Data Collection from a Sensor Network using a Quadcopter

The purpose of this project is to implement an automated data collection drone that fly’s to wireless sensor nodes and collects measurement data. This is intended for sensor networks that are placed too far apart to communicate wirelessly or need to operate on very low power in remote locations. An off the shelf quad copter was outfitted with an Arduino and a ZigBee for collecting data from nodes and storing the data on a micro SD card through openlog. The drone uses a GPS module with provided coordinates for navigation. Each node is constructed using an Arduino, a ZigBee and a temperature sensor

MG-leach: an enhanced leach protocol for wireless sensor network

A wireless sensor network is made up of a large number of small sensor nodes with limited energy resources, which is a real problem for this network. In this article, we will study the ingestion of node energy in these networks at the routing level. In addition, we are modifying one of the most popular routing algorithms for data communication in the WSN: LEACH (Adaptive Hierarchy with Low Power Consumption). The modified version of the LEACH base version "MG_LEACH" uses an intermediate cluster header to transmit data, extend the network lifetime and send more data than the original protocol. Our proposed algorithm is simulated using MATLAB to verify the effectiveness of improving the lifetime of this network. The results of the simulation confirmed that the system was working better than the LEACH basic system and that the network life had been improved. 

Latency-sensitive Service Delivery with UAV-Assisted 5G Networks

In this letter, a novel framework to deliver critical spread out URLLC services deploying unmanned aerial vehicles (UAVs) in an out-of-coverage area is developed. To this end, the resource optimization problem, i.e., resource blocks (RBs) and power allocation, and optimal UAV deployment strategy are studied for UAV-assisted 5G networks to jointly maximize the average sum-rate and minimize the transmit power of UAV while satisfying the URLLC requirements. To cope with the sporadic URLLC traffic problem, an efficient online URLLC traffic prediction model based on Gaussian Process Regression (GPR) is proposed which derives optimal URLLC scheduling and transmit power strategy. The formulated problem is revealed as a mixed-integer nonlinear programming (MINLP), which is solved following the introduced successive minimization algorithm. Finally, simulation results are provided to show our proposed solution approach's efficiency.Comment: Accepted in IEEE Wireless Communications Letter