Distributed Context Information Management
(DICOM)
Research Plan
Jan Lindström
17.11.2004
Abstract
Recent developments in mobile communication and small computing devices have had a tremendous impact on our societies. The main challenge will be software that fulfils the needs of personalized, context-aware services and applications usable anywhere, anyhow, and anytime. The research challenge is to find a light-weight solution that suits in this environment. Therefore, there is great need for distributed context information management to simplify the development of context-aware mobile applications by managing context information gained from multiple sources.Introduction
Mobile device users want to be able to access and manipulate information and services specific to their location, time, and environment. Context information gathered from sensors, networks, device status, user profiles, and other sources could enhance mobile applications usability by letting them adapt to conditions that directly affect their operations [4]. The human dialog is based on context and human way of making decisions is based on context. Therefore, it seems natural to consider using context when building applications that make decisions involving their users [2]. One very simple example of a context-aware application is customizing information based on the current location. However, even if location is the most commonly used context data, there is much more to context than that. As part of the context we can have temperature, time, orientation, affective state, activity, interests, focus of attention, interest/boredom levels, resources available, people nearby, etc. [1,34]. Future mobile terminals will have potential for providing much of this information. Obviously, some pieces of information are harder to obtain than others, but the more we include in the context, the better we can tailor the applications and provide situation-specific services. This is even more important in a mobile environment, since an application running in a mobile environment naturally face with a variety of changes and, therefore, has to adapt itself very quickly in order to deliver the expected results. To achieve true context awareness, however, mobile systems must produce reliable information in the presence of uncertain, rapidly changing, partially true data from multiple heterogeneous sources. Mobile devices equipped with low-cost sensing elements can recognize some aspects of context. However, extracting relevant context information by fusing data from several sensors proves challenging because noise, faulty connections, drift, miscalibration, wear and tear, humidity, and other factors degrade data acquisition [4]. Extracted context overlap, change with time, and yield only partially reliable approximations. Context-aware computing is an important component of the ubiquitous/pervasive paradigm, and its main focus is on applications that can acquire and take advantage of contextual information [2]. The main tasks of a context-aware application involve acquiring context information, reasoning based on the information acquired, and performing an action based on some pre-defined guidelines [17,18]. The development of context-aware applications [5,7] depends on and benefits from the recent advances of several enabling technologies. One of the most important is sensor technology. Today we have sensors for audio/video signals, biometrics (e.g., pulse, skin conductance), motion, and physical environment (e.g., temperature, pressure). Advances in miniaturization, cost-effectiveness, and processing power allows for placing these sensors almost everywhere, as wearable computing research shows [3].Recent research
Recent research has concentrated developing context-aware applications for tourists [8,9,26] and other applications [15,28,32]. Other research has done in application models [10,12,13,24,27], algorithms [11,21,22,25], and protocols [23,29,30,33,35]. However, not much of research has been discussed issues concerning managing context information. The grant challenge is to create a flexible context management framework. The objective is to have efficient means to presenting, maintaining, sharing, protecting, reasoning, and querying context information in both mobile and stationary devices.Objective
The focus of this project will be on the development of a prototype of the server and client software, and in particular, on the algorithms, applications protocols, and overall system architecture. We also intend for this prototype to provide a useful implementation framework for follow-on projects. The key research topics to be pursued are as follows:- 1. Architecture for managing context information in mobile devices and replicated servers.
- 2. Distributed database model, index structures and replication strategies for context information with support for both disconnected and on-line modes of operation.
- 3. Synchronization and consistency of multiple data replicas, with support for both disconnected and on-line modes of operation.
Benefits and Impact
Mobile phones are among the first potentially context-aware devices that people will actually use. For many people, the mobile phone has quickly become an indispensable personal item that is carried around all the time, much like watches, jewellery, or eyeglasses. A phone is wearable, almost always on, has processing and communication capabilities, and is used to hold valuable personal data. Besides PDAs (personal digital assistants) and watches, a phone is the only personal item that can conveniently serve as a mobile platform for context awareness. Mobile phones of the current generation understand very little of what happens around them. Location sensing is starting to become usable, but phones are not yet very useful for interacting with our environment. Much of the information must be manually entered, even though it would often be more natural for us just to point at things and speak commands such as "put this there". This adds to the frustration of phone users, who already have to cope with dozens of different functions. As mobile phones move about in the real world along with their users, they should be able to adapt to changes in their surroundings. Context awareness can enable better user interfaces, where the surrounding situation automatically becomes part of the input. Various other scenarios are possible when considering context awareness, like automatic profile selection, customized information delivery, and allowing or disallowing phone calls based on context. It could also allow novel applications, such as context-sensitive adventure games that take place in the real world.Risks
The challenges are manifold, since a context-aware application developer has to implement different mechanisms capable of dealing with the diverse nature of sensors, smart algorithms for abstracting the raw data and for reasoning, as well as the tasks to be performed. As with any new technology, there are also risks involved. Current context sensing is far from being perfect. Although the state of technology is rapidly improving, there are still issues that are very likely to exist for a long time. One example is faulty automatic context recognition, due in part to inevitable errors related to sensors and in the context recognition process and in part to the fuzziness of the situation models (the transition from one context to another is a fuzzy zone) [31] In certain applications, any resulting malfunctions are intolerable. For instance, as soon as automated ringing-tone silencing fails in an important meeting, the feature will quickly be judged useless. Such mishaps would affect the perceived quality of the products. Therefore, smart decision schemes have to be implemented in order to cope with the uncertain or critical situations. In order to succeed and be accepted, the context-aware phones have to implement very appealing features while also taking care of "gracefully" failing. Another important issue that can affect the deployment of context-aware applications is to determine the balance between how much people are willing to compromise their privacy in exchange for customized services [19], especially when dealing with sensitive data [20], like affective state information. However, these problems could also be solved by building appropriate trust relationships with service providers.References
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