A User Interface for Resource Management in a Mobile Environment

International audienceThis paper describes a user interface for QoS management in mobile IP networks. The paper context is built in conformance with the generic signaling environment, which is standardized by the NSIS IETF working group. In this work, we investigate the use of some techniques of the AI (Artificial Intelligence) domain to implement a user interface called NIA (Negotiation Individual Assistant) in order to determine the QoS profile and negotiate the QoS parameters in the new domain after the handover. Therefore, we use the connectionist learning in the management of the negotiation profiles and the agent technology to help the user to choose the best service provider, dynamically negotiate the QoS on the user's behalf, follow the user's behaviour to be able to anticipate the negotiation and manage the renegotiation. The resource management, presented in this work, provides to mobile terminals the required QoS based on user's mobility and QoS profile. This QoS profile is determined by the NIA

Dynamique de l'environnement : Scénarios, simulations et maquette

IP-SIG/LIV/4.2. Contrat RNRT IPSIGCe livrable traite de la négociation dynamique de SLA/SLS et du maintien des paramètres négociés lors de la mobilité du terminal dans le cadre d'une signalisation NSIS. Il fait suite au livrable 4.1 qui présentait un assistant de négociation, coté terminal utilisateur, permettant de négocier dynamiquement les SLA/SLS avec le/les fournisseurs de service, et plusieurs protocoles conformes à l'environnement NSIS afin de négocier le SLS, et maintenir la qualité de service négociée. Il s'agit dans ce livrable de compléter l'approche retenue en présentant des scénarios d'utilisation, notamment dans le cas du protocole de négociation dynamique de paramètres de SLS, des résultats de simulations, principalement pour le protocole de réservation de ressources à l'avance et une maquette permettant de démontrer la faisabilité de la (re)négociation dynamique de SLA/SLS. Cette dernière comporte un module d'apprentissage et un système multi-agents

Service Level Agreement-based adaptation management for Internet Service Provider (ISP) using Fuzzy Q-learning

Internet access is the vital catalyst for online users, and the number of mobile subscribers is predicted to grow from dramatically in the next few years. This huge demand is the main issue facing the Internet Service Providers (ISPs) who need to handle users’ expectations along with their current resources. An adaptive mechanism within the ISPs architecture is a promising solution to handle such situation. A Service Level Agreement (SLA)is the legal catalyst to monitor any contract violation between end users and ISPs and is embedded within a Quality of Service (QoS) framework. It strengthens and advances the quality of control over the user’s application and network resources and can be further stretched to fulfill the QoS terms through negotiation and re-negotiation. Moreover, the present literature does not focus on the combination of rule-based approaches and adaptation together to update the established learning repository. Therefore, this mainstream of this research in the context of SLAs is to fill in this gap by addressing the combination of rule-base uncertainties and iteration of the learning ability. The key to the proposed architecture is the utilization of self - * capabilities designed to have self-management over uncertainties and the provision of self-adaptive interactions. Thus, the Monitor, Analyse, Plan, Execute and Knowledge Base (MAPE-K) approach is able to deal with this problem together with the integration of Fuzzy and Q-Learning algorithms. The proposed architecture is in the context of autonomic computing. An adaptation manager is the main proposed component to update admission control on the ISP current resources and the ability to manage SLAs. A general methodology type-2 fuzzy logic is applied to ensure the uncertainties and precise decision-making are well addressed in this research. The proposed solution, demonstrating Q-Learning works adaptive with QoS parameters, e.g. Latency, Availability and Packet Loss. With the combination of fuzzy and Q-Learning, we demonstrate that the proposed adaptation manager is able to handle the uncertainties and learning abilities. Q-Learning is able to identify the initial state from various ISPs iterations and update them with appropriate actions, reflecting the reward configurations. The higher the iterations process the higher is the increase the learning ability,rewards and exploration probability. The research outcomes benefit the SLA framework by incorporating the information for SLA policies and Service Level Objectives (SLOs). Lastly, an important contribution is the ability to demonstrate that the MAPE-K approach is a contender for ISP SLA-based frameworks for QoS provision

