Authors:
Jinseok Yang
;
Sameer Tilak
and
Tajana S. Rosing
Affiliation:
UCSD, United States
Keyword(s):
Wireless Sensor Network, Environment Monitoring, Power Management.
Related
Ontology
Subjects/Areas/Topics:
Applications and Uses
;
Environment Monitoring
;
Power Management
;
Sensor Networks
;
Wireless Information Networks
Abstract:
A key problem in sensor networks equipped with renewable energy sources is deciding how to allocate energy
to various tasks (sensing, communication etc.) over time so that the deployed network continues to gather
high-quality data. The state-of-the-art energy allocation algorithm takes into account current battery level and
harvesting energy and fairly allocates as much energy as possible along the time dimension. In this paper we
show that by not considering application-context this approach leads to very high and uniform sampling rates.
However, sampling the environment at fixed predefined intervals is neither possible (need to accommodate
system failures) nor desirable (sampling rate might not capture an important event with desired fidelity). To
that end, in this paper we propose a novel interactive power management technique that adapts sampling rate as
a function of both application-level context (e.g., user request) and system-level context (e.g harvesting energy
availability)
. We vary several key parameters including application request patterns, geographic locations,
time slot length, battery end point voltage and evaluate the performance of our approach in terms of energy
efficiency and accuracy. Our simulations use sensor data and system specifications (battery and solar panel
specs, sensing and communication costs) from a real sensor network deployment. Our results show that the
proposed approach saves significant amounts of energy by avoiding oversampling when application does not
need it while using this saved energy to support sampling at high rates to capture events with necessary fidelity
when needed. The computational complexity of our approach is lower (O(n)) than the state-of-the-art noninteractive
energy allocation algorithm (O(n2)).
(More)