Search Results (12)

Network Music Performance (NMP) refers to network-based remote collaboration when applied to music performances, such as musical education, music production and live music concerts. In NMP, the most important parameter for the Quality of Experience (QoE) of the participants is low end-to-end audio delay. Increasing delays prevent musicians’ synchronization and lead to a suboptimal musical experience. Visual contact between the participants is also crucial for their experience but highly demanding in terms of bandwidth. Since audio compression induces additional coding and decoding delays on the signal path, most NMP systems rely on audio quality reduction when bandwidth is limited to avoid violating the stringent delay limitations of NMP. To assess the delay and quality tolerance limits for NMP and see if they can be satisfied by emerging 5G networks, we asked eleven pairs of musicians to perform musical pieces of their choice in a carefully controlled laboratory environment, which allowed us to set different end-to-end delays or audio sampling rates. To assess the QoE of these NMP sessions, each musician responded to a set of questions after each performance. The analysis of the musicians’ responses revealed that actual musicians in delay-controlled NMP scenarios can synchronize at delays of up to 40 ms, compared to the 25–30 ms reported in rhythmic hand-clapping experiments. Our analysis also shows that audio quality can be considerably reduced by sub-sampling, so as to save bandwidth without significant QoE loss. Finally, we find that musicians rely more on audio and less on video to synchronize during an NMP session. These results indicate that NMP can become feasible in advanced 5G networks. Full article

With the increasing integration of renewable energy, the secure operation of distribution networks faces significant challenges, such as voltage limit violations and increased power losses. To address the issue of reactive power and voltage security under renewable generation uncertainty, this paper proposes a graph-based security reinforcement learning method. First, a graph-enhanced neural network is designed, to extract both topological and node-level features from the distribution network. Then, a primal-dual approach is introduced to incorporate voltage security constraints into the agent’s critic network, by constructing a cost critic to guide safe policy learning. Finally, a dual-critic framework is adopted to train the actor network and derive an optimal policy. Experiments conducted on real load profiles demonstrated that the proposed method reduced the voltage violation rate to 0%, compared to 4.92% with the Deep Deterministic Policy Gradient (DDPG) algorithm and 5.14% with the Twin Delayed DDPG (TD3) algorithm. Moreover, the average node voltage deviation was effectively controlled within 0.0073 per unit. Full article

Ultra-reliable low-latency communication (URLLC) is a cornerstone of beyond 5G and future 6G networks, particularly for mission-critical applications such as the healthcare Internet of Things. In applications such as remote surgery, emergency services, and real-time health monitoring, it is imperative to ensure stringent latency and reliability requirements. However, the energy constraints of wearable and implantable medical devices pose stringent challenges to conventional URLLC methods. This paper proposes an energy-aware URLLC framework that dynamically prioritizes healthcare traffic to optimize transmission energy and reliability. The framework integrates a priority-aware packet scheduler, adaptive transmission control, and edge-enabled reliability management. Extensive Monte Carlo simulations are carried out on various network loads and varying edge computing delays to evaluate performance metrics, like latency, throughput, reliability score, energy consumption, delay violation rate, and Jain’s fairness index. Results illustrate that the suggested technique achieves lower latency, energy consumption, and delay violation rates and higher throughput and reliability scores, sacrificing Jain’s fairness index graciously at peak network overload. This study is a potential research lead for green URLLC in healthcare IoT systems to come. Full article

Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider’s cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360° VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server’s computing capacity (the BS’s bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%). Full article

Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination. Full article

Korea’s industrial accident rate ranks high among Organization for Economic Co-operation and Development countries. Moreover, large-scale accidents have recently occurred. Accordingly, the requirements for management and supervision in industrial sites are increasing; in this context, the “Act on Punishment of Serious Accidents, etc.” has been enacted. Aiming to prevent such industrial accidents, various data collected by devices such as sensors and closed-caption televisions (CCTVs) are utilized to track workers and detect hazardous substances, gases, and fires at industrial sites. In this study, an industrial area requiring such technology is selected. A hazardous situation event is derived, and a dataset is built using CCTV data. A violation corresponding to a hazardous situation event is detected and a warning is provided. The events incorporate requirements extracted from industrial sites, such as those concerning collision risks and the wearing of safety equipment. The precision of the event violation detection exceeds 95% and the response and delay times are under 20 ms. Thus, this system is believed to be used at industrial sites and for other intelligent industrial safety prevention solutions. Full article

Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations. To overcome these challenges, we propose an intelligent detection system that can accurately identify instances of illegal wearing of power construction workers in real-time. Firstly, we integrated the squeeze-and-excitation (SE) module into our convolutional neural network to enhance detection accuracy. This module effectively prioritizes informative features while suppressing less relevant ones, resulting in improved overall performance. Secondly, we present an embedded real-time detection system that utilizes Jetson Xavier NX and Edge-YOLO. This system promptly detects and alerts power construction workers of instances of illegal wearing behavior. To ensure a lightweight implementation, we design appropriate detection heads based on target size and distribution, reducing model parameters while enhancing detection speed and minimizing accuracy loss. Additionally, we employed data augmentation to enhance the system’s robustness. Our experimental results demonstrate that our improved Edge-YOLO model achieves high detection precision and recall rates of 0.964 and 0.966, respectively, with a frame rate of 35.36 frames per second when deployed on Jetson Xavier NX. Therefore, Edge-YOLO proves to be an ideal choice for intelligent real-time detection systems, providing superior accuracy and speed performance compared to the original YOLOv5s model and other models in the YOLO series for safety monitoring at construction sites. Full article

