Grouping and Sponsoring Centric Green Coverage Model for Internet of Things.

energy efficiency green computing internet of things lifetime maximization

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
08 Jun 2021
Historique:
received: 01 05 2021
revised: 01 06 2021
accepted: 04 06 2021
entrez: 2 7 2021
pubmed: 3 7 2021
medline: 8 7 2021
Statut: epublish

Résumé

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

Identifiants

pubmed: 34201100
pii: s21123948
doi: 10.3390/s21123948
pmc: PMC8226805
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Références

Sensors (Basel). 2016 Jan 15;16(1):
pubmed: 26784201
Sensors (Basel). 2016 Aug 27;16(9):
pubmed: 27618902
Sensors (Basel). 2009;9(12):10513-44
pubmed: 22303185
Sensors (Basel). 2021 Jan 30;21(3):
pubmed: 33573209
Sensors (Basel). 2021 Mar 08;21(5):
pubmed: 33800227

Auteurs

Vinod Kumar (V)

School of Computer and Systems Sciences, Jawaharlal Nehru University (JNU), New Delhi 110067, India.

Sushil Kumar (S)

School of Computer and Systems Sciences, Jawaharlal Nehru University (JNU), New Delhi 110067, India.

Rabah AlShboul (R)

Computer Science Department, Faculty of Information Technology, Al al-Bayt University, Mafraq 25113, Jordan.

Geetika Aggarwal (G)

School of Science & Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK.

Omprakash Kaiwartya (O)

School of Science & Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK.

Ahmad M Khasawneh (AM)

Department of Mobile Computing, Amman Arab University, Amman 11953, Jordan.

Jaime Lloret (J)

Integrated Management Coastal Research Institue, Universitat Politecnica de Valencia, 46022 Valencia, Spain.
School of Computing and Digital Technologies, Staffordshire University, Stoke ST4 2DE, UK.

Mahmoud Ahmad Al-Khasawneh (MA)

Faculty of Computer & Information Technology, Al-Madinah International University, Kuala Lumpur 57100, Malaysia.

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Classifications MeSH