Real Time Tracking System and Data Reduction

Today, for various purposes, vehicle tracking systems are used for determining the geographic location of vehicles and transmitting this information to a data center. For detecting the location, a GPS is used, and for the transmission mechanism, a satellite or cell tower is deployed. Tracking systems are producing a massive data since they monitor moving vehicles continuously and report vehicle status. Since the amount of collected data is large and needs a storage unit that can handle all the transmitted data, storage becomes more challenging. The cost of transmitting, processing, storing, and accessing the data grows as the number of vehicles being tracked increases. The amount of data collected by the system depends on the uploading frequency. For example, the amount of data will increase as the uploading frequency (seconds) decreases and vice versa. This work provides a storage management solution that reduces the size of cloud databases, both SQL and NoSQL databases, by eliminating repeated data. One of the causes of massive data in the tracking system is the high uploading frequency that causes a huge amount of repetitive values. We propose two algorithms for minimizing database storage: The Reducing Data Redundancy algorithm and the Data Lifetime algorithm. We implement these two algorithms in the cloud, for both SQL and NoSQL databases. For evaluation, a vehicle tracking system is developed by using Global Positioning System (GPS) and GSM/GPRS module. Our experiments use two different approaches: Static testing for when a vehicle is not in motion mode, and dynamic for when it is. The result of the experiments shows the effectiveness of these two algorithms in decreasing storage size and increasing process time. The system has four parts, which are the tracking unit, cloud database, web application, and Android Application. The tracking unit is installed inside a vehicle to detect the vehicle’s location, speed, and temperature then uploads this information to a cloud database. The main functions of the system are to track a vehicle, transmit the information to the cloud, and send notifications to the system administrator and users. The Android application is designed to receive notifications and view the vehicle’s information such as the current location and temperature. The administrator of the system uses the web application to set constraints for users’ vehicles, such as the temperature range and location restriction

Design and Implementation of Integrated Smart Home Energy Management Systems for Clusters of Buildings

To ensure the balance between power generation and demand at the peak hours, power utilities need to keep additional power generation capacity on standby which usually causes higher operational cost. This will result in variations in hourly to seasonal electricity prices. With a focus on the physical layer integration, we developed a methodology which includes a prototype of a cluster of smart homes with energy management systems (SHEMS) to study and harvest the flexibility in the demand side in the residential building sector by monitoring and controlling the loads at appliances level. For this goal, we developed and fabricated the electronic circuit of five custom-designed smart plugs (DC, MQTT based) and integrated a vendor-based smart plug (AC) to the system. The devices were equally allocated to three home hubs. As opposed to a standalone SHEMS, this methodology is applied at a cluster scale: through awareness of the electricity consumption of all houses, under certain assumptions optimized load patterns can be generated not only to decrease consumers’ electricity bills, but also to meet the grid’s constraints. We crafted 3 scenarios as showcases of the methodology performance, with two electricity price plans and different load configurations. The results show both smart plug types were successful in measuring the loads and communication with other layers resulting in decreased electricity cost. Additionally, using a hybrid cloud-fog based architecture, a function was designed for saving the smart plugs records during cloud service or internet disconnection to enable later synchronization of the local and cloud database