Atif Manzoor

On the Evaluation of Quality of Context

High quality context information plays a vital role in adapting a system to the rapidly changing situations. However, the diversity of the sources of context information and the characteristics of the computing devices strongly impact the quality of context information in pervasive computing environments. Quality of Context parameters can be used to characterize the quality of context information from different aspects. In this paper, we quantify the Quality of Context parameters to present them in a suitable form for use with the applications in pervasive environments. We also present a mechanism to tailor the Quality of Context parameters according to the needs of an application and then evaluate these parameters. Enrichment of context information with Quality of Context parameters enhances the capabilities of context-aware systems to effectively use the context information to adapt to the emerging situations in pervasive computing environments.

Quality of Context: models and applications for context-aware systems in pervasive environments

Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments.

ESCAPE: an adaptive framework for managing and providing context information in emergency situations

Supporting adaptive processes in tackling emergency situations, such as disasters, is a key issue for any emergency management system. In such situations, various teams are deployed in many sites, at the front-end or back-end of the situations, to conduct tasks handling the emergency. Context information is of paramount importance in assisting the adaptation of these tasks. However, there is a lack of middleware supporting the management of context information in emergency situations. This paper presents a novel framework that manages and provides various types of context information required for adapting processes in emergency management systems.

Quality Aware Context Information Aggregation System for Pervasive Environments

Sensing context information and making it available to the people, involved in coordinating a collaborative task, is a preliminary phase in making a system adaptable to the prevailing situation in pervasive environments. However, the diversity of the sources of context information, the characteristics of pervasive environments, and the nature of collaborative tasks pose a stern challenge to the efficient management of context information by sensing a lot of redundant and conflicting information. Quality of Context parameters can be used to resolve the conflicts in context information. In this paper, we present a context aggregation system that detects and removes the duplicates and conflicts from context information by using the policies based on Quality of Context parameters. This system effectively aggregates the continuously evolving context information and efficiently uses the scare resources in pervasive environments.

Using quality of context to resolve conflicts in context-aware systems

Context-aware systems in mobile and pervasive environments face many conflicting situations while collecting sensor data, processing sensor data to extract consistent and coherent high level context information, and disseminating that context information to assist in making decisions to adapt to the continuously evolving situations without diverting human attention. These conflicting situations pose stern challenges to the design and development of context-aware systems by making it extremely complicated and error-prone. Quality of Context parameters can be used to cope with these challenges. In this paper, we discuss the conflicting situations that a context-aware system may face at different layers of its conceptual design and present the conflict resolving policies that are defined on the basis of the Quality of Context parameters. We also illustrate how these policies can be used in different conflicting situations to improve the performance and effectiveness of context-aware systems.

Adaptive Query Routing on Distributed Context - The COSINE Framework

Context-awareness has become a desired key feature of todays mobile systems, yet, its realization still remains a challenge. On the one hand, mobile computing provides great potential for adaptations based on sensed contextual information. On the other hand, the lack of dependability in mobile networks hampers an efficient provision of this information to requesting clients. In this paper we present COSINE, a context management framework for mobile environments. COSINE has been developed on the principles of peer-to-peer computing and establishes context sharing infrastructures consisting of loosely coupled Web services. The services represent modular entities of the applied context model and manage the retrieval, aggregation, query, and provision of context data. Clients access the distributed information transparently via proxies and a self-adaptive routing of queries provides increased fault-tolerance, which is essential in mobile environments.

Vimoware - A Toolkit for Mobile Web Services and Collaborative Computing

Mobile devices are considered to be very useful in ad-hoc and team collaborations, for example in disaster responses, where dedicated infrastructures are not available. Such collaborations normally require flexible and interoperable services while running on mobile devices and being integrated with various other services. Therefore, middleware and toolkits for developing mobile services which can be accessed by using standard interfaces and protocols are in demand. Due to the lack of tools, the support of the development of Web services and collaboration tools on mobile devices is still limited. This paper presents the Vimoware toolkit which allows both developers and users to develop Web services for mobile devices, to conduct ad-hoc team collaborations by executing pre-defined or on-situ flows of tasks, and to test collaboration scenarios.

On modeling, collecting and utilizing context information for disaster responses in pervasive environments

To support collaborative work in disaster responses, various types of context information must be gathered and shared not only within teams at disaster sites, but also among various teams and services in pervasive environments. We propose a representation for describing context information required for disaster responses. This representation is extensible and interoperable enough to include various concepts describing entities existing in disasters and relationships among these entities. We discuss possible techniques based on XML and Web services technologies to gather and share context information among different pervasive applications and services for disaster responses.

Identifying important action primitives for high level activity recognition

Smart homes have a user centered design that makes human activity as the most important type of context to adapt the environment according to peoples needs. Sensor systems that include a variety of ambient, vision based, and wearable sensors are used to collect and transmit data to reasoning algorithms to recognize human activities at different levels of abstraction. Despite various types of action primitives are extracted from sensor data and used with state of the art classification algorithms there is little understanding of how these action primitives affect high level activity recognition. In this paper we utilize action primitives that can be extracted from data collected by sensors worn on human body and embedded in different objects and environments to identify how various types of action primitives influence the performance of high level activity recognition systems. Our experiments showed that wearable sensors in combination with object sensors clearly play a crucial role in recognizing high level activities and it is indispensable to use wearable sensors in smart homes to improve the performance of activity recognition systems.

Service-centric Inference and Utilization of Confidence on Context

The inadequate quality of context forces the context consumers in pervasive environments to reason about the quality and relevance of context to be confident of its worth to perform their functionality. The additional task of analyzing large volumes of context drastically affects the performance of the context consumers to adjust to dynamically changing situations. A single value that presents the quality and relevance of context information tailored to the needs of a particular context consumer may release them from spending resources on context quality analysis and let them concentrate on their main task. In this paper we present a novel technique to combine different Quality of Context (QoC) metrics to infer the value of confidence on context. Our technique also considers the requirements of a particular context consumer regarding QoC metrics while confidence inference. Confidence on context is further provided to the context consumers to select high quality context and use the confidence in their functionality. We have successfully evaluated our approach using two context consumer services and user context collected from a smart home pervasive environment.