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Estimating the home and work locations of users is important for applications such as city planning and personalized recommendations. Although existing approaches can achieve a reasonable precision, they rely on fine-grained sensor data with high sampling rate. Therefore, these approaches come with a high cost and are only studied in small samples of volunteers and thus cannot benefit large-scale Internet users. In this article we propose a method to use crowd sourced location data from mobile devices to estimate the home and work locations of large scale users, leveraging the computation power of the cloud. Experimental results demonstrate our approachachieves a good estimation precision. Moreover, we further study how the estimated home and work locations can be used in two typical applications that are difficult problems using traditional methods but can be elegantly solved by leveraging the proposed crowd-sourced approach.

Existing system	Proposed System
Ø  In existing system, whenever military crew going to mission in desert to catch terrorist, they have to mention one area to form after the mission completed. Ø  So crew uses the walkitalki for communication purpose.   Drawbacks Ø  The terrorist use the signal jammer to cut the walkitalki communication. Ø  This system reduces the protection process of the crew.	Here we introduce a new technology to communicate the crew when using jammer.     The each system is embedded to each soldier in the crew for making communication.   Advantages     It protects the soldiers in the crew from terrorist.        It automatically send back the area location of the soldier, whenever he failed to send the acknowledgement