826 research outputs found
Bio-Inspired Tools for a Distributed Wireless Sensor Network Operating System
The problem which I address in this thesis is to find a way to organise and manage a network
of wireless sensor nodes using a minimal amount of communication. To find a solution I explore
the use of Bio-inspired protocols to enable WSN management while maintaining a low
communication overhead. Wireless Sensor Networks (WSNs) are loosely coupled distributed
systems comprised of low-resource, battery powered sensor nodes. The largest problem with
WSN management is that communication is the largest consumer of a sensor nodeâs energy.
WSN management systems need to use as little communication as possible to prolong their operational
lifetimes. This is the Wireless Sensor Network Management Problem. This problem
is compounded because current WSN management systems glue together unrelated protocols
to provide system services causing inter-protocol interference. Bio-inspired protocols provide a
good solution because they enable the nodes to self-organise, use local area communication, and
can combine their communication in an intelligent way with minimal increase in communication.
I present a combined protocol and MAC scheduler to enable multiple service protocols to
function in a WSN at the same time without causing inter-protocol interference. The scheduler
is throughput optimal as long as the communication requirements of all of the protocols remain
within the communication capacity of the network. I show that the scheduler improves a dissemination
protocolâs performance by 35%. A bio-inspired synchronisation service is presented
which enables wireless sensor nodes to self organise and provide a time service. Evaluation of
the protocol shows an 80% saving in communication over similar bio-inspired synchronisation
approaches. I then add an information dissemination protocol, without significantly increasing
communication. This is achieved through the ability of our bio-inspired algorithms to combine
their communication in an intelligent way so that they are able to offer multiple services
without requiring a great deal of inter-node communication.Open Acces
A Component Platform for Experimenting with Autonomic Composition
International audienceIn this paper, we propose a component-oriented framework that can support autonomic computing and in particular bio-inspired approaches. Starting from the Grid Component Model, a component model targeting at Grid computing and already featuring some autonomicity, we show how such a model can be used in a general autonomic computing context. Indeed the model provides hierarchical structure and reconfiguration for both functional and non-functional levels. This should ease the development of \textit{self-*} and in particular, self-evolving applications. With our approach, even the autonomic strategies themselves can evolve. We consider this model and its implementation as powerful tools for easily experimenting autonomic behaviours
Dynamic deployment of web services on the internet or grid
PhD ThesisThis thesis focuses on the area of dynamic Web Service deployment for grid and
Internet applications. It presents a new Dynamic Service Oriented Architecture
(DynaSOAr) that enables the deployment of Web Services at run-time in response to
consumer requests.
The service-oriented approach to grid and Internet computing is centred on two
parties: the service provider and the service consumer. This thesis investigates the
introduction of mobility into this service-oriented approach allowing for better use of
resources and improved quality of service. To this end, it examines the role of the
service provider and makes the case for a clear separation of its concerns into two
distinct roles: that of a Web Service Provider, whose responsibility is to receive and
direct consumer requests and supply service implementations, and a Host Provider,
whose role is to deploy services and process consumers' requests on available
resources. This separation of concerns breaks the implicit bond between a published
Web Service endpoint (network address) and the resource upon which the service is
deployed. It also allows the architecture to respond dynamically to changes in service
demand and the quality of service requirements. Clearly defined interfaces for each
role are presented, which form the infrastructure of DynaSOAr. The approach taken
is wholly based on Web Services.
The dynamic deployment of service code between separate roles, potentially running
in different administrative domains, raises a number of security issues which are
addressed. A DynaSOAr service invocation involves three parties: the requesting
Consumer, a Web Service Provider and a Host Provider; this tripartite relationship
requires a security model that allows the concerns of each party to be enforced for a
given invocation. This thesis, therefore, presents a Tripartite Security Model and an
architecture that allows the representation, propagation and enforcement of three
separate sets of constraints.
