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In recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.

In this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.

The result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.

The authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.

The purpose of this paper is to make performance improvements and timely critical execution enhancements for operational flight program (OFP). The OFP is core software of autonomous control system of small unmanned helicopter.

In order to meet the time constraints and enhance control application performance, two major improvements were done at real‐time operating system (RTOS) kernel. They are thread scheduling algorithm and lock‐free thread message communication mechanism. Both of them have a direct relationship with system efficiency and indirect relationship with helicopter control application execution stability through improved deadline keeping characteristics.

In this paper, the suitability of earliest deadline first (EDF) scheduling algorithm and non‐blocking buffer (NBB) mechanism are illustrated with experimental and practical applications. Results of this work show that EDF contributes around 15 per cent finer‐timely execution and NBB enhances kernel's responsiveness around 35 per cent with respect to the number of thread context switch and CPU utilization. These apply for OFP implemented over embedded configurable operating system (eCos) RTOS on x86 architecture‐based board.

This paper illustrates an applicability of deadline‐based real‐time scheduling algorithm and lock‐free kernel communication mechanism for performance enhancement and timely critical execution of autonomous unmanned aerial vehicle control system.

This paper illustrates a novel approach to extend RTOS kernel modules based on unmanned aerial vehicle control application execution scenario. Lock‐free thread communication mechanism is implemented, and tested for applicability at RTOS. Relationship between UAV physical and computation modules are clearly illustrated via an appropriate unified modelling language (UML) collaboration and state diagrams. As experimental tests are conducted not only for a particular application, but also for various producer/consumer scenarios, these adequately demonstrate the applicability of extended kernel modules for general use.

Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

The main purpose of the paper is timing analysis of mixed critical applications on the multicore system to identify an efficient task scheduling mechanism to achieve three main objectives improving schedulability, achieving reliability and minimizing the number of cores used. The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

The paper opted for a simulation-based study. The simulation of mixed critical applications, like air traffic control systems and synthetic workloads, is carried out using a litmus-real time testbed on an Ubuntu machine. The heuristic algorithms for task allocation based on utilization factors and task criticalities are proposed for partitioned approaches with multiple objectives.

Both partitioned earliest deadline first (EDF) with the utilization-based heuristic and EDF-virtual deadline (VD) with a criticality-based heuristic for allocation works well, as it schedules the air traffic system with a 98% success ratio (SR) using only three processor cores with transient faults being handled by the active backup of the tasks. With synthetic task loads, the proposed criticality-based heuristic works well with EDF-VD, as the SR is 94%. The validation of the proposed heuristic is done with a global and partitioned approach of scheduling, considering active backups to make the system reliable. There is an improvement in SR by 11% as compared to the global approach and a 17% improvement in comparison with the partitioned fixed-priority approach with only three processor cores being used.

The simulations of mixed critical tasks are carried out on a real-time kernel based on Linux and are generalizable in Linux-based environments.

The rise in transient faults in embedded systems due to the use of low-cost processors has led to the use of fault-tolerant scheduling and mapping techniques.

This paper fulfills an identified need to have multi-objective task scheduling in a mixed critical system. The timing analysis helps to identify performance risks and assess alternative architectures used to achieve reliability in terms of transient faults.

This paper presents the development of a novel model for optimizing the scheduling of crew deployments in repetitive construction projects while considering uncertainty in crew production rates.

The model computations are performed in two modules: (1) simulation module that integrates Monte Carlo simulation and a resource-driven scheduling technique to calculate the earliest crew deployment dates for all activities that fully comply with crew work continuity while considering uncertainty; and (2) optimization module that utilizes genetic algorithms to search for and identify optimal crew deployment plans that provide optimal trade-offs between project duration and crew deployment plan cost.

A real-life example of street renovation is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the stochastic scheduling of crew deployments in repetitive construction projects.

The original contribution of this research is creating a novel multiobjective stochastic scheduling optimization model for both serial and nonserial repetitive construction projects that is capable of identifying an optimal crew deployment plan that simultaneously minimizes project duration and crew deployment cost.

The purpose of this paper is to investigate the scheduling problem of real-time jobs executing on a DVS processor. The jobs must complete their executions by their deadlines and the energy consumption also must be minimized.

