4th Conference on Safety and Security Systems in Europe, Potsdam, Juni 2009
Mesh Network for CBRNE-Reconnaissance
with MUAV Swarms
K. Daniel, B. Dusza, C. Wietfeld
Communication Networks Institute (CNI)
Dortmund University of Technology
1. Introduction
For clarification, containment and crisis
management of large danger areas, such
as fire or gas leakages, a fast and flexible
survey of potentially contaminated areas
is crucial for the success of a rescue effort. This can be achieved efficiently if
the disaster managers are provided with
timely and detailed information of the
situation not only at ground level but also
at greater heights.
At present the rescue personnel are provided with special handheld devices that
can only measure the concentration of
different pollutants at ground level but are
unable to survey and quantify the level of
contamination carried in the atmosphere
by winds and/or billowing columns of
smoke as shown in Fig. 1. Such a measurement is critical to the safety of outlying
communities that may be affected by
these aerial pollutants.
The AirShield project (Airborne Remote
Sensing for Hazard Inspection by Network Enabled Lightweight Drones) is
mandated to provide critical data by
means of aerial surveillance of the disaster area and to deliver important information that will be analyzed to devise and
implement effective and appropriate ac-
Figure 1: Use Case Showing Smoke Propagation
tion plans. AirShield focuses on incidents
that are caused by uncontrolled emissions
of liquid or gaseous CBRNE (chemical,
biological, radiological and nuclear)contaminants.
Instead of sending emergency personnel
with expensive transport and measurement devices, the AirShield project mobilizes a swarm of MUAVs (Micro Unmanned Aerial Vehicles), often simply
known as drones (see Fig. 5), that are
featured with lightweight mobile sensors
for exploration and data collection in the
contaminated area. These wireless interconnected MUAVs are self organizing
and autonomous flying robots that will
collect the necessary information and
transmit it to the Mission Control Center
(MCC) in real-time.
Figure 2: Aerial Mesh Topologies for MUAV Swarms
A gas propagation model will be developed, taking into account geographical
parameters as well as atmospheric and
weather conditions. By using this model
the prediction of the spreading of various
contaminants in the atmosphere will be
possible. The analysis of the received data
and propagation forecasts will provide
decision support by enabling the emergency manager at the MCC to devise
appropriate strategies and allocate requisite resources. The system is planned in
such a way that it can be used by rescue
organizations as well as by private companies (factory fire departments) and
NGOs (Non-Governmental Organization).
In view of the above mentioned challenges the AirShield consortium has been
formed comprising of three industrial
partners, five research institutions and an
end user (fire brigades Dortmund / Germany). AirShield coordinated by the
Communication Networks Institute at TU
Dortmund.
In this paper we present the work in progress of the communication work package
in the AirShield project, which particularly considers a continuous network
planning for the transient MUAV swarm
topology in the aerial remote sensing
network.
2. Aims
An aerial communication network has to
be set up in order to transmit
•
•
•
georeferenced sensor information
telemetric data and
routes and destinations
For this purpose Drone to Ground Station
Links (DGSL) and Inter Drone Links
(IDL) as particularly described in /1/ are
required. The DGSL represent the physical and logical connection between the
MCC and a single MUAV. The IDL enable for realtime calculation of the gas
gradient between all sensor nodes and for
autonomous MUAV swarm navigation.
As shown in Fig. 2 different mesh network configurations are possible. In infrastructure mode there are no physical IDL
and each node is connected to a base
station at ground level. In multihop operation both IDL and DGSL are available in
different configurations. At least there is a
single DGSL, but all MUAV are interconnected. If the nodes are not fully
meshed with each other partitioning occurs that leads to the worst case of an
autarchic cluster when all DGSL are interrupted. A challenging subgoal of the considered work is to avoid or compensate
such autarchic cluster by introducing
channel sensitive mobility behaviour to
the continuously reconfiguring MUAV
swarm.
4. Radio Based Swarm Mobility
The considered swarm performs a
•
•
macroscopic movement and
microscopic/individual
movement
during its mission, that is determined by
the gas/aerosol concentration of the drifting cloud in the troposphere. The macroscopic mobility is mainly based on the
overall concentration gradient. This is
superimposed by the microscopic random
mobility of each node that shall deliver
meaningful measurement values.
