Download Distributed Computing and Internet Technology: 9th by Ofer Feinerman, Amos Korman (auth.), Chittaranjan Hota, PDF

By Ofer Feinerman, Amos Korman (auth.), Chittaranjan Hota, Pradip K. Srimani (eds.)

This e-book constitutes the refereed court cases of the ninth overseas convention on disbursed Computing and net know-how, ICDCIT 2013, held in Bhubaneswar, India, in February 2013.
The forty complete papers provided including five invited talks during this quantity have been conscientiously reviewed and chosen from 164 submissions. The papers disguise a number of learn facets in disbursed computing, net know-how, machine networks, and computing device learning.

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Example text

Photo upload is an example of a user (or application) triggered activity. User takes a photo and the system uploads it to a photo sharing service. The uploading activity is started by a user action (shooting the picture or decision to store it). Finally, voice call activities are started at arbitrary time points either by the user or by calls coming in from the network. When we analyze the combined effect of these three services as depicted on the line Combined (trivial) in Figure 2, we can see that when the starting times of these activities are chosen independently from each other, they occupy a large part of the time axis.

3 Preserving User Privacy Current participatory sensing applications are primarily focused on the collection of data on a large scale. Without any suitable protection mechanism however, the mobile phones are transformed into miniature spies, possibly revealing private information about their owners. Possible intrusions into a user’s privacy include the recording of intimate discussions, taking photographs of private scenes, or tracing a user’s path and monitoring the locations he has visited. Many users are aware of the possible consequences, and may therefore be reluctant to contribute to the sensing campaigns.

The mobile phones are worn around the neck of the participants and automatically take images of the dishes in front of the users. The images document the participants’ food selection and allow for an estimation of the food weight and waste on the plates. , having lunch in a restaurant or eating chips late at night on the sofa). All captured data are uploaded to a personal repository, where the participants can review them to select/discard the information to be shared with their doctors and nutritionists.

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