1 research outputs found
A Resource-Aware and Time-Critical IoT Framework
Internet of Things (IoT) systems produce great
amount of data, but usually have insufficient resources to
process them in the edge. Several time-critical IoT scenarios
have emerged and created a challenge of supporting low latency
applications. At the same time cloud computing became a success
in delivering computing as a service at affordable price with great
scalability and high reliability. We propose an intelligent resource
allocation system that optimally selects the important IoT data
streams to transfer to the cloud for processing. The optimization
runs on utility functions computed by predictor algorithms that
forecast future events with some probabilistic confidence based
on a dynamically recalculated data model. We investigate ways of
reducing specifically the upload bandwidth of IoT video streams
and propose techniques to compute the corresponding utility
functions. We built a prototype for a smart squash court and
simulated multiple courts to measure the efficiency of dynamic
allocation of network and cloud resources for event detection
during squash games. By continuously adapting to the observed
system state and maximizing the expected quality of detection
within the resource constraints our system can save up to 70%
of the resources compared to the naive solution