This article is the result of applied research on the use of potential tourist resources to remove obstacles to the development of tourism marketing and subsequently cause tourism business growth to improve the quality of life in the host communities. The method to achieve this goal is practical and straightforward: First, specific indicators are introduced to measure the rate of destruction/degradation of tourist sites in a period. Then, by referring to the opinions of tourism business experts, official administration managers, and tourists and by using a mathematical model, the financial losses of the tourism business at tourist sites and resulting total economic losses of the tourism business due to the delay in the reconstruction/upgrading of indicators at tourism sites are calculated. A model is developed and simulated for 10 tourist sites in Iran to calculate the damages to 38 indicators in the period of 1978–2019. In the next step, the weaknesses and threats related to Iran’s tourism business are discovered to reform the indicators, especially those related to planning, policymaking, and the democratic rights of tourists and the host community and to the necessary infrastructure and provision of decent services. Field studies reveal that the number of visitors to the 10 tourist sites since 2014 has declined on average from 8% to 25% due to the implementation of Sharia law, the violation of democracy, and the lack of necessary infrastructure. The model that this article proposes for economic growth through the development of the tourism business may feasibly be applied to similar tourist site areas. Full article

In this paper, a novel improved gravitational search algorithm–binary particle swarm optimization (IGSA-BPSO) driven proportional-integral-derivative (PID) controller is proposed to deal with issues of automatic generation control (AGC) of interconnected multi-source (thermal-hydro-gas) multi-area deregulated power systems. The effectiveness and robustness of the proposed controller is compared and analyzed with GSA and PSO-driven PID controllers. The simulated and mathematically formulated results show the superiority of the proposed IGSA-BPSO driven PID controller compared with the other two techniques in settling time, overshoot, and convergence time. The two-area test system considered in this article is integrated with a thermal, hydro, and gas turbine power plant. Integral time multiplied by absolute error (ITAE) is used as the objective function (minimization) by optimization techniques for getting optimum parameters of PID controllers installed in each area. The system’s dynamics are examined using poolco, bilateral, and contract violation cases under a deregulated environment, and the comparative results are shown to analyze the efficacy of the proposed concept. Physical constraints such as generation rate constraints (GRC) and time-delay (TD) have been considered in the system as a realistic approach. This paper considers an accurate AC-DC tie-link model for the proposed AGC mechanism. Dynamic load change condition is tested and verified. The variations of different parameters will be used in the robustness analysis of the proposed system. The comparison shows that the designed controllers are more robust and produce better results than those considered as references. Full article

Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance. Full article

In this study, we consider an edge cloud server in which a lightweight server is placed near a user device for the rapid processing and storage of large amounts of data. For the edge cloud server, we propose a latency classification algorithm based on deadlines and urgency levels (i.e., latency-sensitive and latency-tolerant). Furthermore, we design a task offloading algorithm to reduce the execution time of latency-sensitive tasks without violating deadlines. Unlike prior studies on task offloading or scheduling that have applied no deadlines or task-based deadlines, we focus on a comprehensive deadline-aware task scheduling scheme that performs task offloading by considering the real-time properties of latency-sensitive tasks. Specifically, when a task is offloaded to the edge cloud server due to a lack of resources on the user device, services could be provided without delay by offloading latency-tolerant tasks first, which are presumed to perform relatively important functions. When offloading a task, the type of the task, weight of the task, task size, estimated execution time, and offloading time are considered. By distributing and offloading latency-sensitive tasks as much as possible, the performance degradation of the system can be minimized. Based on experimental performance evaluations, we prove that our latency-based task offloading algorithm achieves a significant execution time reduction compared to previous solutions without incurring deadline violations. Unlike existing research, we applied delays with various network types in the MEC (mobile edge computing) environment for verification, and the experimental result was measured not only by the total response time but also by the cause of the task failure rate. Full article

Deficit Round Robin (DRR) is a fair packet-based scheduling discipline commonly used in wired networks where link capacities do not change with time. However, in wireless networks, especially wireless broadband networks, i.e., IEEE 802.16e Mobile WiMAX, there are two main considerations violate the packet-based service concept for DRR. First, the resources are allocated per Mobile WiMAX frame. To achieve full frame utilization, Mobile WiMAX allows packets to be fragmented. Second, due to a high variation in wireless channel conditions, the link/channel capacity can change over time and location. Therefore, we introduce a Deficit Round Robin with Fragmentation (DRRF) to allocate resources per Mobile WiMAX frame in a fair manner by allowing for varying link capacity and for transmitting fragmented packets. Similar to DRR and Generalized Processor Sharing (GPS), DRRF achieves perfect fairness. DRRF results in a higher throughput than DRR (80% improvement) while causing less overhead than GPS (8 times less than GPS). In addition, in Mobile WiMAX, the quality of service (QoS) offered by service providers is associated with the price paid. This is similar to a cellular phone system; the users may be required to pay air-time charges. Hence, we have also formalized a Generalized Weighted Fairness (GWF) criterion which equalizes a weighted sum of service time units or slots, called temporal fairness, and transmitted bytes, called throughput fairness, for customers who are located in a poor channel condition or at a further distance versus for those who are near the base stations, or have a good channel condition. We use DRRF to demonstrate the application of GWF. These fairness criteria are used to satisfy basic requirements for resource allocation, especially for non real-time traffic. Therefore, we also extend DRRF to support other QoS requirements, such as minimum reserved traffic rate, maximum sustained traffic rate, and traffic priority. For real-time traffic, i.e., video traffic, we compare the performance of DRRF with deadline enforcement to that of Earliest Deadline First (EDF). The results show that DRRF outperforms EDF (higher achievable throughput under the promised delay latency) and maintains fairness under an overload scenario. Full article