Communication Scheduling Research Articles

Terahertz Meets Untrusted UAV-Relaying: Minimum Secrecy Energy Efficiency Maximization via Trajectory and Communication Co-Design

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR's inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR's trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks.

C-Lop: Accurate contention-based modeling of MPI concurrent communication

MPI communication optimization is a crucial stage to optimize high-performance applications. As a formal analysis of MPI communication, the communication performance models have made some achievements in improving the efficiency of collective algorithms and optimizing communication scheduling. However, previous models are difficult to model asynchronous concurrent communication and do not take into account numerous contention factors. In this paper, we present C-Lop, an incremental MPI performance model based on τ-Lop. Firstly, C-Lop proposes a method for asynchronous modeling of concurrent communication. As the only model that considers concurrent transmission, τ-Lop describes the cost of the all processes as a whole without distinguishing the cost of each process. Here, C-Lop uses the idea of asynchronous modeling that describe the cost of the system by averaging the communication cost per process. It can describe the communication cost for some systems with out-of-sync communication more accurately. Moreover, C-Lop introduces the parameter C to represent the contention, and considers the contention of concurrent transmissions on network-on-chip, data reuse, and contention of noncommunication processes to make a more accuracy estimation. Furthermore, parameter C can be customized to fit more application scenarios. In addition, we evaluate several common collective algorithms, a matrix multiplication algorithm (SUMMA), and two kinds of communication in a three-dimensional multi-grid application on the Tianhe-3 prototype, and results show that C-Lop outperforms the competition.

Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation With Bayesian Inference

Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO.

QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices’ priority. This work applies game theory to control devices’ strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices’ strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature.

Energy efficiency maximization for buffer-aided multi-UAV relaying communications

This paper studies a multiple unmanned aerial vehicle (UAV) relaying communication system, where multiple UAV relays assist the blocked communication between a group of ground users (GUs) and a base station (BS). Since the UAVs only have limited-energy in practice, our design aims to maximize the energy efficiency (EE) through jointly designing the communication scheduling, the transmit power allocation, as well as UAV trajectory under the buffer constraint over a given flight period. Actually, the formulated fractional optimization problem is difficult to be solved in general because of non-convexity. To resolve this difficulty, an efficient iterative algorithm is proposed based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, as well as the Dinkel-bach's algorithm. Specifically, the optimization variables of the formulated problem are divided into three blocks and we alternately optimize each block of the variables over iteration. Numerical results verify the convergence of the proposed iterative algorithm and show that the proposed designs achieve significant EE gain, which outperform other benchmark schemes.

An UAV-Assisted Edge Computing Resource Allocation Strategy for 5G Communication in IoT Environment

As the computing capacity of existing mobile devices cannot fully meet the needs of users for communication quality, a computing resource allocation strategy for 5G communication in the Internet of Things (IoT) environment is proposed by applying UAV-assisted edge computing. First, a system model is constructed with the UAV deployed with mobile edge computing (MEC) servers to provide assisted computing services for multiple users on the ground. Based on the optimization of the UAV trajectory, communication scheduling, and the energy consumption model of the UAV, the problem of the total computational cost minimization is formulated. Then, the genetic algorithm is improved by introducing a penalty function to solve this problem, in which selection, crossover, and mutation operations are iterated to obtain the optimal allocation strategy for computational resources. Finally, a simulation platform is constructed to analyze the proposed method. The results show that the total cost and total time of the proposed strategy are better than other comparison strategies.

Joint trajectory and communications design for high air-interface-resource efficiency

Applying aerial devices to collect data from terrestrial sensors is a research hotspot in recent years. In this paper, we consider a novel system model that terrestrial sensors’ data are first collected by low-altitude UAVs and then transferred to a relatively-high-altitude central processor. We formulate the model in such an air-ground integrated system as a non-convex optimization problem of maximizing the air-interface-resource efficiency (AIRE) by jointly optimizing the trajectories and communication schedules. By using techniques of block coordinate descent (BCD) and successive convex approximation (SCA), the proposed problem is relaxed and transformed into three subproblems, which can be solved by iteration. Simulation results show the optimized trajectories and communication schedules, and the designed schemes perform better than the benchmarks.

Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System

In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.

Harnessing data to strategise for digital endowment reporting

Digital endowment reporting is becoming an industry standard in higher education and philanthropy. NC State implemented a digital endowment reporting platform by leveraging efficient project planning and management, a strategic communication schedule and in-depth data analyses for informed decision making. Through the topics explored within, readers can expect to have a better understanding of the preparation needed for a successful digital transition and explore the role datadriven conclusions play in guiding a project of this nature to a rewarding conclusion. A willingness to adapt to issues as they arose to maintain flexibility and a commitment to working cohesively as a team were common threads throughout the project. This paper will detail NC State’s experiences and provide suggestions for how the steps that were taken apply more generally to digital endowment reporting as a whole.

