An Adaptive Packet Aggregation Algorithm (AAM) for Wireless Networks

Packet aggregation algorithms are used to improve the throughput performance by combining a number of packets into a single transmission unit in order to reduce the overhead associated with each transmission within a packet-based communications network. However, the throughput improvement is also accompanied by a delay increase. The biggest drawback of a significant number of the proposed packet aggregation algorithms is that they tend to only optimize a single metric, i.e. either to maximize throughput or to minimize delay. They do not permit an optimal trade-off between maximizing throughput and minimizing delay. Therefore, these algorithms cannot achieve the optimal network performance for mixed traffic loads containing a number of different types of applications which may have very different network performance requirements. In this thesis an adaptive packet aggregation algorithm called the Adaptive Aggregation Mechanism (AAM) is proposed which achieves an aggregation trade-off in terms of realizing the largest average throughput with the smallest average delay compared to a number of other popular aggregation algorithms under saturation conditions in wireless networks. The AAM algorithm is the first packet aggregation algorithm that employs an adaptive selection window mechanism where the selection window size is adaptively adjusted in order to respond to the varying nature of both the packet size and packet rate. This algorithm is essentially a feedback control system incorporating a hybrid selection strategy for selecting the packets. Simulation results demonstrate that the proposed algorithm can (a) achieve a large number of sub-packets per aggregate packet for a given delay and (b) significantly improve the performance in terms of the aggregation trade-off for different traffic loads. Furthermore, the AAM algorithm is a robust algorithm as it can significantly improve the performance in terms of the average throughput in error-prone wireless networks

A New Adaptive Frame Aggregation Method for Downlink WLAN MU-MIMO Channels

Accommodating the heterogeneous traffic demand among streams in the downlink MU-MIMO channel is among the challenges that affect the transmission efficiency since users in the channel do not always have the same traffic demand. Consequently, it is feasible to adjust the frame size to maximize the system throughput. The existing adaptive aggregation solutions do not consider the effects of different traffic scenarios and they use a Poison traffic model which is inadequate to represent the real network traffic scenarios, thus leading to suboptimal solutions. In this study, we propose some adaptive aggregation strategies which employ a novel dynamic adaptive aggregation policy selection algorithm in addressing the challenges of heterogenous traffic demand in the downlink MU-MIMO channel. Different traffic models are proposed to emulate real world traffic scenarios in the network and to analyze the proposed aggregation polices with respect to various traffic models. Finally, through simulation, we demonstrate the performance of our adaptive algorithm over the baseline FIFO aggregation approach in terms of system throughput performance and channel utilization in achieving the optimal frame size of the system

Towards Fairness-Aware Federated Learning

Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL,and overlook the interests of the FL clients. This may result in unfair treatment of clients which discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey which helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest promising future research directions towards fairness-aware federated learning.Comment: 16 pages, 4 figure

Estudi bibliomètric any 2016. EETAC

El present document recull les publicacions indexades a la base de dades Scopus durant el període comprès entre el mesos de gener a desembre de l’any 2016, escrits per autors pertanyents a l’EETAC. Es presenten les dades recollides segons la font on s’ha publicat, els autors que han publicat, i el tipus de document publicat. S’hi inclou un annex amb la llista de totes les referències bibliogràfiques publicades.Postprint (author's final draft

Recent advances in information-centric networking based internet of things (ICN-IoT)

Information-Centric Networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current IP-address based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, caching the content at intermediate nodes to provide reliable, efficient data delivery and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoTs) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoTs attracts both industry and research communities. IoTs is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoTs by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN based caching, naming, security and mobility approaches for IoTs with appropriate classification. Furthermore, we present operating systems (OS) and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoTs

Estudi bibliomètric any 2015. EETAC

El present document recull les publicacions indexades a la base de dades Scopus durant el període comprès entre el mesos de gener a desembre de l’any 2015, escrits per autors pertanyents a l’EETAC. Es presenten les dades recollides segons la font on s’ha publicat, els autors que han publicat, i el tipus de document publicat. S’hi inclou un annex amb la llista de totes les referències bibliogràfiques publicades.Postprint (published version

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Multimedia computer networks quality of service techniques evaluation and development.

The growth in the transmission of time-sensitive applications over computer networks means that Quality of Service (QoS) needs to be managed in an efficient manner. Network QoS management in this thesis refers to evaluation and improvement of QoS provided by integrated wired and wireless computer networks. Evaluation of QoS aims to analyse and quantify network performance with respect of meeting multimedia applications' transmission requirements. QoS improvement involves the ability to take actions to change network performance toward improved operation. Therefore, the main aims of this thesis are: (i) to develop techniques for evaluation QoS in multimedia computer networks, (ii) to develop techniques that uses the information from (i) to manage and improve network performance. Multimedia traffic generates a large amount of data. Collecting this information poses a challenge as it needs to be sufficiently fast and accurate. A contribution of this thesis is that adaptive statistical sampling techniques to sample multimedia traffic were developed and their effectiveness was evaluated. Three different adjustment mechanisms were incorporated into statistical sampling techniques to adjust the traffic sampling rate: simple linear adjustment, quarter adjustment, and Fuzzy Inference System (FIS). The findings indicated that the developed methods outperformed the conventional non-adaptive sampling methods of systematic, stratified and random. The data collected included important QoS parameters, i.e. delay, jitter, throughput, and packet loss that indicated network performance in delivering real-time applications. An issue is that QoS needs evaluation in an informative manner. Therefore, the second contribution of this thesis is that statistical and Artificial Intelligent (AI) techniques were developed to evaluate QoS for multimedia applications. The application's QoS parameters were initially analysed either by Fuzzy C-Means (FCM) clustering algorithm or by Kohonen neural network. The analysed QoS parameters were then used as inputs to a regression model or Multi-Layer Perceptron (MLP) neural network in order to quantify the overall QoS. The proposed QoS evaluation system differentiated the network's QoS into a number of levels (Poor to Good QoS) and based on this information, the overall network's QoS was successfully quantified. In order to facilitate QoS assessment, a portable hand-held device for assessing the QoS in multimedia networks was designed, regression model was implemented on the microcontroller board and its performance was successfully demonstrated.Multimedia applications transmitted over computer networks require a large bandwidth that is a critical issue especially in wireless networks. The challenge is to enable end-to-end QoS by providing different treatments for different classes of traffic and efficient use of network resources. In this thesis, a new QoS enhancement scheme for wireless-wired networks is developed. This scheme consisted of an adaptive traffic allocation algorithm that is incorporated into the network's wireless side to improve the performance of IEEE 802.11e Enhanced Distributed Channel Access (EDCA) protocol, and a Weighted Round Robin (WRR) queuing scheduling mechanism that was incorporated into the wired side. The proposed scheme improved the QoS for Multimedia applications. The average QoS for voice, and video applications were increased from their original values by 72.5%, and 70.3% respectively