Delay Quality Of Service Research Articles

IoT-based MANET performance improvement against jamming attackers in different mobile applications

Internet of Things (IoT) is a cutting-edge technology and effective solution that enables real-time communication between physical objects. In order to manage the mobility of nodes in the IoT, mobile ad hoc networks (MANETs) are crucial. It enables IoT-based systems to construct and connect networks autonomously. IoT based MANET is more vulnerable to Jamming attacks (Jammers) due to the decentralized nature of the ad hoc network. Therefore, secure communication is a crucial requirement for nodes. In this paper, jammers interfere with the IoT MANET communication resulting in order to degrade network performance in terms of delay, throughput and PDR (Packet Delivery Rate) which are the important QoS (Quality of Service) parameters to measure the network performance. The IoT ad hoc system's networking is based on MANET routing protocols so that (GRP, AODV and OLSR) routing protocols had been examined in this paper to improve the QoS (Packet Delivery rate, throughput and delay) of the IoT-MANET system using Riverbed Modeler (17.5) simulation program for different mobile applications. The outcomes showed that the selection of a suitable routing protocol would improve the degradation in delay, throughput and PDR of the ad hoc network.

Traffic prediction in SDN for explainable QoS using deep learning approach

The radical increase of multimedia applications such as voice over Internet protocol (VOIP), image processing, and video-based applications require better quality of service (QoS). Therefore, traffic Predicting and explaining the prediction models is essential. However, elephant flows from those applications still needs to be improved to satisfy Internet users. Elephant flows lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, deep learning models become a good alternative for real-time traffic management. This research aims to design a traffic predicting model that can identify elephant flows to prevent network congestion in advance. Thus, we are motivated to develop elephant flow prediction models and explain those models explicitly for network administrators’ use in the SDN network. H2O, Deep Autoencoder, and autoML predicting algorithms, including XGBoost, GBM and GDF, were employed to develop the proposed model. The performance of Elephant flow prediction models scored 99.97%, 99.99%, and 100% in validation accuracy of under construction error of 0.0003952, 0.001697, and 0.00000408 using XGBoost, GBM, and GDF algorithms respectively. The models were also explicitly explained using Explainable Artificial Intelligence. Accordingly, packet size and byte size attributes need much attention to detect elephant flows.

SyRoC: Symbiotic robotics for QoS-aware heterogeneous applications in IoT-edge-cloud computing paradigm

Smart Robots, as an advanced domain of widespread concern, can be applied in diverse fields to perform compute-intensive Internet of Things (IoT) applications. However, their onboard resource constraints necessitate using the computation offloading technique to extend their capabilities. Additionally, the distributed locations of robots and the diversified quality of service (QoS) requirements of different applications pose challenges to the existing offloading works. To address these concerns, we put forward a collaborative offloading scheme for QoS-aware utility optimization for multi-robot system, and the optimization problem is formulated based on multi-attribute utility theory. Our methodology consists of three critical components: (1) we construct a symbiotic collaboration model that leverages the advantages of multiple computation modes across IoT-edge-cloud infrastructure and tap the potential of multi-hop relay transmission technology to enable efficient collaborative offloading; (2) we develop a QoS-aware preference assessment framework based on analytic hierarchy process to effectively learn the preference weights of various applications executed and prioritizations for key QoS parameters affecting strategy-making of robots; and (3) we apply the game-theoretic approach to devise a symbiotic robotics computation offloading algorithm named SyRoC, which operates in a distributed manner to solve the optimization problem and determine the most effective strategies. To validate our scheme, we conduct a series of experiments on the software simulator, which demonstrate that our proposed scheme not only realizes stable performance on guaranteeing convergence and system utility when robots move dynamically, but also outperforms five typical algorithms in key metrics such as energy consumption (13.43%↓), delay (14.22%↓), and average QoS utility of robots corresponding to the four types of applications executed (12.80%↑, 7.30% ↑, 7.90% ↑,and12.71%↑) in scenarios involving the expansion of robot scales.

User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning.

In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.

Proportionally fair load balancing with statistical quality of service provisioning for aerial base stations

AbstractAerial base stations (ABSs) seem promising to enhance the coverage and capacity of fifth‐generation and upcoming networks. With the flexible mobility of ABSs, they can be positioned in air to maximize the number of users served with a guaranteed quality of service (QoS). However, ABSs may be overloaded or underutilized given inefficient placement, and user association has not been well addressed. Hence, we propose a three‐dimensional ABS placement scheme with a delay‐QoS‐driven user association to balance loading among ABSs. First, a load balancing utility function is designed based on proportional fairness. Then, an optimization problem for joint ABS placement and user association is formulated to maximize the utility function subject to statistical delay QoS requirements and ABS collision avoidance constraints. To solve this problem, we introduce an efficient modified gray wolf optimizer for ABS placement with a greedy user association strategy. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of load balancing and delay QoS provisioning.

