09201 Abstracts Collection -- Self-Healing and Self-Adaptive Systems

From May 10th 2009 to May 15th 2009 the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. Links to extended abstracts or full papers are provided, if available. A description of the seminar topics, goals and results in general can be found in a separate document ``Executive Summary\u27\u27

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

Self-* properties of multi sensing entities in smart environments

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 78-87).Computers and sensors are more and more often embedded into everyday objects, woven into garments, "painted" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks.by Arnaud Pilpré.S.M

Autonomic ubiquitous computing: a home environment management system

Tese de doutoramento em Electrónica Industrial (ramo do conhecimento Informática Industrial)The Ubiquitous Computing and Autonomic Computing reached a point of convergence in which pervasive technology in the environment meets the ability of people to interact with it, making use of all the possibilities made available by this technology. Ubiquitous computing envisions a habitat where the abundance of devices, services and applications allows the physical and virtual worlds to become seamlessly merged. The promise of ubiquitous computing environments is not feasible unless these systems can effectively "disappear". In order to achieve this goal, they need to become autonomic, by managing their own evolution and configuration with minimal user intervention. It is in this context that aspects like self-configuration and self-healing from the autonomic computing concept were adopted in this project. The context awareness and the creation of applications which use that context are the core concern of Ubiquitous Computing Systems and represent the fundamentals for autonomic actions in this type of systems. Such research raises questions on context acquisition, distribution and manipulation, as well as on artificial intelligence algorithms that decide autonomic actions in the environment, having implications in the human interaction with Autonomic Ubiquitous Systems. The research presented in this thesis concentrates on some of those issues. During this project it was developed an experimental setup for context acquisition, in an effortless way, of some activities of a small group of users. This experimental setup was installed in a real home where a young family, a couple and a small child, were actually living. This experimental setup was mainly responsible for the control of the light system of the house, by a network of several inter-connected devices scattered in the home with limited resources. This prototype installation allowed the validation of the system ability, to capture daily life behaviour patterns of the inhabitants. The system architecture was designed based on the concept of a high level and a low level autonomic management system taking from nature the model of the human reflex arc. A reflexive behaviour is managed at a local level by the small devices, with limited resources, high level management is responsible for processing and analysis of the events broadcast by the group of small devices, and run in a centralized mode in a PC. The concept of device information broadcast, to the communication medium, as events was used as an approach to: inter-connect future systems, monitor correct operation of the system devices, capture raw data for estimation of context; allow the visualization of system feedback in user interface devices. Finally, an algorithm using artificial neural networks in combination with simple statistics was developed which allowed the house to learn the routines of its inhabitants, making it truly intelligent by embedding the knowledge about patterns of activities of the users in the devices scattered in the environment, increasing their comfort and, at same time, leading to more energy efficiency. The analysis of the data captured, during two complete years, shows that the reduction of power consumption could be as high as 50%, depending on the profile of the usage of the light.A Computação Ubíqua e a Computação Autónoma atingiram um ponto de convergência no qual a tecnologia dispersa nos ambientes, juntamente com a capacidade das pessoas interagirem, permite tirar partido do seu uso para novas potencialidades. A computação ubíqua vislumbra habitats repletos de dispositivos, serviços e aplicações que permitem a união perfeita do mundo real com o mundo virtual, mas de uma forma natural. A promessa da criação de tais ambientes de computação ubíqua não se tornará possível sem que a complexidade destes sistemas “desapareçam” efectivamente da percepção dos utilizadores. Para que isso seja possível, estes necessitam de ser autónomos, gerindo a sua própria evolução e configuração com o mínimo de intervenção do utilizador. É neste contexto que a noção de Sistemas Ubíquos Autónomos envolvendo as facetas de auto-configuração e auto-reparação derivadas do conceito da computação autónoma, será usada nesta tese. A percepção do contexto e a criação de aplicações que o usam são as principais preocupações na investigação dos Sistemas de Computação Ubíqua, constituindo também a base para as acções autónomas neste tipo de sistemas. Essa investigação levanta questões sobre a forma como o contexto é capturado, distribuído e manipulado. Por outro lado, provoca impacto nos algoritmos de inteligência artificial que efectuam as decisões de acções autónomas no ambiente, afectando consequentemente a interacção humana com os Sistemas Ubíquos Autónomos. A investigação apresentada nesta dissertação concentra-se efectivamente em alguns destes aspectos. Durante a tese foi desenvolvido um sistema experimental com o objectivo de capturar o contexto, de uma forma perceptível, das actividades de um pequeno grupo de utilizadores. Este sistema experimental foi instalado numa casa real, onde vive uma jovem família constituída por uma casal e uma pequena criança. O sistema experimental era responsável por controlar toda a iluminação eléctrica da casa, através de um conjunto de dispositivos, com recursos limitados, conectados em rede e espalhados pela casa. A instalação permitiu validar a capacidade do sistema de capturar os padrões de comportamento quotidiano dos habitantes da casa. A arquitectura do sistema foi projectada baseando-se no conceito de alto-nível e baixo-nivel dos sistemas de gestão autónoma, inspirando-se no modelo dos processos que ocorrem num acto reflexo no corpo humano. As acções de reflexo ou acções básicas são geridas pelo baixo-nivel nos pequenos dispositivos e com recursos limitados, e quanto o gestão de alto-nivel é responsável pelo processamento e analise dos eventos disponíveis no barramento de dados da rede dos pequenos dispositivos. Foi também usado o conceito da difusão (broadcast) da informação, para o barramento de dados, na forma de eventos para permitir: a interligação com sistema futuros, monitorização do correcto funcionamento do sistema, captura da informação para posterior determinação do contexto; e por fim permitir a visualização do estado do sistema na interface com os utilizadores. Por último, foi desenvolvido um algoritmo usando redes neuronais artificiais e em combinação com estatística básica que permite aprender, de uma forma autónoma, as rotinas dos habitantes em casa, conferindo a esta um ambiente inteligente. Desta forma, a casa contém o conhecimento dos padrões quotidianos dos habitantes, aumentando consequentemente o seu conforto e ao mesmo tempo, permitindo melhor eficiência energética. As análises dos dados capturados, durante dois anos completos, mostram que a redução no consumo energético pode chegar os 50%, dependendo do perfil de uso da iluminação.Fundação para a Ciência e a Tecnologia (FCT)Scholarship number SFRH/BD/8290/2004