Energy-Optimal Data Aggregation and Dissemination for the Internet of Things

Established approaches to data aggregation in wireless
sensor networks (WSNs) do not cover the variety of new use
cases developing with the advent of the Internet of Things. In particular,
the current push towards fog computing, in which control,
computation, and storage are moved to nodes close to the network
edge, induces a need to collect data at multiple sinks, rather
than the single sink typically considered in WSN aggregation
algorithms. Moreover, for machine-to-machine communication
scenarios, actuators subscribing to sensor measurements may also
be present, in which case data should be not only aggregated and
processed in-network, but also disseminated to actuator nodes. In
this paper, we present mixed-integer programming formulations
and algorithms for the problem of energy-optimal routing and
multiple-sink aggregation, as well as joint aggregation and
dissemination, of sensor measurement data in IoT edge networks.
We consider optimisation of the network for both minimal total
energy usage, and min-max per-node energy usage. We also
provide a formulation and algorithm for throughput-optimal
scheduling of transmissions under the physical interference model
in the pure aggregation case. We have conducted a numerical
study to compare the energy required for the two use cases, as
well as the time to solve them, in generated network scenarios
with varying topologies and between 10 and 40 nodes. Although
aggregation only accounts for less than 15% of total energy
usage in all cases tested, it provides substantial energy savings.
Our results show more than 13 times greater energy usage for
40-node networks using direct, shortest-path flows from sensors
to actuators, compared with our aggregation and dissemination
solutions.