Communication Scheduling Research Articles

Dynamic Size Message Scheduling for Multi-Agent Communication Under Limited Bandwidth

Dynamic Size Message Scheduling for Multi-Agent Communication Under Limited Bandwidth

Federated deep reinforcement learning based trajectory design for UAV-assisted networks with mobile ground devices

Exploiting highly maneuverable unmanned aerial vehicles (UAVs) has been considered as an efficient way to assist wireless systems, e.g., for applications of data collection. However, several challenges remain to be addressed in the design of such UAV-assisted networks, including multi-UAV joint trajectory determination, data privacy protection, and adaption to the complex channel environment particularly with mobile ground devices (GDs). In this paper, we study a multi-UAV assisted data collection system where UAVs collect data locally from mobile GDs. The aim is to minimize the whole operation time cost via jointly optimizing the UAVs’ three-dimensional (3D) trajectory together with the GDs’ communication scheduling, while satisfying the constraints of no-fly zones (NFZs) and collision avoidance. With a nonconvex feasible set (due to the NFZs), the established problem is nonconvex. Moreover, the randomness of GDs movements significantly reduces the performance of a typical redesign, i.e., determining the UAVs’ trajectory and users’ scheduling before starting the data collection task. To tackle these issues, we first transform the established problem into a Markov decision one, and then propose a multi-agent federated reinforcement learning (MAFRL)-based approach to optimize the dynamic long-term objective via jointly determining UAVs’ 3D trajectory and GD’s communication scheduling. A multi-step propagation technique and a dueling network architecture are adopted to enhance the neural network utilized to train agents, i.e., to accelerate the convergence rate of the proposed method and improve its overall stability. Finally, experimental results reveal the effectiveness of our proposed method in the considered practical scenario.

Joint optimization for energy efficient full-duplex UAV relaying with multiple user pairs

This paper investigates an unmanned aerial vehicle (UAV) assisted amplify-and-forward relaying, where a full-duplex (FD) fixed-wing UAV employs a time-division multiple access scheduling protocol to provide relay services for multiple source–destination user pairs. With the aim of maximizing energy efficiency (EE) of the system, a joint optimization problem is studied so as to jointly fulfill the communication scheduling of multiple user pairs, the transmit power control and the trajectory design of the UAV. Since the optimization variables of the problem are coupled, it is non-convex and hence hard to solve directly. To this end, the initial problem is decomposed into three subproblems corresponding to the optimization of communication scheduling, and transmit power and trajectory of the UAV, respectively. The three subproblems are solved by utilizing the linear programming, the successive convex approximation (SCA), and the Dinkelbach’s algorithm. Then an iterative algorithm based on the block coordinate descent technique is proposed to tackle the joint optimization problem by optimizing the three blocks of variables alternately. Simulation results demonstrate that the proposed algorithm converges efficiently, and the EE of the joint optimization scheme can be significantly improved compared to the benchmark schemes.

Maximizing energy-efficiency for RIS-UAV assisted mobile vehicles in cognitive networks

As an essential technology in the sixth generation of wireless communication, the reconfigurable intelligent surface (RIS) offers transformative solutions for the evolution of intelligent transportation. In the secondary network, RIS is equipped on an unmanned aerial vehicle (UAV) to establish a communication link between secondary base station (SBS) and secondary mobile vehicles (SMVs). At the same time, the communication within the secondary network must not interfere with the primary users (PUs) in the primary network. To achieve the optimal energy efficiency, we need to optimize the RIS passive beamforming, SMVs communication scheduling, SBS radiation power allocation and RIS-UAV trajectory. Since the original problem is difficult to solve, we use an alternating iteration framework to decompose the original problem into four subproblems and solve them with successive convex approximations (SCA). We have developed the CEEM scheme to compare it with benchmark schemes and demonstrate its superior performance, achieving up to a 43.48% improvement. In addition, RIS improves the communication quality by up to 57.53% in the simulation results, which have verified the correctness and effectiveness of the algorithm proposed in this paper.

