing factors of reversible lane safety control are analyzed first, and then determine model parameters of the no-entry zone and the buffer zone in the conflict area. Moreover, model parameters of emptying, exit, and recovery zones in the operation process were determined as well. Additionally, this paper discussed the length of reversible lanes and the timing of signals. The surrogate safety assessment model (SSAM) is adopted to analyze vehicle trajectory data for traffic simulation under different traffic flows. The experiment results show that the proposed reversible lane safety control scheme can keep the vehicle’s traffic conflict rate below 5%, which can effectively ensure the safe operation of the reversible lane.