An Optimized Genetic Algorithm for Cluster Head Election Based on Movable Sinks and Adjustable Sensing Ranges in IoT-Based HWSNs

Nandan, Aridaman Singh; Singh, Samayveer; Kumar, Rajeev; Kumar, Neeraj

doi:10.1109/jiot.2021.3107295

Cited by 34 publications

(22 citation statements)

References 27 publications

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“…Singh et al stated that when a ship is running normally at sea, the ship's main engine, as the ship's main propulsion device, needs to operate continuously for 24 hours, consuming 70%-90% of the ship's total fuel [9]. Nandan et al said that in order to meet the normal operation of the ship and the daily life of the crew, the ship is also equipped with various auxiliary equipment to provide the electricity and heat needed by the ship, consuming 10-30% of the ship's fuel [10]. When the ship's main engine is running, the fuel is burned in the combustor and the chemical energy is mainly converted into mechanical energy and heat energy.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling and Optimization of Ship Waste Heat Utilization System Based on Genetic Algorithm and Sensing

Lyu

2022

International Transactions on Electrical Energy Systems

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In order to solve the optimization problem of ship waste heat utilization system, the modeling and optimization method of ship waste heat utilization system based on genetic algorithm and sensing is proposed. The 6S50ME-C8.2 volume method model was established based on Matlab/Simulink simulation platform. According to the test data of the 6S50ME-C8.2 diesel engine, the simulation value is very close to the test value, which verifies the accuracy of the simulation model. The experimental results show that the thermal efficiency of high-power medium and low-speed two-stroke diesel engines is high, the exhaust temperature of the main engine is generally maintained at about 230°C, and the energy content of exhaust gas is relatively low. If no other measures are taken, the waste gas is directly channeled into the waste heat boiler, which cannot effectively recover the heat in the waste gas. Conclusion. The model can well simulate the actual working condition of the diesel engine, and the exhaust temperature has reached 298°C. The energy quality of waste gas at the inlet of the waste heat boiler is improved to meet the requirements of the waste heat recovery system.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling and Optimization of Ship Waste Heat Utilization System Based on Genetic Algorithm and Sensing

Lyu

2022

International Transactions on Electrical Energy Systems

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“…ML methods such as supervised learning are generally used to learn the statistical model of the wireless sensors' data, which requires effort seeking ML training. In such cases, unsupervised learning models can be employed to determine the statistical model of the sensors' collected data [12].Along with optimum cluster selection in heterogeneous networks, energy harvesting mechanisms can be executed with AI/ML approaches for construction of energy efficient IoT network systems [13], [14].…”

Section: ) Heterogeneous Network (Hetnets)mentioning

confidence: 99%

“…A compressive sensing algorithm is thus presented in [73] where GA optimizes quantity of measured samples, range of transmission and sensing matrix. Cluster head selection in a sensor network, proposed in [13], uses GA which takes density of the nodes and distance among them, their energy consumption and capability of the heterogeneous network devices into account to develop the fitness function of the GA. Machine learning strategies such as backpropagation neural network and unsupervised learning are integrated with GA for indoor localization and network topology control [19], [74].…”

Section: ) Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Energy efficient network coverage provision [73] Routing algorithm Energy efficient high accuracy sensing mechanism [13] Cluster head selection Network lifetime extension, throughput and reduced energy consumption [19] Network topology control by incorporating unsupervised learning and GA Energy efficiency with satisfactory computational complexity [74] CSI fingerprint based indoor localization by incorporating BPNN and GA High localization accuracy, improved data processing and relationship mapping between location fingerprints and coordinates performance in the network with heterogeneous devices if the difference between the survey phase and client phase is large. Therefore, to address this issue, a localization approach with crowdsourcing mechanism is proposed in [10] which uses linear regression for calibration across the training network devices.…”

Section: Algorithmsmentioning

confidence: 99%

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A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

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“…Papers [ 27 – 32 ] discuss the various methods of the CH election process like CHs that are selected based on the amount of residual energy present, distance, and node density. Once a set of CHs has been selected, a message is broadcast by the CHs to the sensor devices in order to initiate cluster formation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

Khan¹,

Singh

Singh³

et al. 2022

Computational Intelligence and Neuroscience

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In today’s environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round.

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An Optimized Genetic Algorithm for Cluster Head Election Based on Movable Sinks and Adjustable Sensing Ranges in IoT-Based HWSNs

Cited by 34 publications

References 27 publications

Modeling and Optimization of Ship Waste Heat Utilization System Based on Genetic Algorithm and Sensing

Modeling and Optimization of Ship Waste Heat Utilization System Based on Genetic Algorithm and Sensing

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

An Optimized Artificial Intelligence System Using IoT Biosensors Networking for Healthcare Problems

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