Scheduling of Millimeter Wave Communications for Ultra-High-Speed Vacuum Tube Train

Scheduling of Millimeter Wave Communications for Ultra-High-Speed Vacuum Tube Train

Loosely Coupled Hybrid Scheduling of Processing and Communication for TSN-Based IMA Systems

Loosely Coupled Hybrid Scheduling of Processing and Communication for TSN-Based IMA Systems

Transmission scheduling of P2P real-time communication based on restless multi-armed bandit

Transmission scheduling of P2P real-time communication based on restless multi-armed bandit

HPS: A Hybrid Proactive Scheduler with adaptive channel selection for industrial 6TiSCH networks

The Industrial Internet of Things (IIoT) plays a vital role in Industry 4.0, thanks to advanced wireless protocols like IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) and their incorporation in the IPv6 stack via 6TiSCH. These protocols rely on carefully constructed schedules generated by a Scheduling Function (SF). The Minimal Scheduling Function (MSF) serves as the standard SF established by the 6TiSCH WG, prescribing the management and adaptation of the communication schedule based on traffic patterns. However, MSF has a notable drawback in adapting to traffic variability and burstiness and fails to account for link quality when updating TSCH cells. To address these limitations, this paper introduces the Hybrid Proactive Scheduler with Adaptive Channel Selection (HPS). HPS calculates cell requirements by considering resource utilization, ETX (Expected Transmission Count), and buffer allocation. It incorporates a fast response mechanism for burst traffic, implemented through an autonomous scheduling mechanism. Additionally, HPS utilizes a whitelisting mechanism to select optimal communication channels. The proposed scheme is implemented in Contiki-ng and evaluated in both simulated environments and real-world testbed. Obtained results demonstrate the potential of HPS to significantly enhance reliability and reduce delays compared to state-of-the-art SFs. Specifically, HPS consistently demonstrates reliability levels surpassing 90% across diverse scenarios accompanied with a noticeable gain in latency and power consumption of approximately 70% and 25%, respectively. These findings highlight the effectiveness of HPS in improving network performance and addressing the limitations of existing SFs.

IoT CONTROLLED LED NOTICE BOARD

The IoT Controlled LED sign board design represents a slice- edge bid aimed at revolutionizing display results for a multitude of purposes including advertising, information dispersion, and aesthetic improvement. At its core, the sign board features an intricate array of LEDs strictly arranged in a grid pattern, each strictly orchestrated by a central microcontroller unit( MCU) harmoniously integrated with a Bluetooth module for flawless wireless communication. This community enables druggies to apply their smartphones or any Bluetooth- enabled bias as important remote controls, decreeing the content showcased on the LED board with unknown ease and perfection. Through a devoted mobile operation, druggies gain a gateway to a realm of creative expression, empowered to transmit a myriad of content types ranging from brief textbook dispatches to witching, robustness and images for immediate display. specially, the system's hallmark lies in its capability to grease real- time content operation, preventing the need for physical intervention and fostering dynamic rigidity to evolving demands and scripts. likewise, the mobile operation's robust point set extends beyond bare content transmission, offering sophisticated tools for communication scheduling and contemporaneous operation of multiple sign boards, thereby accelerating scalability and usability across different surroundings similar as bustling retail spaces, bulging transportation capitals, and bustling public spaces likewise. With its intimately designed interface and multifaceted functionality, the IoT Controlled LED sign board design emerges as a lamp of invention, promising to review the veritably geography of information dispersion and visual communication in the digital age. KEYWORDS: IoT Controlled LED subscribe Board, Microcontroller Unit( MCU), Wireless Communication, Mobile Application, Management, Remote Control.

