Web Based Phone Data Viewer App

An app that lets users see their phone's calls and messages on the web, useful where phones are not allowed. Users register and log in to view the data.

The user installs the app and signs in; credentials are checked against Firebase Authentication or the app's own API. Screens built in XML (or Jetpack Compose) collect the user's input and show results in lists and cards. Data is stored in Firebase Realtime Database / Firestore or a local SQLite database and synced when the phone is online. Notifications and background services alert the user to updates, and an admin side manages the records.

An Activity moves through onCreate, onStart, onResume (running), onPause, onStop and onDestroy. Saving state in onPause or onSaveInstanceState prevents data loss when the user switches apps or rotates the screen.

Firebase gives a hosted real-time database, authentication and push notifications without running our own server. SQLite stores data locally on the phone and works offline. We chose based on whether data must be shared between users.

Packet delivery ratio, end-to-end delay, throughput, packet loss and energy consumption, compared between the existing protocol and the proposed one.

Proactive protocols (DSDV, OLSR) keep routes to all nodes updated at all times. Reactive protocols (AODV, DSR) find a route only when data needs to be sent, which saves overhead in mobile networks.

It follows a three-tier architecture: the browser shows the user interface, the server-side code handles logic and validation, and the MySQL database stores records. Requests travel over HTTP and responses are rendered as HTML or JSON.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.