When we Collecting information about GPS navigation using android, first of all we study the research papers that published already. We have describe following research paper.

Navigation Application Using Android -Harshal Kolhe, Saurabh Upankar, Mukesh Atone, Sanjay kamade. Student, (Department of IT), RGCER, Nagpur

Index Terms: Android, GPS, Navigation, Position, Statistical data, Database

In here trying to implement a device tracking system. They used GPS to track the device. Why they used GPS??

Because the GPS is increasingly being used for a wide range of application. When getting user’s co-ordinate location can track him according to where his android device has been to.

When we studying about navigation we can study about process of monitoring and controlling the moment of craft or vehicle from one place to another and also can determination of position and direction.

What we can do with navigation, we can detect users/any of that device attached location, travelling patterns, location patterns, measure distance to the object, etc.……

I wish to add some facilities for our developing app in feature:

-        Sharing interesting place through social media and collect that sharing info we can gather details about pattern of the customers most interested places…

When we developing such as system:

-        Need IDE such as Android Studio or ECLIPSE

-        Java programming language

-        Database handling such as SQL

-        Design UI

-        Getting user input we can use XML

According to this research paper we have to break whole app to several parts. That mean there is several processes. Each process has its own virtual machine. So, an application’s code runs in isolation from other applications.

Location Based Services are popular but the usage of the same privacy settings for

all users is not appropriate.

LBSes are coupled with smartphone services and these services cause 84%

potential privacy problems and 49% are more comfortable with their own location

information.

        It is true that one of the significant aims of building content recommendation is the construction

of person ahead user profiles. In this case the two problems of feature engineering and the proper

utilization of the signals using data mining and machine learning approaches are faced.

            This paper present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. The five contextual information sources used are: social, historic, task, collection, and user interaction. This study focus on website recommendations rather than search results. This research evaluate the utility of these five sources, and overlaps between them, based on how effectively they predict users’ future interests. The results demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source.

Now it is necessary to determine how something can be aware of such context we have declared.The first context-aware implementations were developed by collected locational information to improve efficiency of systems. For instance: a telephone system which patches through a call directly into a room where a requested person currently is. Since then, there have been many attemps to use and react to contextual information.

Also, One challenge of mobile distributed computing is to exploit the changing environment with a new class of applications that are aware of the context in which they are run. Such context-aware software adapts according to the location of use, the collection of nearby people, hosts, and accessible devices, as well as to changes to such things over time. A system with these capabilities can examine the computing environment and react to changes to the environment. Three important aspects of context are: where you are, who you are with, and what resources are nearby. Context encompasses more than just the user’s location, because other things of interest are also mobile and changing. Context includes lighting, noise level, network connectivity, communication costs, communication bandwidth, and even the social situation.whether you are with your manager or with a co-worker. We are investigating these kinds of applications using the PARCTAB, a small hand held device which uses an infrared-based cellular network for communication. The tab acts as a graphics terminal, and most applications run on remote hosts. This design exploits remote processing power to achieve a smaller and cheaper device. For input, the tab has three finger-operated buttons on the grip, and a touch sensitive screen. For output, the tab has a 128x64 pixel display and a piezo electric speaker. When wired with an infrared transceiver, a room becomes a cell in the infrared network. The bandwidth available for all tabs in a cell is 19,200 bps. The combination of room-sized cells, and tabs that periodically send an identifying packet (Beacon), permits accurate location monitoring even when the device is not being used. The system notifies applications of location changes, and also provides location information to a public service that collects and redistributes information about objects and their locations . Other systems might learn about location by other means: for example, by using global positioning (GPS) or dead-reckoning, or simply by monitoring workstation interactions. An alternative to the tab system’s locating scheme – where the mobile devices beacon – is a scheme where stationary devices broadcast a message identifying their fixed location. Mobile computers can listen to these broadcasts to determine their own locations.

The main objective of a navigation system is to track the current position of a person and dynamically planning the route which leads to the person’s desired location. The well-known GPS or the Global Positioning System has been developed to accomplish the above mentioned objective. However, this is mainly used to navigate while in outdoor rather than in indoor as there are considerable amount of issues occurring in indoors due to poor signal reception inside buildings. Here we focus on indoor navigation as same as outdoor navigation.