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

Neelakandan, S.; Mohan, Prakash; Alotaibi, Youseef; Alghamdi, Saleh; Khalaf, Osamah Ibrahim

doi:10.3390/s22020415

Cited by 109 publications

(56 citation statements)

References 49 publications

(49 reference statements)

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“…Furthermore, the CBR-ICWSN approach comprises an oppositional ABC (OABC)-based routing method for better election of routes. Shafiq et al [15] proposed a robust cluster-based routing protocol (RCBRP) to recognize the routing path by which minimal energy is expended, in order to enhance network lifespan. The presented method was composed of transmission and explored ow methods.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Lakshmanna

Neelakandan²,

Alotaibi

et al. 2022

Sustainability

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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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Section: Literature Reviewmentioning

confidence: 99%

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

Lakshmanna

Neelakandan²,

Alotaibi

et al. 2022

Sustainability

Self Cite

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“…[60] presented research to map out the current state of knowledge on energy efficiency's impact on cloud computing and, in turn, help to build a better picture of the future of this area of study. No emphasis was given to techniques to reduce energy consumption [132][133][134][135][136][137][138][139][140][141][142][143][144][145].…”

Section: Related Surveysmentioning

confidence: 99%

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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“…In [33][34][35], the authors have addressed DSDV and OLSR, DA-AODV protocol and AODV Protocol routing protocols respectively.…”

Section: Related Workmentioning

confidence: 99%

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Hemavathi¹,

Akhila²,

Alotaibi

et al. 2022

Energies

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One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

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

Cited by 109 publications

References 49 publications

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

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

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

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