Deterioration In Quality Of Service Research Articles

Collaborative caching relay algorithm based on recursive deep reinforcement learning in mobile vehicle edge network

With the rapid development of Internet of vehicles (IoV) and the continuous emergence of vehicle information applications, the demand for content in vehicle networking is growing at an alarming speed. Mobile vehicular edge caching is regarded as a promising technology in improving Quality of Service (QoS) and reducing latency. Many caching algorithms have been proposed, which usually place contents in the Road Side Units (RSUs) to provide services to users near them. However, due to the high-speed movement of vehicles and limitations of RSU coverage, caching interrupts often occur frequently, leading to a deterioration in service quality. To deal with this problem, we make full use of Vehicle-to-Vehicle (V2V) collaboration to construct a caching system which does not require RSU support, and propose a Recursive Deep Reinforcement Learning based Collaborative Caching Relay strategy (RDRL-CR). On purpose to minimize the service delay under capacity constraints, the caching problem is formulated as an integer linear programming problem, and caching decisions are achieved through partially observable Markov Decision Process (MDP). Specifically, this strategy utilizes a Graph Neural Network (GNN) to predict vehicle trajectories, and then selects vehicles that can serve as caching nodes by calculating link stability metrics between vehicles. The Long Short Term Memory (LSTM) network is embedded into a deep deterministic strategy gradient algorithm to achieve the final caching decision. Compared with existing caching strategies, the proposed caching strategy in this paper improves the caching hit rate by about 25% and reduces content access latency by about 20%.

Policy-driven strategic transport planning for the Western Province, Sri Lanka

Sri Lanka’s transport sector strives for efficient and effective mobility solutions in terms of the quality and quantity of services provided. However, the modal share of public transport continues to decrease with increasing personal incomes, resulting in the deterioration in service quality of all transport systems in the Western Province. This occurs despite significant government investments in expressways and plans for rapid transit projects. The recent economic crisis in Sri Lanka and the consequent fuel crisis have forced the government to limit investment on transport projects that would be sustainable and bring maximum return on investment. Such selection requires careful consideration of appropriate transport policies and plans. Based on a critical review of existing transport policies and programs, this paper proposes selecting policy-based projects found to be best suited to the Western Province. Several proposals to improve the service quality in different transport modes through better regulation integration of IT and other modern technologies are discussed in terms of selecting policy parameters.

Informer-based QoS prediction for V2X communication: A method with verification using reality field test data

Vehicle-to-everything (V2X) communication plays a critical role in connected and automated driving applications, which requires strict Quality of Service (QoS) performance in terms of delay and reliability since human safety is involved. The QoS of V2X communications is impacted by various factors, such as radio interference, mobility, and user equipment (UE) density, making accurate prediction challenging. Various methods have been exploited to predict QoS deterioration. However, they usually require high computational complexity or may not capture the complex features of V2X communication environment. In this paper, a novel Informer-based QoS prediction model with a causal convolution self-attention mechanism is presented, which achieves high prediction performance with minimal computing resources. Notably, the test data used in the evaluation of the model is collected from the National Intelligent Vehicle and Intelligent Transportation Demonstration Zone in Daxing District, Beijing. The experimental results demonstrate that the proposed method exhibits superior accuracy and stability compared to existing methods such as Back Propagation (BP), ELMAN, LSTM, and CNN.

Spatial–temporal QoS assessment of the EV charging network considering power outages

The rapid expansion of electric vehicle charging networks (EVCNs) makes them critical infrastructures bridging power and traffic systems. The EVCN could be vulnerable during power outages at fast charging stations (FCSs), which are induced by planned maintenance or emergency load shedding. This paper proposes an approach to assess the impact of power outages on the Quality-of-service of the EVCN. The Markov decision process is utilized to model the spatial–temporal randomness of EV movement in a graph-based EVCN. The decision of charging by EV drivers is estimated by a fuzzy logic inference system. The spatial–temporal EV charging load at FCSs is formulated by a queuing-based non-linear optimization problem. Yen’s algorithm is adopted to simulate the EV redistribution phenomenon of searching adjacent healthy FCSs in response to the power outage. Quality-of-service (QoS) indices are derived to assess the potential congestions in the adjacent healthy FCSs. The case studies demonstrate that power outages may cause congestion at peripheral FCSs, exacerbating the QoS of the EVCN. Partial charging may alleviate the QoS deterioration in the event of FCS outages.