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

Uplink Non-Orthogonal Multiple Access With Statistical Delay Requirement: Effective Capacity, Power Allocation, and α Fairness

The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

Manajemen Bandwidth Melalui IP Filter Mikroktik HAP Lite Di FT Uniska

Bandwidth management for clients connected to the internet network is needed in the upload and download process. In this study used additional devices in the form of a mikrotik router and research sites at the Faculty of Engineering UNISKA, Kediri. The study discusses Quality of Service (QoS) with 4 indicators, namely throughput, packet loss, delay, and jitter. The results showed that the speedtest application bandwidth did not exceed the specified limit. And in the wireshark application, it is shown that the calculation of QoS throughput for each user is not more than 100 Kbps, then the standard parameters according to TIPHON are categorized as "BAD" with an index of "0". In the QoS packet loss, each user gets a value of 0, then the standard parameters according to TIPHON are categorized as "PERFECT" with the index "4". In the QoS delay, each user gets a value less than 100 s, then the standard parameters according to TIPHON are categorized as "PERFECT" with the index "4". And on the QoS jitter of each user, the value is below the number 1, then the standard parameters according to TIPHON are categorized as "GOOD" with an index of "3".

Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting.

As a key enabler of the access traffic steering, switching and splitting (ATSSS) feature, multipath transport can leverage the simultaneous use of several network paths and support seamless failover to improve both communication throughput and resilience. Therefore, a traffic scheduling strategy is necessary to determine the best network path combination that may improve the performance of multipath transport. To address this need, we developed a multiservice-type based transmission (MSTT) traffic scheduling optimization strategy, which involves three steps. First, the user equipment (UE) selects the number of data stream transmission paths, considering the service utility function, and either transmits all data streams via the 3GPP network or sends two streams, one via the 3GPP network and the other via the non-3GPP network. Second, the proposed method is used to select the transmission path for each data stream based on load balancing. Finally, an algorithm for optimizing traffic scheduling is formulated by applying the convex optimization problem to maximize the effective network capacity under a Delay Quality of Service (DQoS) constraint. The proposed traffic scheduling strategy is validated through simulation experiments. The results indicate that user satisfaction and effective capacity realized are always better than when using the always-best-connected and fixed-ratio power-allocation algorithms.

Resource matching algorithm under delay QoS constraint for uplink visible light communication based on supermartingale theory

We propose a supermartingale-based resource matching algorithm to guarantee delay quality of service (QoS) for the visible light communication uplink system. A network queuing model with multiple bursty arrivals is established, and multipacket reception technology is considered to alleviate terminal collisions. We introduce the supermartingale theory to evaluate the delay QoS performance, and a tighter delay-violation probability bound is derived. Relying on the supermartingale analysis, we design a search algorithm to achieve resource matching under a delay QoS constraint. The simulation results prove that the proposed algorithm guarantees delay QoS and saves service resources.

Dynamic Power Allocation for Uplink NOMA With Statistical Delay QoS Guarantee

Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes.

Manajemen Bandwidth Melalui IP Filter Mikroktik Hap Lite di FT UNISKA

Bandwidth management for clients connected to the internet network is needed in the upload and download process. In this study used additional devices in the form of a mikrotik router and research sites at the Faculty of Engineering UNISKA, Kediri. The study discusses Quality of Service (QoS) with 4 indicators, namely throughput, packet loss, delay, and jitter. The results showed that the speedtest application bandwidth did not exceed the specified limit. And in the wireshark application, it is shown that the calculation of QoS throughput for each user is not more than 100 Kbps, then the standard parameters according to TIPHON are categorized as "BAD" with an index of "0". In the QoS packet loss, each user gets a value of 0, then the standard parameters according to TIPHON are categorized as "PERFECT" with the index "4". In the QoS delay, each user gets a value less than 100 s, then the standard parameters according to TIPHON are categorized as "PERFECT" with the index "4". And on the QoS jitter of each user, the value is below the number 1, then the standard parameters according to TIPHON are categorized as "GOOD" with an index of "3".

