Evidence map›Paper›PMID 37687941›Full record

ArticleSensors (Basel, Switzerland)2023

Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks.

Allam Balaram, Rajendiran Babu, Miroslav Mahdal, Dowlath Fathima, Neeraj Panwar, Janjhyam Venkata Naga Ramesh, Muniyandy Elangovan

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

1 citing paper in PubMed, 12 citations in OpenAlex.

  1. 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 at 6 institutions in 3 countries.

Allam BalaramDepartment of Computer Science and Engineering, MLR Institute of Technology, Hyderabad 500043, India.ORCID 0000-0002-2312-2700
Rajendiran BabuDepartment of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Chennai 603203, India.
Miroslav MahdalDepartment of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 70800 Ostrava, Czech Republic.ORCID 0000-0002-9720-2201
Dowlath FathimaDepartment of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia.ORCID 0000-0002-9516-1485
Neeraj PanwarSchool of Computing, Graphic Era Hill University, Dehradun 248002, India.
Janjhyam Venkata Naga RameshDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522302, India.ORCID 0000-0002-2084-8864
Muniyandy ElangovanDepartment of Biosciences, Saveetha School of Engineering, Saveetha Nagar, Thandalam 602105, India.ORCID 0000-0003-2349-3701
Graphic Era University · INKoneru Lakshmaiah Education Foundation · INSaudi Electronic University · SASaveetha University · INSRM Institute of Science and Technology · INVSB - Technical University of Ostrava · CZ

Funding

European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems within the Operational Programme Research, Development, and Education CZ.02.1.01/0.0/0.0/16_019/0000867European Union under the REFRESH - Research Ex-cellence For REgion Sustainability and High-tech Industries via the Operational Programme Just Transition CZ.10.03.01/00/22_003/0000048
6 · The paper itself

Abstract

Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.

Indexed as

dual mechanismexploitationexplorationkrill herdlatencystability

Identifiers

PMID37687941
PMCPMC10490580
OpenAlexW4386246444

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.