Quality Of Service Requirements Research Articles

Quality of option B + prevention of mother-to-child transmission of HIV services in public hospitals of Gamo zone, Southern Ethiopia, mixed approach

BackgroundPrevention of mother-to-child transmission service is a comprehensive package of services planned to reduce the risk of mother-to-child transmission of HIV. It is very crucial to determine the level of quality of PMTCT services in this study area since other studies in our country omitted several variables in each category of the Donobedian model. Therefore, this study aimed to determine the level of quality of option B + PMTCT of HIV services.MethodsAn institution-based cross-sectional study design with both quantitative and qualitative data collection method was employed. Donabedian’s model was used to assess the level of quality of PMTCT service. A total of 422 pregnant women were used to assess the level of satisfaction of clients. An inventory of resources and direct observation was done to assess the quality of the input and output component of the Donobedian model respectively. In addition to satisfaction items, 12 output-related items were also used to assess quality in the output dimension. Finally, those hospitals that scored above 90% in each component of the Donovedian model were categorized as having good quality. Finally, twelve in-depth interviews were conducted to explore barriers to the quality of option B + PMTCT services. The qualitative data were analyzed using the thematic analysis method and finally, it was presented with the quantitative result through triangulation.ResultsNo hospitals simultaneously met the requirements for good quality in all three dimensions of option B + PMTCT service quality. Only one hospital out of the four hospitals met the requirements for good quality of PMTCT service in the input dimension. Regarding the process and output dimension's quality of PMTCT services, two of the hospitals met the criteria for good quality. One hospital out of the total exhibited poor performance in all three dimensions of service quality for option B + PMTCT services.ConclusionAccording to this study no hospitals simultaneously met the requirements for good quality in all three dimensions of option B + PMTCT service quality. PMTCT unit performance must be continuously monitored, reviewed, and supervised. To obtain the minimum required resources primary hospitals must be supported.

A Cluster-Based V2V Approach for Mixed Data Dissemination in Urban Scenario of IoVs

Data dissemination in Internet of Vehicles (IoVs) is attracting more and more attention. However, few efforts have been made on mixed data dissemination in urban scenario. The rigid Quality of Service (QoS) requirements of Emergency Messages (EMs), the varied QoS tolerant degrees of Service Messages (SMs), and the complex transmission environment in urban scenario pose great challenges. In this paper, a Cluster-based Vehicle-to-Vehicle (V2V) approach for Mixed Data Dissemination (CMDD) is proposed to tackle the aforementioned challenges in urban scenario of IoVs. The proposed approach consists of two main algorithms: the bus-based clustering and the mixed data scheduling. To deal with the complex and harsh transmission environment in urban scenario, we propose to use buses, which have fixed driving routes and schedules as well as sufficient resources, to act as cluster heads. Meanwhile, multiple characteristics are deeply analyzed and artfully utilized in the design of the clustering algorithm, to achieve efficient data dissemination from buses to ordinary vehicles. To meet the rigid and personalized QoS requirements of EMs/SMs under stringent resources, a new priority calculation method is proposed and mixed data are scheduled accordingly. Simulation results show that the proposed approach achieves remarkable performance advantage in terms of data download delay, emergency warning ratio and service response ratio.

RIS-Assisted Scheduling for High-Speed Railway Secure Communications

With the rapid development of high-speed railway systems and railway wireless communication, the application of ultra-wideband millimeter wave band is an inevitable trend. However, the millimeter wave channel has large propagation loss and is easy to be blocked. Moreover, there are many problems such as eavesdropping between the base station (BS) and the train. As an emerging technology, reconfigurable intelligent surface (RIS) can achieve the effect of passive beamforming by controlling the propagation of the incident electromagnetic wave in the desired direction. We propose a RIS-assisted scheduling scheme for scheduling interrupt flows and improving quality of service (QoS). In the propsed scheme, an RIS is deployed between the BS and multiple mobile relays (MRs). By jointly optimizing the beamforming vector and the discrete phase shift of the RIS, the constructive interference between direct link signals and reflected link signals can be achieved, and the channel capacity of eavesdroppers is guaranteed to be within a controllable range. Finally, the purpose of maximizing the number of successfully scheduled flows and satisfying their QoS requirements can be practically realized. Extensive simulations demonstrate that the proposed scheme has superior performance regarding the number of completed flows and the system secrecy capacity over four baseline schemes in literature.