A prototype implementation of DynaSOAr is used to evaluate the claims made, and
the results show that a significant benefit in terms of round-trip execution time for
data-intensive applications is achieved. Additional benefits in terms of parallel
deployments to satisfy multiple concurrent requests are also shown
Internet of Things Strategic Research Roadmap
Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual âthingsâ have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network
GPU Computing for Cognitive Robotics
This thesis presents the first investigation of the impact of GPU
computing on cognitive robotics by providing a series of novel experiments in
the area of action and language acquisition in humanoid robots and computer
vision. Cognitive robotics is concerned with endowing robots with high-level
cognitive capabilities to enable the achievement of complex goals in complex
environments. Reaching the ultimate goal of developing cognitive robots will
require tremendous amounts of computational power, which was until
recently provided mostly by standard CPU processors. CPU cores are
optimised for serial code execution at the expense of parallel execution, which
renders them relatively inefficient when it comes to high-performance
computing applications. The ever-increasing market demand for
high-performance, real-time 3D graphics has evolved the GPU into a highly
parallel, multithreaded, many-core processor extraordinary computational
power and very high memory bandwidth. These vast computational resources
of modern GPUs can now be used by the most of the cognitive robotics models
as they tend to be inherently parallel. Various interesting and insightful
cognitive models were developed and addressed important scientific questions
concerning action-language acquisition and computer vision. While they have
provided us with important scientific insights, their complexity and
application has not improved much over the last years. The experimental
tasks as well as the scale of these models are often minimised to avoid
excessive training times that grow exponentially with the number of neurons
and the training data. This impedes further progress and development of
complex neurocontrollers that would be able to take the cognitive robotics
research a step closer to reaching the ultimate goal of creating intelligent
machines. This thesis presents several cases where the application of the GPU
computing on cognitive robotics algorithms resulted in the development of
large-scale neurocontrollers of previously unseen complexity enabling the
conducting of the novel experiments described herein.European Commission Seventh Framework
Programm
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Engineering Self-Adaptive Collective Processes for Cyber-Physical Ecosystems
The pervasiveness of computing and networking is creating significant opportunities for building valuable socio-technical systems. However, the scale, density, heterogeneity, interdependence, and QoS constraints of many target systems pose severe operational and engineering challenges. Beyond individual smart devices, cyber-physical collectives can provide services or solve complex problems by leveraging a âsystem effectâ while coordinating and adapting to context or environment change. Understanding and building systems exhibiting collective intelligence and autonomic capabilities represent a prominent research goal, partly covered, e.g., by the field of collective adaptive systems. Therefore, drawing inspiration from and building on the long-time research activity on coordination, multi-agent systems, autonomic/self-* systems, spatial computing, and especially on the recent aggregate computing paradigm, this thesis investigates concepts, methods, and tools for the engineering of possibly large-scale, heterogeneous ensembles of situated components that should be able to operate, adapt and self-organise in a decentralised fashion. The primary contribution of this thesis consists of four main parts. First, we define and implement an aggregate programming language (ScaFi), internal to the mainstream Scala programming language, for describing collective adaptive behaviour, based on field calculi. Second, we conceive of a âdynamic collective computationâ abstraction, also called aggregate process, formalised by an extension to the field calculus, and implemented in ScaFi. Third, we characterise and provide a proof-of-concept implementation of a middleware for aggregate computing that enables the development of aggregate systems according to multiple architectural styles. Fourth, we apply and evaluate aggregate computing techniques to edge computing scenarios, and characterise a design pattern, called Self-organising Coordination Regions (SCR), that supports adjustable, decentralised decision-making and activity in dynamic environments.Con lo sviluppo di informatica e intelligenza artificiale, la diffusione pervasiva di device computazionali e la crescente interconnessione tra elementi fisici e digitali, emergono innumerevoli opportunitĂ per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessitĂ âsi pensi ai livelli, scale, eterogeneitĂ , e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un âeffetto sistemaâ che emerge dalla coordinazione e l'adattamento di componenti fra loro, l'ambiente e il contesto. Comprendere e costruire sistemi in grado di esibire intelligenza collettiva e capacitĂ autonomiche Ăš un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. PerciĂČ, traendo ispirazione e partendo dall'attivitĂ di ricerca su coordinazione, sistemi multiagente e self-*, modelli di computazione spazio-temporali e, specialmente, sul recente paradigma di programmazione aggregata, questa tesi tratta concetti, metodi, e strumenti per l'ingegneria di
ensemble di elementi situati eterogenei che devono essere in grado di lavorare, adattarsi, e auto-organizzarsi in modo decentralizzato. Il contributo di questa tesi consiste in quattro parti principali. In primo luogo, viene definito e implementato un linguaggio di programmazione aggregata (ScaFi), interno al linguaggio Scala, per descrivere comportamenti collettivi e adattativi secondo l'approccio dei campi computazionali. In secondo luogo, si propone e caratterizza l'astrazione di processo aggregato per rappresentare computazioni collettive dinamiche concorrenti, formalizzata come estensione al field calculus e implementata in ScaFi. Inoltre, si analizza e implementa un prototipo di middleware per sistemi aggregati, in grado di supportare piĂč stili architetturali. Infine, si applicano e valutano tecniche di programmazione aggregata in scenari di edge computing, e si propone un pattern, Self-Organising Coordination Regions, per supportare, in modo decentralizzato, attivitĂ decisionali e di regolazione in ambienti dinamici
Opinions and Outlooks on Morphological Computation
Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals â e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system â and plants, but it has also been observed at the cellular and even at the molecular level â as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others
French Roadmap for complex Systems 2008-2009
This second issue of the French Complex Systems Roadmap is the outcome of the
Entretiens de Cargese 2008, an interdisciplinary brainstorming session
organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It
capitalizes on the first roadmap and gathers contributions of more than 70
scientists from major French institutions. The aim of this roadmap is to foster
the coordination of the complex systems community on focused topics and
questions, as well as to present contributions and challenges in the complex
systems sciences and complexity science to the public, political and industrial
spheres
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