Special Electrical Engineering Seminar
Moore B280
Optimal transmission policies for energy harvesting communication devices
Michele Zorzi,
Professor,
Department of Information Engineering,
University of Padova, Italy & University of California, San Diego,
Energy Harvesting (EH) is a new paradigm in Wireless Sensor Networks
(WSNs): sensor nodes are powered by energy harvested from the ambient,
rather than by non-rechargeable batteries, thus enabling a potentially
perpetual operation of the WSN. However, Energy Harvesting poses new
challenges in the design of WSNs, in that energy availability is random and
fluctuates over time, thus calling for radically different energy management
solutions. In this talk we investigate the following fundamental question:
how should the harvested energy be managed to ensure optimal performance?
First, we consider a sensor powered by EH which senses data of varying
importance and reports them judiciously to a Fusion Center. Assuming that
data transmission incurs an energy cost, our objective is to identify
low-complexity policies that achieve close-to-optimal performance, in terms
of maximizing the average long-term importance of the reported data. We
first consider schemes that rely on the assumption of perfect knowledge of
the amount of energy available in the battery. Subsequently, we investigate
the design of operation policies that maximize the long-term reward under
imperfect knowledge of the State-Of-Charge (SOC). Moreover, for both
scenarios, we explore the impact of time-correlation in the EH process,
showing that simple adaptation to the state of the EH process yields
close-to-optimal performance, without requiring full knowledge of the SOC of
the battery.
(WSNs): sensor nodes are powered by energy harvested from the ambient,
rather than by non-rechargeable batteries, thus enabling a potentially
perpetual operation of the WSN. However, Energy Harvesting poses new
challenges in the design of WSNs, in that energy availability is random and
fluctuates over time, thus calling for radically different energy management
solutions. In this talk we investigate the following fundamental question:
how should the harvested energy be managed to ensure optimal performance?
First, we consider a sensor powered by EH which senses data of varying
importance and reports them judiciously to a Fusion Center. Assuming that
data transmission incurs an energy cost, our objective is to identify
low-complexity policies that achieve close-to-optimal performance, in terms
of maximizing the average long-term importance of the reported data. We
first consider schemes that rely on the assumption of perfect knowledge of
the amount of energy available in the battery. Subsequently, we investigate
the design of operation policies that maximize the long-term reward under
imperfect knowledge of the State-Of-Charge (SOC). Moreover, for both
scenarios, we explore the impact of time-correlation in the EH process,
showing that simple adaptation to the state of the EH process yields
close-to-optimal performance, without requiring full knowledge of the SOC of
the battery.
Event Series
Electrical Engineering Systems Seminar Series
Event Sponsors