Wireless sensor network calibration technique for low-altitude unmanned aerial vehicle localization in paddy field

Abstract

This paper presents the use of the received signal strength indicator (RSSI) from the RF signal to estimate the distance from a point where the signal is transmitted to the point where the signal is received. This can be a challenge as in the paddy field, the watery and dry conditions, as well as the height of the paddy plant can affect signal transmission during this estimation process. Two low-cost ground beacons, Beacon1 and Beacon2 (The coordinator), are used and placed in a known location with a fixed distance across the paddy field, which becomes the reference point during the distance estimation for the unmanned aerial vehicle (UAV). These signals are analyzed by using the non-right-angle trigonometry computation, to estimate the distance between the transmitter and the receiver. The estimated distance is compared with the measured value to determine the efficiency of this approach. The calibration trendlines of these beacons in the open, watery and dry paddy fields are discussed and presented. It is found that the dry paddy field gives less RSSI mean error and proved that humidity can contribute to the distance estimation error.

Software calibration for AK8963 magnetometer based on optimal ellipsoidal fitting

“…Some known calibration methods using Helmholtz coils require placing the magnetometers to be calibrated in an area where a homogeneous magnetic field is created by the Helmholtz coils. These techniques have the disadvantage that it becomes necessary to use coils with a very large radius to be able to calibrate the magnetometer, which poses space problems [14]- [18]. In addition, other methods have the advantage of calibrating the magnetometer in its environment of use.…”

“…As shown in Figure 7, the adjustment algorithm can essentially accurately calculate the position of the center of the ellipsoid, which represents the offsets of the three axes of the magnetometer. The fitting parameters of the ellipsoid U and c are given in (17) and (18).…”

Quantum drones and the future of military warfare

“…Because WSN is application-specific, it is difficult to create bottleneck control rules that are appropriate for all types of IoD implementations. Crowding escape and restriction in WSN strive to reduce packet loss due to congestion while guaranteeing that all network flows are allocated equal capacity [33], [59]- [63].…”

“…There are two categories of drones: commercial drones and military drones. Both urban and rural areas might reap significant benefits from the IoD due to the fact that drones can be positioned or relocated at any time and in any location [33], [60]. The internet of quantum drones (IoQD) offers the following basic features [59], [62]- [65]: -A safe and confidential messaging exchange: It is possible for the encrypted message to be transferred between any two entities in a secure manner thanks to quantum communication.…”

“…Crowding escape and restriction in WSN strive to reduce packet loss due to congestion while guaranteeing that all network flows are allocated equal capacity. WSN has the potential to be utilized in a variety of applications, including but not limited to the following: traffic monitoring, pipeline monitoring, landslide detection, rehabilitation applications, border patrol, laboratory tutoring, precision agriculture, real-time soccer game monitoring, real-time healthcare monitoring, asset tracking, and military applications [1], [33], [66]. For all of these important applications, an authorized user (external party) need direct access to real-time data from specific chosen sensor nodes.…”