The need for vehicle tracking system in real time is growth continues due to increase the cases of theft. This type of system in real time needs to transmit large data with huge number of HTTP request to the server to keep tracking and monitoring in real time, thus causes spend extremely high cost every month for transportation the information on tracking vehicles to server therefor the needs for reducing the number of transportation and data size that transmits in each HTTP request to save expenses. This paper designed and implement an integrated vehicle tracking system in real time to track vehicle anywhere and anytime. This system is divided into two parts: vehicle tracking part and monitoring part. Tracking part is represented by installation the electronic devices in the vehicle using modern Global Positioning System (GPS), microcontroller Arduino UNO R3 and SIM800L GSM/GPRS modem. GPS is determined location of the vehicle via received coordinates from satellites such as latitude and latitude with accuracy ranging approximately 2.5 meters; the coordinates faked to add a type of protection to information on vehicles without effecting on characterizing real time tracking before sending it via a General Packet Radio service (GPRS). The monitoring part is in the cloud and will receive the coordinates and displays it on a map in a web page. The main contribution of this system is it reduced data size that sent from in-vehicle device via selected only necessary data for tracking vehicle from NEMA sentences of GPS and reduced number of HTTP request that sent to remote server via constrain the transmission of information with the movement of vehicles, since when vehicle moved the coordinates each 10s and did not send anything when the vehicle stopped thus will reduce the cost of expenses every month. This system can be utilized to track and monitoring the vehicles of large universities, companies, organization and also can be used in army vehicles and police vehicles.    

This paper proposes to use machine learning techniques with ultrasonic sensors to predict the behavior and status of a person when they live solely inside their house. The proposed system is tested on a single room. A grid of ultrasonic sensors is placed in the ceiling of a room to monitor the position and the status of a person (standing, sitting, lying down). The sensors readings are wirelessly communicated through a microcontroller to a cloud. An intelligent system will read the sensors values from the cloud and analyses them using machine learning algorithms to predict the person behavior and status and decide whether it is a normal situation or abnormal. If an abnormal situation is concluded, then an alert with be risen on a dashboard, where a care giver can take an immediate action. The proposed system managed to give results with accuracy exceeding 90%. Results out of this project will help people with supported needed, for example elderly people, to live their life as independent as possible, without too much interference from the caregivers. This will also free the care givers and allows them to monitors more units at the same time.