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The rapid advancement of intelligent manufacturing has led to an increasing demand for computing resources in industrial computing tasks. As a new computing paradigm, edge-cloud collaborative computing (E3C) fills the delay gap of traditional cloud computing for industrial computing tasks. Nevertheless, the E3C performance is heavily contingent upon task scheduling, which plays a pivotal role in influencing the effectiveness of E3C task execution. In this paper, we tackle the task scheduling problem by introducing a novel scheduling model and algorithm. Firstly, we establish a task scheduling optimization model to precisely carve the joint multi-server cache sharing and delay-aware task scheduling problem. We formulate the joint task scheduling model as a constrained combinatorial optimization problem and prove its NP-hardness. Simultaneously, given the heightened security requirements of manufacturing E3C compared to conventional E3C, we address the task security concerns during the scheduling process by incorporating task privacy levels and encryption techniques to safeguard the shared task caches in the established model. Secondly, to solve the near-optimal joint strategy composed of scheduling, caching and sharing strategies derived from the established model, we propose a scheduling algorithm based on the improved artificial bee colony algorithm. Finally, we conduct extensive experiments to verify our scheduling model and algorithm. Experimental results substantiate that our multi-server cache sharing mechanism can further decrease the task execution delay by 31.13% in comparison to the conventional task scheduling. Furthermore, the proposed scheduling algorithm demonstrates superior performance in terms of solution accuracy compared to existing algorithms.

Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

Ever-increasing computational demand introduced by the expanding scale of Internet of Things (IoT) devices poses significant concerns in terms of energy consumption in a fog-cloud environment. Due to the limited resources of IoT devices, energy-efficient task offloading becomes even more challenging for time-sensitive tasks. In this paper, we propose a reinforcement learning-based framework, namely Adaptive Q-learning-based Energy-aware Task Offloading (AQETO), that dynamically manages the energy consumption of fog nodes in a fog-cloud network. Concurrently, it considers IoT task delay tolerance and allocates computational resources while satisfying deadline requirements. The proposed approach dynamically determines energy states of each fog node using Q-learning depending on workload fluctuations. Moreover, AQETO prioritizes allocation of the most urgent tasks to minimize delays. Extensive experiments demonstrate the effectiveness of AQETO in terms of the minimization of fog node energy consumption and delay and the maximization of system efficiency.

Reversible logic is a design paradigm suitable to energy-efficient digital systems and has lately received recognition as a viable option for designing sophisticated digital applications. It possesses the capacity to revolutionize progress in quantum and low-power computing. This research presents an innovative reversible gate designed with merely 10 transistors. The suggested gate includes capabilities for several logic operations such as XOR, XNOR, NOT, NAND, NOR, half-adder and parity generator. This proposed design can attain a minimal quantum cost, which is strongly advocated in quantum computing. We have verified the design’s resilience using Monte Carlo and process corner analysis after modeling it with Cadence Virtuoso at 45 nm technology and 1 V supply voltage. This confirms the robustness of the design amidst variation. In contrast to current reversible gates, the proposed structure achieves an 18 percent reduction in delay and a 12 percent reduction in power delay product, rendering it an appealing option for high-speed and low-energy circuit design. To illustrate its adaptability in alternative designs, we have utilized it to the construction of a 4-bit binary-to-Gray code converter circuit.

In the context of cloud systems, the effectiveness of placing modules for optimal reliability and end-to-end delay (EED) is directly linked to the success of scheduling distributed scientific workflows. However, the measures used to evaluate these aspects (reliability and EED) are in conflict with each other, making it impossible to optimize both simultaneously. Thus, we introduce a scheduling algorithm for distributed scientific workflows that focuses on enhancing reliability while maintaining specific EED limits. This is particularly important given the inevitable failures of processing servers and communication links. To achieve our objective, we first develop an artificial intelligence-based model that merges an improved version of the wild horse optimization technique with a levy flight approach. This hybrid approach enhances the ability to explore new possibilities effectively. Additionally, we establish a viable strategy for sharing mapping decisions and stored information among processing servers, promoting scalability and robustness—essential qualities for large-scale distributed systems. This strategy not only boosts local search capabilities but also prevents premature convergence of the algorithm. The primary goal of this study is to pinpoint resource placements that strike a balance between global exploration and local exploitation. This entails effectively harnessing the search space and minimizing the inclination toward resources with a high likelihood of failures. Through experimentation in various system configurations, our proposed method consistently outperformed competing workflow scheduling algorithms. It achieved notably higher levels of reliability while adhering to the same EED constraints.

