Evidence map›Paper›PMID 36366109›Full record

ArticleSensors (Basel, Switzerland)2022

A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings.

Abdullah Safi, Zulfiqar Ahmad, Ali Imran Jehangiri, Rohaya Latip, Sardar Khaliq Uz Zaman, Muhammad Amir Khan, Rania M Ghoniem

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. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Mechanism-Guided Thermoelectric Strategies for Smart Fire Prevention.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. 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

7 authors.

Abdullah SafiDepartment of Computer Science and Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan.
Zulfiqar AhmadDepartment of Computer Science and Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan.ORCID 0000-0002-6278-9516
Ali Imran JehangiriDepartment of Computer Science and Information Technology, Hazara University Mansehra, Mansehra 21120, Pakistan.
Rohaya LatipDepartment of Communication Technology and Network, Universiti Putra Malaysia (UPM), Serdang 43400, Malaysia.ORCID 0000-0002-6462-1944
Sardar Khaliq Uz ZamanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan.ORCID 0000-0002-0261-6541
Muhammad Amir KhanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan.ORCID 0000-0003-3669-2080
Rania M GhoniemDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. PNURSP2022R138
6 · The paper itself

Abstract

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0-600 m) and heights above the ground (0-2 m) in an open environment and indoor (1st Floor-3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.

Indexed as

Electric Power SuppliesSmokeHumidityMonitoring, PhysiologicTemperatureSmokefire detectionInternet of ThingsLoRaWANLPWANresponse time

Identifiers

PMID36366109
PMCPMC9657799

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.