From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Actas da 10ª Conferência sobre Redes de Computadores

Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio

An SDN QoE Monitoring Framework for VoIP and video applications

Τα τελευταία χρόνια έχει σημειωθεί ραγδαία άνοδος του κλάδου των κινητών επικοινωνιών, αφού η χρήση των κινητών συσκευών εξαπλώνεται με ταχύτατους ρυθμούς και αναμένεται να συνεχίσει τη διείσδυσή της στην καθημερινότητα των καταναλωτών. Το γεγονός αυτό, σε συνδυασμό με τους περιορισμούς που θέτει η τρέχουσα δομή των δικτύων επικοινωνιών, καθιστά αναγκαία την ανάπτυξη νέων δικτύων με αυξημένες δυνατότητες, ώστε να είναι δυνατή η εξυπηρέτηση των χρηστών με την καλύτερη δυνατή ποιότητα εμπειρίας και ταυτόχρονα τη βέλτιστη αξιοποίηση των πόρων του δικτύου. Μία νέα δικτυακή προσέγγιση αποτελεί η δικτύωση βασισμένη στο λογισμικό (Software Defined Networking - SDN), η οποία αφαιρεί τον έλεγχο από τις συσκευές προώθησης του δικτύου, και οι αποφάσεις λαμβάνονται σε κεντρικό σημείο. Η ποιότητα υπηρεσίας που αντιλαμβάνεται ο χρήστης, ή αλλιώς ποιότητα εμπειρίας, κρίνεται ζήτημα υψηλής σημασίας στα δίκτυα SDN. Η παρούσα διπλωματική εργασία έχει ως στόχο την παρουσίαση της τεχνολογίας SDN, την επισκόπηση της υπάρχουσας έρευνας στο πεδίο της ποιότητας εμπειρίας σε SDN δίκτυα και στη συνέχεια την ανάπτυξη μίας SDN εφαρμογής η οποία παρακολουθεί και διατηρεί την ποιότητας εμπειρίας σε υψηλά επίπεδα για εφαρμογές VoIP και video. Πιο συγκεκριμένα, η εφαρμογή SQMF (SDN QoE Monitoring Framework) παρακολουθεί περιοδικά στο μονοπάτι μετάδοσης των πακέτων διάφορες παραμέτρους του δικτύου, με βάση τις οποίες υπολογίζει την ποιότητα εμπειρίας. Εάν διαπιστωθεί ότι το αποτέλεσμα είναι μικρότερο από ένα προσδιορισμένο κατώφλι, η εφαρμογή αλλάζει το μονοπάτι μετάδοσης, και έτσι η ποιότητα εμπειρίας ανακάμπτει. Η δομή της παρούσας διπλωματικής εργασίας είναι η εξής: Στο κεφάλαιο 1 παρουσιάζεται η σημερινή εικόνα των δικτύων επικοινωνιών και οι προβλέψεις για τη μελλοντική εικόνα, καθώς και οι προκλήσεις στις οποίες τα σημερινά δίκτυα δε θα μπορούν να αντεπεξέλθουν. Στη συνέχεια στο κεφάλαιο 2 περιγράφεται αναλυτικά η τεχνολογία SDN ως προς την αρχιτεκτονική, το κύριο πρωτόκολλο που χρησιμοποιεί, τα σενάρια χρήσης της, την προτυποποίηση, τα πλεονεκτήματα και τα μειονεκτήματά της. Το κεφάλαιο 3 εισάγει την έννοια της ποιότητας εμπειρίας του χρήστη και παραθέτει ευρέως γνωστά μοντέλα υπολογισμού της για διάφορους τύπους εφαρμογών, που χρησιμοποιούνται στην παρούσα εργασία. Σχετικές υπάρχουσες μελέτες στο πεδίο της ποιότητας εμπειρίας σε δίκτυα SDN αλλά και συγκριτικός πίνακας μπορούν να βρεθούν στο κεφάλαιο 4. Τα επόμενα κεφάλαια αφορούν στην εφαρμογή SQMF που υλοποιήθηκε στα πλαίσια της παρούσας διπλωματικής εργασίας: το κεφάλαιο 5 περιγράφει αναλυτικά όλα τα προαπαιτούμενα εργαλεία και οδηγίες για την ανάπτυξη του SQMF, ενώ το κεφάλαιο 6 παρουσιάζει παραδείγματα όπου η ποιότητα εμπειρίας ενός δικτύου μπορεί να υποστεί μείωση. Τέλος, το κεφάλαιο 7 αναλύει σε βάθος τις σχεδιαστικές προδιαγραφές, τη λογική και τον κώδικα του SQMF και παρέχει επίδειξη της λειτουργίας του και αξιολόγησή του, ενώ το κεφάλαιο 8 συνοψίζει επιγραμματικά τα συμπεράσματα της παρούσας εργασίας και ανοιχτά θέματα για μελλοντική έρευνα.Lately, there has been a rapid rise of the mobile communications industry, since the use of mobile devices is spreading at a fast pace and is expected to continue its penetration into the daily routine of consumers. This fact, combined with the limitations of the current communications networks’ structure, necessitates the development of new networks with increased capabilities, so that users can be served with the best possible quality of service and at the same time with the optimal network resources utilization. A new networking approach is Software Defined Networking (SDN) which decouples the control from the data plane, transforming the network elements to simple forwarding devices and making decisions centrally. The quality of service perceived by the user, or quality of experience (QoE), is considered to be a matter of great importance in software defined networks. This diploma thesis aims at presenting SDN technology, reviewing existing research in the field of QoE on SDN networks and then developing an SDN application that monitors and preserves the QoE for VoIP and video applications. More specifically, the developed SDN QoE Monitoring Framework (SQMF) periodically monitors various network parameters on the VoIP/video packets transmission path, based on which it calculates the QoE. If it is found that the result is less than a predefined threshold, the framework changes the transmission path, and thus the QoE recovers. The structure of this diploma thesis is the following: Chapter 1 presents the current state of communications networks and predictions for the future state, as well as the challenges that current networks will not be able to cope with. Chapter 2 then describes in detail the SDN technology in terms of architecture, main control-data plane communication protocol, use cases, standardization, advantages and disadvantages. Chapter 3 introduces the concept of QoE and lists well-known QoE estimation models for various applications types, some of which were used in this thesis. Relevant existing studies in the field of QoE on SDN networks as well as a comparative table can be found in chapter 4. The following chapters concern the framework implemented in the context of this diploma thesis: Chapter 5 describes in detail all the required tools and instructions for the development of SQMF, while Chapter 6 presents examples where the QoE in a network can face degradation. Finally, Chapter 7 analyzes in depth SQMF's design principles, logic and code files, provides a demonstration of its operation and evaluates it, whereas Chapter 8 briefly summarizes the conclusions and of this thesis and future work points

