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Uncertainty Quantification in Internet of Battlefield Things 21

architectures become necessary to ensure their applicability, particularly in
the context of ML applications. To help illustrate these challenges, a moti-
vating scenario is provided.

2.2.1 Detecting Vehicle-Borne IEDs in Urban Environments
As part of an ongoing counterinsurgency operation by coalition forces in the
country of Aragon, focus is placed on monitoring of insurgent movements
and activities. Vehicle-borne improvised explosive devices (VBIEDs) have
become more frequently used by insurgents in recent months, requiring
methods for quick detection and interception. Recent intelligence reports
have provided details on the physical appearance of IED-outfitted vehicles
in the area. However, due to the time constraints in confirming detections of
VBIEDs, methods for autonomous detection become desirable. To support
VBIED detection, an IoBT infrastructure has been deployed by coalition
forces consisting of a mix of unattended ground sensors (UGSs) and
unmanned aerial systems (UASs). In turn, supervised learning methods
are employed over sensor data gathered from both sources.
Recent intelligence has indicated that VBIEDs may be used in a city
square during the annual Aragonian Independence Festival. A local custom
for this festival involves decoration of vehicles with varying articles (includ-
ing flags and Christmas tree lighting). A UAS drone is tasked with patrolling
the airspace over one of the inbound roadways and recoding images of
detected vehicles. However, due to the decorations present on many civilian
vehicles, confidence in VBIED classification by the UAS is significantly
reduced. To mitigate this, the drone flies along a 3-mile stretch of road
for 10 minutes to gather new images of the decorated vehicles. In each case
the drone generates a classification of each vehicle as VBIED or not, each
with a particular confidence value. For low-confidence readings, the drone
contacts a corresponding UGS to do the following: (i) take a high-resolution
image, and (ii) take readings for presence of explosives-related chemicals in
the air nearby, where any detectable explosives confirms the vehicle is a
VBIED. Since battery power for the UGS is limited, along with available
network bandwidth, the UAS should only request UGS readings when
especially necessary. Following receipt of data from a UGS, the UAS per-
forms retraining of the classifier to improve the accuracy of future VBIED
classification attempts. Over a short period, the UAS has gathered additional
training data to support detection of VBIEDs. Eventually, the drone passes
over a 1-mile stretch of road lacking UGSs. At this point the UAS must clas-
sify detected vehicles without UGS support (Fig. 2.1).