xecution environment, is widely recognized as [...]]]> To fully enable the great potential of service provisioning scenarios considering a growing diversity of users, constant Internet access and several devices, context-awareness, known as the ability to provide services with full awareness of current e

xecution environment, is widely recognized as one of the cornerstones to build modern mobile and ubiquitous systems.

Service adaptation is triggered by received context data: hence, context data have to be timely delivered to let services promptly adapt to the current execution context. However, the middleware has to transparently manage and route huge amounts of context data, while ensuring timely delivery to mobile nodes: especially in wide-area mobile networks, that can lead to non-negligible overhead, thus hindering both system scalability and reliability.

Existing systems already exploit contextual attributes to trigger management functions and to adapt services. For instance, Google and Facebook dynamically adapt to the current characteristics of mobile devices, Web clients, and connectivity (such as the available bandwidth).

By considering mainly four context dimensions: computing, physical, time and user, different context-aware behaviors can be realized to adapt services so to make them satisfactory for final user and to fit current execution environment characteristics. Toward this goal, the quality of the context data is a fundamental issue since it can compromise the correctness of adaptation operations.

Although many benefits can be provided with context-aware adaptation, still, an in-depth analysis of the context data distribution is missing. Starting from the core assumption that only effective and efficient context data distribution can pave the way to the deployment of truly context-aware services, Bellavista et al. (2012) published a paper aiming at putting together current research efforts to derive an holistic view of the existing literature. They presented a unified architectural model and a new taxonomy for context data distribution, by considering and comparing a large number of solutions.

BELLAVISTA, P., CORRADI, A., FANELLI, M., AND FOSCHINI, L. (2012). A Survey of Context Data Distribution for Mobile Ubiquitous Systems. 2012. ACM Comput. Surv. 44, 4 (August 2012)

It works simply enough: the user loads th buy cheap cialis

e app onto the phone, and sets [...]]]> A new context-aware adaptive application is available in the market. Named PhoneGuard, it considers the context of the user, a speed higher than 10 miles per hour, to disable the phone’s texting functions.

It works simply enough: the user loads th

e app onto the phone, and sets a password. The app, which is always on, pings GPS satellites every few seconds, and if it determines that the phone is in a moving vehicle, it renders the phone almost useless. If you try to open the text messaging app — or any other app, for that matter — while driving, a “No Texting Vehicle” message covers the screen. (You are able to answer the phone, but you can’t dial out.)

It aims to reduce accidents caused by the distraction of users who text while driving. However, once too many people are wedded to the idea of doing so, it is likely that some people will naturally look for a workaround.

Some users have complained about the app’s tendency to drain the phone’s battery, since PhoneGuard constantly pings GPS satellites to establish the user’s speed. After seven hours of use with only PhoneGuard running, the battery on a Droid2, for instance, dropped from 100 percent to zero.

The fact that PhoneGuard is free is a huge breakthrough for the category. Since the dangers of driving-while-texting became more public, app developers have sought to capitalize on the issue, by selling apps similar to PhoneGuard and then charging a monthly service to use the app. PhoneGuard has found a way to offer this potentially lifesaving technology for free.

PhoneGuard is available for Android and BlackBerry.

The original article can be retrieved at “App Smart Extra: An App Aimed to Curb Distracted Driving“.