Real-Time Energy Monitoring in IoT-enabled Mobile Devices

Shivaraman, Nitin; Saki, Seima; Liu, Zhiwei; Ramanathan, Saravanan; Easwaran, Arvind; Steinhorst, Sebastian

doi:10.48550/arxiv.2004.14804

Cited by 2 publications

(1 citation statement)

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“…Primarily these systems can be divided into two subcategories. One category provides realtime monitoring of systems using non-intrusive methods [5], [6], [4]. Because of their non-invasive nature, these methods can be easily integrated into any system.…”

Section: Introductionmentioning

confidence: 99%

Development of Open-Source, Edge Energy Management System for Tactical Power Networks

Khan¹,

Wali²,

Farrukh³

et al. 2022

Preprint

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<p> Microgrids specialized for tactical operations have been subjected to several challenges. These tactical power networks are islanded and have a relatively low power generation capacity. Meeting power requirements of military equipment, having intermittent and highly inductive nature, exposes microgrids to severe stresses. Existing methodologies to monitor and control the impact of load variations require sophisticated equipment and trained personnel. The objective of this research paper is to present an open-source edge energy monitoring system (EEMS) for efficient demand management of tactical networks. The proposed system is capable of capturing all minute operational artifacts, including harmonic distortions and power quality of these networks. A variable gain amplifier circuit enables the proposed EMS to sense all the signals in a wide range of power with higher resolution. The proposed system utilizes raspberry pi as an edge device to meet the low power requirements of tactical networks. The novel concurrent programming approach adopted in the proposed EMS, effectively handles the large amount of data acquired from the network. This parallel processing of acquired data speeds up the execution process. All electrical parameters obtained during this process are stored in an encrypted local database that can be utilized for fault analysis and load prediction. Further integration of machine learning tools in proposed EMS assists in automated power network reconfiguration and tuning under harsh battlefield situations </p>

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Section: Introductionmentioning

confidence: 99%