Intuitive Human Governance of Autonomic Pervasive Computing Environments

O’Donnell, Tony; Lewis, David; Wade, Vincent

doi:10.1109/wowmom.2005.57

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…By using properly defined semantics, and a suitable reasoner, meaningful feedback can be generated to the user based on examining the semantic constraints of the model [14]. This feedback can prompt the user to add whatever additional information is required to make the model sufficient.…”

Section: A Environment Configuration Semanticsmentioning

confidence: 99%

Semantics for the intuitive governance of autonomic user‐centered pervasive computing environments

O’Donnell

Lewis

Wade

2006

International Journal of Pervasive Computing and Communications

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.Abstract-As Pervasive Computing environments become more richly populated with sensors and computing power it may be possible for the environment to observe and interpret user actions and pre-emptively provide accurate support for those actions. This requires accurate inference of user intent. Though individual user acts may be inferable from environmental context sensing, the inference of intent for which appropriate support might be offered is more challenging. Numerous researchers have used probabilistic techniques such as Bayesian Analysis techniques to attempt such inference. Unlike a desktop environment, however, pervasive computing environments are extremely heterogeneous, so any heuristics used must be tailored to the physical environment and the users in question. Thus these techniques are only likely to be accurate if configured with accurate knowledge of routine user behavior. Many existing approaches attempt to learn such knowledge from operational data, but this often requires user involvement in training and expert configuration of probabilistic processing structure. In this paper we examine a complimentary approach where users are presented with an intuitive interface to support the direct configuration of probabilistic structure by users with appropriate knowledge. By treating an intent inference system for a particular pervasive computng environment as an autonomic system, we approach the problem as the design of an intuitive governance interface for this system. We then present the design and usability evaluation of this governance interface.

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Section: A Environment Configuration Semanticsmentioning

confidence: 99%

Semantics for the intuitive governance of autonomic user‐centered pervasive computing environments

O’Donnell

Lewis

Wade

2006

International Journal of Pervasive Computing and Communications

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“…Systems that have these characteristics are termed as self-managing systems. Autonomic pervasive computing [2] maintains characteristics from both autonomic computing and pervasive computing environment. Like pervasive computing, devices running in this area should be context and situation aware and these devices form an ad-hoc ephemeral network.…”

Section: Introductionmentioning

confidence: 99%

Self-healing for autonomic pervasive computing

Ahmed

Ahamed

Sharmin

et al. 2007

Proceedings of the 2007 ACM Symposium on Applied Computing

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Self-healing is one of the main challenges to growing autonomic pervasive computing. Fault detection and recovery are the main steps of self-healing. Due to the characteristics of pervasive computing the self-healing becomes difficult. In this paper, the challenges of self-healing have been addressed and an approach to develop a self-healing service for autonomic pervasive computing is presented. The self-healing service has been developed and integrated into the middleware named MARKS+ (Middleware Adaptability for Resource discovery, Knowledge usability, and Self-healing). The self-healing approach is being evaluated on a test bed of PDAs. An application is being developed by using the proposed service.

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Intuitive Human Governance of Autonomic Pervasive Computing Environments

Abstract: Abstract

Cited by 2 publications

References 11 publications

Semantics for the intuitive governance of autonomic user‐centered pervasive computing environments

Semantics for the intuitive governance of autonomic user‐centered pervasive computing environments

Self-healing for autonomic pervasive computing

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