Deep reinforcement learning-based collaborative routing algorithm for clustered MANETs

Flexible adaptation to differentiated quality of service (QoS) is quite important for future 6G network with a variety of services. Mobile ad hoc networks (MANETs) are able to provide flexible communication services to users through self-configuration and rapid deployment. However, the dynamic wireless environment, the limited resources, and complex QoS requirements have presented great challenges for network routing problems. Motivated by the development of artificial intelligence, a deep reinforcement learning-based collaborative routing (DRLCR) algorithm is proposed. Both routing policy and subchannel allocation are considered jointly, aiming at minimizing the end-to-end (E2E) delay and improving the network capacity. After sufficient training by the cluster head node, the Q-network can be synchronized to each member node to select the next hop based on local observation. Moreover, we improve the performance of training by considering historical observations, which can improve the adaptability of routing policies to dynamic environments. Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion. In addition, the effectiveness of the routing policy in a dynamic environment is verified.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

An Energy-Effective and QoS-Guaranteed Transmission Scheme in UAV-Assisted Heterogeneous Network

In this article, we consider a single unmanned aerial vehicle (UAV)-assisted heterogeneous network in a disaster area, which includes a UAV, ground cellular users, and ground sensor users. The cellular data and sensing data are transmitted to UAVs by cellular users and sensor users, due to the outage of the ground wireless network caused by the disaster. In this scenario, we aim to minimize the energy consumption of all the users, to extend their communication time and facilitate rescue. At the same time, cellular users and sensor users have different rate requirements, hence the quality of service (QoS) of the users should be guaranteed. To solve these challenges, we propose an energy-effective relay selection and resource-allocation algorithm. First, to solve the problem of insufficient coverage of the single UAV network, we propose to perform multi-hop transmission for the users outside the UAV’s coverage by selecting suitable relays in an energy-effective manner. Second, for the cellular users and sensor users inside the coverage of the UAV but with different QoS requirements, we design a non-orthogonal multiple access (NOMA)-based transmission scheme to improve spectrum efficiency. Deep reinforcement learning is exploited to dynamically adjust the power level and allocated sub-bands for inside users to reduce energy consumption and improve QoS satisfaction. The simulation results show that the proposed NOMA transmission scheme can achieve 9–17% and 15–32% performance gain on the reduction of transmit power and the improvement of QoS satisfaction, respectively, compared with state-of-the-art NOMA transmission schemes and orthogonal multiple access scheme.

Swarm and Location-Based QoS Routing Algorithm in MEO/LEO Double-Layered Satellite Networks

In this paper, we mainly research on the QoS (Quality of Service) routing algorithms for MEO/LEO (medium Earth orbit/low Earth orbit) double-layered satellite networks. In this type of networks, the rapidly changing network topology due to relative motion of satellites is one of the main challenges when designing an efficient routing algorithm. Specifically, the issues of high rerouting overhead and traffic routing with diverse QoS requirements remain to be resolved. This paper proposed a M-BMDP (modified bandwidth constrained minimum delay path) routing algorithm based on swarm and location for MEO/LEO double-layered satellite networks. This algorithm forms a set of LEO groups according to the footprint of MEO satellites and chooses the relative MEO satellites as its group manager. For delay sensitive traffic, the algorithm can improve the QoS as the cost of packet loss based on hop limit. And for users located in reversed crevice zone, the traffic can route through one MEO satellite to reduce the time delay. The simulation results show that the M-BMDP algorithm performs better in rerouting delay, overhead and pack loss rate compared with existing solutions.

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

Uncertainty quantification and consideration in ML-aided traffic-driven service provisioning

The network traffic prediction problem has been extensively studied in the literature through the application of statistical linear models and more recently through the application of machine learning (ML). In fact, ML has proven its capabilities on accurately modeling the non-linear nature of network traffic, outperforming conventional statistical linear models. Without doubt, model accuracy constitutes an important evaluation metric. In a network environment, however, where uncertainty may lead to erroneous service provisioning decisions (e.g., violations of the quality-of-service (QoS) requirements), it does not provide any information of how much the traffic predictions can be trusted. Hence, in this work the focus is on addressing traffic prediction uncertainty by leveraging the capabilities of Monte Carlo (MC) dropout inference. The proposed framework is compared with a margin-based technique, traditionally used to compensate for traffic prediction uncertainty, demonstrating that the MC dropout inference framework results in significant spectrum savings, as opposed to the baseline, myopic, margin-based scheme. Even though a small penalty is observed on the unpredictable traffic, this is successfully handled on-line with a reduced operational overhead compared to the case where prediction uncertainty is completely ignored. Importantly, it is shown that, unlike the margin-based framework, the MC dropout inference framework can be used for the provisioning of services with diverse QoS requirements.

QoS‐based routing scheme in software‐defined networks using fuzzy analytic hierarchy process

SummaryRecently, with the rapid development of the Internet of Things (IoT) and its real‐time applications, the network is required to guarantee the differential quality of service (QoS) requirements of the various data flows (telemedicine, video, and live streaming) of various IoT services. Software‐defined network (SDN) as emerging network technology separates the control logic from data planes of networks and is a promising technique to guarantee the QoS requirements of different services. In SDN, the controller has global visibility of the whole network devices such as switches and routers. Hence, controllers can dynamically optimize QoS requirements and resources of the network. On the other hand, providing a QoS‐aware routing scheme is an important challenge. Hence, in this paper by capitalizing on the fuzzy analytic hierarchy process (AHP), a new QoS‐aware routing method is proposed in SDNs. The proposed scheme considers the combination of different QoS criteria such as bandwidth, route length, reliability, and bit error rate for selecting an appropriate route between sender and receiver nodes. The simulation results indicated that the proposed method improves throughput and end‐to‐end delay. Comparison with other routing algorithms shows that the proposed method performs better load balancing under different network topologies.

Power Optimization of a Three-Node Two-Way Relay-Assisted Power-Line Communication System

A smart grid system requires two-way communication links. Because power-line infrastructure is widely available, power-line communication (PLC) is preferred as the communication technology in smart grid systems. A power-line, on the other hand, was not designed to carry data and thus suffers from disturbances that attenuate the transmitted signal. Therefore, it is beneficial to provide spatial diversity to improve the system performance. Optimal power allocation has always been an issue to be solved in relay-aided communication systems. Using a two-way multicarrier relay configuration, this paper investigates power optimization of a relay-assisted PLC system with the consideration of the quality of service (QoS) constraints. Further, we investigate the combination of half-duplex and full-duplex nodes. Since the QoS constrained power allocation problem is highly non-convex, an alternating optimization (AO) algorithm is proposed to decompose the optimization problem into sub-problems. The AO algorithm is an iterative algorithm where it uses the newly obtained parameters (e.g., power) to find a new set of other parameters. Simulation results show that the proposed system successfully reduces the total power required by the system compared to the conventional bidirectional direct transmission (BDT) system and the relay-assisted two-way information exchange (R2WX) system under the same QoS requirement.

