Requirements Of Real-time Services Research Articles

Edge Server Deployment for Health Monitoring With Reinforcement Learning in Internet of Medical Things

The Internet of Medical Things (IoMT) has recently gained a lot of interest in the health care industry. IoMT enables real-time and omnipresent monitoring of a patient's health status, resulting in massive amounts of medical data being generated. The centralized massive data processing places enormous strain on the typical cloud computing, rendering it incapable of supporting a variety of real-time health care applications. Therefore, edge computing that moves application programs and data processing from central infrastructure to the edge nodes has attracted wide attention. However, adopting existing edge server (ES) deployment strategies for IoMT is not suitable due to the decentralized and high real-time service requirements of IoMT systems. In particular, traditional ES deployment strategies in IoMT system confront major load imbalance across ESs, latency issues, and energy consumption concerns. To address these challenges, a deployment strategy of ESs based on the state-action-reward-state-action (SARSA) learning, named ESL, is designed. Specifically, ESs are quantified by evaluating the silhouette coefficient (SC) and the sum of squared errors. Then, through fuzzy C-means (FCM) algorithm, the preliminary division of health monitoring units (HMUs) and the initial locations of ESs are obtained. Finally, SARSA learning is adopted to determine the deployment of ESs. Furthermore, extensive experiments and analyses confirm that ESL achieves the core objective of optimizing load balancing among ESs while also optimizing request-response latency and request processing energy consumption.

FOLD: Fog-dew infrastructure-aided optimal workload distribution for cloud robotic operations

In our fast-paced, technology-driven world, multi-robot systems have emerged as crucial solutions to tackle contemporary challenges, from industrial automation to disaster response, especially where the scope of human interventions is significantly constrained. In such scenarios, a notable number of event-driven operations trigger robots to perform a substantial amount of tasks. Nonetheless, completion of the tasks proves challenging due to the limited computational capabilities inherent to many robotic systems. Although cloud computing solutions can be integrated to address these limitations by distributing the workload to clouds, ensuring optimized performance remains a formidable challenge due to the communication bottlenecks encountered by the robots. Moreover, the presence of robots’ energy constraints and stringent real-time service requirements further exacerbate this workload distribution problem. In response to the aforementioned challenges, this paper introduces a fog-dew-enabled robotic system designed to mitigate latency and energy consumption while orchestrating crucial workload distribution decisions among robots. The execution of decision-making tasks is conceptualized as a multi-objective optimization problem. Due to the NP-hardness of the multi-objective optimization, we propose an innovative solution based on a meta-heuristic Binary Particle Swarm Optimization algorithm. Through rigorous experimentation conducted via simulation within the iFogSim2 simulator, our results demonstrate that the proposed algorithm surpasses the current state-of-the-art in simultaneously optimizing latency and energy consumption.

GAN-powered heterogeneous multi-agent reinforcement learning for UAV-assisted task offloading

The flexible and highly mobile unmanned aerial vehicle (UAV) with computing capabilities can improve the quality of experience (QoE) of ground users (GUs) according to real-time service requirements by performing flight maneuvers. In this study, we investigate a task offloading scheme and trajectory optimization in a multi-UAV-assisted system, where UAVs offload a portion of multiple GUs’ computational tasks. The optimization problem is formulated to jointly minimize the energy consumption of UAVs and the task latency of GUs by optimizing trajectory, task allocation and offloading proportion. This paper proposes a heterogeneous multi-agent reinforcement learning (MARL)-based approach to solve the issue in high dimensions and limited states, where UAV and GU are treated separately as two different types of agents. Due to the high cost and low sample efficiency of online training of RL algorithms, a generative adversarial network (GAN)-powered auxiliary training mechanism is proposed, which reduces the overhead of interacting with the real world and makes the agent’s policy appropriate for real-world execution environment via offline training with generated environment states. Numerical evaluation results demonstrate that the proposed algorithm outperforms other benchmark algorithms in terms of energy consumption and task latency.

