Saad Liaquat Kiani

Survey of Context Provisioning Middleware

In the scope of ubiquitous computing, one of the key issues is the awareness of context, which includes diverse aspects of the users situation including his activities, physical surroundings, location, emotions and social relations, device and network characteristics and their interaction with each other. This contextual knowledge is typically acquired from physical, virtual or logical sensors. To overcome problems of heterogeneity and hide complexity, a significant number of middleware approaches have been proposed for systematic and coherent access to manifold context parameters. These frameworks deal particularly with context representation, context management and reasoning, i.e. deriving abstract knowledge from raw sensor data. This article surveys not only related work in these three categories but also the required evaluation principles.

Cloud Based Big Data Analytics for Smart Future Cities

ICT is becoming increasingly pervasive to urban environments and providing the necessary basis for sustainability and resilience of the smart future cities. Often ICT tools for a smart city deal with different application domains e.g. land use, transport, energy, and rarely provide an integrated information perspective to deal with sustainability and socioeconomic growth of the city. Smart cities can benefit from such information using Big, and often real-time cross-thematic, data collection, processing, integration and sharing through inter-operable services deployed in a Cloud environment. However, such information utilisation requires appropriate software tools, services and technologies to collect, store, analyse and visualise large amounts of data from the city environment, citizens and various departments and agencies at city scale. This paper presents a theoretical perspective on the smart cities focused Big data processing and analysis by proposing a Cloud-based analysis service that can be further developed to generate information intelligence and support decision-making in smart future cities context.

ContextML: A light-weight context representation and context management schema

Context representation is a fundamental process in developing context aware systems for the pervasive world. We present a light weight XML based context representation schema called ContextML in which context information is categorized into scopes and related to different types of entities (e.g. user, device). The schema is also applied for encoding management messages in order to allow for a flexible framework supporting gradual plug & play extendibility and mobility. ContextML is tailored to be used for REST-based communication between the framework components. Explanation of the schema is provided with the help of real world examples. Moreover, the European C-CAST testbed is introduced, embracing a variety of context providers and application domains.

A Federated Broker Architecture for Large Scale Context Dissemination

Dissemination of context data from context producing entities to context consuming entities is a fundamental functional task of context aware systems. Broker based approaches have been successfully demonstrated in a number of prototype context-aware systems. With the increase in sensing capabilities of mobile devices, such devices are not merely consumers of context information any more but also have the ability to be \emph{providers} of context gathered through integrated sensors. In such a provider-consumer model, where context dissemination is aided by a central broker, device-broker interaction can become a communication and computation bottleneck in presence of multiple context providers and consumers in mobile devices. In this paper we present a theoretical model for a large scale context-aware system based on a federation of multiple context brokers and discuss the concept of a mobile broker to facilitate the participation of mobile devices in context provision and consumption efficiently. The utilisation of an asynchronous event-based publish-subscribe paradigm is focused as a key element.

Federated broker system for pervasive context provisioning

Software systems that provide context-awareness related functions in pervasive computing environments are gaining momentum due to emerging applications, architectures and business models. In most context-aware systems, a central broker performs the functions of context acquisition, processing, reasoning and provisioning to facilitate context-consuming applications, but demonstrations of such prototypical systems are limited to small, focussed domains. In order to develop modern context-aware systems that are capable of accommodating emerging pervasive/ubiquitous computing scenarios, are easily manageable, administratively and geographically scalable, it is desirable to have multiple brokers in the system divided into administrative, network, geographic, contextual or load based domains. Context providers and consumers may be configured to interact only with their nearest, relevant or most convenient broker. This setup demands inter-broker federation so that providers and consumers attached to different brokers can interact seamlessly, but such a federation has not been proposed for context-aware systems. This article analyses the limiting factors in existing context-aware systems, postulates the design and functional requirements that modern context-aware systems need to accommodate, and presents a federated broker based architecture for provisioning of contextual information over large geographical and network spans.

A framework for cloud-based context-aware information services for citizens in smart cities

BackgroundIn the context of smart cities, public participation and citizen science are key ingredients for informed and intelligent planning decisions and policy-making. However, citizens face a practical challenge in formulating coherent information sets from the large volumes of data available to them. These large data volumes materialise due to the increased utilisation of information and communication technologies in urban settings and local authorities’ reliance on such technologies to govern urban settlements efficiently. To encourage effective public participation in urban governance of smart cities, the public needs to be facilitated with the right contextual information about the characteristics and processes of their urban surroundings in order to contribute to the aspects of urban governance that affect them such as socio-economic activities, quality of life, citizens well-being etc. The cities on the other hand face challenges in terms of crowd sourcing with quality data collection and standardisation, services inter-operability, provisioning of computational and data storage infrastructure.FocusIn this paper, we highlight the issues that give rise to these multi-faceted challenges for citizens and public administrations of smart cities, identify the artefacts and stakeholders involved at both ends of the spectrum (data/service producers and consumers) and propose a conceptual framework to address these challenges. Based upon this conceptual framework, we present a Cloud-based architecture for context-aware citizen services for smart cities and discuss the components of the architecture through a common smart city scenario. A proof of concept implementation of the proposed architecture is also presented and evaluated. The results show the effectiveness of the cloud-based infrastructure for the development of a contextual service for citizens.

Context-aware service utilisation in the clouds and energy conservation

Ubiquitous computing environments are characterised by smart, interconnected artefacts embedded in our physical world that provide useful services to human inhabitants unobtrusively. Mobile devices are becoming the primary tools for human interaction with these embedded artefacts and for the utilisation of services available in smart computing environments such as clouds. Advancements in the capabilities of mobile devices allow a number of user and environment related context consumers to be hosted on these devices. Without a coordinating component, these context consumers and providers are a potential burden on device resources; specifically the effect of uncoordinated computation and communication with cloud-enabled services can negatively impact battery life. Therefore energy conservation is a major concern in realising the collaboration and utilisation of mobile device based context-aware applications and cloud based services. This paper presents the concept of a context-brokering component to aid in coordination and communication of context information between mobile devices and services deployed in a cloud infrastructure. A prototype context broker is experimentally analysed for effects on energy conservation when accessing and coordinating with cloud services on a smart device, with results signifying reduction in energy consumption.

Effect of caching in a broker based context provisioning system

Caching is a well-established mechanism used in distributed systems for improving overall performance. In this paper, we analyse the effect of using a context cache in a broker-based context provisioning system. An experiment is carried out using a simulation based on our framework architecture of context consumers, context broker and context providers exchanging different types of context data over time. The results show notable improvement in the context query response time.

Provision of an integrated data analysis platform for computational neuroscience experiments

Purpose – The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach – The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings – The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value – The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clin...

Intelligent grid enabled services for neuroimaging analysis

This paper reports our work in the context of the neuGRID project in the development of intelligent services for a robust and efficient Neuroimaging analysis environment. neuGRID is an EC-funded project driven by the needs of the Alzheimers disease research community that aims to facilitate the collection and archiving of large amounts of imaging data coupled with a set of services and algorithms. By taking Alzheimers disease as an exemplar, the neuGRID project has developed a set of intelligent services and a Grid infrastructure to enable the European neuroscience community to carry out research required for the study of degenerative brain diseases. We have investigated the use of machine learning approaches, especially evolutionary multi-objective meta-heuristics for optimising scientific analysis on distributed infrastructures. The salient features of the services and the functionality of a planning and execution architecture based on an evolutionary multi-objective meta-heuristics to achieve analysis efficiency are presented. We also describe implementation details of the services that will form an intelligent analysis environment and present results on the optimisation that has been achieved as a result of this investigation.