Title: Energy-Efficient Sensor Selection  for Data Quality and Load Balancing
Speaker: Farshid Hassani Bijarbooneh, Uppsala University

Abstract:
It is common to deploy stationary sensors in large geographical
environments for monitoring purposes. In such cases, the
monitored data are subject to data loss due to poor link quality
or node failures. Fortunately, the sensing data are highly
correlated both spatially and temporally. In this paper, we
consider such networks in general, and jointly take into account
the link quality estimates, and the spatio-temporal correlation
of the data to minimise energy consumption by selecting sensors
for sampling and relaying data. In particular, we propose a
multi-phase adaptive sensing algorithm with belief propagation
protocol (ASBP), which can provide high data quality and reduce
energy consumption by turning on only a small number of nodes in
the network. We explore the correlation of data, formulate the
sensor selection problem and solve it using constraint
programming (CP) and greedy search. We then use our Bayesian
inference technique to reconstruct the missing sensing data. ASBP
is evaluated based on the data from real deployment of a WSN, and
we show that while maintaining a satisfactory level of data
quality and prediction accuracy, ASBP successfully provides load
balancing among sensors and preserves 80% more energy compared to
the case where all sensor nodes are actively involved.