By Stephan Sigg, Petteri Nurmi, Flora Salim
This publication constitutes the complaints of the seventh foreign convention on cellular Computing, purposes, and providers (MobiCASE 2015) held in Berlin, Germany, in November 2015. The sixteen complete and four poster papers have been conscientiously reviewed and chosen from forty three submissions, and are provided including four papers from the 1st Workshop on scenario reputation by means of Mining Temporal info (SIREMETI 2015). The convention papers disguise the next themes: clever caching, job acceptance and crowdsourcing, cellular frameworks, middleware, interactive functions and mobility.
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Extra resources for Mobile Computing, Applications, and Services: 7th International Conference, MobiCASE 2015, Berlin, Germany, November 12-13, 2015, Revised Selected Papers
The relationship between acceleration and running), while others cannot. Of course, the combinations shown are representative of our data set, where activities were taking place in an urban environment. For example, it is normal to see Wi-Fi sometimes taking particular prominence. This would probably not be the case if environments were more heterogeneous. 4 Recognition Using a Combination of Multiple Metrics It makes sense to use a classiﬁer such as an SVM when combining multiple metrics to deduce an activity, which can be seen as a more advanced feature set.
Linux, NodeJS). One focus of our benchmarking is task completion time, not only computational power is relevant, but also network latency. Thus, we test three diﬀerent located clouds (US West, central Europe, Asia Paciﬁc) with same computational power to get the impact of network latency . In Table 2, linear distances between our measurement conducting location (Darmstadt, Germany) and the cloud data centers are listed. Only considering the distance, clouds are at a disadvantage compared to nearby located cloudlets regarding latency because of physical constraints: information cannot propagate faster than the speed of light (∼3 · 108 m/s) when dealing with long distance.
In this paper, we intend to go one step further by presenting a sensing system that combines the data collected by one smartwatch and one smartphone. The platform that we have developed makes use of commercially-available devices and can be used to analyse the activity of a monitored user in great detail. Possible applications range from sports tracking systems to human behaviour modeling. Our contribution addresses four complementary objectives. (1) The design of an energy-eﬃcient sensing system, using a streamlined fusion of data collected on two devices (a smartphone and a smartwatch).