An Optimized LTE-Based Technique for Drone Base Station Dynamic 3D Placement and Resource Allocation in Delay-Sensitive M2M Networks

Drones are expected to facilitate extending wireless networks’ access for both human users and smart machine-type-communication devices (MTCDs) with strict and diverse quality of service (QoS) requirements. In this paper, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of the MTCDs deployed over a large geographical area. The resulting technique jointly optimizes the drone's 3D positioning to maximize coverage and allocates the network resources in such a way that gives high priority to the delay-sensitive machine-to-machine (M2M) traffic. This optimization algorithm determines the optimal bound of the solution. Since it cannot be used in real-time operations due to its computational complexity, we also introduce a heuristic technique that offers a near-optimal solution with much reduced complexity. We conduct several simulation evaluations to assess the proposed techniques. These results are compared to those of other drone placement approaches. The comparisons show that the proposed techniques offer significantly better results in the communication coverage while fulfilling the diverse QoS requirements of the deployed M2M network. Moreover, the heuristic-based technique is shown to succeed in finding solutions close to the optimal bound with a considerable reduction of complexity over the exact algorithm.

SO-RAN: Dynamic RAN Slicing via Joint Functional Splitting and MEC Placement

Next-Generation (NG) mobile networks are expected to revolutionize wireless communication systems, facilitating the deployment of critical applications with stringent Quality of Service (QoS) requirements. Multi-access Edge Computing (MEC) and Radio Access Network (RAN) slicing are considered vital mechanisms of upcoming NG systems as they allow creation of end-to-end isolated RAN slices and increase of serving mobile data traffic on edge. The efficient deployment of RAN functions promises flexible and disaggregated architectures for the diversified needs of NG networks. In this work, we propose a novel optimization approach that minimizes RAN/MEC economic costs and maximizes served traffic. This framework creates isolated RAN slices by optimal placement of RAN and MEC functions per slice, which solves the problem of operating cost-efficient edge networks and increases the served traffic with diverse QoS requirements. We first formulate our framework as a convex and linear problem and then decompose it using a distributed method that accelerates the performance and guarantees the optimal global solution. Our results show that our proposed work better uses the spectrum, and there is a trade-off between the achieved throughput and the cost incurred to the network.

Energy Efficiency Optimization for MISO-NOMA SWIPT System with Heterogeneous QoS Requirements

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

Secrecy Rate Maximization for Multicarrier-Based Cognitive Radio Networks With an Energy Harvesting Jammer

Cognitive radio (CR) is becoming an increasingly significant component of Internet-of-Things technologies because it overcomes the challenge of spectrum resource scarcity for wireless communications. For the cooperative CR network (CRN) based on the multicarrier system, this article investigates the physical layer security (PLS) to guarantee information privacy for the secondary network while guaranteeing the quality of service (QoS) of the primary network. First, we propose one communication scheme for this type of CRN. The secondary transmitter (ST) and secondary jammer (SJ) first harvest RF energy from the primary signal transmitted by the primary transmitter (PT). Then, ST transmits confidential information with the spectrum vacated by the primary network to the secondary receiver (SR), and SJ simultaneously generates the jamming signal to interfere with the eavesdropper (ED). Second, for the scenarios with the instantaneous channel state information (CSI) and the statistical CSI of the ED, respectively, we aim to maximize the secrecy rate of the secondary network while guaranteeing the QoS requirement of the primary network by jointly optimizing the time allocation between the two phases and the transmit power allocation for ST and SJ over subcarriers. For each scenario, the secrecy rate maximization is formulated as the nonconvex optimization problem. Finally, we design efficient algorithms for both two nonconvex problems. The optimal time allocation is obtained by the 1-D search method at the outer layer, and the optimal transmit power allocation is obtained by the difference of concave (DC) programming at the inner layer, where the Lagrange duality method is used to solve the convex approximation problem. Simulation results verify that the proposed scheme essentially improves the secrecy rate of the secondary network compared to benchmark schemes.

