ReviewHeliyon2024
Micro energy harvesting for IoT platform: Review analysis toward future research opportunities.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Future directions and emerging trends of sustainable energy harvesting: innovations in photovoltaic and thermoelectric systems.RSC advances · 2026Review
- A Survey of Modern Data Acquisition and Analysis Systems for Environmental Risk Monitoring in Aquatic Ecosystems.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Micro-energy harvesting (MEH) is a technology of renewable power generation which is a key technology for hosting the future low-powered electronic devices for wireless sensor networks (WSNs) and, the Internet of Things (IoT). Recent technological advancements have given rise to several resources and technologies that are boosting particular facets of society. Many researchers are now interested in studying MEH systems for ultra-low power IoT sensors and WSNs. A comprehensive study of IoT will help to manage a single MEH as a power source for multiple WSNs. The popular database from Scopus was used in this study to perform a review analysis of the MEH system for ultra-low power IoT sensors. All relevant and important literature studies published in this field were statistically analysed using a review analysis method by VOSviewer software, and research gaps, challenges and recommendations of this field were investigated. The findings of the study indicate that there has been an increasing number of literature studies published on the subject of MEH systems for IoT platforms throughout time, particularly from 2013 to 2023. The results demonstrate that 67% of manuscripts highlight problem-solving, modelling and technical overview, simulation, experimental setup and prototype. In observation, 27% of papers are based on bibliometric analysis, systematic review, survey, review and based on case study, and 2% of conference manuscripts are based on modelling, simulation, and review analysis. The top-cited articles are published in 5 different countries and 9 publishers including IEEE 51%, Elsevier 16%, MDPI 10% and others. In addition, several MEH system-related problems and challenges are noted to identify current limitations and research gaps, including technical, modelling, economic, power quality, and environmental concerns. Also, the study offers guidelines and recommendations for the improvement of future MEH technology to increase its energy efficiency, topologies, design, operational performance, and capabilities. This study's detailed information, perceptive analysis, and critical argument are expected to improve MEH research's viable future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.