The two-phase energy-efficient scheduling algorithm is proposed to solve the scheduling problem for real-time jobs. In the off-line phase, the maximum instantaneous total density and instantaneous total density (ITD) are proposed to derive the speed of the processor for each time instance. The derived speeds are saved for run time. In the on-line phase, the authors set the processor speed according to the derived speeds and set a timer to expire at the corresponding end time instance of the used speed.

When the DVS processor executes a job at a proper speed, the energy consumption of the system can be minimized.

This paper does not consider jobs with precedence constraints. It can be explored in the further work.

The experimental results of the proposed schemes are presented to show the effectiveness.

The experimental results show that the proposed scheduling algorithm, ITD, can achieve energy saving and make the processor fully utilized.

In client/server distributed systems, the server is often the bottleneck. Improving the server performance is thus crucial for improving the overall performance of distributed information systems. Real‐time system is required to complete its work and deliver its services on a timely basis. The purpose of this paper is to propose a new scheduling algorithm for real‐time distributed system (client/server model) to achieve the above‐mentioned goal.

The ant colony optimization (ACO) algorithms are computational models inspired by the collective foraging behavior of ants. They provide inherent parallelism and robustness. Therefore, they are appropriate for scheduling of tasks in soft real‐time systems. During simulation, results are obtained with periodic tasks, measured in terms of success ratio and effective CPU utilization; and compared with results of earliest deadline first (EDF) algorithm in the same environment.

Analysis and experiments show that the proposed algorithm is equally efficient during underloaded conditions. The performance of EDF decreases as the load increases, but the proposed algorithm works well in overloaded conditions also. Because of this type of property, the proposed algorithm is more suitable for the situation when future workload of the system is unpredictable.

The application of ACO algorithms for scheduling of client/server real‐time distributed system, never found before in the literature. The new concept proposed in this paper will be of great significance to both theoretical and practical research in scheduling of distributed systems in the years to come.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

The total capacity of ambulances in metropolitan cities is often less than the post-disaster demand, especially in the case of disasters such as earthquakes. However, because earthquakes are a rare occurrence in these cities, it is unreasonable to maintain the ambulance capacity at a higher level than usual. Therefore, the effective use of ambulances is critical in saving human lives during such disasters. Thus, this paper aims to provide a method for determining how to transport the maximum number of disaster victims to hospitals on time.

The transportation-related disaster management problem is complex and dynamic. The practical solution needs decomposition and a fast algorithm for determining the next mission of a vehicle. The suggested method is a synthesis of mathematical modeling, scheduling theory, heuristic methods and the Voronoi diagram of geometry. This study presents new elements for the treatment, including new mathematical theorems and algorithms. In the proposed method, each hospital is responsible for a region determined by the Voronoi diagram. The region may change if a hospital becomes full. The ambulance vehicles work for hospitals. For every patient, there is an estimated deadline by which the person must reach the hospital to survive. The second part of the concept is the way of scheduling the vehicles. The objective is to transport the maximum number of patients on time. In terms of scheduling theory, this is a problem whose objective function is to minimize the sum of the unit penalties.

The Voronoi diagram can be effectively used for decomposing the complex problem. The mathematical model of transportation to one hospital is the P‖ΣU_j problem of scheduling theory. This study provides a new mathematical theorem to describe the structure of an algorithm that provides the optimal solution. This study introduces the notion of the partial oracle. This algorithmic tool helps to elaborate heuristic methods, which provide approximations to the precise method. The realization of the partial oracle with constructive elements and elements proves the nonexistence of any solution. This paper contains case studies of three hospitals in Tehran. The results are close to the best possible results that can be achieved. However, obtaining the optimal solution requires a long CPU time, even in the nondynamic case, because the problem P‖ΣU_j is NP-complete.

This research suggests good approximation because of the complexity of the problem. Researchers are encouraged to test the proposed propositions further. In addition, the problem in the dynamic environment needs more attention.

If a large-scale earthquake can be expected in a city, the city authorities should have a central control system of ambulances. This study presents a simple and efficient method for the post-disaster transport problem and decision-making. The security of the city can be improved by purchasing ambulances and using the proposed method to boost the effectiveness of post-disaster relief.

The population will be safer and more secure if the recommended measures are realized. The measures are important for any city situated in a region where the outbreak of a major earthquake is possible at any moment.

This paper fulfills an identified need to study the operations related to the transport of seriously injured people using emergency vehicles in the post-disaster period in an efficient way.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.