The MUAV nodes are capable to measure
the signal to noise ratio (SNR) of the
channel. This is a usual key figure for the
strength and quality of the received signal
of moving robots /2/. For the IDL a free
loss propagation model can be assumed.
In contrast to the IDL fading effects have
to be taken into account for the DGSL.
Depending on the SNR measurements,
which are broadcasted within the MUAV
swarm, each node adapts its motion in
order to keep the mesh network alive and
prevent interruptions. In particular a bioinspired ant behaviour is considered for
an explorer node (EN) that consciously
leaves the swarm mesh area as shown in
Fig. 4 (I) for channel measurements in the
target area. If an out-of-coverage-area
with no DGSL coverage is detected (II),
the EN stops and waits at the edge of this
area (III) as long as the swarm is crossing
the “RF dead zone”. In the meanwhile the
EN routes all information from the re-
Figure 4: Channel Sensitive Mesh Mobility
maining MUAV swarm over the IDL and
DGSL to the MCC at ground level (III).
When the out-of-coverage-area is crossed
over by the swarm, the EN is following
ground area but not the aerial user space.
Thus, dedicated radio access, e.g. with
Mobile WiMAX, is a recommendable
alternative.
Figure 5: MUAV Predecessor (md4-200)
(IV) and resuming its mission. The algorithm is considered to work with more
than a single EN, e.g. if an EN is working
in relay mode, a faraway MUAV takes
over the explorer role at the other flank of
the moving MUAV mesh network.
3. MUAV Mesh Network
Different communication technologies are
exploited for the MUAV mesh network.
For the IDL different WLAN derivatives
are considered. IEEE 802.11a/h works at
5 GHz and allows for higher transmit
powers that overcompensate the higher
attenuation on the channel. The ad-hoc
mode is indispensable for setting up a
mobile ad hoc network with WLAN. The
WLAN ad-hoc mode is also the preliminary for using hybrid IEEE 802.11s routing protocols. IEEE 802.11p, as discussed
in /5/, supports deployment in high vehicular environments. The issue at the
present time is, that no off the shelf components are available that join all the
mentioned specified features.
For the DGSL Long Term Evolution
(LTE), High Speed Packet Access
(HSPA) or MobileWiMAX /4/ are suitable cellular radio technologies. The challenge of using third party networks lies in
the directivity of the widely used antennas, which are usually illuminating the
Acknowledgements
Our work has been conducted within the
AirShield-project (Airborne Remote Sensing for Hazard Inspection by Network
Enabled Lightweight Drones), which is
part of the nationwide security research
program funded by the German Federal
Ministry of Education and Research
(BMBF) (13N9834).
References
/1/ Daniel K., Dusza, B., Lewandowski, A., Wietfeld
C.: “AirShield: A System-of-Systems MUAV Remote Sensing Architecture for Disaster Response”, IEEE International Systems Conference
(SysCon), Vancouver, March 2009.
/2/ Lindhe, M., Johansson, K. “Using robot mobility to
exploit multipath fading” Wireless communications
in networked robotics” IEEE Wireless Communications, Feb. 2009
/3/ Boulat A., Desnoyers, P. J. “Exact Distributed
Voronoi Cell Computation in Sensor Networks“
Proc. of the International Conference on Information Processing in Sensor Networks (IPSN '07),
Cambridge, MA, April 2007
/4/ Yang, Z., Mohammed, A. “Evaluation of WiMAX
Uplink Performance in High Altitude Platforms
Cellular System”, 4th International Symposium on
Wireless
Communication
Systems
(ISWCS),
Trondheim, 2007
/5/ Jiang, D., Delgrossi, L. “IEEE 802.11p: Towards an
International Standard for Wireless Access in Vehicular Environments”, IEEE Vehicular Technology Conference VTC Spring, 2008
Contact:
Kai Daniel, Björn Dusza, Christian Wietfeld
TU Dortmund, Communication Networks Institute (CNI)
Otto-Hahn-Str. 6, 44227 Dortmund, Germany
e-mail: {Kai.Daniel, Bjoern.Dusza, Christian.Wietfeld}@tu-dortmund.de