Detection and classification of multiple power quality disturbances in Microgrid network using probabilistic based intelligent classifier

Suganthi, S.; Vinayagam, Arangarajan; Veerasamy, Veerapandiyan; Deepa, A. R.; Abouhawwash, Mohamed; Thirumeni, Mariammal

doi:10.1016/j.seta.2021.101470

Cited by 41 publications

(15 citation statements)

References 42 publications

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“…Algorithm 2: (continued) The proposed scheme results are evaluated with the existing data aggregation methods like ASAS [25], APPA [26], EHDA [30] and SPPDA [41], in terms cost of communication in terms exchanged bytes, energy cost for AN and SN, storage cost in terms of data transmission and computation cost [42][43][44][45][46][47][48][49][50].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Paillier Cryptography Based Message Authentication Code for IoMT Security

Devi¹,

Kuruba²,

Nam³

et al. 2023

Computer Systems Science and Engineering

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Health care visualization through Internet of Things (IoT) over wireless sensor network (WSN) becomes a current research attention due to medical sensor evolution of devices. The digital technology-based communication system is widely used in all application. Internet of medical thing (IoMT) assisted healthcare application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human participation. These smart medical devices may consume with limited resources and also the data generated by these devices are large in size. These IoMT based applications suffer from the issues such as security, anonymity, privacy, and interoperability. To overcome these issues, data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and decryption. This article proposed a well-organized data aggregation and secured transmission approach. The data generated by the sensor are collected and compressed. Aggregator nodes (AN) received the compressed data and concatenate it. The concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptography-based encryption with Message Authentication code (MAC). Fog node extracts the forwarded data from AN using Fog message extractor method (FME) with decryption. The proposed system ensures data integrity, security and also protects from security threats. This proposed model is simulated in Network Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security, privacy and also reduces the communication cost. Fog node usages in between Aggregator and base station, will reduce the cloud server/base station computational overhead and storage cost. The proposed ideology is compared with existing data aggregation schemes in terms of computational cost, storage cost, communication cost and energy cost. Cost of communication takes 18.7 ms which is much lesser than existing schemes.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Paillier Cryptography Based Message Authentication Code for IoMT Security

Devi¹,

Kuruba²,

Nam³

et al. 2023

Computer Systems Science and Engineering

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“…In GASONeC, delivery ratio, BS length, predicted energy intake and overall SN number are used to find a dynamical, optimum solution. The proposed HGWACO helps to minimize the power consumption and also reduce the load balancing [28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Devi¹,

Kuruba²,

Nam³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks (WSNs) are projected to have a wide range of applications in the future. The fundamental problem with WSN is that it has a finite lifespan. Clustering a network is a common strategy for increasing the lifetime of WSNs and, as a result, allowing for faster data transmission. The clustering algorithm's goal is to select the best cluster head (CH). In the existing system, Hybrid grey wolf sunflower optimization algorithm (HGWSFO)and optimal cluster head selection method is used. It does not provide better competence and output in the network. Therefore, the proposed Hybrid Grey Wolf Ant Colony Optimisation (HGWACO) algorithm is used for reducing the energy utilization and enhances the lifespan of the network. Black hole method is used for selecting the cluster heads (CHs). The ant colony optimization (ACO) technique is used to find the route among origin CH and destination. The open cache of nodes, transmission power, and proximity are used to improve the CH selection. The grey wolf optimisation (GWO) technique is the most recent and well-known optimiser module which deals with grey wolves' hunting activity (GWs). These GWs have the ability to track down and encircle food. The GWO method was inspired by this hunting habit. The proposed HGWACO improves the duration of the network, minimizes the power consumption, also it works with the large-scale networks.The HGWACO method achieves 25.64% of residual energy, 25.64% of alive nodes, 40.65% of dead nodes also it enhances the lifetime of the network.

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“…Making appropriate usage information about the location and speed of a mobile node to choose a cluster head can help WSNs balance their energy usage [31][32][33][34][35][36][37][38][39][40][41][42][43]. The method first categorized sensor nodes before constructing a CH.…”

Section: Related Workmentioning

confidence: 99%