Skip to main content

Review of Article: Codeword-based Data Collection Protocol for Optical Unmanned Aerial Vehicle Networks

Review of Article: Codeword-based Data Collection Protocol for Optical Unmanned Aerial Vehicle Networks

In the world of data transfer, optical data collection protocols for optical communication have a transfer rate of up to 2.5 Gbit/s.  Light of Sight (LOS) Free Space Optical (FSO) communication can support the high level of data connectivity required when multiple airborne Unmanned Aerial Vehicles (UAVs) are involved in a cooperative sensing scenario (Ramdhan, 2016).  This article proposes the protocol where collected data is ferried to a location where it can be analyzed, processed, stored, or forwarded to remote locations with cloud capabilities and viewing the collected data.
This dissemination of data through FSO has unique features for large bandwidth, high data rate, license free requirement, low power and low mass requirement (Ramdhan, 2016).  This surpasses the traditional RF method of data transfer in speed, security, and freedom to use without frequency allocation.  This data collection protocol is geared toward multiple UAV network architecture; three nodes are responsible for the transfer of data. 
3 Nodes
  • Sensor Nodes (Level 1)
    • Responsible for data collection randomly located in the UAV network
  • Drone Nodes (Level 2)
    • Receive the collected data from sensor nodes and ferry the information to a processing unit.
  • Data Collection Nodes (Level 3)
    • Relay nodes are responsible for information collected from the nearest drone. This node performs functions such as data compression, fusion, etc. 
Optical encoding and decoding is implemented within the data collection protocol of the UAV network.  The network architecture is based on two trees.  The first tree is the identification tree that uses the optical codewords to identify nodes in the network.  The second tree is the collection tree that picks up data from UAVs and routes it to the root drone to be delivered to a collection node.  Each UAV in the network is identified by a structured codeword and serves as an optical identifier (Ramdhan, 2016).
The inter-UAV FSO network has 3 basic link types.  These link types are: “ground to UAV, UAV to ground, and UAV to UAV (Ramdhan, 2016).”
This is an interesting article and displays the great advantages using FSO data transfer with incredible speed and efficiency when working in network of UAVs that work in applications such as Search and Rescue, fire monitoring, agriculture, and mining.  This almost perfect method for data transfer comes with handicaps.  Optical beams between these airborne terminals require a direct line of sight (DLOS) and can be interrupted by terrain or degraded by atmospheric conditions.

References:
Ramdhan, N., Sliti, M., & Boudriga, N. (2016). Codeword-based data collection protocol for optical unmanned aerial vehicle networks. Paper presented at the 35-39. doi:10.1109/HONET.2016.7753446

Link:
http://ieeexplore.ieee.org.ezproxy.libproxy.db.erau.edu/document/7753446/

Comments

Popular posts from this blog

ADS-B Detect, Sense and Avoid Sensor Selection for Unmanned Aerospace Systems

Introduction There is a need for a more efficient and safer environment in support of existing aeronautical operations that reduce the risk of collisions for manned and unmanned aircraft.  Operators of Small Unmanned Aerospace Systems (sUAS) under 55 pounds hold a responsibility to safe flight in the airspace in which they are permitted.  Payload weight on aircraft this small is significant and should be kept to a minimum for operating efficiency.  Weight requirement and cost effectiveness are key factors for Sense and Avoid (SAA) sensor selection.  A Traffic Collision and Avoidance System (TCAS) are too large and heavy for sUAS.  SAA technology for UAS is part of a much bigger picture.  Each development brings UAS closer to their consent in the National Airspace System (NAS).  NASA conducts collaborative research “with the Federal Aviation Administration (FAA), the Radio Technical Commission for Aeronautics (RTCA) and commercial aerospace enti...

Complimenting Sensors for Navigation in Urban Canyons

Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis Journal of Aerospace Information Systems             This article from the Journal of Aerospace Information Systems analyzes alternative methods for Unmanned Aerospace Systems (UAS) navigation within urban environments.   Navigation accuracy by Global Positioning System (GPS) is severely degraded due to urban canyons, where accuracy is particularly poor.   An urban canyon is best described as area flanked by tall buildings.   Although Global Navigation Satellite System (GNSS) is unreliable in the vicinity of dense urban structure it can be used in combination with other complimentary sensors to provide position and velocity measurement.             Urban UAS missions related to law enforcement, traffic surveillance, riot control, and anti-terrorism are all challenged b...