A machine learning management model for QoE enhancement in next-generation wireless ecosystems

Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring a good quality of service (QoS) will be one of the major challenges of next-generation wireless systems on account of a variety of factors that are beyond the control of network and service providers. In this context, ITU-T is working on updating the various Recommendations related to QoS and users\u27 quality of experience (QoE). Considering the ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next-generation wireless ecosystems taking advantage of big data and machine learning. The results from a case study conducted to validate the model in real-world Wi-Fi deployment scenarios are also presented

QoE Enhancement in Next Generation Wireless Ecosystems: A Machine Learning Approach

Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring quality of service (QoS) will be one of the major challenges on account of a variety of factors that are beyond the control of network and service providers in these environments. In this context, ITU-T is working on defining new Recommendations related to QoS and users\u27 quality of experience (QoE) for the 5G era. Considering the new ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next generation wireless ecosystems taking advantage of big data and machine learning (ML). The methodological approach is based on the use of supervised and unsupervised ML techniques in order to identify both the KQIs relevant for the users and the network performance (NP) anomalies. The proposed methodology links the NP and QoE via inductive ML algorithms and provides information about the areas where corrective actions are required. The results from a case study conducted to validate the model in real-world Wi-Fi deployment scenarios are also presented

Real-life performance of protocol combinations for wireless sensor networks

Wireless sensor networks today are used for many and diverse applications like nature monitoring, or process and wireless building automation. However, due to the limited access to large testbeds and the lack of benchmarking standards, the real-life evaluation of network protocols and their combinations remains mostly unaddressed in current literature. To shed further light upon this matter, this paper presents a thorough experimental performance analysis of six protocol combinations for TinyOS. During these protocol assessments, our research showed that the real-life performance often differs substantially from the expectations. Moreover, we found that combining protocols is far from trivial, as individual network protocols may perform very different in combination with other protocols. The results of our research emphasize the necessity of a flexible generic benchmarking framework, powerful enough to evaluate and compare network protocols and their combinations in different use cases

Supporting protocol-independent adaptive QoS in wireless sensor networks

Next-generation wireless sensor networks will be used for many diverse applications in time-varying network/environment conditions and on heterogeneous sensor nodes. Although Quality of Service (QoS) has been ignored for a long time in the research on wireless sensor networks, it becomes inevitably important when we want to deliver an adequate service with minimal efforts under challenging network conditions. Until now, there exist no general-purpose QoS architectures for wireless sensor networks and the main QoS efforts were done in terms of individual protocol optimizations. In this paper we present a novel layerless QoS architecture that supports protocol-independent QoS and that can adapt itself to time-varying application, network and node conditions. We have implemented this QoS architecture in TinyOS on TmoteSky sensor nodes and we have shown that the system is able to support protocol-independent QoS in a real life office environment

Evaluating rate-estimation for a mobility and QoS-aware network architecture

In a nearby future wireless networks will run applications with special QoS requirements. FHMIP is an effective scheme to reduce Mobile IPv6 handover disruption but it does not deal with any other specific QoS requirement. Therefore new traffic management schemes are needed in order to provide QoS guarantees to real-time applications and this implies network mobility optimizations and congestion control support. Traffic management schemes should deal with QoS requirements during handover and should use some resource management strategy in order to achieve this. In this article a new resource management scheme for DiffServ QoS model is proposed, to be used by access routers as an extension to FHMIP micromobility protocol. In order to prevent QoS deterioration, access routers pre-evaluate the impact of accepting all traffic from a mobile node, previous to the handover. This pre-evaluation and post decision on whether or not to accept any, or all, of this new traffic is based on a measurement based admission control procedure. This mobility and QoS-aware network architecture, integrating a simple signaling protocol, a traffic descriptor, and exhibiting adaptive behavior has been implemented and tested using ns-2. All measurements and decisions are based on DiffServ class-of-service aggregations, thus avoiding large flow state information maintenance. Rate estimators are essential mechanisms to the efficiency of this QoS-aware overall architecture. Therefore, in order to be able to choose the rate estimator that better fits this global architecture, two rate estimators - Time Sliding Window (TSW) and Exponential Moving Average (EMA) - have been studied and evaluated by means of ns-2 simulations in QoS-aware wireless mobility scenarios.Nuno V. Lopes was supported by an FCT Grant (SFRH/BD/35245/2007

Uncertainty and Congestion Elimination in 4G Network Call Admission Control using Interval Type-2 Intuitionistic Fuzzy Logic

The management and control of the global growth and complex nature of wireless Fourth Generation (4G) Networks elicits the need for Call Admission Control (CAC). However, CAC faces the challenge of network congestion, thereby deteriorating the network Quality of Service (QoS) due to inherent imprecision and uncertainties in the QoS data which leads to difficulties in measuring some objective and constraints of QoS using crisp values. Previous researches have shown the strength of Interval Type-2 Fuzzy Logic System (IT2FLS) in coping adequately with linguistic uncertainties. Intuitionistic fuzzy sets (IFSs) have indicated their ability to further reduce uncertainty by handling conflicting evaluation involving membership (M), nonmembership (NM) and hesitation. This paper applies the Interval Type-2 Intuitionistic Fuzzy Logic System (IT2IFLS) in solving CAC problem in order to achieve a better QoS in 4G Networks

Ubiquitous Internet in an integrated satellite-terrestrial environment: The SUITED solution

yesThe current Internet architecture appears to not be particularly suited to addressing the emerging needs of new classes of users who wish to gain access to multimedia services made available by ISPs, regardless of their location, while in motion and with a guaranteed level of quality. One of the main objectives of so-called nextgeneration systems is to overcome the limitations of today¿s available Internet by adopting an approach based on the integration of different mobile and fixed networks. The SUITED project moves in this direction since it aims at contributing to the design and deployment of the global mobile broadband system (GMBS), a unique satellite/terrestrial infrastructure ensuring nomadic users access to Internet services with a negotiated QoS. A description of the main features of the GMBS architecture, characterized by the integration of a multisegment access network with a federated ISP network is given in this article. The GMBS multimode terminal is schematically described, and an overview of the so-called QoS-aware mobility management scheme, devised for such a heterogeneous scenario,is provided

A Comparative Study of Prioritized Handoff Schemes with Guard Channels in Wireless Cellular Networks

Mobility management has always been the main challenge in most mobile systems. It involves the management of network radio channel resource capacity for the purpose of achieving optimum quality of service (QoS) standard. In this era of wireless Personal Communication Networks such as Global System for Mobile Communication (GSM), Wireless Asynchronous Transfer Mode (WATM), Universal Mobile Telecommunication System (UMTS), there is a continuous increase in demand for network capacity. In order to accommodate the increased demand for network capacity (radio resource) over the wireless medium, cell sizes are reduced. As a result of such reduction in cell sizes, handoffs occur more frequently, and thereby result in increased volume of handoff related signaling. Therefore, a handoff scheme that can handle the increased signaling load while sustaining the standard QoS parameters is required.This work presents a comparative analysis of four popular developed handoff schemes. New call blocking probability, forced termination probability and throughput are the QoS parameters employed in comparing the four schemes. The four schemes are:RCS-GC,MRCS-GC, NCBS-GC, and APS-GC. NCBS-GChas the leased new call blocking probability while APS-GC has the worst. In terms of forced termination probability, MRCS-GC has the best result, whileRCS-GChas the worst scheme.MRCS-GC delivers the highest number of packets per second while APS-GC delivers the least. These performance metrics are computed by using the analytical expressions developed for these metrics in the considered models in a Microsoft Excel spreadsheet environment.http://dx.doi.org/10.4314/njt.v34i3.2