IntroductionAs a result of advances in communication, computation, sensor and energy storage technologies, as well as carbon fiber-reinforced plastic materials, micro unmanned aerial vehicles (UAV) are available at affordable prices. On this basis many new application areas, such as the in-depth reconnaissance and surveillance of major incidents, will be possible. Uncontrolled emissions of liquid or gaseous contaminants in cases of volcanic eruptions, large fires, industrial incidents, or terrorist attacks can be analyzed by utilizing UAV (Figure 1). Hence, the use of cognitive Unmanned Aerial Systems (UAS) for distributing mobile sensors in incident areas is in general a significant value added for remote sensing, reconnaissance, surveillance, and communication purposes.1Figure 1: Deployment Scenario: Chemical Plume Detection with an Autonomous Micro UAV Mesh Network.In the near future police departments, fire brigades and other homeland security organizations will have access to medium- and small-size UAV and will integrate them in their work flow. The use of non-military frequencies and civil communication technologies gains in importance for purposes of safety and security missions, since the frequency pool is limited and nearly exhausted. In particular, regionally organized public authorities and small rescue organizations like fire brigades often have insufficient access to frequencies and expensive communication equipment. Thus, using civil mobile communication systems is often the only effective workaround for homeland security organizations.This is also one of the major issues for wireless communication in the area of unmanned aerial systems (UAS). Besides flight regulation, wireless communication is an important aspect of UAS as telemetry information (navigation, control, guidance) and sensor data usually have to be transmitted to a mission control center (MCC) in nearly real-time. Today, there is no viable alternative for this type of transmission besides using civil mobile communication networks. Unfortunately, there is no foreseeable solution in terms of frequency assignment for UAS. For efficient sensor coverage of large industrial and incident areas, fast and flexible strategies for collecting sensor data through an autonomous, reliably connected UAV need to be developed. In this article we focus on the civilian concepts of operations (CONOPS) for UAV, in particular for small-scale UAV. Viable concepts on the system level for leveraging public wireless communication networks for UAV-based cognitive remote sensing are presented with respect to both existing constraints and user requirements.The article is structured as follows: we first present the current state of the art and related research activities in the area of UAS communication. Civilian concepts of operations (CONOPS) for purposes of homeland security are discussed in the next section. Subsequent sections address the requirements, concepts and solutions for Air-to-Air (A2A), Air-to-Ground (A2G), and UAS-backend communication. On this basis we then show a methodology for agent-based UAV-mobility for areas with insufficient communication. The article ends suggestions for future research.Related Work and ProjectsSeveral research investigations have been done in the area of UAS. However, UAS communication aspects mostly address proprietary communication systems and usually do not consider public wireless infrastructures since these systems have been mostly deployed by military organizations in the past. Hence, we identify a demand for more in-depth contributions for UAS communication by means of public wireless networks.Tiwari and others have studied the placement planning problem of an airborne network.2 They offer a toolbox to optimize the ground coverage while maintaining a certain degree of reliability and connectivity. By introducing practical scenarios for deployment, the interaction between communication design and mobility planning is shown. …
[1]
Christian Wietfeld,et al.
AirShield: A system-of-systems MUAV remote sensing architecture for disaster response
,
2009,
2009 3rd Annual IEEE Systems Conference.
[2]
Yee Hui Lee,et al.
Investigation of Low Altitude Air-to-Ground Channel over a Tropical Sea Surface at C Band
,
2009
.
[3]
Joseph Yadegar,et al.
Topology control for future Airborne Networks
,
2009,
MILCOM 2009 - 2009 IEEE Military Communications Conference.
[4]
Eric W. Frew,et al.
Airborne Communication Networks for Small Unmanned Aircraft Systems
,
2008,
Proceedings of the IEEE.
[5]
Christian Wietfeld,et al.
Protocol Design and Delay Analysis for a MUAV-Based Aerial Sensor Swarm
,
2010,
2010 IEEE Wireless Communication and Networking Conference.
[6]
Christian Wietfeld,et al.
Channel Aware mobility for self organizing wireless sensor swarms based on low altitude platforms
,
2010,
2010 7th International Symposium on Wireless Communication Systems.
[7]
Zhu Han,et al.
Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles
,
2009,
IEEE Transactions on Vehicular Technology.
[8]
Barry E. Mullins,et al.
A novel communications protocol using geographic routing for swarming UAVs performing a Search Mission
,
2012,
2009 IEEE International Conference on Pervasive Computing and Communications.
[9]
Krishna M. Sivalingam,et al.
Efficient data gathering in distributed hybrid sensor networks using multiple mobile agents
,
2008,
2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE '08).
[10]
Andrew R. Nix,et al.
Path Loss Models for Air-to-Ground Radio Channels in Urban Environments
,
2006,
2006 IEEE 63rd Vehicular Technology Conference.
[11]
A. Tiwari,et al.
Towards a mission planning toolbox for the Airborne Network: Optimizing ground coverage under connectivity constraints
,
2008,
2008 IEEE Aerospace Conference.
[12]
Anna Scaglione,et al.
Cooperative OTH communications for airborne networks: Opportunities and challenges
,
2009,
2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.
[13]
Christian Wietfeld,et al.
A communication aware steering strategy avoiding self-separation of flying robot swarms
,
2010,
2010 5th IEEE International Conference Intelligent Systems.