Evidence map›Paper›PMID 32438545›Full record

ArticleSensors (Basel, Switzerland)2020

Lightweight Proof of Game (LPoG): A Proof of Work (PoW)'s Extended Lightweight Consensus Algorithm for Wearable Kidneys.

Adarsh Kumar, Deepak Kumar Sharma, Anand Nayyar, Saurabh Singh, Byungun Yoon

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Internet of Things for Smart Community Solutions.Sensors (Basel, Switzerland) · 2022
    Article
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

5 authors.

Adarsh KumarDepartment of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India.ORCID 0000-0003-2919-6302
Deepak Kumar SharmaDepartment of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India.ORCID 0000-0002-3181-2541
Anand NayyarGraduate School, Duy Tan University, Da Nang 550000, Vietnam.ORCID 0000-0002-9821-6146
Saurabh SinghDepartment of Industrial & Systems Engineering, Dongguk University, Seoul 04620, Korea.ORCID 0000-0003-1118-9569
Byungun YoonDepartment of Industrial & Systems Engineering, Dongguk University, Seoul 04620, Korea.ORCID 0000-0002-1110-4011

Funding

National Research Foundation of Korea NRF-2019R1A2C1085388
6 · The paper itself

Abstract

In healthcare, interoperability is widely adopted in the case of cross-departmental or specialization cases. As the human body demands multiple specialized and cross-disciplined medical experiments, interoperability of business entities like different departments, different specializations, the involvement of legal and government monitoring issues etc. are not sufficient to reduce the active medical cases. A patient-centric system with high capability to collect, retrieve, store or exchange data is the demand for present and future times. Such data-centric health processes would bring automated patient medication, or patient self-driven trusted and high satisfaction capabilities. However, data-centric processes are having a huge set of challenges such as security, technology, governance, adoption, deployment, integration etc. This work has explored the feasibility to integrate resource-constrained devices-based wearable kidney systems in the Industry 4.0 network and facilitates data collection, liquidity, storage, retrieval and exchange systems. Thereafter, a Healthcare 4.0 processes-based wearable kidney system is proposed that is having the blockchain technology advantages. Further, game theory-based consensus algorithms are proposed for resource-constrained devices in the kidney system. The overall system design would bring an example for the transition from the specialization or departmental-centric approach to data and patient-centric approach that would bring more transparency, trust and healthy practices in the healthcare sector. Results show a variation of 0.10 million GH/s to 0.18 million GH/s hash rate for the proposed approach. The chances of a majority attack in the proposed scheme are statistically proved to be minimum. Further Average Packet Delivery Rate (ADPR) lies between 95% to 97%, approximately, without the presence of outliers. In the presence of outliers, network performance decreases below 80% APDR (to a minimum of 41.3%) and this indicates that there are outliers present in the network. Simulation results show that the Average Throughput (AT) value lies between 120 Kbps to 250 Kbps.

Indexed as

BlockchainKidneys, ArtificialWearable Electronic DevicesAlgorithmsConsensusHumansattacksbit-exchangeblockchainchallenge-responsecryptocurrencygame theorygash ratehealthcarelightweightness

Identifiers

PMID32438545
PMCPMC7287825

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.