LTSA-PCA : Tool support for compositional reliability analysis

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Software systems are constructed by combining new and existing services and components. Models that represent an aspect of a system should therefore be compositional to facilitate reusability and automated construction from the representation of each part. In this paper we present an extension to the LTSA tool  that provides support for the specification, visualisation and analysis of composable probabilistic behaviour of a component-based system using Probabilistic Component Automata (PCA). These also include the ability to specify failure scenarios and failure handling behaviour. Following composition, a PCA that has full probabilistic information can be translated to a DTMC model for reliability analysis in PRISM. Before composition, each component can be reduced to its interface behaviour in order to mitigate state explosion associated with composite representations, which can significantly reduce the time to analyse the reliability of a system. Moreover, existing behavioural analysis tools in LTSA can also be applied to PCA representations.

LTSA-PCA software
Examples
Demonstration Video
P. Rodrigues, E. Lupu and J. Kramer LTSA-PCA: Tool Support for Compositional Reliability Analysis, ICSE 2014, (formal demonstrations), Hyderabad, May 31 – June 7, 2014. download preprint of the paper.

On Efficient Meta-Data Collection for Crowdsensing

Dickens, L. and Lupu, E.  On Efficient Meta-Data Collection for Crowdsensing. In Crowdsensing Workshop at PerCom, 2014. (To appear.)

Building trustworthy systems that themselves rely on, or integrate, semi-trusted information sources is a challenging aim, but doing so allows us to make good use of floods of information continuously contributed by individuals and small organisations. This paper addresses the problem of quickly and efficiently acquiring high quality meta-data from human contributors, in order to support crowdsensing applications.

Crowdsensing (or participatory sensing) applications have been used to sense, measure and map a variety of phenomena, including: individuals’ health, mobility & social status; fuel & grocery prices; air quality & pollution levels; biodiversity; transport infrastructure; and route-planning for drivers & cyclists. Crowdsensing applications have an on-going requirement to turn raw data into useful knowledge, and to achieve this, many rely on prompt human generated meta-data to support and/or validate the primary data payload. These human contributions are inherently error prone and subject to bias and inaccuracies, so multiple overlapping labels are needed to cross-validate one another. While probabilistic inference can be used to reduce the required label overlap, there is a particular need in crowdsensing to minimise the overhead and improve the accuracy of timely label collection. This paper presents three general algorithms for efficient human meta-data collection, which support different constraints on how the central authority collects contributions, and three methods to intelligently pair annotators with tasks based on formal information theoretic principles. We test our methods’ performance on challenging synthetic data-sets, based on r eal data, and show that our algorithms can significantly lower the cost and improve the accuracy of human meta-data labelling, with a corresponding increase in the average novel information content from new labels.

 

Daniele Sgandurra

Daniele joined the group as a Research Associate after having completed a PhD at University of Pisa, and having worked at IIT-CNR as a PostDoc Researcher. Daniele’s main research fields include virtualization and cloud security, threat modeling, mobile security, malware analysis and  critical infrastructures and risk management.

His personal homepage can be found here. Daniele is now a Lecturer in the Security Group at Royal Holloway, University of London

Luke Dickens

Luke Dickens
Luke Dickens

Luke joined the group as a Research Associate after having completed a PhD at Imperial College London under the supervision of Dr Alessandra Russo. Luke focuses on machine learning techniques and is working on techniques for dealing with partially trusted sources of information in particular in crowdsourcing scenarios. Luke is now a lecturer at University College London.

Policy Refinement

Layered refinement with interleaved transformations
Layered refinement with interleaved transformations

Refining policies from high level goals to enforceable specifications in asemi-automated and principled ways remains one of the most significant challenges in policy based systems. We have on two occasions attempted to tackle this challenges in collaboration with Dr Alessandra Russo at Imperial, Dr Arosha Bandara at the Open University and Dr Jorge Lobo at IBM. The first attempt wast done during the Dr Bandara’s PhD thesis. …

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Self-Managed Cell

smc
Self-Managed Cell Architecture

The Self-Managed Cell is an architectural pattern for building autonomous pervasive systems. It was developed in collaboration with Prof. Joe Sventek at the University of Glasgow, and with my colleagues Dr. Narnaker Dulay and Prof. Morris Sloman at Imperial College.

Enrico Scalavino

enricoscalavinoEnrico designed a framework for secure dissemination of information in partially disconnected environments as part of the Consequence project. This was in particular applied to information exchanges between first responder organisations in crisis management scenario. His work also investigated the protection of derived data. After successfully completing his PhD under the supervision of Dr Emil Lupu, Enrico took a position with Morgan Stanley. He is now a Senior Software engineer at Microsoft.

