2,274 research outputs found
Synchronous Counting and Computational Algorithm Design
Consider a complete communication network on nodes, each of which is a
state machine. In synchronous 2-counting, the nodes receive a common clock
pulse and they have to agree on which pulses are "odd" and which are "even". We
require that the solution is self-stabilising (reaching the correct operation
from any initial state) and it tolerates Byzantine failures (nodes that
send arbitrary misinformation). Prior algorithms are expensive to implement in
hardware: they require a source of random bits or a large number of states.
This work consists of two parts. In the first part, we use computational
techniques (often known as synthesis) to construct very compact deterministic
algorithms for the first non-trivial case of . While no algorithm exists
for , we show that as few as 3 states per node are sufficient for all
values . Moreover, the problem cannot be solved with only 2 states per
node for , but there is a 2-state solution for all values .
In the second part, we develop and compare two different approaches for
synthesising synchronous counting algorithms. Both approaches are based on
casting the synthesis problem as a propositional satisfiability (SAT) problem
and employing modern SAT-solvers. The difference lies in how to solve the SAT
problem: either in a direct fashion, or incrementally within a counter-example
guided abstraction refinement loop. Empirical results suggest that the former
technique is more efficient if we want to synthesise time-optimal algorithms,
while the latter technique discovers non-optimal algorithms more quickly.Comment: 35 pages, extended and revised versio
ABOUT THE COMPLEXITY OF LIVING SYSTEMS MODELS
In this paper we attempt an overview of the philosophical implications of complex systems thought, and investigate how this alternative viewpoint affects our attempts to design and utilise models for living systems. We classify the types of complex system that relate to self-organisation. The overall requirements for self-organising modeling are considered and some alternative ways of looking at some specific problems that may arise are explored. As a novelty, the paper proposes various ways of moving forward in the area of practical model design.complex systems, models, practical desing
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 “system effect” 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 “dynamic collective computation” 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 opportunità per la costruzione di sistemi socio-tecnici di nuova generazione. Tuttavia, l'ingegneria di tali sistemi presenta notevoli sfide, data la loro complessità—si pensi ai livelli, scale, eterogeneità, e interdipendenze coinvolti. Oltre a dispositivi smart individuali, collettivi cyber-fisici possono fornire servizi o risolvere problemi complessi con un “effetto sistema” 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 capacità autonomiche è un importante problema di ricerca studiato, ad esempio, nel campo dei sistemi collettivi adattativi. Perciò, traendo ispirazione e partendo dall'attività 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 più 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, attività decisionali e di regolazione in ambienti dinamici
A new analysis of debris mitigation and removal using networks
Modelling studies have shown that the implementation of mitigation guidelines, which aim to reduce the amount of new debris generated on-orbit, is an important requirement of future space activities but may be insufficient to stabilise the near-Earth debris environment. The role of a variety of mitigation practices in stabilising the environment has been investigated over the last decade, as has the potential of active debris removal (ADR) methods in recent work. We present a theoretical approach to the analysis of the debris environment that is based on the study of networks, composed of vertices and edges, which describe the dynamic relationships between Earth satellites in the debris system. Future projections of the 10 cm and larger satellite population in a non-mitigation scenario, conducted with the DAMAGE model, are used to illustrate key aspects of this approach. Information from the DAMAGE projections are used to reconstruct a network in which vertices represent satellites and edges encapsulate conjunctions between collision pairs. The network structure is then quantified using statistical measures, providing a numerical baseline for this future projection scenario. Finally, the impact of mitigation strategies and active debris removal, which can be mapped onto the network by altering or removing edges and vertices, can be assessed in terms of the changes from this baseline. The paper introduces the network methodology, highlights the ways in which this approach can be used to formalise criteria for debris mitigation and removal. It then summarises changes to the adopted network that correspond to an increasing stability and changes that represent a decreasing stability of the future debris environment
Self-stabilising Byzantine Clock Synchronisation is Almost as Easy as Consensus
We give fault-tolerant algorithms for establishing synchrony in distributed
systems in which each of the nodes has its own clock. Our algorithms
operate in a very strong fault model: we require self-stabilisation, i.e., the
initial state of the system may be arbitrary, and there can be up to
ongoing Byzantine faults, i.e., nodes that deviate from the protocol in an
arbitrary manner. Furthermore, we assume that the local clocks of the nodes may
progress at different speeds (clock drift) and communication has bounded delay.
In this model, we study the pulse synchronisation problem, where the task is to
guarantee that eventually all correct nodes generate well-separated local pulse
events (i.e., unlabelled logical clock ticks) in a synchronised manner.
