Energy Efficient and Quality-of-Service Aware Routing using Underwater Wireless Sensor Networks

Sathya, P.; Sengottuvelan, P.

doi:10.14569/ijacsa.2022.0130158

Cited by 2 publications

(8 citation statements)

References 7 publications

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“…The PUCOA is compared over the Cuckoo Search Optimization Algorithm with Energy Efficient and QOS Aware (CSOA-EQ) [ 6 ], Grey Wolf Optimization Algorithm (GWO) [ 2 ], Jelly Fish Optimization (JFO), Marriage Honeybee Optimization (MHO), COOT Optimization (COOT), Sewing Training Based Optimization (STBO), CHO [ 23 ] and POA [ 22 ] in terms of distance, energy consumption and link quality for choosing the optimal path is represented in “ Fig 8(a) ” to “ Fig 8(c) ”. To choose the optimal path, it is necessary to reduce the distance and energy use.…”

Section: Resultsmentioning

confidence: 99%

“…In 2022, P. Sathya, & P. Sengottuvelan [ 6 ] presented a novel energy efficient approach with quality of service aware based on routing with utilizing underwater wireless sensor networks. The path was selected with the proposed approach by establishing differential equation in the suggested method in accordance with the residual energy and energy utilization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Using paper [ 5 ], the author improved the efficiency of energy but designing a reliable and topology independent protocols are still a difficult problem. In paper [ 6 ], the author presented a novel energy efficient approach with quality of service aware based on routing with utilizing underwater wireless sensor networks and it utilized least amount of energy with enhanced network dependability. Yet, it is difficult in concerning the average packet latency and the network longevity.…”

Section: Introductionmentioning

confidence: 99%

“…This is because earlier research has shown that multi-hop data deliveries are ideal in terms of power consumption for underwater systems [5]. One of the most popular techniques for developing future sensor approaches and sea perception networks is wireless data transfer in ocean waters [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Kaveripakam,

Chinthaginjala

2023

PLoS ONE

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The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel’s time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Kaveripakam,

Chinthaginjala

2023

PLoS ONE

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“…These data can be used to assess the impact of human activities on marine ecosystems and identify potential hazards. Additional examples of research related to this parameter include [186,187].…”

Section: Surface and Underwater Monitoring Servicesmentioning

confidence: 99%

Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters

Alahmari,

Mehmood,

Alzahrani

et al. 2023

Sustainability

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The rise in the service economy has been fueled by breakthroughs in technology, globalization, and evolving consumer patterns. However, this sector faces various challenges, such as issues related to service quality, innovation, efficiency, and sustainability, as well as macro-level challenges such as globalization, geopolitical risks, failures of financial institutions, technological disruptions, climate change, demographic shifts, and regulatory changes. The impacts of these challenges on society and the economy can be both significant and unpredictable, potentially endangering sustainability. Therefore, it is crucial to comprehensively study services and service economies at both holistic and local levels. To this end, the objective of this study is to develop and validate an artificial-intelligence-based methodology to gain a comprehensive understanding of the service sector by identifying key parameters from the academic literature and public opinion. This methodology aims to provide in-depth insights into the creation of smarter, more sustainable services and economies, ultimately contributing to the development of sustainable future societies. A software tool is developed that employs a data-driven approach involving the use of word embeddings, dimensionality reduction, clustering, and word importance. A large dataset comprising 175 K research articles was created from the Scopus database, and after analysis, 29 distinct parameters related to the service sector were identified and grouped into 6 macro-parameters: smart society and infrastructure, digital transformation, service lifecycle management, and others. The analysis of over 112 K tweets collected from Saudi Arabia identified 11 parameters categorized into 2 macro-parameters: private sector services and government services. The software tool was used to generate a knowledge structure, taxonomy, and framework for the service sector, in addition to a detailed literature review based on over 300 research articles. The conclusions highlight the significant theoretical and practical implications of the presented study for autonomous capabilities in systems, which can contribute to the development of sustainable, responsible, and smarter economies and societies.

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Energy Efficient and Quality-of-Service Aware Routing using Underwater Wireless Sensor Networks

Cited by 2 publications

References 7 publications

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction

Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters

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