1 research outputs found
Automating Computational Placement for the Internet of Things
PhD ThesisThe PATH2iot platform presents a new approach to distributed data analytics for Internet of
Things applications. It automatically partitions and deploys stream-processing computations
over the available infrastructure (e.g. sensors, field gateways, clouds and the networks that
connect them) so as to meet non-functional requirements including network limitations and
energy. To enable this, the user gives a high-level declarative description of the computation as
a set of Event Processing Language queries. These are compiled, optimised, and partitioned
to meet the non-functional requirements using a combination of distributed query processing
techniques that optimise the computation, and cost models that enable PATH2iot to select the
best deployment plan given the non-functional requirements. This thesis describes the resulting
PATH2iot system, illustrated with two real-world use cases. First, a digital healthcare analytics
system in which sensor battery life is the main non-functional requirement to be optimized.
This shows that the tool can automatically partition and distribute the computation across a
healthcare wearable, a mobile phone and the cloud - increasing the battery life of the smart watch
by 453% when compared to other possible allocations. The energy cost of sending messages over
a wireless network is a key component of the cost model, and we show how this can be modelled.
Furthermore, the uncertainty of the model is addressed with two alternative approaches: one
frequentist and one Bayesian The second use case is one in which an acoustic data analytics for
transport monitoring is automatically distributed so as enable it to run over a low-bandwidth
LORA network connecting the sensor to the cloud. Overall, the paper shows how the PATH2iot
system can automatically bring the benefits of edge computing to the increasing set of IoT
applications that perform distributed data analytics