Communication Delays Research Articles

A Robust Distributed MPC Framework for Multiagent Consensus With Communication Delays

A Robust Distributed MPC Framework for Multiagent Consensus With Communication Delays

Distributed Optimal Control of Energy Storages in DC Microgrids for Operation Loss Minimization With Communication Delays

Distributed Optimal Control of Energy Storages in DC Microgrids for Operation Loss Minimization With Communication Delays

An efficient Protocol for Enhanced Flynet Communication in Presence of Partitioning

This paper presents an adaptive fault-tolerant resource allocation protocol tailored for FlyNet, a dynamic aerial network characterized by its mobility and three-dimensional operational space. Addressing the challenges of network partitioning and resource allocation in aerial networks, the proposed algorithm dynamically adjusts to the network's changing topology, ensuring consistent and efficient resource management. Through robust fault tolerance mechanisms, the protocol enhances FlyNet's reliability, maintaining seamless communication and optimal resource utilization even amid node mobility and disruptions. Simulation results demonstrate the algorithm's effectiveness in adapting to dynamic environments, maximizing resource utilization, and minimizing communication delays.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Gain-scheduled filter design with different time delays for T-S fuzzy systems

In this article, the authors study a problem of parameter-varying systems that gain-scheduled [Formula: see text] filtering T-S fuzzy problems by appearing of time-varying delays. Our paper also aims to establish filters that deal with communication delays. Traditional studies often study delay-free filters, but these filters have a more general form. In addition, a nonlinear fractional transformation structure has been incorporated into this filter, which allows its parameters to vary. The authors implement fuzzy Lyapunov–Krasovskii functionals used to intend the delayed fuzzy filters of this study, and delay-dependent circumstances are obtained for stability and performance analysis. The solution of the presented linear matrix inequalities (LMIs) provides the filter parameters. This method is finally justified with an example that illustrates its effectiveness.

Lightweight Flow‐Based Policy Enforcement for SDN‐Based Multi‐Domain Communication

ABSTRACTAlthough software‐defined networking (SDN) is commonly employed for intra‐domain communication, inter‐domain communication still heavily relies on conventional routing methods, specifically BGP‐based routers. The BGP router plays a crucial role in managing control and data planes, but this traditional approach hinders the exploitation of SDN advantages. Previous studies demonstrated the use of BGP for inter‐domain and end‐to‐end communication. This paper advocates for the adoption of a fully SDN‐based data plane packet switching strategy through the introduction of LPEES, a lightweight policy framework tailored for SDN‐based inter‐domain communication. LPEES strategically confines BGP's functionality to the control plane, preserving SDN benefits. Evaluation results confirm the effectiveness of LPEES compared to the BGP routing approach, as measured by throughput and various network quality of service (QoS) metrics. Additionally, LPEES streamlines inter‐domain communication by utilizing a trust‐based routing policy approach that can establish trust between communicating domains. The presented solution's main advantage is that it loosens the burden on the administrator by requiring less human interference to check the inter‐domain communication security and privacy. Our evaluations show LPEES outperform the BGP‐based in terms of throughput as LPEES achieves a 27 Gbps versus 22 Gbps in the traditional approach. Based on our experiments, LPEES also enhances the communication delay by an average of 17% compared to the traditional BGP‐based approach.

A Fair MAC Protocol Based on Dual Bandwidth Allocation Iterations in Underwater Acoustic Sensor Networks

ABSTRACTMost existing passive allocation‐based media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) overlook the fairness issue in channel bandwidth allocation, resulting in certain nodes occupying channel resources for extended periods, thereby leading to low channel utilization. To address this issue, this paper proposes a fair MAC protocol based on dual bandwidth allocation iterations (FBA‐MAC). Firstly, by limiting the maximum number of packets and the maximum number of available time slots that the host node can receive in each communication cycle, the protocol reduces collisions and collisions more effectively, thus controlling the communication delay. Secondly, in the process of channel bandwidth allocation, a dual bandwidth allocation iterative method was used to allocate appropriate channel bandwidth for each member node according to its communication requirements and the generation time of its generated data packets. Finally, in order to minimize the time interval of each packet arriving at the host node to reduce the communication delay, the transmission scheduling scheme was adaptively adjusted by combining the results of channel bandwidth allocation to maximize the channel utilization. Simulation results indicate that compared to the other four MAC protocols, FBA‐MAC demonstrates significant advantages in terms of fairness (10% higher), achieving higher network throughput (10% higher) and shorter end‐to‐end delay. Therefore, FBA‐MAC exhibits superior adaptability and efficiency in dynamic network environments.