Performansi Software Defined Network Controller Pada Streaming Video Menggunakan Real-time Transport Protocol

Computer network conditions that are dynamic and also complex make network operators often make mistakes in the configuration of a network device. However, these mistakes can be resolved by the emergence of a centralized programming approach which is now known as Software Defined Network (SDN). In addition, with the development of information technology, several services in computer networks also grabbed a lot of attention from the world, such as video streaming services. A video streaming service that is so massive is capable of having an impact on data traffic on computer networks. In SDN networks, controllers are a core component of this architecture. Currently, many different types of SDN controllers can be used, however, choosing a controller in an SDN network must be an important concern. This is related to the performance of the SDN controller which is a requirement for SDN network development. This research will analyze the performance of the video streaming service using the Real-time Transport Protocol (RTP) streaming protocol in testing two SDN controllers, namely Pox and Opendaylight. Based on the tests that have been carried out, it produces Quality of Service (QoS) values in the form of throughput, delay, and jitter. The Pox controller excels in the value of QoS delay and throughput. As for the jitter value, both Pox and Opendaylight controllers have good indexes.

Multi-Agent Reinforcement Learning Based Distributed Transmission in Collaborative Cloud-Edge Systems

Millimeter-wave (mmWave) cloud-edge collaboration has emerged as an effective solution for low-latency transmission by harnessing the potentials of cloud and edge nodes (eNodes) as well as abundant bandwidth. In a mmWave dense network, centralized processing and coordinated multi-points processing require large amounts of signaling exchange overhead, which in turn increase the latency. As such, distributed mechanism is demanded. In this paper, we investigate joint user scheduling and beam selection strategies for eNodes and formulate the problem as a constrained Markov decision process. The objective and constraints are long-term average network delay cost minimization and instantaneous quality of service (QoS) guarantee for each user equipment. To update the distributed strategies, we apply the multi-agent reinforcement learning technique and propose a hybrid learning framework by extending the actor-critic model, where centralized training and decentralized execution are implemented. We utilize the Lagrange technique to design the reward functions. Additionally, a novel actor network architecture and an exploration scheme are proposed. Simulation results validate the effectiveness of the proposed intelligent distributed algorithm with a high degree of scalability, and show superior performance in terms of long-term average network delay cost and QoS satisfaction rates compared with other methods.

Energy Efficiency Maximization for UAV‐Assisted Emergency Communication Networks

Using unmanned aerial vehicles (UAVs) in emergency communications is a promising technology because of their flexible deployment, low cost, and high mobility. However, due to the limited energy of the onboard battery, the service duration of the UAV is greatly limited. In this paper, we study an emerging energy‐efficient UAV emergency network, where a UAV works as an aerial base station to serve a group of users with different statistical quality‐of‐service (QoS) constraints in the downlink. In particular, the energy efficiency of the UAV is defined as the sum effective capacity of the downlink users divided by the energy consumption of the UAV, which includes the energy consumed by communication and the energy consumed by hovering. Then, we formulate an optimization problem to maximize the energy efficiency of the UAV by jointly optimizing the UAV’s altitude, downlink transmit power, and bandwidth allocation while meeting a statistical delay QoS requirement for each user. The formulated optimization problem is a nonlinear nonconvex optimization problem of fractional programming, which is difficult to solve. In order to deal with the nonconvex optimization problem, the following two steps are used. First, we transform the fractional objective function into a tractable subtractive function. Second, we decompose the original optimization problem into three subproblems, and then, we propose an efficient iterative algorithm to obtain the energy efficiency maximization value by using the Dinkelbach method, the block coordinate descent, and the successive convex optimization technique. Extensive simulation results show that our proposed algorithm has significant energy savings compared with a benchmark scheme.

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality-of-Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this article, we propose a novel framework based on software-defined networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an integer linear programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterward, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the nonoccurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.

Multiple-Phase Energy Detection and Effective Capacity Based Resource Allocation Against Primary User Emulation Attacks in Cognitive Radio Networks

Cognitive radio (CR) is regarded as an effective approach to avoid the inefficient use of spectrum. However, CRNs have more special security problems compared with the traditional wireless communication systems due to its open and dynamic characteristics. Primary user emulation attack (PUEA) is a common method which can hinder secondary users (SUs) from accessing the spectrum by transmitting signals who has the similar characteristics of the primary users’ (PUs) signals, and then the SUs’ quality of service (QoS) cannot be guaranteed. To handle this issue, we first design a multiple-phase energy detection scheme based on the cooperation of multiple SUs to detect the PUEA more precisely. Second, a joint SUs scheduling and power allocation scheme is proposed to maximize the weighted effective capacity of multiple SUs with a constraint of the average interference to the PU. The simulation results show that the proposed method can effectively improve the effective capacity of the secondary users compared with the traditional overlay scheme which cannot be aware of the existence of PUEA. Also the good delay QoS guarantee for the secondary users is provided.

