The growing use of mobile technologies
in Africa provides an opportunity for mobile phone-based
mobile devices come equiped with a rich set of sensors such as
accelemoter (for orientation), GPS (for location), digital compass (for
direction) and microphone (for sound). The availability of portable and
rich-sensor mobile devices (e.g., smartphones) has given raise to a new
paradigm known as participatory-sensing [3,6].
Using their mobile devices, ordinary citizens and communities collect
and share data about their environment as well as well being. Several
participatory sensing applications have been conceived ranging from
noise environmental monitoring to health well being . Building on
this trend, this masters research seeks to leverage the availability of
a rich set of sensors such as GPS, accelerometer, camera and compass
that come embedded in today’s mobile devices for infrastructure
monitoring with a focus on the transportation sector.
This masters thesis aims to undertake
research to explore the use of mobile sensor technology in road infrastructure
monitoring in the urban areas of a developing country setting where roads are characterised by highly uneven road surfaces.
The research activities will include:
(1) design and/or adaptation of exisiting algorithms and data
structures for highly uneven road surfaces. (2) building an open data
repository of raw and aggregated data from mobile sensors such as
accelerometer and GPS (3) investigating real-time and adaptive
visualization (e.g., enriched with context-awareness) (4) exploring
automatic "garbage collection" techniques or "self-destructing"
data structures for stale data (i.e., removing of datasets
corresponding to repaired road damages) (5) performing pilot case
studies, (6) investigating privacy issues and (7) conducting a comparative study of automated participatory
sensing vs explicit reporting rwith a view of exploring how the two can
complement each other. The exact scope to be refined with the student.
This project requires the student to have (or develop) good
programming skills. Knowledge of mobile applications programming and AI
concepts such as machine-learning is a plus but not a requirement.
Bainomugisha < baino at cis dot mak dot ac dot ug >