Dynamic clustering based risk aware congestion control technique for vehicular network

In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there’s no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study’s conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Multi-Agent Reinforcement Learning for Smart Community Energy Management

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

Robust state agreement in switching networks of nonlinear agents with measurement noises and communication delays

Robust state agreement in switching networks of nonlinear agents with measurement noises and communication delays

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Routing protocols, as a crucial component of the internet of things (IoT), play a significant role in data collection and environmental monitoring tasks. However, existing clustering routing protocols suffer from issues such as uneven network energy consumption, high communication delays, and inadequate adaptation to topology changes. To address these issues, this study proposes an adaptive routing algorithm to balance energy consumption and delay using game theory and deep Q-network (DQN) algorithms (EDRP-GTDQN). Specifically, EDRP-GTDQN evaluates the importance of node positions using node centrality and integrates a game-theoretic-based approach to select optimal cluster heads in terms of node centrality and residual energy. Moreover, graph convolutional networks (GCN) and DQN are incorporated to construct transmission paths for cluster heads, adapt to network topology changes, and balance energy consumption and performance. Furthermore, a cluster rotation mechanism is employed to optimize overall network energy consumption and prevent the formation of hotspots. Experimental results demonstrate that EDRP-GTDQN achieves average performance improvements of 19.76%, 30.04%, 44.2%, and 61.42% in average energy consumption, network lifetime, and average end-to-end delay compared to conventional routing protocols such as EECRAIFA, MRP-GTCO, DEEC, and MH-LEACH. Therefore, EDRP-GTDQN is undoubtedly an effective solution to reduce energy consumption and enhance service quality in wireless sensor networks.

Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.

Prenatal maternal diabetes, comorbidities, and risk for neurodevelopmental impairment in the first two years.

Maternal diabetes is a known risk for neurodevelopmental delay in offspring, and often presents with comorbid metabolic conditions, such as obesity and hypertension. However, their combined effects on neurodevelopmental outcomes remain unclear. We investigated the independent and combined associations of maternal diabetes and comorbidities with the risk of neurodevelopmental delay in children aged 12 and 24 months. A prospective longitudinal cohort of children from Pregnancy during the COVID-19 Pandemic study. Neurodevelopmental screening at 12 and 24 months was conducted using the Ages and Stages Questionnaire, which assesses domain-specific development. Maternal diabetes was not associated with neurodevelopmental risks either at 12 or 24 months. However, in combined analyses, maternal diabetes and pre-pregnancy overweight were associated with an increased risk of neurodevelopmental delay in personal-social skills (odds ratio [OR], 1.75 [95%CI,1.01-3.01]) at 24 months, though not at 12 months. Maternal diabetes and pre-pregnancy obesity were also associated with an increased risk for neurodevelopmental delay in communication (OR, 1.71 [95%CI,1.01-2.82]) and personal-social skills (OR, 2.01 [95%CI,1.03-3.73]) at 24 months. Furthermore, maternal diabetes and hypertensive disorders of pregnancy (HDP) had higher rates of positive screening for delay in fine-motor skills (OR, 3.54 [95%CI, 1.28-8.41]) at 12 months but not at 24 months. Post-hoc analysis revealed an independent association of maternal pre-pregnancy obesity, but not overweight, with an increased risk of neurodevelopmental delay in communication, fine-motor, and personal-social skills (ORs ranging from 1.44 to 1.71) at 24 months but not 12 months. Similarly, there was an independent association of maternal HDP with an increased risk of neurodevelopmental delay in fine-motor and and personal-social skills (ORs ranging from 2.01 to 2.19) at 24 months. Maternal diabetes with comorbid conditions is likely to increase the risk of neurodevelopmental delay during infancy than individual exposure, suggesting the persistent influence of prenatal exposure on offspring neurodevelopment. 1. Identifying modifiable prenatal risk factors for neurodevelopmental impairment in offspring is crucial for targeted interventions and providing support to mothers during pregnancy, which can lead to improved child health outcomes. 2. Maternal diabetes was not associated with neurodevelopmental delays in children at 12 or 24 months. 3. Maternal diabetes in combination with pre-pregnancy overweight or obesity increased the risk of cognitive delay at 24 months. 4. Maternal pre-pregnancy obesity but not overweight, and hypertensive disorders of pregnancy independently increased risks of cognitive and motor delays at 24 months.

Digital-Twin Predictive Control of Nonlinear Systems With Time Delays, Unknown Dynamics, and Communication Delays.

With the advancement of computing technology and big data technology, digital twins have gradually been applied in various fields, such as manufacturing, energy, and healthcare. This article studies the predictive control of nonlinear dynamic systems using digital twins. Based on a digital-twin control system framework, predictive control is discussed for three different nonlinear systems with time delays: 1) known nonlinear systems; 2) unknown nonlinear systems; and 3) unknown nonlinear cyber-physical systems. Both a digital-twin predictive control strategy and a digital-twin control predictor are proposed to compensate for time delays and communication delays actively. With the strategy and predictor, the digital-twin controller of a time-delay nonlinear system can be designed to achieve the desired performance based on the nonlinear system without time delays, which vastly simplifies the controller design procedure. A digital model is constructed using data to deal with unknown nonlinear dynamics. The three different closed-loop digital-twin predictive control systems are analyzed to derive a unified stability criterion. The simulation results show how the proposed digital-twin predictive control method performs well for nonlinear systems with time delays, unknown dynamics, and/or communication delays.