As wireless networks continue to advance, virtual reality (VR) transmission over wireless connections is progressively transitioning from concept to practical application. Although this technology can significantly enhance the VR user experience, its development bottleneck lies in the computing capacity of devices and transmission latency. Considering the limited computational resources of VR devices for rendering tasks, multi-access edge computing (MEC) servers are introduced to provide powerful computing capabilities. To cope with transmission latency, reconfigurable intelligent surface (RIS) enhances links between base stations (BSs) and users. Based on these two technologies, we propose a RIS-assisted VR streaming model, where BSs are equipped with MEC servers to assist data rendering. Firstly, the user association, power control, and RIS phase shift optimization problems in the VR transmission system are jointly modeled and analyzed, establishing a long-term minimization of the interaction delay model. Secondly, by modeling the optimization problem as a Markov decision process (MDP), a joint optimization framework based on multi-agent deep reinforcement learning (MADRL) is proposed. In this framework, we have separately designed two dedicated algorithms for discrete and continuous variables. Furthermore, multiple agents can provide feedback based on user experience and cooperate with each other to improve the joint strategy. Finally, the performance and superiority of the proposed solution and algorithm are validated through simulation experiments in different application scenarios.

Air Traffic Flow Management (ATFM) delay refers to the difference between a flight’s Target Take-Off Time (TTOT) and its Calculated Take-Off Time (CTOT), reflecting congestion levels in the air traffic network. ATFM delays are assigned to balance demand and capacity at key points in the network. The traditional First-Come, First-Served (FCFS) approach allocates delays strictly in the order flights are ready to depart, which is simple but inflexible. This study proposes a dynamic priority-based aircraft sequencing method at critical waypoints under multi-resource scenarios, aiming to reduce ATFM delays. An improved Constrained Position Shifting (CPS) constraint is introduced into the optimization model to enhance the influence of flight priority during decision-making. Additionally, three different priority strategies are designed to compare their respective impacts on ATFM delay. Finally, a dynamic priority-based ATFM delay optimization model is developed to address the identified challenges. Experimental results demonstrate that, compared with the FCFS scheme, the three priority strategies achieve maximum ATFM delay reductions of 30.5%, 44.1%, and 19.9%, respectively. The proposed model effectively allocates shorter delays to critical flights, optimizing resource utilization and improving the operational efficiency of the air route network. The research provides a reference framework for air traffic managers in allocating spatiotemporal resources across multiple congestion hotspots. By aligning priorities with network-wide efficiency goals, it overcomes traditional model limitations, avoids local optima, and supports globally optimal ATFM policy and practice.

As smart homes and Internet of Things (IoT) ecosystems continue to expand, the need for energy-efficient and low-latency communication has become increasingly critical. One of the key challenges in these systems is minimizing delay time while ensuring reliable and efficient communication between devices. This study focuses on optimizing delay time in ZigBee sensor networks used in smart-home systems. A Smart–Adaptive Communication Distribution Algorithm is proposed, which dynamically adjusts the communication between network nodes based on real-time network conditions. Experimental measurements were conducted under various scenarios to evaluate the performance of the proposed algorithm, with a particular focus on reducing delay and enhancing overall network efficiency. The results demonstrate that the proposed algorithm significantly reduces delay times compared to traditional methods, making it a promising solution for delay-sensitive IoT applications. Furthermore, the findings highlight the importance of adaptive communication strategies in improving the performance of ZigBee-based sensor networks.

In this work, an efficient full-swing (FS)-gate diffusion input (GDI) logic style is used for implementing full adder (FA) and arithmetic logic unit (ALU) circuits. The performance of the ALU in delay time, power consumption, and area terms is strongly dependent on the performance efficiency of the FA circuits utilized in its construction. In this research paper, multi-efficient power and high-speed ALU circuits are proposed with outputs in full-swing form and improved area. The proposed circuit is evaluated and tested using the Cadence Virtuoso simulation package in the 65 nm CMOS process. The proposed ALUs reduce power consumption by 20.01% and 4% compared with the ALU implemented by the traditional CMOS technique and GDI-based unit, respectively. Also, four ALU designs utilizing four efficient FA circuits are presented. The proposed ALUs provide more than 74.4–23.3% improvement in the power-delay product (PDP) term compared with existing circuits. The simulation experiments reveal that with the proposed ALUs, the circuits achieve superior performance compared with previous and existing ALU designs, and the power efficiency of these units improves in a range of 18.7–20.1% over the CMOS-based units. Also, the various technology nodes and their impact are considered in this paper.

With the advent of 6G technologies, satellite communication networks are in urgent need of innovative bearer technologies to meet the demands of government and enterprise private lines as well as computing power networks. We propose optical service unit-based optical inter-satellite links (OISL-OSU) as a solution to address the current limitations in fine-grained service bearing within optical transport networks (OTNs), thereby enhancing the flexibility and efficiency of satellite optical networks. Comparative tests were conducted between OISL-OSU and existing packet-switching technologies in multi-service satellite optical transport networks. Through hardware-in-the-loop simulation verification, key performance indicators such as delay optimization, bandwidth utilization rate, and flexible resource adjustment capability were systematically evaluated. Experimental results demonstrate that OISL-OSU technology exhibits superior performance in delay optimization and fine-grained service bearing. The flexible mapping and multiplexing mechanism of OISL-OSU significantly improves resource utilization efficiency, decreases transmission delay, and strengthens hard-pipe connection capabilities.