Asymptotic Performance Analysis of NOMA Uplink Networks Under Statistical QoS Delay Constraints

In this article, we study the performance of an uplink non-orthogonal multiple access (NOMA) network under statistical quality of service (QoS) delay constraints, captured through each user's effective capacity (EC). We first propose novel closed-form expressions for the EC in a two-user NOMA network and show that in the high signal-to-noise ratio (SNR) region, the “strong” NOMA user, referred to as U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , has a limited EC, assuming the same delay constraint as the “weak” user, referred to as U1. We demonstrate that for the weak user U1, OMA and NOMA have comparable performance at low transmit SNRs, while NOMA outperforms OMA in terms of EC at high SNRs. On the other hand, for the strong user U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , NOMA achieves higher EC than OMA at small SNRs, while OMA becomes more beneficial at high SNRs. Furthermore, we show that at high transmit SNRs, irrespective of whether the application is delay tolerant, or not, the performance gains of NOMA over OMA for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and OMA over NOMA for U <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> remain unchanged. When the delay QoS of one user is fixed, the performance gap between NOMA and OMA in terms of total EC increases with decreasing statistical delay QoS constraints for the other user. Next, by introducing pairing, we show that NOMA with user-pairing outperforms OMA, in terms of total uplink EC. The best pairing strategies are given in the cases of four and six users NOMA, raising once again the importance of power allocation in the optimization of NOMA's performance.

Bisection and Exact Algorithms Based on the Lagrangian Dual for a Single-Constrained Shortest Path Problem

We propose two new algorithms called BiLAD and ExactBiLAD for the well-known Single-Constrained Shortest Path (SCSP) problem. It is a fundamental problem in quality-of-service (QoS) routing, where one seeks a source-destination path with the least cost satisfying a delay QoS constraint in a network. As pointed out by Juttner et al. , there is no widely accepted algorithm with polynomial time to the SCSP problem because the SCSP problem is NP-hard. The remarkable feature of BiLAD is that it ensures that the length of iteratively updated angle interval is shrunk at least at a constant ratio. With the help of this feature, we prove its polynomial time complexity. To the best of our knowledge, this is the first time that the polynomial time complexity is proved in details. The numerical results show that, in most QoS routing test instances, the performance of BiLAD is close to their primal optimal solutions. The proposed modified Dijkstra procedure, whose complexity is the same as that of the Dijkstra algorithm, also accelerates BiLAD. In the second part of the paper, based on the information obtained by BiLAD, we design an exact algorithm–ExactBiLAD, in which an optimal solution to the SCSP problem is finally obtained by scanning the steadily reduced optimal-path-candidate triangle area. The simulation results indicate that ExactBiLAD needs only a dozen times of executing the modified Dijkstra algorithm regardless of the network size or the average node degree. Distinguished from many other exact algorithms, ExactBiLAD has a satisfactory performance in the practical computation.

QoS Driven Power Allocation in Secure Multicarrier Full-Duplex Relay Systems

In this paper, we propose the quality-of-service (QoS) driven robust power allocation policy with channel uncertainty in the secure multicarrier full-duplex (FD) relay communication system, where the statistical delay QoS is given by the secure effective capacity (SEC). To maximize the SEC under the constraints of both the total system power and the residual loopback interference (LI) power of the FD relay, an optimization problem is formulated and the corresponding power allocation strategy has been presented in the case of perfect channel state information (CSI) and imperfect CSI. Specially, considering the imperfect CSI, we propose a robust power allocation scheme, where the channel uncertainty is modeled by a bounded region. Furthermore, the worst-case method and the Bernstein approximations are utilized to deal with the uncertainty. After the Taylor approximation based simplification of the formulated optimization problem, the optimal solution is obtained by subgradient descent algorithm based on Lagrangian dual method and Karush-Kuhn-Tucker (KKT) conditions. With known error range of CSI, numerical results and analysis illustrate that the proposed robust power allocation strategy can satisfy the statistical delay QoS requirement very well. Furthermore, the trade-off between the SEC and the robustness can be achieved with channel uncertainty.