A parallel rotator for FFT/IFFT applied in multi-carrier wireless communication systems

In this paper, the authors propose a new parallel rotator for FFT and IFFT applied in multi-carrier radio communication systems. The proposed algorithm serves some primary purposes. First, it reduces the computational complexity, thus simplifying the hardware implementation. Second, it speeds up the convergence of the algorithm, resulting in reduced processing latency to meet the requirements of real-time services. Third, it improves rotational accuracy. In addition, the algorithm also significantly lessens the consumption of hardware and power resources. The phase rotator's hardware architecture is then proposed and implemented on the FPGA using the Vivado System Generator for DSP Tool (SYSGEN). The FPGA experimental results indicate that the proposed algorithm's computational complexity, processing delay, and hardware resource usage are significantly reduced when compared to some recent algorithms.

Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems

Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1%, and reduces the collision rate with SUs and the collision rate with PUs by 46.4% and 6.8%, respectively.

A Novel Traffic Shaping Algorithm for SDN-Sliced Networks using a New WFQ Technique

Managing traditional networks comes with number of challenges due to their limitations, in particular, because there is no central control. Software-Defined Networking (SDN) is a relatively new idea in networking, which enables networks to be centrally controlled or programmed using software applications. Novel traffic shaping (TS) algorithms are proposed for the implementation of a Quality of Service (QoS) bandwidth management technique to optimise performance and solve network congestion problems. Specifically, two algorithms, namely “Packet tagging, Queueing and Forwarding to Queues” and “Allocating Bandwidth”, are proposed for implementing a Weighted Fair Queuing (WFQ) technique, as a new methodology in an SDN-sliced testbed to reduce congestion and facilitate a smooth traffic flow. This methodology aimed at improving QoS that does two things simultaneously, first, making traffic conform to an individual rate using WFQ to make the appropriate queue for each packet. Second, the methodology is combined with buffer management, which decides whether to put the packet into the queue according to the proposed algorithm defined for this purpose. In this way, the latency and congestion remain in check, thus meeting the requirements of real-time services. The Differentiated Service (DiffServ) protocol is used to define classes in order to make network traffic patterns more sensitive to the video, audio and data traffic classes, by specifying precedence for each traffic type. SDN networks are controlled by floodlight controller(s) and FlowVisor, the slicing controller, which characterise the behaviour of such networks. Then, the network topology is modelled and simulated via the Mininet Testbed emulator platform. To achieve the highest level of accuracy, The SPSS statistical package Analysis of Variance (ANOVA) is used to analyse particular traffic measures, namely throughput, delay and jitter as separate performance indices, all of which contribute to QoS. The results show that the TS algorithms do, indeed, permit more advanced allocation of bandwidth, and that they reduce critical delays compared to the standard FIFO queueing in SDN.

Improved Delay Priority Resource Scheduling with Low Packet Loss Rate for MBMS in LTE Systems

An improved delay priority resource scheduling algorithm with low packet loss rate for multimedia broadcast multicast service (MBMS) in long term evolution (LTE) systems is proposed in this paper. Real-time services in LTE systems require lower delay and packet loss rate. However, it is difficult to meet the QoS requirements of real-time services using the current MBMS resource scheduling algorithm. The proposed algorithm in this paper jointly considers user delay information and real-time channel conditions. By introducing the user delay information, the lower delay and fairness of users are guaranteed. Meanwhile, by considering the channel conditions of users, the packet loss rate can be effectively reduced, improving the system throughput. Simulation results show that under the premise of ensuring the delay requirements of real-time services, the proposed algorithm achieves a lower packet loss rate compared to other existing algorithms. Furthermore, it can achieve a good balance between system throughput and user fairness.

