Evidence map›Paper›PMID 35009926›Full record

ArticleSensors (Basel, Switzerland)2022

On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks.

Sandrine Mukase, Kewen Xia, Abubakar Umar, Eunice Oluwabunmi Owoola

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sandrine MukaseSchool of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.ORCID 0000-0003-2805-9920
Kewen XiaSchool of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.ORCID 0000-0003-3968-481X
Abubakar UmarSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China.
Eunice Oluwabunmi OwoolaSchool of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.

Funding

Hebei Province Natural Science Foundation No.E2021202179Key Research and Development Project from Hebei Province No.19210404D, No.20351802D, No.21351803DNational Natural Science Foundation of China No.U1813222, No.42075129
6 · The paper itself

Abstract

Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes.

Indexed as

energy consumptionenergy renewable management systemparticle swarm optimizationwireless energy transferwireless renewable sensor networks

Identifiers

PMID35009926
PMCPMC8749933

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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.