Co-Design of Bandwidth-Aware Communication Scheduler and Cruise Controller for Multiple High-Speed Trains

Co-Design of Bandwidth-Aware Communication Scheduler and Cruise Controller for Multiple High-Speed Trains

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the inter-vehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

Population-Invariant MADRL for AoI-Aware UAV Trajectory Design and Communication Scheduling in Wireless Sensor Networks

Population-Invariant MADRL for AoI-Aware UAV Trajectory Design and Communication Scheduling in Wireless Sensor Networks

Transmission Scheduling of Millimeter Wave Communication for High-Speed Railway in Space-Air-Ground Integrated Network

Transmission Scheduling of Millimeter Wave Communication for High-Speed Railway in Space-Air-Ground Integrated Network

QoS-Aware Scheduling of Airship-Assisted Millimeter Wave Communications for High-Speed Railway

QoS-Aware Scheduling of Airship-Assisted Millimeter Wave Communications for High-Speed Railway

Enabling Metaheuristics with Deep Learning based Resource Allocation in Unmanned Aerial Vehicles Wireless Networks

Unmanned aerial vehicle (UAV) network offers a variety of applications in public safety, disaster management, advertising and broadcasting, overload situation, etc. Due to the dynamic characteristics of MU, it is challenging to provide robust transmission services to mobile users (MU). Resource allocation (RA), including sub-channel, serving user, and transmit power, is a crucial problem; also, it is critical to enhance the coverage and energy efficiency of UAV-enabled communication protocol. Furthermore, system resources are limited (for example, spectrum, and transmission power) and UAV transmission coverage and on-board energy are limited. In order to meet the QoE of any user with limited UAV energy and limited resource system, we jointly enhance UAV trajectory, user communication scheduling, and bandwidth allocation and transmit power to satisfy user QoE requirements and increase energy efficiency. Thus, the study proposes a new mud ring optimization with deep belief network-based resource allocation scheme (MRODBN-RAS) technique for UAV-enabled wireless networks. The proposed MRODBN-RAS approach focuses on the effectual accomplishment of the computational and energy-effective decision. Besides, the MRODBN-RAS technique assumed the UAV as a learning agent by forming RA decisions as actions. In addition, the MRODBN-RAS technique designed a reward function to reduce the weighted resource utilization. The MRODBN-RAS technique uses DBN model with hyperparameter tuning using MRO algorithm to allocate the resources. The design of the MRO algorithm helps in the optimal selection of the hyperparameter related to the DBN model. The simulation results of the MRODBN-RAS method are examined under various measures. The extensive comparison study highlighted the better performance of the MRODBN-RAS approach over existing techniques.

Sliding mode consensus control for multi-agent systems under multi-node round-robin protocol

This work investigates the consensus issue for nonlinear multi-agent systems (MASs) via the multi-node round-robin protocol (MRRP) and sliding mode control (SMC) method. The MRRP is used to schedule information transmissions among agents. On the one hand, considering the limited communication ability of the leader, the leader could take turns sending information to several nearby followers. On the other hand, for reasonably scheduling communication resources among followers, the number of follower’s sensor nodes could be automatically adjusted to transmit their information according to the size of the consensus error. Under the MRRP, a token-dependent sliding mode controller is designed to achieve consensus. And controller gains are obtained via the optimized algorithm. Eventually, two numerical simulation instances are furnished to substantiate the efficacy of the proposed SMC strategy under MRRP.

Research on flow scheduling of train communication based on time-sensitive network

The rapid development of intelligent rail transportation equipment is raising higher requirements for the real-time performance of Train Communication Network (TCN) data transmission. Time-Sensitive Network (TSN) has gained widespread attention in train communication due to its advantages of high transmission rates and deterministic latency. Firstly, for addressing the flow scheduling problem in TSN, a TCN topology model supports TSN is designed. It establishes a multi-objective flow scheduling model based on a priority strategy, aiming to minimize average response time and makespan while ensuring a scheduling success rate. Then, to solve the routing selection problem, a joint scheduling algorithm based on multi-level routing selection is adopted, providing feasible scheduling solutions. Finally, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is introduced to iteratively and selectively optimize the scheduling solutions. This approach resolves the flow ordering problem while achieving optimization for the scheduling model. The feasibility of this scheduling approach is validated through simulations, and the results demonstrate significant advantages of the proposed model and algorithm in terms of scheduling success rate and real-time performance, providing effective solutions for flow scheduling in TCN.

