Donghyun Seo | Searcharxiv

arXiv2021

EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes with Energy-Information Dynamic Co-Optimization

Intensive research on energy harvested sensor nodes with traditional battery powered devices has been driven by the challenges in achieving the stringent design goals of battery lifetime, information accuracy, transmission distance, and cost. This challenge is further amplified by the inherent power intensive nature of long-range communication when sensor networks are required to span vast areas such as agricultural fields and remote terrain. Solar power is a common energy source is wireless sensor nodes, however, it is not reliable due to fluctuations in power stemming from the changing seasons and weather conditions. This paper tackles these issues by presenting a perpetually-powered, energy-harvesting sensor node which utilizes a minimally sized solar cell and is capable of long range communication by dynamically co-optimizing energy consumption and information transfer, termed as Energy-Information Dynamic Co-Optimization (EICO). This energy-information intelligence is achieved by adaptive duty cycling of information transfer based on the total amount of energy available from the harvester and charge storage element to optimize the energy consumption of the sensor node, while employing in-sensor analytics (ISA) to minimize loss of information. This is the first reported sensor node < 35cm2 in dimension, which is capable of long-range communication over > 1Km at continuous information transfer rates of upto 1 packet/second which is enabled by EICO and ISA.

cs.NI↗

arXiv2020

Localization of directed polymers in continuous space

The first main goal of this article is to give a new metrization of the Mukherjee--Varadhan topology, recently introduced as a translation-invariant compactification of the space of probability measures on Euclidean spaces. This new metrization allows us to achieve our second goal which is to extend the recent program of Bates and Chatterjee on localization for the endpoint distribution of discrete directed polymers to polymers based on general random walks in Euclidean spaces. Following their strategy, we study the asymptotic behavior of the endpoint distribution update map and study the set of its distributional fixed points satisfying a variational principle. We show that the distributdion concentrated on the zero measure is a unique element in this set if and only if the system is in the high temperature regime. This enables us to prove that the asymptotic clustering (a natural continuous analogue of the asymptotic pure atomicity property) holds in the low temperature regime and that the endpoint distribution is geometrically localized with positive density if and only if the system is in the low temperature regime.

math.PR↗