Evidence map›Paper›PMID 28832495›Full record

ArticleSensors (Basel, Switzerland)2017

A Fair Contention Access Scheme for Low-Priority Traffic in Wireless Body Area Networks.

Shagufta Henna, Muhammad Sajeel, Faisal Bashir, Muhammad Asfand-E-Yar, Muhammad Tauqir

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. 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. Review
  2. 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.

Shagufta HennaDepartment of Computer Science, Bahria University, Islamabad 44000, Pakistan. Shinna.buic@bahria.edu.pk.
Muhammad SajeelDepartment of Computer Science, Bahria University, Islamabad 44000, Pakistan. sajeel@isb.phoenix.com.pk.
Faisal BashirDepartment of Computer Science, Bahria University, Islamabad 44000, Pakistan. hodcsib@Bahria.edu.pk.
Muhammad Asfand-E-YarDepartment of Computer Science, Bahria University, Islamabad 44000, Pakistan. m.asfandyar@bui.edu.pk.
Muhammad TauqirDepartment of Computer Science, Bahria University, Islamabad 44000, Pakistan. tauqir130@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, wireless body area networks (WBANs) have attracted significant consideration in ubiquitous healthcare. A number of medium access control (MAC) protocols, primarily derived from the superframe structure of the IEEE 802.15.4, have been proposed in literature. These MAC protocols aim to provide quality of service (QoS) by prioritizing different traffic types in WBANs. A contention access period (CAP)with high contention in priority-based MAC protocols can result in higher number of collisions and retransmissions. During CAP, traffic classes with higher priority are dominant over low-priority traffic; this has led to starvation of low-priority traffic, thus adversely affecting WBAN throughput, delay, and energy consumption. Hence, this paper proposes a traffic-adaptive priority-based superframe structure that is able to reduce contention in the CAP period, and provides a fair chance for low-priority traffic. Simulation results in ns-3 demonstrate that the proposed MAC protocol, called traffic- adaptive priority-based MAC (TAP-MAC), achieves low energy consumption, high throughput, and low latency compared to the IEEE 802.15.4 standard, and the most recent priority-based MAC protocol, called priority-based MAC protocol (PA-MAC).

Indexed as

Wireless TechnologyComputer Communication Networkslow-priority trafficMAC superframe structurePA-MACpriority-based MACWBAN

Identifiers

PMID28832495
PMCPMC5621062

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.