Robust Quality Of Service Research Articles

Unlocking QoS Potential: Integrating IoT services and Monte Carlo Control for heterogeneous IoT device management in gateways

The exponential surge in IoT devices has presented significant challenges in managing network traffic and maintaining Quality of Service (QoS) in IoT networks. To tackle these challenges, ongoing efforts are focused on developing innovative solutions that leverage advanced traffic management techniques, robust QoS mechanisms, and enhanced security protocols. Addressing these obstacles is crucial for the continued growth and success of the IoT system, enabling seamless integration of smart devices and revolutionizing multiple industries and sectors by providing unparalleled connectivity and intelligence. This article proposes an innovative approach to enhance the management of heterogeneous IoT devices and ensure Quality of Service (QoS) in IoT networks. Integrating device categories into the QoS policy aims to optimize overall performance while addressing the diverse QoS requirements of different device categories. The benefits of considering device categories include tailored resource allocation, improved user satisfaction, customized QoS handling, and scalability. The proposed approach utilizes the Monte Carlo Control algorithm to learn an optimal QoS policy through simulations of traffic scenarios. Our paper highlights Monte Carlo Control’s (MCC) rapid learning in diverse IoT settings, peaking at 50 episodes, compared to Q-learning’s 100 episodes and dynamic programming’s 200 episodes. Specifically, in audio IoT, MCC achieved 730 packets/second throughput with a 50 ms delay, outperforming QL (723 packets/second, 134 ms) and DP (700 packets/second, 171 ms). These results underscore MCC’s vital role in elevating IoT Quality of Service, making it pivotal for system performance and user experience optimization.

Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction.

Enhanced reinforcement learning assisted dynamic power management model for internet‐of‐things centric wireless sensor network

The exponential rise in the demands of the wireless communication system has alarmed industries to achieve more efficient and quality-of-service (QoS) centric wireless communication networks. The decentralised and infrastructure-less nature of wireless sensor networks (WSNs) enable it to be one of the most sought and used wireless network globally. Its cost-efficiency and functional robustness towards low-power lossy networks make it suitable for internet-of-things (IoT) applications. In recent years, IoT technologies have been used in diverse applications, including Smart City Planning and Management (SCPM). Although, mobile-WSN has played a decisive role in IoT enabled SCPM, its routing optimality and power transmission have always remained challenging. Noticeably, major existing researches address mainly on routing optimisation and very few efforts are made towards dynamic power management (DPM) under non-linear network conditions. With this motive, in this study, a highly robust and efficient QoS – centric reinforcement learning-based DPM model has been developed for mobile-WSN to be used in SCPM. Unlike classical reinforcement learning methods, the authors’ proposed advanced reinforcement learning-based DPM model exploits both known and unknown network parameters and state-activity values, including bit-error probability, channel state information, holding time, buffer cost etc. to perform dynamic switching decision. The key objective of the proposed model is to ensure optimal QoS oriented DPM and adaptive switching control to yield reliable transmission with the maximum possible resource utilisation. To achieve it, they proposed model has been developed as a controlled-Markov decision problem by applying hidden Markov model it obtains known and unknown parameters, which are subsequently learnt using an enhanced reinforcement learning to yield maximum resource utilisation while maintaining low buffer cost, holding cost and bit-error probability to retain the QoS provision.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering.

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

Risk Assessment for Cloud Computing

Abstract—The broad implementation of cloud computing has brought a dramatic change to multiple industries, which derives from the development of the Internet-related technologies. Cloud service providers have untangled a lot of issues to maintain a robust quality of service. Despite the utilization of proficient techniques in resource management that has made optimal planning possible, as one resource is allocated to multiple users the risk of resources unavailability in cloud computing is still present. The level of risk is evaluated based on the probability of an incident occurring, which is mapped against the estimated negative impact. The probability of an incident occurring can be determined by the threat exploiting the vulnerability. All though a zero-risk scenario is impractical, effective risk assessment strategies equipped with efficient mitigation mechanisms can decrease the negative impact. As of now there are three types of cloud computing systems public, private and hybrid each with different levels of security. Most In this paper, we examine the various risk assessment models with regards to a Cloud Computing and touching upon Information Assets and Risk, Privacy and Confidentiality Concerns, Data Governance.
 IndexTerms—Cloud Computing,Risk Assessment,Risk Impact. Risk Management

A Novel Optimal Joint Resource Allocation Method in Cooperative Multicarrier Networks: Theory and Practice.

