Evidence map›Paper›PMID 41353471›Full record

ArticleScientific reports2025

Hybrid approach for evaluating dynamic scheduling mechanisms in IoT cognitive sensors for enhanced network performance.

Muhammad Nawaz Khan, Mohsin Shah, Toqeer Mahmood, Rehan Tariq Chohan, Arshad Khan

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Muhammad Nawaz KhanDepartment of Smart Security, Gachon University, Seongnam, 13120, Republic of Korea.
Mohsin ShahDepartment of Computer Engineering, Gachon University, Seongnam, 13120, Republic of Korea.
Toqeer MahmoodDepartment of Computer Science, National Textile University, Faisalabad, 37610, Pakistan.
Rehan Tariq ChohanFaculty of Information Science and Technology, The National University of Malaysia, Qatar (UKM-Qatar Campus), Lusail, Qatar. rehan.chohan@ukm.qa.
Arshad KhanYoobee colleges of Creative Innovation, 1010, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet of Things is an intelligent network of cognitive sensors that are embedded into nearby objects and appliances to create a connected environment. It collects, processes, and transmits data for automation and decision-making while providing ubiquitous services. Sensors create primarily redundant data, with several repeating bit streams and matching patterns. Due to resource restrictions, sensors are carefully scheduled to activate and deactivate sensing operations. As a result, it is highly recommended to create schemes that can handle large volumes of data while maintaining a careful strategy for data collection. This article has identified and analyzed various approaches for comprehensively scheduling cognitive sensors and then evaluated the efficiency of the three schemes, CADS, EASS, and EDASS. Based on these schemes, a hybrid approach is proposed. The hybrid scheme, "Hybrid of CADS, EASS, and EDASS" (HCEE), is recommended to be used in those areas where each scheme is implemented and evaluated for applicability individually. Using the best features of these schemes, one combined scheme can be used to provide better results than those three in their individual forms. The basic three systems were evaluated with HCEE, for communication overhead and latency, node processing and buffering, the number of messages in the network, and energy usage in various scenarios. Every scheme has its primary goal, based on specific factors and a unique set of applications. Each of them exhibits unique behavior and has a variety of uses, but using one scheme in all these scenarios has achieved better results than any of these schemes. In performance analysis, CADS and EASS are more energy efficient with 67.58 and 64.25 mean values, respectively. HCEE has an extra message overhead of 6.61% compared to the average of the three schemes. HCEE has a transmission delay of 60.07% compared to other systems, and requires less buffer than CADS but more than EASS and EDASS.

Indexed as

AdaptiveCognitive sensorsDynamicEnergy efficiencyInternet of thingsLatencyScheduling

Identifiers

PMID41353471
PMCPMC12770530

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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.