HIDE: User centred Domotic evolution toward Ambient Intelligence

Pervasive Computing and Ambient Intelligence (AmI) visions are still far from being achieved, especially with regard to Domotics and home applications. According to the vision of Ambient Intelligence (AmI), the most advanced technologies are those that disappear: at maturity, computer technology should become invisible. All the objects surrounding us must possess sufficient computing capacity to interact with users, the surroundings and each other. The entire physical environment in which users are immersed should thus be a hidden computer system equipped with the appropriate software in order to exhibit intelligent behavior. Even though many implementations have started to appear in several contexts, few applications have been made available for the home environment and the general public. This is mainly due to the segmentation of standards and proprietary solutions, which are currently confusing the market with a sparse offer of uninteroperable devices and systems. Although modern houses are equipped with smart technological appliances, still very few of these appliances can be seamlessly connected to each other. The objective of this research work is to take steps in these directions by proposing, on the one hand, a software system designed to make today’s heterogeneous, mostly incompatible domotic systems fully interoperable and, on the other hand, a feasible software application able to learn the behavior and habits of home inhabitants in order to actively contribute to anticipating user needs, and preventing emergency situations for his health. By applying machine learning techniques, the system offers a complete, ready-to-use practical application that learns through interaction with the user in order to improve life quality in a technological living environment, such as a house, a smart city and so on. The proposed solution, besides making life more comfortable for users without particular needs, represents an opportunity to provide greater autonomy and safety to disabled and elderly occupants, especially the critically ill ones. The prototype has been developed and is currently running at the Pisa CNR laboratory, where a home environment has been faithfully recreated

ACUTA Journal of Telecommunications in Higher Education

In This Issue Roundtable Discussion: Convergence: Dealing with Change Convergence: A Framework for Change Planning for Competition and Convergence Voice and Telephony over IP: Risk Dimensions and Solutions Cybervandals: Fending Off Attacks Why Vendors See Colleges as a Key Market Segment Campus Profile: Clarion University of Pennsylvania Called to Serve: The Conscripted Consultant Column

Service Oriented Mobile Computing

La diffusione di concetti quali Pervasive e Mobile Computing introduce nell'ambito dei sistemi distribuiti due aspetti fondamentali: la mobilità dell'utente e l'interazione con l'ambiente circostante, favorite anche dal crescente utilizzo di dispositivi mobili dotati di connettività wireless come prodotti di consumo. Per estendere le funzionalità introdotte nell'ambito dei sistemi distribuiti dalle Architetture Orientate ai Servizi (SOA) e dal paradigma peer-to-peer anche a dispositivi dalle risorse limitate (in termini di capacità computazionale, memoria e batteria), è necessario disporre di un middleware leggero e progettato tenendo in considerazione tali caratteristiche. In questa tesi viene presentato NAM (Networked Autonomic Machine), un formalismo che descrive in modo esaustivo un sistema di questo tipo; si tratta di un modello teorico per la definizione di entità hardware e software in grado di condividere le proprie risorse in modo completamente altruistico. In particolare, il lavoro si concentra sulla definizione e gestione di un determinato tipo di risorse, i servizi, che possono essere offerti ed utilizzati da dispositivi mobili, mediante meccanismi di composizione e migrazione. NSAM (Networked Service-oriented Autonomic Machine) è una specializzazione di NAM per la condivisione di servizi in una rete peer-to-peer, ed è basato su tre concetti fondamentali: schemi di overlay, composizione dinamica di servizi e auto-configurazione dei peer. Nella tesi vengono presentate anche diverse attività applicative, che fanno riferimento all'utilizzo di due middleware sviluppati dal gruppo di Sistemi Distribuiti (DSG) dell'Università di Parma: SP2A (Service Oriented Peer-to-peer Architecture), framework per lo sviluppo di applicazioni distribuite attraverso la condivisione di risorse in una rete peer-to-peer, e Jxta-Soap che consente la condivisione di Web Services in una rete peer-to-peer JXTA. Le applicazioni realizzate spaziano dall'ambito della logistica, alla creazione di comunità per l'e-learning, all'Ambient Intelligence alla gestione delle emergenze, ed hanno come denominatore comune la creazione di reti eterogenee e la condivisione di risorse anche tra dispositivi mobili. Viene inoltre messo in evidenza come tali applicazioni possano essere ottimizzate mediante l'introduzione del framework NAM descritto, per consentire la condivisione di diversi tipi di risorse in modo efficiente e proattivo