With the increasing demands for better transmission speed and robust quality of service (QoS), the capacity constrained backhaul gradually becomes a bottleneck in cooperative wireless networks, e.g., in the Internet of Things (IoT) scenario in joint processing mode of LTE-Advanced Pro. This paper focuses on resource allocation within capacity constrained backhaul in uplink cooperative wireless networks, where two base stations (BSs) equipped with single antennae serve multiple single-antennae users via multi-carrier transmission mode. In this work, we propose a novel cooperative transmission scheme based on compress-and-forward with user pairing to solve the joint mixed integer programming problem. To maximize the system capacity under the limited backhaul, we formulate the joint optimization problem of user sorting, subcarrier mapping and backhaul resource sharing among different pairs (subcarriers for users). A novel robust and efficient centralized algorithm based on alternating optimization strategy and perfect mapping is proposed. Simulations show that our novel method can improve the system capacity significantly under the constraint of the backhaul resource compared with the blind alternatives.

Real-Time QoS Routing Protocols in Wireless Multimedia Sensor Networks: Study and Analysis.

Many routing protocols have been proposed for wireless sensor networks. These routing protocols are almost always based on energy efficiency. However, recent advances in complementary metal-oxide semiconductor (CMOS) cameras and small microphones have led to the development of Wireless Multimedia Sensor Networks (WMSN) as a class of wireless sensor networks which pose additional challenges. The transmission of imaging and video data needs routing protocols with both energy efficiency and Quality of Service (QoS) characteristics in order to guarantee the efficient use of the sensor nodes and effective access to the collected data. Also, with integration of real time applications in Wireless Senor Networks (WSNs), the use of QoS routing protocols is not only becoming a significant topic, but is also gaining the attention of researchers. In designing an efficient QoS routing protocol, the reliability and guarantee of end-to-end delay are critical events while conserving energy. Thus, considerable research has been focused on designing energy efficient and robust QoS routing protocols. In this paper, we present a state of the art research work based on real-time QoS routing protocols for WMSNs that have already been proposed. This paper categorizes the real-time QoS routing protocols into probabilistic and deterministic protocols. In addition, both categories are classified into soft and hard real time protocols by highlighting the QoS issues including the limitations and features of each protocol. Furthermore, we have compared the performance of mobility-aware query based real-time QoS routing protocols from each category using Network Simulator-2 (NS2). This paper also focuses on the design challenges and future research directions as well as highlights the characteristics of each QoS routing protocol.

IJARCCE - Computer and Communication Engineering

QoS (Quality of Service) aware spectrum sensing and channel allocation in cognitive radio wireless mesh networks is a continuous practice due to the divergent scope of communication in wireless mesh networks. Henceforth the current research is moving in a direction to find effective solutions towards QoS aware spectrum sensing and channel allocation. But all of these solutions are specific to one or two QoS factors. According to the real-time practices the QoS assessment by one or two factors is impractical. Moreover majority of current approaches are delivering the computational complexity as O(n2), which due to the magnification of number evolution against the increment in number of channel availability in cognitive radio wireless mesh networks. In this context here we devised a Neighbor Conduct Sensitive QoS variance assessment strategy for cooperative spectrum sensing and channel allocation strategy, which enables to assess the QoS state of a spectrum under possible malicious cooperation that is based on multiple number of QoS factors and also should stabilize the computational complexity to O(n*log(n)). The experiment results are indicating the significance of the proposed model towards scalable and robust QoS variance aware spectrum sensing and channel allocation strategy for cognitive radio wireless mesh networks.

