Eila Ovaska

Supporting context awareness in smart environments: a scalable approach to information interoperability

A still open issue in pervasive systems is how to effectively support the development and run-time execution of smart applications in wide-scale and open deployment scenarios. That calls for i) easy and low-cost interoperability with legacy systems/services and ii) scalability in terms of both overhead and performance results for different classes of applications with different quality requirements. In this position paper, we report the research work we are doing within an ongoing project on the exploitation of context awareness for scalability in smart environments, where interoperability is achieved at the information level via semantic technologies. In particular, the paper shows how context-aware middleware facilities can help in dynamically determining personalized views on a shared information space, implemented as coordinated repositories of Resource Description Format triples. The advantages are relevant in terms of usability (automatic discarding of unsuitable resource/service components) and overhead reduction (limitation of quality monitoring space).

Knowledge based quality-driven architecture design and evaluation

Modelling and evaluating quality properties of software is of high importance, especially when our every day life depends on the quality of services produced by systems and devices embedded into our surroundings. This paper contributes to the body of research in quality and model driven software engineering. It does so by introducing; (1) a quality aware software architecting approach and (2) a supporting tool chain. The novel approach with supporting tools enables the systematic development of high quality software by merging benefits of knowledge modelling and management, and model driven architecture design enhanced with domain-specific quality attributes. The whole design flow of software engineering is semi-automatic; specifying quality requirements, transforming quality requirements to architecture design, representing quality properties in architectural models, predicting quality fulfilment from architectural models, and finally, measuring quality aspects from implemented source code. The semi-automatic design flow is exemplified by the ongoing development of a secure middleware for peer-to-peer embedded systems.

The reliability estimation, prediction and measuring of component-based software

Reliability is a key driver of safety-critical systems such as health-care systems and traffic controllers. It is also one of the most important quality attributes of the systems embedded into our surroundings, e.g. sensor networks that produce information for business processes. Therefore, the design decisions that have a great impact on the reliability of a software system, i.e. architecture and components, need to be thoroughly evaluated. This paper addresses software reliability evaluation during the design and implementation phases; it provides a coherent approach by combining both predicted and measured reliability values with heuristic estimates in order to facilitate a smooth reliability evaluation process. The approach contributes by integrating the component-level reliability evaluation activities (i.e. the heuristic reliability estimation, model-based reliability prediction and model-based reliability measuring of components) and the system-level reliability prediction activity to support the incremental and iterative development of reliable component-based software systems. The use of the developed reliability evaluation approach with the supporting tool chain is illustrated by a case study. The paper concludes with a summary of lessons learnt from the case studies.

Smart-M3 and OSGi: The interoperability platform

The design of applications that may benefit from the data gathered from the surrounding physical environment represents a challenging and promising research field. Because of the heterogeneity of such systems interoperability is becoming more difficult to achieve at the service, information and device levels. In this paper, we report on the first attempt to design an interoperability platform by integrating a “Semantic Web” information sharing infrastructure, namely Smart-M3 (Multi-part, Multi-device and Multi-vendor), within the OSGi (Open Service Gateway initiative) framework, enhancing the information level interoperability of the former with the service level interoperability of the latter. Such an interoperability platform can grant the unanticipated evolution of existing Information and Communication Technology (ICT) systems by providing a generic and simple solution to develop what are generally called “Smart Space Applications”. As proof of concept a maintenance scenario will be discussed and detailed.

Context-awareness in smart spaces

Any of our living environments can form a smart space that provides services and applications for the users according to their situations, the computing and communication facilities of the environment and the surroundings of the user. The ability to take into account the context of the user, and the digital and physical environment makes a space smart. This paper introduces a set of new capabilities that are required for achieving context-awareness in smart spaces. First, sensor data from the environment is acquired and aggregated. Second, the context data is interpreted and represented and possibly enhanced with external information. Thereafter, context reasoning is possible. Finally, situation based context reasoning is made by application agents that exploit domain ontologies for tuning context information to the situation in hand. The use of the introduced context-awareness concept is exemplified by an emergency scenario of a smart city.

