Evidence map›Paper›PMID 31726741›Full record

ArticleSensors (Basel, Switzerland)2019

Mobility Aware Duty Cycling Algorithm (MADCAL) A Dynamic Communication Threshold for Mobile Sink in Wireless Sensor Network.

Craig Thomson, Isam Wadhaj, Zhiyuan Tan, Ahmed Al-Dubai

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. 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
1.1field-weighted citation impact, top 19% of its field
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, 8 citations in OpenAlex.

  1. Article
  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

4 authors at 1 institution in 1 country.

Craig ThomsonSchool of Computing, Edinburgh Napier University, 10 Colinton Rd, Edinburgh EH10 5DT, UK.ORCID 0000-0001-7320-7262
Isam WadhajSchool of Computing, Edinburgh Napier University, 10 Colinton Rd, Edinburgh EH10 5DT, UK.
Zhiyuan TanSchool of Computing, Edinburgh Napier University, 10 Colinton Rd, Edinburgh EH10 5DT, UK.
Ahmed Al-DubaiSchool of Computing, Edinburgh Napier University, 10 Colinton Rd, Edinburgh EH10 5DT, UK.
Edinburgh Napier University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The hotspot issue in wireless sensor networks, with nodes nearest the sink node losing energy fastest and degrading network lifetime, is a well-referenced problem. Mobile sink nodes have been proposed as a solution to this. They do not completely remove the hotspot problem though, with nodes the sink passes most closely still expending more energy than others. This study proposes a lightweight algorithm, located in the media access control (MAC) layer of static nodes and utilising knowledge of predictable sink node mobility. This is in order to create a dynamic communication threshold between static nodes and the sink, within which static nodes awaken, lessening competition for sink communication between nodes. In utilising predictable mobility and factors already known to the static node, such as location and interference range, there is no need for energy-consuming messaging. Analysis and simulation results, tested on a lightweight implementation of a carrier-sense multiple-access-based MAC protocol, show a significant improvement in energy consumption in both controlled and random environments, with frame delivery improved to the point where sink speed is negated. This is when compared to the existing duty cycling approach.

Indexed as

duty cyclingdynamic thresholdmobile sinkmobility awarenesswireless sensor network

Identifiers

PMID31726741
PMCPMC6891301
OpenAlexW2984177694

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.