MobiPADS: a reflective middleware for context-aware mobile computing

distributed computing services that essentially abstract the underlying network services to a monolithic “black box. ” In a mobile operating environment, the fundamental assumption of middleware abstracting a unified distributed service for all types of applications operating over a static network infrastructure is no longer valid. In particular, mobile applications are not able to leverage the benefits of adaptive computing to optimize its computation based on current contextual situations. In this paper, we introduce the Mobile Platform for Actively Deployable Service (MobiPADS) system. MobiPADS is designed to support context-aware processing by providing an executing platform to enable active service deployment and reconfiguration of the service composition in response to environments of varying contexts. Unlike most mobile middleware, MobiPADS supports dynamic adaptation at both the middleware and application layers to provide flexible configuration of resources to optimize the operations of mobile applications. Within the MobiPADS system, services (known as mobilets) are configured as chained service objects to provide augmented services to the underlying mobile applications so as to alleviate the adverse conditions of a wireless environment. Index Terms—Middleware, mobile applications, mobile computing support services, mobile environments.

Supporting ethnographic studies of ubiquitous computing in the wild

Ethnography has become a staple feature of IT research over the last twenty years, shaping our understanding of the social character of computing systems and informing their design in a wide variety of settings. The emergence of ubiquitous computing raises new challenges for ethnography however, distributing interaction across a burgeoning array of small, mobile devices and online environments which exploit invisible sensing systems. Understanding interaction requires ethnographers to reconcile interactions that are, for example, distributed across devices on the street with online interactions in order to assemble coherent understandings of the social character and purchase of ubiquitous computing systems. We draw upon four recent studies to show how ethnographers are replaying system recordings of interaction alongside existing resources such as video recordings to do this and identify key challenges that need to be met to support ethnographic study of ubiquitous computing in the wild

Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine

Activity-Based Computing aims to capture the state of the user and its environment by exploiting heterogeneous sensors in order to provide adaptation to exogenous computing resources. When these sensors are attached to the subject’s body, they permit continuous monitoring of numerous physiological signals. This has appealing use in healthcare applications, e.g. the exploitation of Ambient Intelligence (AmI) in daily activity monitoring for elderly people. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone inertial sensors. As these mobile phones are limited in terms of energy and computing power, we propose a novel hardware-friendly approach for multiclass classification. This method adapts the standard Support Vector Machine (SVM) and exploits fixed-point arithmetic for computational cost reduction. A comparison with the traditional SVM shows a significant improvement in terms of computational costs while maintaining similar accuracy, which can contribute to develop more sustainable systems for AmI.Peer ReviewedPostprint (author's final draft