Evaluating the Quality of Social Media Data in Big Data Architecture

The use of freely available online data is rapidly increasing, as companies have detected the possibilities and the value of these data in their businesses. In particular, data from social media are seen as interesting as they can, when properly treated, assist in achieving customer insight into business decision making. However, the unstructured and uncertain nature of this kind of big data presents a new kind of challenge: how to evaluate the quality of data and manage the value of data within a big data architecture? This paper contributes to addressing this challenge by introducing a new architectural solution to evaluate and manage the quality of social media data in each processing phase of the big data pipeline. The proposed solution improves business decision making by providing real-time, validated data for the user. The solution is validated with an industrial case example, in which the customer insight is extracted from social media data in order to determine the customer satisfaction regarding the quality of a product.

Requirements of an Open Data Based Business Ecosystem

Emerging opportunities for open data based business have been recognized around the world. Open data can provide new business opportunities for actors that provide data, for actors that consume data, and for actors that develop innovative services and applications around the data. Open data based business requires business models and a collaborative environment-called an ecosystem-to support businesses based on open data, services, and applications. This paper outlines the open data ecosystem (ODE) from the business viewpoint and then defines the requirements of such an ecosystem. The outline and requirements are based on the state-of-the-art knowledge explored from the literature and the state of the practice on data-based business in the industry collected through interviews. The interviews revealed several motives and advantages of the ODE. However, there are also obstacles that should be carefully considered and solved. This paper defines the actors of the ODE and their roles in the ecosystem as well as the business model elements and services that are needed in open data based business. According to the interviews, the interest in open data and open data ecosystems is high at this moment. However, further research work is required to establish and validate the ODE in the near future.

Architecture and Knowledge-Driven Self-Adaptive Security in Smart Space

Dynamic and heterogeneous smart spaces cause challenges for security because it is impossible to anticipate all the possible changes at design-time. Self-adaptive security is an applicable solution for this challenge. This paper presents an architectural approach for security adaptation in smart spaces. The approach combines an adaptation loop, Information Security Measuring Ontology (ISMO) and a smart space security-control model. The adaptation loop includes phases to monitor, analyze, plan and execute changes in the smart space. The ISMO offers input knowledge for the adaptation loop and the security-control model enforces dynamic access control policies. The approach is novel because it defines the whole adaptation loop and knowledge required in each phase of the adaptation. The contributions are validated as a part of the smart space pilot implementation. The approach offers reusable and extensible means to achieve adaptive security in smart spaces and up-to-date access control for devices that appear in the space. Hence, the approach supports the work of smart space application developers.

Comparison of Adaptive Information Security Approaches

Dynamically changing environments and threat landscapes require adaptive information security. Adaptive information security makes it possible to change and modify security mechanisms at runtime. Hence, all security decisions are not enforced at design-time. This paper builds a framework to compare security adaptation approaches. The framework contains three viewpoints, that is, adaptation, security, and lifecycle. Furthermore, the paper describes five security adaptation approaches and compares them by means of the framework. The comparison reveals that the existing security adaptation approaches widely cover the information gathering. However, the compared approaches do not describe how to decide a method to perform a security adaptation. Similarly, means how to provide input knowledge for the security adaptation is not covered. Hence, these research areas have to be covered in the future. The achieved results are applicable for software developers when selecting a security adaptation approach and for researchers when considering future research items.

Ontology-Based Security Adaptation at Run-Time

This paper describes how software is able to autonomously adapt its security mechanisms based on knowledge from security ontology. Security adaptation is required because a software’s environment changes during run-time. Thus, all security requirements cannot be defined beforehand. To achieve security adaptation, we have combined a security ontology that defines security mechanisms, security objectives, and high level security measurements. The run-time security adaptation utilises this security ontology to adapt security mechanisms or their parameters to fulfil security requirements for each environment and usage situation. The novelty of this approach comes from the utilisation of ontologies and security measurements, which makes adaptation flexible. We validate our security adaptation with a case study in a smart space environment. The case study proves that security adaptation is able to work autonomously without other user actions.