By Pang Wu, Huan-Kai Peng, Jiang Zhu, Ying Zhang (auth.), Joy Ying Zhang, Jarek Wilkiewicz, Ani Nahapetian (eds.)
This booklet constitutes the completely refereed post-conference complaints of the 3rd overseas convention on cellular Computing, purposes, and companies (MobiCASE 2011) held in l. a., CA, united states, in the course of October 24-27, 2010. The 18 revised complete papers provided including 12 revised poster papers have been conscientiously reviewed and chosen from various submissions. The convention papers are geared up in seven technical periods, protecting the subjects of cellular pervasive functions, process matters, location-aware providers, cellphone established platforms, cellular internet and providers, instruments for cellular environments, and cellular software improvement issues.
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Extra info for Mobile Computing, Applications, and Services: Third International Conference, MobiCASE 2011, Los Angeles, CA, USA, October 24-27, 2011. Revised Selected Papers
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It is a programming model for processing and generating large data sets. Under MapReduce, the computation takes a set of input key/value pairs, and produces a set of output key/value pairs. The user specifies a Map function that takes an input pair to generate a set of intermediate key/value pairs. All intermediate values associated with the same intermediate key are grouped together and passed to the Reduce function. The Reduce function accepts an intermediate key and a Mobile MapReduce: Minimizing Response Time 45 set of values for that key.
We sort the LC in decreasing order of weight (Line 13). Then we could find the minima of the curvature of the T in Equation 1 by adding one by one node for our computation (Line 19-28). Here we assume the a, d, and bw be the average of those values of the total nodes considered so far. We also calculate the potential number of chunks to be executed by each node here based on the weight heuristic (Line 30). Then we find out the nodes having some chunks of the data in prior and sort them in decreasing order of ids (Line 34) in list LCRD and give them priority if they have better performance considering affinity data (Line 36-46).
Nahapetian Data Fusion The sensors used in the Smart Shoe are heterogeneous, including accelerometers, gyroscopes, and pressure sensors. Additionally, the sensor signals are transmitted over a wireless connection either directly or indirectly to a computer. Transmission over the Bluetooth connection can be lossy. Additionally, the data collected from the various sensors are not necessarily time-synced with each other. All of these challenges with the sensor signals require post-processing of the data using a data fusion approach.