Grouping and sponsoring centric green coverage model for internet of things

Vinod Kumar, Sushil Kumar, Rabah Alshboul, Geetika Aggarwal, Omprakash Kaiwartya, Ahmad M. Khasawneh, Jaime Lloret, Mahmoud Ahmad Al-Khasawneh

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number3948
Issue number12
Publication statusPublished - 2 Jun 2021
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgments: This project was technically supported by the School of Science and Technology, Nottingham Trent University, UK. The authors, therefore, gratefully acknowledge the technical and financial support.

Funding Information:
Funding: This research was partially supported by the Al al-Bayt University, Jordan, and partially by the Jawaharlal Nehru University, New Delhi, India.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


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