1,329 research outputs found
Energy Management Policies for Energy-Neutral Source-Channel Coding
In cyber-physical systems where sensors measure the temporal evolution of a
given phenomenon of interest and radio communication takes place over short
distances, the energy spent for source acquisition and compression may be
comparable with that used for transmission. Additionally, in order to avoid
limited lifetime issues, sensors may be powered via energy harvesting and thus
collect all the energy they need from the environment. This work addresses the
problem of energy allocation over source acquisition/compression and
transmission for energy-harvesting sensors. At first, focusing on a
single-sensor, energy management policies are identified that guarantee a
maximal average distortion while at the same time ensuring the stability of the
queue connecting source and channel encoders. It is shown that the identified
class of policies is optimal in the sense that it stabilizes the queue whenever
this is feasible by any other technique that satisfies the same average
distortion constraint. Moreover, this class of policies performs an independent
resource optimization for the source and channel encoders. Analog transmission
techniques as well as suboptimal strategies that do not use the energy buffer
(battery) or use it only for adapting either source or channel encoder energy
allocation are also studied for performance comparison. The problem of
optimizing the desired trade-off between average distortion and delay is then
formulated and solved via dynamic programming tools. Finally, a system with
multiple sensors is considered and time-division scheduling strategies are
derived that are able to maintain the stability of all data queues and to meet
the average distortion constraints at all sensors whenever it is feasible.Comment: Submitted to IEEE Transactions on Communications in March 2011; last
update in July 201
Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization
Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management
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