Alberto Egon Schaeffer Filho

albertoAlberto did his PhD thesis under the supervision of Dr Emil Lupu and contributed significantly to our work on the Self Managed Cells. In particular, his work focussed on means of realising autonomous interactions, compositions and federations of cells in a recursive fashion. After completing his PhD, Alberto became a Research Associate at Lancaster University. He is now an Associate Professor at the Federal University of Rio Grande do Sul (UFRGS). His web page is here.

Ponder2

ponder2
Le Penseur

Ponder2 combines a general-purpose, distributed object management system with a Domain Service, Obligation Policy Interpreter, Command Interpreter and Authorisation Enforcement. The Domain Service provides an hierarchical structure for managing objects. The Obligation Policy Interpreter handles Event, Condition, Action rules (ECA). The Command Interpreter accepts a set of commands, compiled from a high-level language called PonderTalk, via a number of communications interfaces which may perform invocations on a ManagedObjectregistered in the Domain Service. The Authorisation Enforcement caters for both positive and negative authorisation policies, provides the ability to specify fine grained authorisations for every object and implements domain nesting algorithms for conflict resolution. …

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Kevin Twidle

kevinDr. Kevin Twidle has been a long time member and associate of the Distributed Software Engineering Group at Imperial College. In recent years he has been the main designer and implementer of the Ponder2 software, which was used within Imperial and by outside organisations in a broad range of contexts including for the management of Body Sensor Networks for eHealth, Autonomous Vehicles, Heterogeneous Networks, Mobile Workflows and Distributed Authorisation Systems. He is now an IT consultant based in France but working world wide.

Sye Loong Keoh

slkSye-Loong obtained his PhD in 2005 under the supervision of Dr. Emil Lupu. He then worked with us as a Research Associate until 2008 when he joined Philips Research in Eindhoven. In 2013, Sye-Loong left industry to return to academia and was appointed as an Assistant Professor with the University of Glasgow in Singapore. His current  web page is here.

Consequence: Context-Aware Data Centric Information Sharing

fp7logo“Consequence” was a collaborative research project partly funded by the European Commission, which ran between 2008 and 2011. The project built a comprehensive framework for controlled dissemination of information based on Data Sharing Agreements and a combination of technologies based on rights management and usage control policies. The framework was demonstrated within the context of sharing large scale scientific data and also for controlled sharing of information between first responder organisations in crisis management scenarios. More details, videos, papers and project deliverable are available from the Consequence web site.

Arosha Bandara

aroshabandaraDr Arosha Bandara obtained his PhD under the supervision of Dr Emil Lupu and Dr Alessandra Russo. He is now with the Open University and still collaborates frequently with Imperial.

CoCo Cloud: Confidential and Compliant Clouds

fp7logoFP7, Partners: Hewlett-Packard, The Italian National Research Council, Imperial College London, University of Oslo, SAP, Atos, AGID, Bird & Bird, and Grupo Hospitalario Quirón.

The project aims to facilitate data sharing in cloud environments by providing end-to-end data centric security from the client to the cloud based on the (semi-)automated enforcement ofData Sharing Agreements. These agreements may reflect legal, contractual or user defined preferences, which may be conflicting and thus an appropriate balance and model for their enforcement must be found.

 

Privacy Dynamics: Learning from the Wisdom of Groups

EP/K033425/1
EP/K033425/1

The project aims to study privacy management by investigating how individuals learn and benefit from their membership of social or functional groups, and how such learning can be automated and incorporated in modern mobile and ubiquitous technologies that increasingly pervade society.  The project will focus on addressing the privacy concerns of individuals in the context of their use of pervasive technologies, such as Smartphones and Clouds and aims to contribute in three research areas: (1) engineering of adaptive systems that guide their users to manage their privacy; (2) the development of logic-based machine learning techniques to alleviate the cognitive and physical load of personalising users’ privacy requirements; and (3) empirical investigation of the privacy behaviour of and in groups, in the context of both collaboration and conflict. This is a joint project with The Open University and the University of Exeter, funded by the EPSRC (Grant No EP/K033425/1). At Imperial the project is led by Dr Alessandra Russo.

 

Catalysing economic growth: releasing the value of big data

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EP/K039504/1

How will Big Data affect innovation, growth and well-being in the UK economy? By Big Data we mean very large or complex datasets that are constantly accumulating in society because of the dramatically increased ability to sense, capture, store and analyse information about social, economic, or scientific phenomena. We anticipate that Big Data and associated analytics may ultimately transform how societies and communities view themselves, and how governments, large corporations, and entrepreneurial startup companies relate to those populations. Therefore it is essential to consider how Big Data might contribute to economic growth and generate opportunities for innovation for UK companies. …

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