IACRA: Lifetime Optimization by Invulnerability-Aware Clustering Routing Algorithm Using Game-Theoretic Approach for Wsns.

Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount of energy consumed for data transmission, how to prolong the network lifespan by designing various hierarchical routing protocols has attracted more and more attention. As a result, numerous achievements have emerged successively. However, these presented mechanisms can rarely guarantee the satisfactory quality of service (QoS), while lowering the energy cost level of WSNs. Meanwhile, invulnerability is undoubtedly an excellent quantitative index to assess QoS. Therefore, it is critical to develop a practical routing method to optimize network lifetime by considering both invulnerability and energy efficiency. Game theory is suitable for such a critical problem as it can be used in node or at network level to encourage the decision-making capabilities of WSNs. In this paper, a novel invulnerability-aware clustering routing algorithm (IACRA) using game-theoretic method is proposed to solve the predicament. The core features of the addressed game-theory-based routing protocol include integral invulnerability awareness, optimal cluster head selection in hierarchical routing, distance-aware cluster head discovery, and cluster rotation update mechanism for lifetime optimization. Particularly, the integral network invulnerability based on weighted fusion is constructed for further defining the profit model by combining the invulnerability indicators used to evaluate the local and whole network. Meanwhile, the optimal probability function of every node elected as CH in per cluster is established through the game between invulnerability and node energy consumption. In addition, the cluster update mechanism base on cluster rotation is proposed to avoid the rapid death of nodes with large energy consumption for maximizing network lifetime. The experimental results indicated a significant improvement in energy balance as well as in invulnerability compared with the other three kinds of well-known clustering routing protocols including GEEC (Game-theory-based energy efficient clustering routing protocol), HGTD (Hybrid, game-theory-based distributed clustering protocol), and EEGC (Efficient energy-aware and game-theory-based clustering protocol). Concretely, at the 400 communication rounds, the invulnerability of IACRA was higher than that of GEEC, HGTD, and EEGC by 77.56%, 29.45% and 15.90%, respectively, and the average residual energy of IACRA was 8.61%, 18.35% and 6.36% larger than that of GEEC, HGTD, and EEGC, respectively. Based on these results, the proposed protocol can be utilized to increase the capability of WSNs against deterioration of QoS and energy constraints.

Expected service dimensions and service levels for paratransit considering future mobility needs in emerging countries

• Mobility-as-a-Service (MaaS) promises improved modal efficiency and integration among public transit and flexible paratransit services (FPS). • Key service dimensions and issues of flexible paratransit services are analysed in the context of MaaS. • Expected service level for these service dimensions are determined using the level of service (LOS) approach based on user perception. • Ride matching as per expected waiting time and service hours via MaaS agencies would ensure proper demand-supply match and fleet size determination. In emerging countries, flexible transport systems (FTS) are mostly informal and are provided by individual paratransit operators/drivers. These services are largely unregulated thus resulting in uncoordinated operations with poor service quality. Earnings and working conditions of paratransit drivers/operators are also poor due to loosely regulated fleet size and other operational and regulatory issues which results in further deterioration of service quality. This research identifies key service dimensions and issues of paratransit services and also determines the expected service level for these service dimensions using the level of service approach based on user perception. LOS scales are designed for different service attributes using a five-point satisfaction and importance rating system. For quantitative attributes, five-level LOS scales are determined by applying the Law of Successive Interval Scaling, whereas for qualitative attributes, the same has been developed using the frequency of response for each level of importance for each sub-attribute. Subsequently, we explore various policies and regulatory measures towards reform and redesign of paratransit services and other mobility needs. Operator profitability can be achieved both through the controlled entry of operators through the issue of permits and/or by relaxing the current service level of some service aspects which are at LOS B level of service. The LOS scales can help in future service design of paratransit services in emerging countries which can offer an affordable alternative to ridesourcing and ride-sharing using cars. The study is conducted in two medium-sized cities in India namely Rajkot and Vishakapatnam.

Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters

Predicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft’s Azure cloud environment, and its efficiency in other providers’ computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS/IaaS) over the tested period.

A Novel Federated Fog Architecture Embedding Intelligent Formation

Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internet-of-Things (IoT) while end-users are running critical real-time services. In parallel, federated fogs are not effective when formed without considering the performance perceived by the end-users. This article presents a novel architecture for the federated fog concept and proposes an adaptive and intelligent federation formation approach using Genetic Algorithm and Machine Learning models. Fog federations serve as a solution for fog providers to offer the required QoS they serve. Such a concept allows efficient distribution of load among multiple fog providers that share their resources. Throughout this process, the issue of QoS deterioration, due to local overloads, is relatively solved. Hence, the end users can enjoy a delay-free experience when using real-time applications. Real data is used to evaluate the proposed architecture and formation mechanism. The results show a notable improvement in the throughput as well as a decrease in the response time for the services requested.

Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about service problems. With the popularity of over-the-top services, this problem has become increasingly serious. Based on the service perception data crowd-sensed from massive smartphones in the mobile network, we first investigated the application of multi-label ReliefF, a well-known method of feature selection, in determining the feature weights of the perception data and propose a unified multi-label ReliefF (UML-ReliefF) algorithm. Then a feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm is proposed for the key quality indicators (KQI) prediction, by combining the UML-ReliefF and ML-kNN together in the learning. The experimental results for web browsing service show that UML-ReliefF can effectively identify the most influential features of the data and thus, lead to better performance for KQI prediction. The experiments also show that the feature-weighted KQI prediction is superior to its unweighted counterpart, since the former takes full advantage of all the features in the learning. Although there is still much room of improvement in the precision of the prediction, the proposed method is highly potential for network engineers to find the deterioration of service quality promptly and take measures before it is too late.

Factors Effecting Employee Turnover: A Study to Understand Organizational Retention Strategies

The resignation or leaving of employees, whether better or lower performing, created gaps causing understaffing, deterioration of service quality and further overburdening of remaining employees. This research basically aims at exploring and investigating the impact of factors including career growth opportunities, workplace environment, managerial support, rewards & recognition system, and work-life balance on ‘turnover intention’ specifically, it will deal with job satisfaction factors such as general working conditions, general working conditions, pay and promotion, work relationships, use of skills and abilities and work activities in relation to job satisfaction and employee turnover. To achieve the research objective, this study utilized the descriptive research design that employed mainly the survey questionnaires to gather information. Key findings revealed a positive significant correlation between the following factors on job satisfaction: pay and promotion, work relationships and the use of skills and abilities positively correlated to employee job satisfaction.

Anomalous Quality of Service Deterioration at Roundabouts Caused by Rainfall

The paper investigated the extent to which rainfall influences the quality of service delivery at multilane roundabouts using a novel quality of service approach. Quality of service is defined as how well roundabouts operate based on road users and road providers’ perception of service quality. Delay and reserve capacity were used respectively as proxies for road users and road providers’ perception of service quality. The entry and circulating traffic data were recorded continuously for eight weeks under dry, light, moderate, and heavy rainfall weather conditions at each surveyed roundabout, then collated, analysed and compared. Linear regression with dummy variable was used to model the roundabout entry capacity and a corrector factor was added to modify the regression function. The corrector factor considered different entry radii and entry angles of surveyed roundabouts. Multi-criteria quality of service table with travel time as proxy for road users and speed as proxy for road providers’ perception of service delivery was developed from peak traffic data and used to determine the extent of deterioration. The multi-criteria table introduced in the paper is a clear departure from the speed-based criteria used in many studies. The results show a significant increase in time delay and a decrease in reserve capacity relative to rainfall. The paper has concluded that rainfall has an anomalous negative effect on the quality of service at multilane roundabouts. The findings could be used in a variety of ways in traffic management to predict the travel time at roundabouts under rainy conditions and to prescribe speed limits accordingly.

