Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Sharada, K. A.; Mahesh, T. R.; chandrasekaran, Saravanan; Shashikumar, R.; Kumar, V. Vinoth; Annand, Jonnakuti Rajkumar

doi:10.1038/s41598-024-55099-1

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…Different datasets were used to carry out the classification process. In [38], the CWRU dataset was assessed on the inception capsule network for fault diagnosis.…”

Section: Review Of the Related Workmentioning

confidence: 99%

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Mahesh,

Chandrasekaran,

Ram

et al. 2024

IEEE Access

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The detection of faulty bearings is an essential step in guaranteeing the safe and efficient operation of rotating machinery. Bearings, which also transmit the loads and pressures generated by the machinery, support the rotating shafts. A common method for bearing fault diagnostics is using signal processing techniques. In terms of accuracy, dependability, and sensitivity to various fault types and severity levels, these techniques do, however, have significant limits. To address these limitations, practitioners often integrate signal processing with advanced techniques like machine learning and data analytics to enhance diagnostic accuracy, reliability, and overall effectiveness in bearing fault detection and predictive maintenance. Exploration of various models has demonstrated enhanced results in managing nonlinear data to a certain degree; however, these models face challenges when dealing with intricately complex patterns. Moreover, CNNs can automatically learn relevant features from raw sensor data, capturing intricate patterns and relationships in the data without the need for manual feature engineering. CNNs can be optimized for scalability and real-time processing, essential for applications requiring quick decisions and computationally less expensive for large datasets compared to other models. An optimized one-dimensional Convolutional Neural Network (1D CNN) using different kernel sizes is proposed for predicting and finding bearing problems to overcome these constraints. This method creates a feature vector by applying many filters of various sizes to the input signal. Using the created feature vector, the input signal can be divided into many categories, such as healthy or unhealthy. In comparison to other methods, the proposed technology performs better and offers a high accuracy of 99.52% in bearing fault identification. The 1D CNN model with multiple kernel sizes excels in preserving data structure during dimensionality reduction, as confirmed by comparing t-Distributed Stochastic Neighbor Embedding plots. Particularly, the optimized 1D CNN with multiple kernel sizes accurately classifies faults with minimal errors, showcasing its fault classification proficiency compared with the other state-of-the-art methods. The visualization underscores the methodology's efficacy in discerning intricate fault patterns within the data.

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“…Different datasets were used to carry out the classification process. In [38], the CWRU dataset was assessed on the inception capsule network for fault diagnosis.…”

Section: Review Of the Related Workmentioning

confidence: 99%

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Mahesh,

Chandrasekaran,

Ram

et al. 2024

IEEE Access

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“…These attacks occur when rogue nodes are damaged in your network. Network security is defined as the retention of personal information and identity verification 5 . Another major problem faced by such networks is the communication between ground drones, which ensures confidentiality, and availability.…”

Section: Introductionmentioning

confidence: 99%

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

Chandrasekar,

Shanmugavalli,

Mahesh

et al. 2024

Sci Rep

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In wireless networking, the security of flying ad hoc networks (FANETs) is a major issue, and the use of drones is growing every day. A distributed network is created by a drone network in which nodes can enter and exit the network at any time. Because malicious nodes generate bogus identifiers, FANET is unstable. In this research study, we proposed a threat detection method for detecting malicious nodes in the network. The proposed method is found to be most effective compared to other methods. Malicious nodes fill the network with false information, thereby reducing network performance. The secure ad hoc on-demand distance vector (AODV) that has been suggested algorithm is used for detecting and isolating a malicious node in FANET. In addition, because temporary flying nodes are vulnerable to attacks, trust models based on direct or indirect reliability similar to trusted neighbors have been incorporated to overcome the vulnerability of malicious/selfish harassment. A node belonging to the malicious node class is disconnected from the network and is not used to forward or forward another message. The FANET security performance is measured by throughput, packet loss and routing overhead with the conventional algorithms of AODV (TAODV) and reliable AODV secure AODV power consumption decreased by 16.5%, efficiency increased by 7.4%, and packet delivery rate decreased by 9.1% when compared to the second ranking method. Reduced packet losses and routing expenses by 9.4%. In general, the results demonstrate that, in terms of energy consumption, throughput, delivered packet rate, the number of lost packets, and routing overhead, the proposed secure AODV algorithm performs better than the most recent, cutting-edge algorithms.

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An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

Chang,

He,

2024

Telecommun Syst

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Improved energy efficiency using adaptive ant colony distributed intelligent based clustering in wireless sensor networks

Cited by 8 publications

References 22 publications

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning

Secure malicious node detection in flying ad-hoc networks using enhanced AODV algorithm

An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

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