Improve Data Storage Efficiency with Service-oriented Architecture underlying the Internet of Things

ABSTRACT Sensor hardware technology and network bandwidth have developed rapidly. Through the combination of the Internet and different types of sensors, the Internet of Things (IoT) can facilitate the development of applications, and it has become a hot issue in many countries. The IoT can facilitate the development of applications such as smart cities, smart healthcare and smart homes. In the IoT, people can exchange information to anything, such as people and objects, objects and machines, and even between machines. However, in order to develop related application services of the IoT, hundreds of millions of objects will exchange data on the Internet, and the amount of data in the IoT is very large. In order to provide the different real-time service requirements, the Service-oriented Architecture underlying IoT (SoAIoT) and the data storage operating mechanism of SoAIoT are constructed in the study. The operating mechanism of SoAIoT will reduce the load of network, improve the quality of data processing scheduling, and provide users with different services to meet service needs. Finally, in order to evaluate whether the proposed operating mechanism of SoAIoT can achieve the goal of our research, experimental examples are given through the application of the earth and rock flow monitoring.

Elastic QoS Scheduling with Step-by-Step Propagation in IEEE 802.11e Networks with Multimedia Traffic

The spreading diffusion of wireless devices and the crowded coexistence of multimedia applications greedy of bandwidth and with strict requirements stress the service provisioning offered by wireless technologies. WiFi is a reference for wireless connectivity and it requires a continuous evolution of its mechanism in order to follow increasingly demanding service needs. In particular, despite the evolution of physical layer, some critical contexts, such as industrial networks, telemedicine, telerehabilitation, and virtual training, require further refined improvements in order to ensure the respect of strict real-time service requirements. In this paper an in-depth analysis of Dynamic TXOP HCCA (DTH) MAC enhanced centralized scheduler is illustrated and it is further refined introducing a new improvement, DTH with threshold. DTH and DTH with threshold can be integrated with preexisting centralized schedulers in order to improve their performances, without any overprovisioning that can negatively impact on the admission control feasibility test. Indeed, without modifying the centralized scheduler policy, they combine together the concepts of reclaiming transmission time and statistical estimation of the traffic profile in order to provide, at each polling, an instantaneous transmission time tailored to the variable traffic requirements, increasing, when necessary, the service data rate. These mechanisms can coexist with advanced physical layer-based solutions, providing the required service differentiation. Experimental results and theoretical analysis, based on elastic scheduler theory, show that they are effective especially in the case of Variable Bit Rate traffic streams in terms of transmission queues length, packets loss, delay, and throughput.

QoS‐guaranteed energy and throughput joint optimisation in multi‐user multiple‐input multiple‐output systems

This study proposes a finely quality of service (QoS)-guaranteed algorithm to jointly optimise the throughput and energy efficiency in MU-multiple-input multiple-output systems. The latency requirements of real-time services are guaranteed by user scheduling in the first stage. The energy efficiency and system throughput are jointly optimised while the required transmission rate is finely guaranteed in the second stage. Simulation results show that the algorithm can achieve a good balance between the energy efficiency and throughput, and is superior to the existing schemes in terms of QoS guarantee.

A new Channel- and QoS-Aware scheduling Scheme for Real-time Services in LTE Network

This paper proposes a new scheduling scheme which is based on Channel-and QoS-Aware for real-time traffics in LTE down-link direction. The real-time services (VoIP, Video, etc.) are very sensitive to network impairments such as delay, packet loss, jit-ter, etc. The proposed scheduling scheme considers Maximum Queue Size (MQS) as an essential factor for the priority metric and the combination of well-known M-LWDF-based algorithms such as M-LWDF, VT-MLWDF and Queue-HOL-MLWDF. The simulation results show that the proposed scheme has the performance which not only satisfies QoS requirements of real-time services but also outperforms the M-LWDF, VT-MLWDF and Queue-HOL-MLWDF schedulers in terms of Packet Loss Rate (PLR), cell throughput, Fairness Index (FI) and Spectral Efficiency (SE), especially for Video flow. The proposed sched-uler also significantly improves the delay in comparison with the VT-MLWDF and Queue-HOL-MLWDF schedulers ones for both VoIP and Video flows. The performance evaluation is compared in terms of Delay, PLR, Throughput, FI and SE for M-LWDF, VT-MLWDF, Queue-HOL-MLWDF and the proposed schedulers.