Compared to prior work, we achieve exponential improvements in stabilisation
time and the number of communicated bits, and give the first sublinear-time
algorithm for the problem:
- In the deterministic setting, the state-of-the-art solutions stabilise in
time and have each node broadcast bits per time
unit. We exponentially reduce the number of bits broadcasted per time unit to
while retaining the same stabilisation time.
- In the randomised setting, the state-of-the-art solutions stabilise in time
and have each node broadcast bits per time unit. We
exponentially reduce the stabilisation time to while each node
broadcasts bits per time unit.
These results are obtained by means of a recursive approach reducing the
above task of self-stabilising pulse synchronisation in the bounded-delay model
to non-self-stabilising binary consensus in the synchronous model. In general,
our approach introduces at most logarithmic overheads in terms of stabilisation
time and broadcasted bits over the underlying consensus routine.Comment: 54 pages. To appear in JACM, preliminary version of this work has
appeared in DISC 201
Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution
Epileptic seizures are characterised by abnormal brain dynamics at multiple
scales, engaging single neurons, neuronal ensembles and coarse brain regions.
Key to understanding the cause of such emergent population dynamics, is
capturing the collective behaviour of neuronal activity at multiple brain
scales. In this thesis I make use of the larval zebrafish to capture single
cell neuronal activity across the whole brain during epileptic seizures.
Firstly, I make use of statistical physics methods to quantify the collective
behaviour of single neuron dynamics during epileptic seizures. Here, I
demonstrate a population mechanism through which single neuron dynamics
organise into seizures: brain dynamics deviate from a phase transition.
Secondly, I make use of single neuron network models to identify the synaptic
mechanisms that actually cause this shift to occur. Here, I show that the
density of neuronal connections in the network is key for driving generalised
seizure dynamics. Interestingly, such changes also disrupt network response
properties and flexible dynamics in brain networks, thus linking microscale
neuronal changes with emergent brain dysfunction during seizures. Thirdly, I
make use of non-linear causal inference methods to study the nature of the
underlying neuronal interactions that enable seizures to occur. Here I show
that seizures are driven by high synchrony but also by highly non-linear
interactions between neurons. Interestingly, these non-linear signatures are
filtered out at the macroscale, and therefore may represent a neuronal
signature that could be used for microscale interventional strategies. This
thesis demonstrates the utility of studying multi-scale dynamics in the larval
zebrafish, to link neuronal activity at the microscale with emergent properties
during seizures
Cooperative mobility maintenance techniques for information extraction from mobile wireless sensor networks
Recent advances in the development of microprocessors, microsensors, ad-hoc wireless networking and information fusion algorithms led to increasingly capable Wireless Sensor Networks (WSNs). Besides severe resource constraints, sensor nodes mobility is considered a fundamental characteristic of WSNs.
Information Extraction (IE) is a key research area within WSNs that has been characterised in a variety of ways, ranging from a description of its purposes to reasonably abstract models of its processes and components. The problem of IE is a challenging task in mobile WSNs for several reasons including: the topology changes rapidly; calculation of trajectories and velocities is not a trivial task; increased data loss and data delivery delays; and other context and application specific challenges. These challenges offer fundamentally new research problems.
There is a wide body of literature about IE from static WSNs. These approaches are proved to be effective and efficient. However, there are few attempts to address the problem of IE from mobile WSNs. These attempts dealt with mobility as the need arises and do not deal with the fundamental challenges and variations introduced by mobility on the WSNs.
The aim of this thesis is to develop a solution for IE from mobile WSNs. This aim is achieved through the development of a middle-layer solution, which enables IE approaches that were designed for the static WSNs to operate in the presence of multiple mobile nodes. This thesis contributes toward the design of a new self-stabilisation algorithm that provides autonomous adaptability against nodes mobility in a transparent manner to both upper network layers and user applications. In addition, this thesis proposes a dynamic network partitioning protocol to achieve high quality of information, scalability and load balancing.
The proposed solution is flexible, may be applied to different application domains, and less complex than many existing approaches. The simplicity of the solutions neither demands great computational efforts nor large amounts of energy conservation. Intensive simulation experiments with real-life parameters provide evidence of the efficiency of the proposed solution. Performance experimentations demonstrate that the integrated DNP/SS protocol outperforms its rival in the literature in terms of timeliness (by up to 22%), packet delivery ratio (by up to 13%), network scalability (by up to 25%), network lifetime (by up to 40.6%), and energy consumption (by up to 39.5%). Furthermore, it proves that DNP/SS successfully allows the deployment of static-oriented IE approaches in hybrid networks without any modifications or adaptations
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