SERVICE QUALITY IN ELECTRICITY DISTRIBUTION IN BRAZIL: A MALMQUIST APPROACH

ABSTRACTThe paper undertakes a dynamic analysis for service quality in the electricity distribution in Brazil between 2010 and 2014 based on Malmquist indexes constructed upon Data Envelopment Analysis (DEA) distance functions. The motivation for the less usual consideration of efficiency frontiers for service quality, that does not rely on traditional inputs and outputs but rather on quality indicators, builds on previous static applications in the context of telecommunications. The analysis considers undesirable technical indicators for quality as inputs and desirable consumer satisfaction indicators for quality as outputs in the efficiency frontier analysis. The bootstrap‐corrected Malmquist indexes indicated service quality deterioration in 38.1 per cent, quality stagnation in 40.5 per cent and quality improvement only in 21.4 per cent of the cases. When one decomposes the Malmquist index, the evidence does not suggest relevant frontier shifts and indicates a dominant role for the catch‐up effect. Finally, Malmquist indexes are regressed against variables that may portray heterogeneities, such as firm size, service area, density, ownership and regional dummies in terms of a well‐known two‐stage procedure. The mostly weak significance results reinforce the plausibility of the initial analysis.

Nash Bargaining Approach for Fair and Efficient LTE–WiFi Aggregation

As the demand for mobile traffic is growing, radio frequency bandwidths often become insufficient for guaranteeing specific performance. Utilizing unlicensed 5 GHz broadband bandwidths, the LWA system enables the synergistic use of LTE and WiFi systems based on link aggregation techniques, without critical deterioration of service quality. This provides considerable benefits to the mobile operators when they successfully operate two networks and maintain an optimal balance in managing network resources. This study addresses balancing and fairness for newly emerging LWA systems. In particular, we develop a two-stage LWA access framework based on the Nash bargaining game. The framework further improves fairness and efficiency in the LTE–WiFi resource utilization by incorporating user segmentations on the basis of user characteristics, such as service contract and usage patterns. Extensive experiments using a network simulator with an NS–3 unlicensed band produce good performance in terms of fairness and efficiency. The proposed scheme demonstrates better outcomes compared to the existing methods while concurrently using radio resources in the LWA system in a fair manner. Moreover, our approach presents a simple and fast decision-making process and requires simple user profiles—contract status and traffic generation history. Therefore, the proposed scheme is far lighter and more flexible than traditional approaches that require extensive information exchanges between heterogeneous networks.

Does Regulation Trade Off Quality against Inequality? The Case of German Architects and Construction Engineers

AbstractWe exploit an exogenous price increase of 10 per cent for architectural services to answer the question how price regulation affects income inequality and service quality. Using individual‐level data from the German microcensus for the years 2006–2012, we find a significant reform effect of 8 per cent on personal net income for self‐employed architects and construction engineers. This group moved from the second lowest to the highest quintile of the net income distribution. This increase in inequality is associated with a deterioration of service quality. The reform reduced average scores of a peer ranking for architects by 18 per cent.

Real time transit demand prediction capturing station interactions and impact of special events

Demand for public transportation is highly affected by passengers’ experience and the level of service provided. Thus, it is vital for transit agencies to deploy adaptive strategies to respond to changes in demand or supply in a timely manner, and prevent unwanted deterioration in service quality. In this paper, a real time prediction methodology, based on univariate and multivariate state-space models, is developed to predict the short-term passenger arrivals at transit stations. A univariate state-space model is developed at the station level. Through a hierarchical clustering algorithm with correlation distance, stations with similar demand patterns are identified. A dynamic factor model is proposed for each cluster, capturing station interdependencies through a set of common factors. Both approaches can model the effect of exogenous events (such as football games). Ensemble predictions are then obtained by combining the outputs from the two models, based on their respective accuracy. We evaluate these models using data from the 32 stations on the Central line of the London Underground (LU), operated by Transport for London (TfL). The results indicate that the proposed methodology performs well in predicting short-term station arrivals for the set of test days. For most stations, ensemble prediction has the lowest mean error, as well as the smallest range of error, and exhibits more robust performance across the test days.