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

A situational awareness model for data analysis on 5G mobile networks : the SELFNET analyzer framework

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 14-07-2017Se espera que las redes 5G provean un entorno seguro, con able y de alto rendimiento con interrupciones m nimas en la provisi on de servicios avanzados de red, sin importar la localizaci on del dispositivo o cuando el servicio es requerido. Esta nueva generaci on de red ser a capaz de proporcionar altas velocidades, baja latencia y mejor Calidad de Servicio (QoS) comparado con las redes actuales Long Term Evolution (LTE). Para proveer estas capacidades, 5G propone la combinaci on de tecnolog as avanzadas tales como Redes De nidas por Software (SDN), Virtualizaci on de las Funciones de Red (NFV), Redes auto-organizadas (SON) e Inteligencia Arti cial. De manera especial, 5G ser a capaz de solucionar o mitigar cambios inesperados o problemas t picos de red a trav es de la identi caci on de situaciones espec cas, tomando en cuenta las necesidades del usuario y los Acuerdos de Nivel de Servicio (SLAs). Actualmente, los principales operadores de red y la comunidad cient ca se encuentran trabajando en estrategias para facilitar el an alisis de datos y el proceso de toma de decisiones cuando eventos espec cos comprometen la salud de las redes 5G. Al mismo tiempo, el concepto de Conciencia Situacional (SA) y los modelos de gesti on de incidencias aplicados a redes 5G est an en etapa temprana de desarrollo. La idea principal detr as de estos conceptos es prevenir o mitigar situaciones nocivas de manera reactiva y proactiva. En este contexto, el proyecto Self-Organized Network Management in Virtualized and Software De ned Networks (SELFNET) combina los conceptos de SDN, NFV and SON para proveer un marco de gesti on aut onomo e inteligente para redes 5G. SELFNET resuelve problemas comunes de red, mientras mejora la calidad de servicio (QoS) y la Calidad de Experiencia (QoE) de los usuarios nales...5G networks hope to provide a secure, reliable and high-performance environment with minimal disruptions in the provisioning of advanced network services, regardless the device location or when the service is required. This new network generation will be able to deliver ultra-high capacity, low latency and better Quality of Service (QoS) compared with current Long Term Evolution (LTE) networks. In order to provide these capabilities, 5G proposes the combination of advanced technologies such as Software De ned Networking (SDN), Network Function Virtualization (NFV), Self-organized Networks (SON) or Arti cial Intelligence. In particular, 5G will be able to face unexpected changes or network problems through the identi cation of speci c situations, taking into account the user needs and the Service Level Agreements (SLAs). Nowadays, the main telecommunication operators and community research are working in strategies to facilitate the data analysis and decision-making process when unexpected events compromise the health in 5G Networks. Meanwhile, the concept of Situational Awareness (SA) and incident management models applied to 5G Networks are also in an early stage. The key idea behind these concepts is to mitigate or prevent harmful situations in a reactive and proactive way. In this context, Self-Organized Network Management in Virtualized and Software De ned Networks Project (SELFNET) combines SDN, NFV and SON concepts to provide a smart autonomic management framework for 5G networks. SELFNET resolves common network problems, while improving the QoS and Quality of Experience (QoE) of end users...Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu