Review paper on Design and Optimization of Energy Efficient Wireless Sensor Network Model for Complex Networks

Kuthe, Annaji; Sharma, Ashish

doi:10.1109/iscon52037.2021.9702421

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…The FSCs are processed via use of a Bioinspired model that uses Genetic Algorithm (GA) for selection of optimal routing nodes. These nodes are selected based on their residual energy & distance matrices, it combines the way network use over time QoS and energy level into a tness score [40,41,42]. The nodes are clustered using destination aware Fan Shaped clustering model to improve QoS in different structure.…”

Section: Literature Reviewmentioning

confidence: 99%

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Kuthe,

Khandelwal

2024

Preprint

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One of the best methods for increasing energy efficiency in large-scale wireless networks is sleep scheduling. The scalability of existing sleep scheduling algorithms is limited in real-time applications due to their higher complexity or lack of coordination. Furthermore, these models' routing performance needs to be adjusted for various use scenarios. This essay suggests designing a low complexity sleep-scheduling model with iterative bioinspired learning for better routing efficiency in the context of diverse networks in order to integrate these qualities. At first, the suggested model gathers progressively larger data sets on node location, energy usage, fault frequency, and additional quality of service measures. These parameter sets are used to train a set of scalar nodes (nodes with low efficiency levels), that decide initial sleep scheduling cycles for high-efficiency multimedia nodes. The sleep schedules are co-ordinated by multimedia nodes during their inter-node communications, which assists the scalar nodes to iteratively improve sleep-and-wake cycles. These cycles are decided by a Bacterial Foraging Optimization (BFO) process, which assists in improving energy efficiency without compromising on QoS performance levels. The BFO process is cascaded with a fault-tolerant Grey Wolf Particle Swarm Optimizer (GWPSO), that evaluates high-efficiency routing paths that can provide low delay, low energy consumption, with lower probability of faults. Due to these operations, the proposed model is able to reduce communication delay by 4.9%, and energy needed for communication by 8.5%, while improving throughput by 3.4%, and consistency of routing-performance by 1.9% when compared with existing routing techniques. This performance was observed to be consistent even under node-level faults for heterogenous network scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Kuthe,

Khandelwal

2024

Preprint

Self Cite

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“…The importance of Decision making has been reported by many researchers in varied fields. Some of them being E-LEACH protocol, Smart Home Appliances Controller Using IOT [10,11].…”

Section: Proposed Workmentioning

confidence: 99%

Water Quality Analysis Using Machine Learning

Kuthe¹,

Bhake²,

Bhoyar³

et al. 2022

IJRASET

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Human beings are dependent on the natural resources that stands for its quality. Climatic changes and environmental impacts have always been under observation. The quality of the products is always measured before use. Water, an inevitable resource ,has got a serious significance in checking its quality due to the influence of various external factors like industrial effluents, acid rain etc. This paper provide same theology for assessing the water quality that uses statistical quality control technique and Machine Learning algorithms to scale up the classification accuracy. The classification is focused on deciding if the water is suitable for drinking purpose. The goal of this study is to develop a water quality prediction model with the help of water quality factors using Artificial Neural Network (ANN) and time-series analysis.

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“…Therefore, achieving a balance between energy efficiency and security becomes crucial in the design of wireless sensor networks. In literature, several schemes have already been proposed to address the energy efficiency and security challenges in WSNs, but they often fail to provide a comprehensive and integrated solution [7], [8]. Many of these schemes are based on certain assumptions and specific design considerations, which may not be able to adapt to the dynamic and unpredictable nature of WSN environments.…”

mentioning

confidence: 99%

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

Puttaswamy,

Kanakapura Shivaprasad

2024

IJECE

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This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

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Review paper on Design and Optimization of Energy Efficient Wireless Sensor Network Model for Complex Networks

Cited by 9 publications

References 15 publications

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

LCSMIBR: Design of a Low complexity Sleep-scheduling Model with Iterative Bioinspired learning for improved Routing efficiency under heterogeneous network condition

Water Quality Analysis Using Machine Learning

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

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