Quality-aware Content Adaptation in Digital Video Streaming

User-generated video has attracted a lot of attention due to the success of Video Sharing Sites such as YouTube and Online Social Networks. Recently, a shift towards live consumption of these videos is observable. The content is captured and instantly shared over the Internet using smart mobile devices such as smartphones. Large-scale platforms arise such as YouTube.Live, YouNow or Facebook.Live which enable the smartphones of users to livestream to the public. These platforms achieve the distribution of tens of thousands of low resolution videos to remote viewers in parallel. Nonetheless, the providers are not capable to guarantee an efficient collection and distribution of high-quality video streams. As a result, the user experience is often degraded, and the needed infrastructure installments are huge. Efficient methods are required to cope with the increasing demand for these video streams; and an understanding is needed how to capture, process and distribute the videos to guarantee a high-quality experience for viewers. This thesis addresses the quality awareness of user-generated videos by leveraging the concept of content adaptation. Two types of content adaptation, the adaptive video streaming and the video composition, are discussed in this thesis. Then, a novel approach for the given scenario of a live upload from mobile devices, the processing of video streams and their distribution is presented. This thesis demonstrates that content adaptation applied to each step of this scenario, ranging from the upload to the consumption, can significantly improve the quality for the viewer. At the same time, if content adaptation is planned wisely, the data traffic can be reduced while keeping the quality for the viewers high. The first contribution of this thesis is a better understanding of the perceived quality in user-generated video and its influencing factors. Subjective studies are performed to understand what affects the human perception, leading to the first of their kind quality models. Developed quality models are used for the second contribution of this work: novel quality assessment algorithms. A unique attribute of these algorithms is the usage of multiple features from different sensors. Whereas classical video quality assessment algorithms focus on the visual information, the proposed algorithms reduce the runtime by an order of magnitude when using data from other sensors in video capturing devices. Still, the scalability for quality assessment is limited by executing algorithms on a single server. This is solved with the proposed placement and selection component. It allows the distribution of quality assessment tasks to mobile devices and thus increases the scalability of existing approaches by up to 33.71% when using the resources of only 15 mobile devices. These three contributions are required to provide a real-time understanding of the perceived quality of the video streams produced on mobile devices. The upload of video streams is the fourth contribution of this work. It relies on content and mechanism adaptation. The thesis introduces the first prototypically evaluated adaptive video upload protocol (LiViU) which transcodes multiple video representations in real-time and copes with changing network conditions. In addition, a mechanism adaptation is integrated into LiViU to react to changing application scenarios such as streaming high-quality videos to remote viewers or distributing video with a minimal delay to close-by recipients. A second type of content adaptation is discussed in the fifth contribution of this work. An automatic video composition application is presented which enables live composition from multiple user-generated video streams. The proposed application is the first of its kind, allowing the in-time composition of high-quality video streams by inspecting the quality of individual video streams, recording locations and cinematographic rules. As a last contribution, the content-aware adaptive distribution of video streams to mobile devices is introduced by the Video Adaptation Service (VAS). The VAS analyzes the video content streamed to understand which adaptations are most beneficial for a viewer. It maximizes the perceived quality for each video stream individually and at the same time tries to produce as little data traffic as possible - achieving data traffic reduction of more than 80%

Journal of Telecommunications and Information Technology, 2019, nr 2

kwartalni

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe