99 research outputs found
Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?
The increasing popularity of live video streaming from mobile devices, such as Facebook Live, Instagram Stories, Snapchat, etc. pressurizes the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of quality of experience (QoE) as the basis for network control, customer loyalty, and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: 1) adaptation; 2) energy efficiency; and 3) multipath content delivery. Discussions, challenges, and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided
Seamless multimedia delivery within a heterogeneous wireless networks environment: are we there yet?
The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing usersâ quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided
Prediction-based techniques for the optimization of mobile networks
MenciĂłn Internacional en el tĂtulo de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition
of several random phenomena, most of which, related to human activities, such as mobility,
communications and network usage. However, when observed in their totality, the many individual
components merge into more deterministic patterns and trends start to be identifiable and
predictable.
In this thesis we analyze a recent branch of network optimization that is commonly referred to
as anticipatory networking and that entails the combination of prediction solutions and network
optimization schemes. The main intuition behind anticipatory networking is that knowing in
advance what is going on in the network can help understanding potentially severe problems and
mitigate their impact by applying solution when they are still in their initial states. Conversely,
network forecast might also indicate a future improvement in the overall network condition (i.e.
load reduction or better signal quality reported from users). In such a case, resources can be
assigned more sparingly requiring users to rely on buffered information while waiting for the
better condition when it will be more convenient to grant more resources.
In the beginning of this thesis we will survey the current anticipatory networking panorama
and the many prediction and optimization solutions proposed so far. In the main body of the work,
we will propose our novel solutions to the problem, the tools and methodologies we designed to
evaluate them and to perform a real world evaluation of our schemes.
By the end of this work it will be clear that not only is anticipatory networking a very promising
theoretical framework, but also that it is feasible and it can deliver substantial benefit to current
and next generation mobile networks. In fact, with both our theoretical and practical results we
show evidences that more than one third of the resources can be saved and even larger gain can
be achieved for data rate enhancements.Programa Oficial de Doctorado en IngenierĂa TelemĂĄticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge OrtĂn Gracia.- Vocal: Guevara Noubi
Device characteristics-based differentiated energy-efficient adaptive solution for multimedia delivery over heterogeneous wireless networks
Energy efïŹciency is a key issue of highest importance to mobile wireless device users, as those devices are powered by batteries with limited power capacity. It is of very high interest to provide device differentiated user centric energy efficient multimedia content delivery based on current application type, energy-oriented device features and user preferences. This thesis presents the following research contributions in the area of energy efïŹcient multimedia delivery over heterogeneous wireless networks:
1. ASP: Energy-oriented Application-based System proïŹling for mobile devices: This proïŹling provides services to other contributions in this thesis. By monitoring the running applications and the corresponding power demand on the smart mobile device, a device energy model is obtained. The model is used in conjunction with applicationsâ power signature to provide device energy constraints posed by running applications.
2. AWERA
3. DEAS: A Device characteristics-based differentiated Energy-efïŹcient Adaptive Solution for video delivery over heterogeneous wireless networks. Based on the energy constraint, DEAS performs energy efïŹcient content delivery adaptation for the current application. Unlike the existing solutions, DEAS takes all the applications running on the system into account and better balances QoS and energy efïŹciency.
4. EDCAM
5. A comprehensive survey on state-of-the-art energy-efïŹcient network protocols and energy-saving network technologies
Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures
The advent of communication technologies marks a transformative phase in
critical infrastructure construction, where the meticulous analysis of failures
becomes paramount in achieving the fundamental objectives of continuity,
security, and availability. This survey enriches the discourse on failures,
failure analysis, and countermeasures in the context of the next-generation
critical communication infrastructures. Through an exhaustive examination of
existing literature, we discern and categorize prominent research orientations
with focuses on, namely resource depletion, security vulnerabilities, and
system availability concerns. We also analyze constructive countermeasures
tailored to address identified failure scenarios and their prevention.
Furthermore, the survey emphasizes the imperative for standardization in
addressing failures related to Artificial Intelligence (AI) within the ambit of
the sixth-generation (6G) networks, accounting for the forward-looking
perspective for the envisioned intelligence of 6G network architecture. By
identifying new challenges and delineating future research directions, this
survey can help guide stakeholders toward unexplored territories, fostering
innovation and resilience in critical communication infrastructure development
and failure prevention
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Internet of Things and Sensors Networks in 5G Wireless Communications
The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic
Gestion conjointe de ressources de communication et de calcul pour les réseaux sans fils à base de cloud
