An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication

Venu, D.; Mayuri, A.V.R.; Neelakandan, S.; Murthy, G. L. N.; Arulkumar, N.; Shelke, Nilesh M.

doi:10.1016/j.ijleo.2021.168545

Cited by 52 publications

(12 citation statements)

References 20 publications

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“…Table 1 and Figure 4 illustrate the network lifetime analysis of the MCR-UWSN technique with existing techniques. From the figure, it is apparent that the MCR-UWSN technique has offered the maximum network lifetime [47]. With respect to FND, the MCR-UWSN technique has attained a higher FND of 852 rounds, whereas the LEACH, LEACH-ANT, CUWSN, EOCA, and ACOCR techniques achieved a lower FND of 424, 560, 629, 689, and 805 rounds, respectively.…”

Section: Performance Validationmentioning

confidence: 96%

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Neelakandan¹,

Mohan

Alotaibi

et al. 2022

Sensors

143

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In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

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Section: Performance Validationmentioning

confidence: 96%

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Neelakandan¹,

Mohan

Alotaibi

et al. 2022

Sensors

143

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“…When the CHs are chosen and clusters are optimally generated, the TLBO-MHR algorithm is applied to generate optimal routes in the IoT-assisted WSN. TLBO is a populationbased technique simulated by the procedure of teacher as well as learner [29][30][31][32][33][34][35][36]. Differing from other heuristic techniques, TLBO requires fewer approaches to certain parameters, which is an essential reason for choosing TLBO technique to optimize problems.…”

Section: Design Of Tlbo-mhr Techniquementioning

confidence: 99%

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Neelakandan²,

et al. 2022

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The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

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“…The healthcare IoT data sets and performance criteria for the proposed MR-LSGDM strategy are briefly outlined in this section [ 31 ]. The complete approach was developed using the MATLAB 2021a tool on a Core i3-3110M processor running Windows 8 with 2 GB RAM, and it was tested on 8 healthcare IoT data sets ( Table 1 ) [ 32 ]. Over 30 separate runs, the new BBO-FCM approach was compared to existing algorithms such as CNN 2016, CNN 2018, CNN-SF, CNN-LSTM, lightweight CNN, and CNN-BiLSTM in terms of intracluster distance, purity index, standard deviation, root mean square error, accuracy, and F -measure [ 33 ].…”

Section: Performance Validationmentioning

confidence: 99%

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

Harshavardhan

Boyapati²,

Neelakandan

et al. 2022

Journal of Healthcare Engineering

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Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.

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An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication

Cited by 52 publications

References 20 publications

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

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