Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

System assessment of WUSN using NB-IoT UAV-aided networks in potato crops

Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors & x2019; battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor & x2019;s density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001