Strategies for Combatting Health Disparities in Prioritization Schema for Medically-Necessary Time-Sensitive Orthopaedic Surgeries

Abstract: A decrease in non-emergent procedure volume was observed during the COVID-19 pandemic to conserve protective equipment, increase hospital capacity, and limit nosocomial infection. Decreasing COVID-19 infection rates, paired with increasing hospital financial pressure and concerns for patient welfare, have prompted the development of guidelines for re-introduction of medically-necessary time-sensitive (MeNTS) procedures. Such protocols have received criticism for potentially perpetuating inequities disfavoring vulnerable populations. Limited access to testing supplies and protective equipment, coupled with higher incidence of medical comorbidities attributable to social determinants of health, disadvantages vulnerable populations in seemingly objective prioritization schema. Here, we detail both an analysis of current guidelines as well as strategies aimed at mitigating these disparities (including prioritizing essential infrastructure workers, implementing questionnaires, improving scheduling communication, tracking patients via ZIP codes and insurance status, facilitating post-operative rehabilitation, acknowledging physician bias, and favoring lottery selection over first-come, first-served). These guidelines and strategies can apply to future pandemics and even routine prioritization schema.

An enhanced MSU‐TSCH scheduling algorithms for industrial wireless sensor networks

SummaryThe reliability and latency requirements of wireless sensor network‐based smart grid communications are met in large part by MAC protocols. Developed by IEEE, time slotted channel hopping (TSCH), a method that is effective, dependable, and predictable. However, the TSCH standard does not enable mobility and does not offer any solution for scheduling, especially in IEEE 802.15.4 TSCH, the most recent generation of extremely dependable and low‐power MAC protocols. Recently, MSU‐TSCH a time‐frequency communication schedule was proposed to provide mobility to TSCH. Despite its distinctive qualities, MSU‐TSCH has the drawback of computing the TSCH schedule at each node independently of its traffic load, which can significantly increase the communication delay. Due to this limitation, MSU‐TSCH is not suitable for some delay‐sensitive smart grid applications. In this article, we provide an improved MSU‐TSCH‐based TSCH protocol, called Mobility‐TSCH, that dynamically adjusts time slot assignments based on traffic volume and latency requirement. Moreover, the protocol Mobility‐TSCH adapts to topology changes and supports mobility. Additionally, it optimizes time slot allocation by prioritizing nodes closest to the sink. The performance and evaluation analysis of Mobility‐TSCH compared to the original MSU‐TSCH, reveal that the communication delay is greatly reduced, decreases the average end‐to‐end latency, the high packet delivery ratio (PDR), Increase the performance of both metrics, parent connectivity ratio (PCR) and convergence time (CT), while maintaining a minimal overhead signaling.

Autonomous Scheduling for Reliable Transmissions in Industrial Wireless Sensor Networks

Deploying Internet of Things (IoT) on low-power lossy wireless sensor/actuator networks (LLN) in harsh industrial environments presents challenges such as dynamic link qualities due to noise, signal attenuations and spurious interferences. However, the critical demand for industrial applications is reliability of data delivery on low-cost low-power sensor/actuator devices. To address these issues, this paper proposes a fully autonomous scheduling approach, called Auto-Sched, which ensures reliability of data delivery for both downlink and uplink traffic scheduling and enhances network robustness against node/link failures. To ensure reliability, Auto-Sched assigns retransmission time slots based on the reliability constraints of the communication link. To avoid collision issues, Auto-Sched creates an upward pipeline-like communication schedule for uplink end-to-end data delivery, and a downward pipeline-like communication schedule for downlink scheduling. For enhancing network robustness, we propose a simple algorithm for real-time autonomous schedule reconstruction, when node or link failures occur, with minimal influence on communication overhead. Performance evaluations quantified the performance of our proposed approaches under a variety of scenarios comparing them with existing approaches.