Tech Briefs Magazine - June 2021 - 52

Software
LogFire
The software automatically checks mission operations logs.
NASA's Jet Propulsion Laboratory, Pasadena, California
ule-based systems seem natural for
runtime verification (RV)/program
monitoring. From a specification notation
point of view, rule-based systems
appear quite suitable for expressing the
kind of properties the runtime verification
community normally writes.
Specifications written in a rule system
have an operational flavor, which can
be seen as a disadvantage or an advantage,
depending on the viewpoint. The
operational flavor makes specifications
longer than in declarative temporal
logic or regular expressions; however,
they are natural to write. Once the core
idea is mastered, writing rules is
straightforward, like programming.
More declarative specifications can be
trickier to get right. This observation is
similar to the observation that it may be
easier to formulate a nontrivial property
as a state machine than as a temporal
logic formula or a regular expression.
JPL's rovers and spacecraft produce
telemetry streams when operating. A
R
telemetry stream is essentially a sequence
of events that is stored on the ground as a
log in persistent memory. Mission operations
need to produce automated checks
that these logs are correct. LogFire can
be used for checking these logs. LogFire,
for example, allows operations engineers
to automatically ensure that a rover performs
the correct steps.
LogFire reads in a log file and checks
it against a formal specification. The
specification can be formulated in a
rule-based language. The rule-based
specification language is implemented
as an API in the Scala programming
language. This re sults in a very powerful
specification language since one can
mix rules and traditional programming.
The
definition of the rule-based language
as an API in Scala has the appearance
of a DSL (Domain-Specific Lan -
guage) due to Scala's support for definition
of such (internal) DSLs. The
rule engine, which is based on the well
known RETE algorithm, has in addition
been augmented with the notion
of events, which are instantaneous in
contrast to long-living facts with which
rule engines normally work. Finally, an
indexing method has optimized the en -
gine to handle events that carry data.
LogFire furthermore allows specification
patterns to be easily encoded to generate
rules. It is relatively straightforward
to define specification patterns as fragments
of temporal logic and time lines
- instances of which are translated to
rules. An interesting nuance is that these
templates allow data-parameterized
events. Experiments have been performed
comparing the resulting implementation
with six other runtime verification
and rule-based systems.
NASA seeks to freely license this software
and make it available for general use for
open source projects. Please contact NASA's
Licensing Concierge at Agency-PatentLicensing@mail.nasa.gov
or call us at 202358-7432
to initiate licensing discussions.
WiFi RSSI Sensor Tracker for the ISS
This software could also be used for indoor navigation assistance for the visually impaired.
Lyndon B. Johnson Space Center, Houston, Texas
S
ensors aboard the International
Space Station (ISS), including personal
CO2 monitors, require location
tracking in order to correlate their timestamped
data with positional information.
Labeling of data based on visual
inspection is expensive and impractical
for tracking many sensors. A cheap and
efficient solution is to utilize the one
additional measurement device these
sensors have; namely, their WiFi or Blue -
tooth signal strength readings.
Using these signal strength readings,
this software aims to provide approximate
location information in time for
individual sensor units. The purpose of
the Module-level ISS Sensor Tracker
(MIST) project is to set a standard data
52
Cov
format and protocol for recording WiFi
signal strength information so that it
could later be used to locate sensors
onboard the ISS. Google and Apple have
proprietary solutions for location services
that combine GPS data with WiFi
strength readings to give an accurate estimate
of a cellphone's position; however,
no such method exists for tracking WiFienabled
devices on the ISS.
Support-vector machines (SVM) were
chosen as the algorithm to pursue. The
main motivation is the high accuracy relative
to the number of samples required
for training, as training data is the main
cost aboard the ISS. A Python application
and library for both logging WiFi
information and predicting positions
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ToC
was developed. A pipeline of techniques
was constructed that gives highly accurate
performance for the data sets collected.
With a simple moving-window filter,
the predictor can correctly identify
which module a sensor is in with >95%
accuracy. Further improvements will be
able to bring this much closer to 100%.
The software program consists of two
operating modes: training and runtime.
For training, WiFi RSSI data is collected
using standard Linux command line utilities
and is stored in a csv format. The RSSI
readings at each time step are correlated
to a numbered location; in this case, the
ISS module. This data is run through a set
of preprocessing functions that strips away
bad/sparse readings and arranges the
Tech Briefs, June 2021

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Tech Briefs Magazine - June 2021

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