Collaborating to Integrate Education and Practice: A Model of a Physical Therapy Academic–Clinical Partnership

Background and Purpose. Strong academic–clinical partnerships have been advocated to optimize the training of the next generation of health professionals and to best meet the needs of patients and society. Despite the benefits of partnerships, there is a lack of literature describing successful models for collaboration between physical therapist (PT) schools and academic medical center hospitals. The purpose of this article was to describe the formal partnership between the Northwestern University, Feinberg School of Medicine, Department of Physical Therapy and Human Movement Sciences (DPTHMS) and Shirley Ryan AbilityLab in Chicago, Illinois. Method/Model Description and Evaluation. The partnership is presented through the lens of D'Amour's 4-dimensional model of collaboration highlighting 4 critical dimensions of collaboration: shared goals and activities, internalization, formalization, and governance. Outcomes. The establishment of this partnership has fostered the institutions' achievement of mutual goals supporting their interconnected visions and missions. Examples of the collaboration's shared goal and activities include: the tripling of clinical education experiences for professional Doctor of Physical Therapy (DPT) students; establishment of 2 post professional PT residency programs; proliferation of DPT teaching opportunities for Shirley Ryan AbiltyLab clinical PTs with formal university academic appointments for 6 Shirley Ryan Ability Lab PTs; and clinical appointments for 7 of 34 eligible DPTHMS faculty members. The internalization dimension is evidenced by regularly scheduled communications; promotion of opportunities for mutual acquaintanceship through shared boards and teams; socialization opportunities; and trust-building across institutions. The primary example of formalization is the legal collaboration agreement signed in 2013 and amended in 2019. Partnership governance is achieved through a structure of designated liaisons, shared boards, and regular interinstitutional leadership meetings. Discussion and Conclusion. D'Amour's structuration model describes predictors for successful interinstitutional clinical–academic partnerships. The model's 4 collaboration dimensions facilitate the assessment of partnership success for both established partnerships and for institutions in the planning stage of such collaborations.

Real-time task scheduling for FPGA-based multicore systems with communication delay

Efficient scheduling is critical for achieving high performance in the multicore computing environment. This paper focuses on the scheduling problem for the real-time applications in the FPGA-based multicore structure with an objective to minimize the makespan under hardware resource constraints. To address this problem, we propose a priority-driven scheduling algorithm, called the Real-Time Priority-driven Algorithm (ReTPA). In this method, we first allocate the tasks into software and hardware computing units based on their hardware resource constraints. Then scheduling is performed to minimize the makespan. We further extend ReTPA with communication scheduling to improve the scheduling efficiency in the communication-intensive applications, which is called Communication-based Real-Time Priority-driven Algorithm (CReTPA). Our experimental results confirm the efficiency of the proposed algorithms in applications with different scales or different computation-communication ratios. Besides, we compare the two proposed algorithms with a classic priority-driven algorithm, HEFT, an advanced priority-driven algorithm, HETS, and two advanced genetic algorithms, GAA and MGAA, proposed by Abdallah et al. in 2019. The performance comparisons illustrate that our algorithms, ReTPA and CReTPA, both show the outstanding performance and CReTPA obtains the most reasonable solutions within the shortest scheduling time for communication-intensive applications.

DynaComm: Accelerating Distributed CNN Training Between Edges and Clouds Through Dynamic Communication Scheduling

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.

Generating Multi-Issued Session Key by Using Semi Quantum Key Distribution With Time-Constraint

Information security refers to protect the information from unauthorized access or modification. Quantum Key Distribution (QKD) is a way to generate a key preventing those malicious activities. One of QKD protocol, namely Semi-quantum key distribution (SQKD) protocol, is designed to allow two users to establish a secure secret key when either of them is limited to performing certain “classical” operations. It is proven to be secure from any type of attack. However, it will be a problem in the multi-session communication since the SQKD activities follow the number of the session. In this paper, we propose two modified SQKDs with time-constraint approach. Time-constraint is beneficial in QKD activity since it could generate session key between two parties within a certain time-constraint. By setting the number of session key and its time-constraint before QKD activities, many scheduled communications would be prepared well. Furthermore, BAN Logic analysis is applied to analyze the goal of the protocol, the considered assumptions, wasted phase, and the demand for data encryption. Finally, the performance analysis of the protocols is presented, and it shows a better performance compared with other certain QKDs.

Reinforcement Learning TDMA-Based MAC Scheduling in the Industrial Internet of Things: A Survey

As technology progresses, almost all Industrial Internet of Things applications are becoming more data-driven. Hence, more advanced ways of communication are required to both save energy and become efficient to collect huge amounts of data. Researchers have now agreed that the root improvement of IIoT communication can start from a lower layer which is Data Link. Time Division Medium Access-based protocols on this layer have drawn academia's attention to decrease the energy consumption of IIoT devices and achieve higher levels of throughput. Yet, a problem arises, that is, developing a scheduler has always been troublesome while designing a Medium Access Control protocol. Scheduling communication in TDMA-based MAC protocols is considered an NP-hard problem. It is one of the topics highly debated among researchers to make the scheduling adaptive to the huge variation of IIoT application requirements. Conventional scheduling algorithms may suffer from the rapid changes in sensor network topology, network throughput, and applications’ delay targets. Therefore, Reinforcement Learning algorithms are being integrated into scheduling techniques because of their adaptability and efficiency. This paper surveys RL-based TDMA MAC protocols and compares them in terms of several unified features. Each protocol is explained and discussed with others regarding its advantages and drawbacks.

Adaptive Scheduling for Joint Communication and Radar Detection: Tradeoff Among Throughput, Delay, and Detection Performance

In this paper, we focus on the tradeoff design between communication and radar detection for a joint communication and radar (CommRadar) system by leveraging a low-complexity hardware architecture. To achieve this, we exploit the communication traffic diversity to design an adaptive scheduling policy. Through adjusting the CommRadar mode selection, radar steering direction and communication user scheduling, and time allocation between communication and radar detection, we could better utilize the un-occupied transmission time to improve radar detection performance when the data traffic is light, and balance the time allocation between communication and radar detection when the data traffic is heavy. Moreover, for our adaptive scheduling method, we have provided a quantitative performance bound for data throughput, queueing delay, and radar detection performance of both short-term and long-term. By conducting extensive simulation studies, we have demonstrated that our method could greatly reduce the communication performance loss when integrating the radar functionality, while still guaranteeing radar detection performance.

6TiSCH Low Latency Autonomous Scheduling for Industrial Internet of Things

With the advancements in the Internet of Things (IoT), machine-to-machine communication, big-data, and the associated environment, a new model of the Industrial Internet of Things (IIoT) has emerged. The IIoT brings sensors, intelligent machines and tools, instruments, and analytics together for applications like manufacturing, robotics, and many others. Most of these applications require adaptive and autonomous behavior, Quality of Service (QoS), efficient resource allocation, and reservation. One of these crucial challenges is to build and maintain a data communication schedule. Time Slotted Channel Hopping (TSCH) based network operation promises required QoS for low-power applications and enables high reliability. 6TiSCH layer is being developed by standardizing the protocol stack to achieve industrial performance requirements by using IPv6 over IEEE802.15.4e TSCH MAC. 6TiSCH aims to manage the schedule and configure it with the topology and traffic requirements in the industrial environment. This paper proposes a novel low latency autonomous scheduling scheme for the 6TiSCH networks. It generates a segmented schedule for the network where all source nodes can send application data packets to the root node in a single slotframe. The performance of the proposed technique is compared with existing scheduling techniques. Our scheme outperforms the other techniques. The result shows that the latency is reduced up to 41% in comparison with the other scheduling schemes. The proposed scheme has a lower radio duty cycle as the node’s ON time is reduced, making it more energy efficient and reliable.

Improving the overall equipment effectiveness (OEE) on the chicken bowl printing machine by using the theory of change perspective

This article discusses how to improve the overall equipment effectiveness (OEE) of the chicken bowl printing machine. OEE is important to measure machine performance in the production process. OEE will affect the costs incurred during production activities, such as product defects, how effective the performance of machines is, and how much time is available for production. The failure-tags analysis was used to identify the category factors causing engine failure. The why-why analysis was used to identify the root cause of the problem, and the theory of change was used to explain what should be implemented by the company to improve the OEE value. In 2020, only December had a value above standard by 95%, based on the Seiichi Nakajima OEE has a good value if the value >85%. The OEE values From January to May were fell from 73% to 34% and then kept increasing to 73% in November. The increase of the OEE value can be achieved by implementing a communication schedule, monitoring and evaluating the performance of maintenance staff to achieve a fast maintenance machine process, and applying maintenance rules and guidelines.

Radar-Aided Communication Scheduling Algorithm for 5G and Beyond Networks

Radar and communication coexistence is an upcoming technology with numerous research opportunities in the medium access control (MAC) layer, particularly in scheduling and radio resource management (RRM). More efficient scheduling algorithms are needed with the wide range of applications that the wireless environment is experiencing. We investigate an echo-based scenario in the radar-aided vehicular communication system in which an echo is reflected from a target. Unlike the conventional scheduling mechanisms where signal-to-interference-plus-noise ratio (SINR) is exploited, this paper proposes a new radar-aided communication scheduling algorithm by utilizing parameters such as range and velocity with the classical SINR measurements. The proposed algorithm schedules the available resources by extracting information from the radar echo. The proposed radar-aided communication scheduling scheme provides a more flexible design by adding new parameters, resulting in a more efficient algorithm in a broad variety of scenarios. The proposed scheme is beneficial for B5G communication systems that allow localization and sensing as key features of next-generation wireless networks.

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks (VANETs) subject to finite communication resource. First, a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input. Under such a platoon model, the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader, while attaining improved communication efficiency. Toward this aim, a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed. One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status. Then, a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived. Finally, simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Codesign of communication scheduling and controller of networked control systems

Networked control systems have received increasing attention from many researchers because of their vast potential. The insertion of communication networks in a controlled system brings network-induced defects, which are mainly caused by limited network resources. This paper proposes a codesign of periodic communication scheduling and a controller using sparsity for efficient use of the network while improving initial control performance. The effectiveness of the proposed method is verified through two numerical simulations.