On-demand Bandwidth and Stability Based Unicast Routing in Mobile Adhoc Networks

Abstract Characteristics of mobile ad hoc networks (MANETs) such as lack of central coordination, dynamic topology and limited resources pose a challenging problem in quality of service (QoS) routing. Providing an efficient, robust and low overhead QoS unicast route from source to destination is a critical issue. Bandwidth and route stability are the major important QoS parameters for applications where long duration connections are required with stringent bandwidth requirements for multimedia applications. This paper proposes an On-demand Bandwidth and Stability based Unicast Routing scheme (OBSUR) in MANET by adding additional QoS features to existing Dynamic Source Routing (DSR) protocol. The objective of the OBSUR is to provide QoS satisfied, reliable and robust route for communicating nodes. The scheme works in following steps. (1) Each node in the network periodically (small regular intervals) estimates bandwidth availability, node and link stability, buffer availability, and stability factor between nodes. (2) Construction of neighbor stability and QoS database at every node which is used in route establishment process. (3) The unicast path is constructed by using route request and route reply packets with the help of route information cache, and (4) route maintenance in case of node mobility and route failures. Simulation results show that there is an improvement in terms of traffic admission ratio, control overhead, packet delivery ratio, end to end delay and throughput as compared to Route Stability Based QoS Routing (RSQR) in MANETs.

Quality‐of‐service‐aware cluster‐based service discovery approach for mobile ad hoc networks

SUMMARYThis paper presents a quality‐of‐service (QoS) distributed service discovery approach for mobile ad hoc network environments. The approach builds upon a clustered topology, where the clusterhead (CH) is assigned additional roles having to do with maintaining a directory of services in the network and aggregating and computing QoS scores about service providers (SPs) from requesting nodes (RNs) and the providers themselves. To reduce the amount of overhead traffic, the design makes extensive use of piggybacking for relaying and updating the CHs with QoS scores. A mobile device that is interested in a certain service submits a request to its CH, which uses cached QoS data to return a ranked list of SPs that offer the type of requested service. On the basis of its interaction with the SP, the device sends the CH next time it makes a request a score reflecting its perception of the SP's QoS. Over time, the CH develops robust QoS data that it uses to help devices obtain the best available service. Theoretical analysis and experimental evaluation based on simulations prove the advantages of the proposed system and the effectiveness of its operations. Copyright © 2013 John Wiley & Sons, Ltd.

A Survey of QoS Based Routing Protocols for Wireless Sensor Networks

With the increasing demand for real time applications in the Wireless Senor Network (WSN), real time critical events anticipate an efficient quality-of-service (QoS) based routing for data delivery from the network infrastructure. Designing such QoS based routing protocol to meet the reliability and delay guarantee of critical events while preserving the energy efficiency is a challenging task. Considerable research has been focused on developing robust energy efficient QoS based routing protocols. In this paper, we present the state of the research by summarizing the work on QoS based routing protocols that has already been published and by highlighting the QoS issues that are being addressed. The performance comparison of QoS based routing protocols such as SAR, MMSPEED, MCMP, MCBR, and EQSR has also been analyzed using ns-2 for various parameters.

A Reliable and Robust QoS Routing Protocol for Mobile Wireless Sensor Networks

The problem of guaranteeing Quality-of-Service (QoS) routing in wireless sensor networks (WSNs) with node mobility is a difficult problem, even without possible interference from external radio sources. Difficulties arise because node mobility can cause frequent network topology changes, communication channels can have high error rates, the jitter rate is high and several different applications can be sharing the use of the communication medium. In this paper we propose a reliable and robust QoS routing protocol for WSNs based on a Combined Weight (CW) Value. The CW is based on the QoS parameters link quality, residual energy and available bandwidth. In addition to this, we propose an adaptive rate control mechanism in order to avoid congestion losses. By simulation results, we show that our proposed protocol is reliable and robust when compared to existing routing protocol. General Terms Combined Weight, Adaptive Rate.