Supporting the Mind in Space: Psychological Tools for Long-Duration Missions.

The psychological well-being of astronauts is becoming just as vital as their physical and technical readiness as space missions extend into deep space. Long-duration missions pose unique challenges, such as isolation, confinement, communication delays, and microgravity, which can significantly affect mental health and cognitive performance. This commentary discusses the need for innovative mental health support systems, including automated psychotherapy, as well as Earth-based training methods like mindfulness and relaxation techniques, to address the psychological demands of space travel. By integrating these approaches into pre-mission preparation and in-flight routines, astronauts can develop self-regulation strategies to manage stress, improve focus, and enhance emotional resilience. Automated psychotherapy available 24-7 provides real-time confidential support when communication with Earth is delayed. As space exploration moves forward, the success of missions will depend not only on technological advancements but also on the development of psychological countermeasures that prioritize mental health alongside physical well-being. This paper emphasizes the importance of continued research and collaboration to refine and test these tools in analog environments, ensuring astronauts are mentally and emotionally prepared for the challenges of space.

Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems

The increasing complexity of deep space missions introduces significant challenges in maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. This research proposes a novel framework for the autonomous, data-driven fault diagnosis of spacecraft propulsion systems. Leveraging data generated from a spacecraft propulsion system simulation model. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. The framework's effectiveness is demonstrated through implementation on a microcomputer, showing promising results in terms of diagnostic accuracy and processing speed, thus highlighting its potential for onboard spacecraft application. This study not only advances the autonomous capabilities of spacecraft in deep space but also contributes to the broader field of Prognostics and Health Management (PHM) by providing a scalable, efficient approach to fault diagnosis in critical spacecraft systems. The proposed framework demonstrates a promising approach to optimizing diagnostic tasks for spacecraft systems. However, the trade-offs observed necessitate a careful consideration of task-specific requirements and the potential need for adjustments to maintain a high level of accuracy alongside computational efficiency.

Research on Longitudinal Control of Electric Vehicle Platoons Based on Robust UKF–MPC

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state–space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF–MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF–MPC controller outperforms the MPC and UKF–MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error and exhibits stronger robustness and stability.

LFC of multi-area power system using a robust full-order terminal sliding mode controller considering communication delay

LFC of multi-area power system using a robust full-order terminal sliding mode controller considering communication delay

Space Radiology: Emerging Nonsonographic Medical Imaging Techniques and the Potential Applications for Human Spaceflight.

Space medicine is a multidisciplinary field that requires the integration of medical imaging techniques and expertise in diagnosing and treating a wide range of acute and chronic conditions to maintain astronaut health. Medical imaging within this domain has been viewed historically through the lens of inflight point-of-care ultrasound and predominantly research uses of cross-sectional imaging before and after flight. However, space radiology, a subfield defined here as the applications of imaging before, during, and after spaceflight, will grow to necessitate the involvement of more advanced imaging techniques and subspecialist expertise as missions increase in length and complexity. While the performance of imaging in spaceflight is limited by equipment mass and volume, power supply, radiation exposure, communication delays, and personnel training, recent developments in nonsonographic modalities have opened the door to their potential for in-mission use. Additionally, improved exam protocols and scanner technology in combination with artificial intelligence algorithms have greatly advanced the utility of possible pre- and postflight studies. This article reviews the past and present of space radiology and discusses possible use cases, knowledge gaps, and future research directions for radiography, fluoroscopy, computed tomography, and magnetic resonance imaging within space medicine, including both the performance of new exam types for new indications and the increased extraction of information from exams already routinely obtained. Through thoughtfully augmenting the use of these tools, medical mission risk may be reduced substantially through preflight screening, inflight diagnosis and management, and inflight and postflight surveillance.

An effective and efficient UAV leader selection scheme in swarm of UAVs

Therefore, this study aims to introduce a novel framework for leader and coordinator selection in a swarm of UAVs that may further reduce communication delay and increase the data transmission rate. The proposed reliable framework avoids single-point failure. We use an integrated greedy approach for leader selection using better channel gain, enhanced data transmission rate, and minimal round-trip delays as metrics. Furthermore, the UAV coordinator is selected based on the nearest distance. In addition, this study also proposes an efficient and iterative algorithm for the effective selection of UAV leaders and coordinators. The experimental results indicate that our proposed protocol minimizes delay by 20% and increases the data transmission rate by 102bps as compared with existing methods.