Dynamic point selection CoMP enabled hybrid powered green cellular networks

Integration of renewable energy (RE) harvester has emerged as a long-term solution for the next generation cellular networks for reducing their operational expenditures and CO2 footprints. However, the dynamic nature of RE generation could lead to energy outage and service quality deterioration. Thus, utilization of commercial grid supply in conjunction with RE generators is a more realistic option for sustainable network operations. Therefore, this article proposes an energy-efficient hybrid framework for achieving envisaged green cellular networks. Under the proposed framework, each base station (BS) is equipped with on-site solar energy harvester as the primary energy source along with the traditional grid electricity supplying additional energy demand. Afterward, dynamic point selection type coordinated multi-point transmission technique is integrated for selecting the best serving BS for a user. A comprehensive investigation is carried out in the context of downlink LTE-A cellular networks for evaluating energy efficiency performance of the proposed framework.

Fuzzy Logic-Based Quality of Service Evaluation for Multimedia Transmission over Wireless Ad Hoc Networks

This paper proposes a fuzzy logic (FL) model for evaluation of quality of service (QoS) in multimedia transmission over ad hoc networks as an effective mechanism for QoS management. It aims at minimizing the negative effects of major QoS parameters, (jitter, delay, packet loss) sustaining efficiency and reliability of data deliveries and improving overall system performance. Both triangular membership function (TMF) and Gaussian membership function (GMF) are adopted to demonstrate their effects in FL-QoS evaluation. The proposed approaches are implemented in Matlab/Simulink. Results indicate that input conditions have varying level of influences on the output based on the MFs used. It is generally observed that QoS fuzzy control with TMF gives a better performance than GMF. Typically, it is also noted that when the input conditions are selected at 50[Formula: see text]ms delay, 5[Formula: see text]ms jitter and 50% packet loss, we obtain 79% and 71% output responses with TMF and GMF, respectively. This shows that TMF method can control QoS in a multimedia transmission over ad hoc wireless network more effectively with good service output response than GMF. Generally, the paper shows that FL is capable of measuring network performance and predicting any QoS deterioration without complex mathematical calculation, to provide an improved QoS for customers’ satisfaction.

Trading service quality for safety: a cautionary tale from the French ‘Robien law’ on elevator safety

To deal with elevator accidents the French ‘Robien law’ mandated safety upgrades for ‘old’ elevators. Available statistics suggest that the law reduced fatal accidents but also coincides with an unprecedented deterioration in service quality, multiplying breakdowns and downtime. We exploit a unique data set of more than 3500 elevators over 10 years to investigate the law’s impact. Using a difference-in-difference setting, our results indicate that the law led to a considerable increase in the number of failures and downtime. We also find evidence that the situation was exacerbated by the fact that the higher failure rates were not met by a sufficient increase in maintenance staff.

A data-driven agent-based model of congestion and scaling dynamics of rapid transit systems

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of congestion and scaling dynamics. By adjusting our model, we can estimate the effective capacity of the RTS trains as well as replicate the penultimate station effect, where commuters travel backwards to the preceding station to catch a seat, sacrificing time for comfort. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. We find that increasing the current population (2million) beyond a factor of approximately 10% leads to an exponential deterioration in service quality. We also show that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality. Finally, our model can be used to generate simulated data for statistical analysis when such data are not empirically available, as is often the case.

Simulating Congestion Dynamics of Train Rapid Transit Using Smart Card Data

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains—all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of approximately 10% leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for statistical analysis when such data are not empirically available, as is often the case.