A Robust GA-based QoS Routing Algorithm for Solving Multi-constrained Path Problem

To support networked multimedia applications, it is important for a network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill certain constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is NP-complete and finding an exact solution can be difficult. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. However, the actual link metrics in a QoS-aware network is dynamic and may continuously change over time and since the path given by the routing algorithm is computed using the state information available to the router, which may or may not be up-to-date, it is possible that a feasible path returned by the algorithm may turn out to be no longer valid. This paper presents a GA-based QoS routing algorithm for solving the general k -constrained problem which has the capability to return multiple feasible paths in a single run. This makes the algorithm more robust in the case that the rate of change of state information in the network is higher than the rate of state information received by the router. Simulation results show that this algorithm consistently achieve higher feasibility ratio relative to existing well-known MCP routing algorithms when state information in the router lags behind the network.

DRACON: QoS Management for Large-Scale Distributed Real-Time Databases

The demand for real-time data services is increasing in many large-scale distributed real-time applications including advanced traffic control, global environment control, and the nation-wide electric power grid control. However, providing quality-of-service (QoS) for data services in such large-scale and geographically distributed environment is a challenging task. In particular, both unpredictable communicational delays and computational workloads of large-scale distributed systems can lead to large number of deadline misses. We have designed a distributed real-time database architecture called DRACON (Decentralized data Replication And CONtrol), which enables QoS guarantees for large-scale distributed real-time applications. DRACON couples cluster-based replica-sharing and a decentralized control structure to address communication and computational unpredictability, simultaneously. The cluster-based replica-sharing mechanism not only enables scalable and bounded-delay access to remote data with high probability, but also decouples clusters to have less interaction, allowing a decentralized, thus scalable, QoS control structure. The simulation study demonstrates that DRACON’s decentralized QoS control structure combined with a decentralized replica-sharing structure provides robust and predictable QoS guarantees in a highly scalable manner.

An integrated approach with feedback control for robust Web QoS design

There is a growing demand for provisioning of different levels of quality of service (QoS) on scalable Web servers to meet changing resource availability and to satisfy different client requirements. In this paper, we investigate the problem of providing proportional QoS differentiation with respect to response time on Web servers. We first present a processing rate allocation scheme based on the foundations of queueing theory. It provides different processing rates to requests of different client classes so as to achieve the differentiation objective. At application level, process is used as the resource allocation principal for achieving processing rates on Apache Web servers. We design and implement an adaptive process allocation approach, guided by the queueing-theoretical rate allocation scheme, on an Apache server. This application-level implementation, however, shows weak QoS predictability because it does not have fine-grained control over the consumption of resources that the kernel consumes and hence the processing rate is not strictly proportional to the number of processes allocated. We then design a feedback controller and integrate it with the queueing-theoretical approach. It adjusts process allocations according to the difference between the target response time and the achieved response time using a proportional integral derivative controller. Experimental results demonstrate that this integrated approach can enable Web servers to provide robust proportional response time differentiation.

Adaptive QoS control of multimedia transmission over band-limited networks

Consumers increasingly demand high quality of service (QoS) for multimedia applications. Application-layer QoS control mechanisms that do not make specific references to coding semantics tend to provide the most robust solutions for delivery of multimedia data over band-limited networks. We propose an adaptive and robust QoS algorithm for band-limited networks that integrates explicit receiver-sender rate control, rate shaping with multiple quantization parameters and adaptive forward error correction (FEC). Experimental results indicate that an average improvement of 0.5 dB PSNR can be achieved compared to other methods.

CEDAR: a core-extraction distributed ad hoc routing algorithm

We present CEDAR, a core-extraction distributed ad hoc routing algorithm for quality-of-service (QoS) routing in ad hoc network environments, CEDAR has three key components: (a) the establishment and maintenance of a self-organizing routing infrastructure called the core for performing route computations; (b) the propagation of the link-state of high bandwidth and stable links in the core through increase/decrease waves; and (c) a QoS-route computation algorithm that is executed at the core nodes using only locally available state. The performance evaluations show that CEDAR is a robust and adaptive QoS routing algorithm that reacts quickly and effectively to the dynamics of the network while still approximating the performance of link-state routing for stable networks.