QoS-aware congestion control with online learning

In emerging applications such as industrial control and autonomous driving, end-to-end deterministic quality of service (QoS) transmission guarantee has become an urgent problem to be solved. Internet congestion control algorithms are essential to the performance of applications. However, existing congestion control schemes follow the best-effort principle of data transmission without the perception of application QoS requirements. To enable data delivery within application QoS constraints, we leverage an online learning mechanism to design Crimson, a novel congestion control algorithm in which each sender continuously observes the gap between current performance and pre-defined QoS. Crimson can change rates adaptively that satisfy application QoS requirements as a result. Across many emulation environments and real-world experiments, our proposed scheme can efficiently balance the different trade-offs between throughput, delay and loss rate. Crimson also achieves consistent performance over a wide range of QoS constraints under diverse network scenarios.

A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks.

Video conferencing is one of the advanced technologies for users that allows online communication despite long distances. High quality communication and ongoing support for the principles of video conferencing service that can be achieved through Software-Defined Networking (SDN). SDN is a new architecture for computer networks that separates the control plane from the data plane to improve network resources and reduce operating costs. All routing decisions and control mechanisms are made by a device called a controller. Traffic engineering can be well implemented in SDN because the entire network topology is known to the controller. Considering SDN features, user requests can be dynamically routed according to current network status and Quality of Service (QoS) requirements. In general, the purpose of SDN routing algorithms is to maximize the acceptance rate of user requests by considering QoS requirements. In this literature, most routing studies to provide satisfactory video conferencing services have focused solely on bandwidth. Nevertheless, some studies have considered both delay and bandwidth constraints. In this paper, a Fuzzy Delay-Bandwidth Guaranteed Routing (FDBGR) algorithm is proposed that considers both delay and bandwidth constraints in routing. The proposed fuzzy system is based on rules that can postpone requests with high resource demands. Also, the purpose of the FDBGR is to distribute the network workload evenly for all requests, where this is done by maintaining the capacity to accept future requests. The combination of conventional routing algorithms and SDN provides remarkable improvements in mobility, scalability and the overall performance of the networks. Simulations are performed on different scenarios to evaluate the performance of the FDBGR compared to state-of-the-art methods. Besides, FDBGR has been compared with a number of most related previous works such as H-MCOP, MH-MCOP, QoMRA, QROUTE and REDO based on criteria such as number of accepted requests, average path length, energy consumption, load balancing, and average delay. The simulation results clearly prove the superiority of the proposed algorithm with an average delay of 48ms in different topologies for video conferencing applications.

Prioritization-Driven Congestion Control in Networks for the Internet of Medical Things: A Cross-Layer Proposal

Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices' transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet's network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.

Intelligent Random Access for Massive-Machine Type Communications in Sliced Mobile Networks

With the emerging Internet of Things paradigm, massive Machine-Type Communication (mMTC) has been identified as one of the prominent services that enables a broad range of applications with various Quality of Service (QoS) requirements for 5G-and-beyond networks. However, it is very difficult to employ a monolithic physical network to support various mMTC applications with differentiated QoS requirements. Moreover, in ultra-dense mobile networks, the scarcity of the preamble and Physical Downlink Control CHannel (PDCCH) resources may easily lead to resource collisions when a large number of devices access the network simultaneously. To tackle these issues, in this paper, we propose a network slicing-enabled intelligent random access framework for mMTC. First, by tailoring a gigantic physical network into multiple lightweight network slices, fine-grained QoS provisioning can be accomplished, and the collision domain of Random Access (RA) can be effectively reduced. In addition, we propose a novel concept of sliced preambles (sPreambles), based on which the transitional RA procedure is optimized, and the issue of preamble shortage is effectively relieved. Furthermore, with the aim of alleviating PDCCH resource shortage and improving transmission efficiency, we propose a learning-based resource-sharing scheme that can intelligently multiplex the PDCCH resources in the naturally dynamic environment. Simulation results show that the proposed framework can efficiently allocate resources to individual mMTC devices while guaranteeing their QoS requirements in random access processes.

Runtime Management of Service Level Agreements through Proactive Resource Provisioning for a Cloud Environment

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.