Mobile Edge Cloud brings the cloud closer to mobile users by moving the cloud computational efforts from the internet to the mobile edge. We adopt a local mobile edge cloud computing architecture, where small cells are empowered with computational and storage capacities. Mobile usersâ offloaded computational tasks are executed at the cloud-enabled small cells. We propose the concept of small cells clustering for mobile edge computing, where small cells cooperate in order to execute offloaded computational tasks. A first contribution of this thesis is the design of a multi-parameter computation offloading decision algorithm, SM-POD. The proposed algorithm consists of a series of low complexity successive and nested classifications of computational tasks at the mobile side, leading to local computation, or offloading to the cloud. To reach the offloading decision, SM-POD jointly considers computational tasks, handsets, and communication channel parameters. In the second part of this thesis, we tackle the problem of small cell clusters set up for mobile edge cloud computing for both single-user and multi-user cases. The clustering problem is formulated as an optimization that jointly optimizes the computational and communication resource allocation, and the computational load distribution on the small cells participating in the computation cluster. We propose a cluster sparsification strategy, where we trade cluster latency for higher system energy efficiency. In the multi-user case, the optimization problem is not convex. In order to compute a clustering solution, we propose a convex reformulation of the problem, and we prove that both problems are equivalent. With the goal of finding a lower complexity clustering solution, we propose two heuristic small cells clustering algorithms. The first algorithm is based on resource allocation on the serving small cells where tasks are received, as a first step. Then, in a second step, unserved tasks are sent to a small cell managing unit (SCM) that sets up computational clusters for the execution of these tasks. The main idea of this algorithm is task scheduling at both serving small cells, and SCM sides for higher resource allocation efficiency. The second proposed heuristic is an iterative approach in which serving small cells compute their desired clusters, without considering the presence of other users, and send their cluster parameters to the SCM. SCM then checks for excess of resource allocation at any of the network small cells. SCM reports any load excess to serving small cells that re-distribute this load on less loaded small cells. In the final part of this thesis, we propose the concept of computation caching for edge cloud computing. With the aim of reducing the edge cloud computing latency and energy consumption, we propose caching popular computational tasks for preventing their re-execution. Our contribution here is two-fold: first, we propose a caching algorithm that is based on requests popularity, computation size, required computational capacity, and small cells connectivity. This algorithm identifies requests that, if cached and downloaded instead of being re-computed, will increase the computation caching energy and latency savings. Second, we propose a method for setting up a search small cells cluster for finding a cached copy of the requests computation. The clustering policy exploits the relationship between tasks popularity and their probability of being cached, in order to identify possible locations of the cached copy. The proposed method reduces the search cluster size while guaranteeing a minimum cache hit probability.Cette thĂšse porte sur le paradigme « Mobile Edge cloud» qui rapproche le cloud des utilisateurs mobiles et qui dĂ©ploie une architecture de clouds locaux dans les terminaisons du rĂ©seau. Les utilisateurs mobiles peuvent dĂ©sormais dĂ©charger leurs tĂąches de calcul pour quâelles soient exĂ©cutĂ©es par les femto-cellules (FCs) dotĂ©es de capacitĂ©s de calcul et de stockage. Nous proposons ainsi un concept de regroupement de FCs dans des clusters de calculs qui participeront aux calculs des tĂąches dĂ©chargĂ©es. A cet effet, nous proposons, dans un premier temps, un algorithme de dĂ©cision de dĂ©portation de tĂąches vers le cloud, nommĂ© SM-POD. Cet algorithme prend en compte les caractĂ©ristiques des tĂąches de calculs, des ressources de lâĂ©quipement mobile, et de la qualitĂ© des liens de transmission. SM-POD consiste en une sĂ©rie de classifications successives aboutissant Ă une dĂ©cision de calcul local, ou de dĂ©portation de lâexĂ©cution dans le cloud.Dans un deuxiĂšme temps, nous abordons le problĂšme de formation de clusters de calcul Ă mono-utilisateur et Ă utilisateurs multiples. Nous formulons le problĂšme dâoptimisation relatif qui considĂšre lâallocation conjointe des ressources de calculs et de communication, et la distribution de la charge de calcul sur les FCs participant au cluster. Nous proposons Ă©galement une stratĂ©gie dâĂ©parpillement, dans laquelle lâefficacitĂ© Ă©nergĂ©tique du systĂšme est amĂ©liorĂ©e au prix de la latence de calcul. Dans le cas dâutilisateurs multiples, le problĂšme dâoptimisation dâallocation conjointe de ressources nâest pas convexe. Afin de le rĂ©soudre, nous proposons une reformulation convexe du problĂšme Ă©quivalente Ă la premiĂšre puis nous proposons deux algorithmes heuristiques dans le but dâavoir un algorithme de formation de cluster Ă complexitĂ© rĂ©duite. LâidĂ©e principale du premier est lâordonnancement des tĂąches de calculs sur les FCs qui les reçoivent. Les ressources de calculs sont ainsi allouĂ©es localement au niveau de la FC. Les tĂąches ne pouvant pas ĂȘtre exĂ©cutĂ©es sont, quant Ă elles, envoyĂ©es Ă une unitĂ© de contrĂŽle (SCM) responsable de la formation des clusters de calculs et de leur exĂ©cution. Le second algorithme proposĂ© est itĂ©ratif et consiste en une formation de cluster au niveau des FCs ne tenant pas compte de la prĂ©sence dâautres demandes de calculs dans le rĂ©seau. Les propositions de cluster sont envoyĂ©es au SCM qui Ă©value la distribution des charges sur les diffĂ©rentes FCs. Le SCM signale tout abus de charges pour que les FCs redistribuent leur excĂšs dans des cellules moins chargĂ©es.Dans la derniĂšre partie de la thĂšse, nous proposons un nouveau concept de mise en cache des calculs dans lâEdge cloud. Afin de rĂ©duire la latence et la consommation Ă©nergĂ©tique des clusters de calculs, nous proposons la mise en cache de calculs populaires pour empĂȘcher leur rĂ©exĂ©cution. Ici, notre contribution est double : dâabord, nous proposons un algorithme de mise en cache basĂ©, non seulement sur la popularitĂ© des tĂąches de calculs, mais aussi sur les tailles et les capacitĂ©s de calculs demandĂ©s, et la connectivitĂ© des FCs dans le rĂ©seau. Lâalgorithme proposĂ© identifie les tĂąches aboutissant Ă des Ă©conomies dâĂ©nergie et de temps plus importantes lorsquâelles sont tĂ©lĂ©chargĂ©es dâun cache au lieu dâĂȘtre recalculĂ©es. Nous proposons ensuite dâexploiter la relation entre la popularitĂ© des tĂąches et la probabilitĂ© de leur mise en cache, pour localiser les emplacements potentiels de leurs copies. La mĂ©thode proposĂ©e est basĂ©e sur ces emplacements, et permet de former des clusters de recherche de taille rĂ©duite tout en garantissant de retrouver une copie en cache
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