New multicast scheme based on dynamic mobility prediction in mobile IPv6 environment

A new mobile multicast scheme called mobility prediction based mobile multicast(MPBMM) was proposed. In MPBMM, when a mobile node (MN) roams among subnets during a multicast session, MN predicts the next subnet, to which MN will attach, by the information of its position and mobility speed, consequently speeds up the handoff procedure. Simulation results show that the proposed scheme can minimize the loss of multicast packets, reduce the delay of subnet handoff, decrease the frequency of multicast tree reconfiguration, and optimize the delivery path of multicast packets. When MN moves among subnets at different speeds (from 5 to 25 m/s), the maximum loss ratio of multicast packets is less than0.2%, the maximum inter-arrival time of multicast packets is 117 ms, so the proposed scheme can meet the QoS requirements of real-time services. In addition, MPBMM can support the mobility of multicast source.

Power and Spreading Gain Allocation in CDMA Networks for Prioritized Data Services under Power Constraints

A power and spreading gain allocation strategy is considered for effectively providing data services for mobile users with different levels of priorities in a DS-CDMA system supporting real-time and non-real-time services. Specifically, the uplink in the DS-CDMA system is considered subject to a constraint on total power received at the base station caused by non-real-time data services. The constraint is imposed to meet QoS requirements of real-time services. The priority level of a data user is specified by the weighting factor assigned to the data throughput of the user. Our strategy implements a relative prioritization that affords a trade-off between spectral efficiency and strict prioritization.

Analytical performance evaluation of downlink beamforming with power control by transmit power limited channel inversion

We investigate the transmission performance of a downlink BPSK transmission system which uses beamforming combined with cross-polarised antenna arrays and power control in a Rayleigh fading environment to fulfil QoS requirements of real time services. A power control method termed “power control by transmit power limited channel inversion” is applied in the system. Power limitation in the downlink and the effect on the bit error rate performance is especially interesting for mobile stations at the cell borders of a cellular communication system. For this non-linear transmit model we derive analytical formulas for the average transmit power and the bit error rate performance. The exact validity of the results is verified by Monte-Carlo simulations. We also show the interaction of power control and beamforming, and the dependency of the gain in transmit power from the SNR, an effect that is not visible without transmit power limited power control.

Dynamic weighted cell multiplexing method for real-time traffic in ATM

A new cell multiplexing algorithm is proposed to satisfy the service requirements of real time traffic in ATM networks. The proposed algorithm is capable of assigning higher weights to real time traffic than to non-real time traffic, thereby guaranteeing the quality of service of real time traffic. Simulation results show that the proposed algorithm has better performance than other cell multiplexing algorithms in terms of the mean delay time and the maximum queue length.

Link-sharing and resource management models for packet networks

Discusses the use of link-sharing mechanisms in packet networks and presents algorithms for hierarchical link-sharing. Hierarchical link-sharing allows multiple agencies, protocol families, or traffic types to share the bandwidth on a link in a controlled fashion. Link-sharing and real-time services both require resource management mechanisms at the gateway. Rather than requiring a gateway to implement separate mechanisms for link-sharing and real-time services, the approach in the paper is to view link-sharing and real-time service requirements as simultaneous, and in some respect complementary, constraints at a gateway that can be implemented with a unified set of mechanisms. While it is not possible to completely predict the requirements that might evolve in the Internet over the next decade, the authors argue that controlled link-sharing is an essential component that can provide gateways with the flexibility to accommodate emerging applications and network protocols. >