A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

Singh, Samayveer; Malik, Arun; Kumar, Rajeev; Singh, Pradeep

doi:10.1002/dac.4956

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Due to the promising solutions that UAVs provide in different application areas, UAVs has attracted research attention in IoT applications in order to facilitate data harvesting and relaying [74]. This is very important for cases where the IoT devices are deployed in areas where there are no wireless network facilities, such as in rural farmlands or offshore locations, to help transmit the data to the decision-making centers.…”

Section: Data Harvesting From Iot Devicesmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Section: Data Harvesting From Iot Devicesmentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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show abstract

“…To survive, a network of underwater sensors must adapt and reorganize due to the increasing forces present in the underwater environment [ 11 , 12 , 13 ]. Self-optimization is crucial for UWSNs to decrease power drainage and, as a result, increase the network’s lifetime [ 14 , 15 ]. Among other things, this is because of problems with power consumption, propagation delays, and synchronization.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks

Khan,

Singh,

Yadav

et al. 2023

Sensors

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Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network’s lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

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Jewel Beetle-Batteryless WSN Eco Monitoring System

Iyer¹,

Bakshi²

2023

Lecture Notes in Electrical Engineering

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A proficient data gathering technique for unmanned aerial vehicle‐enabled heterogeneous wireless sensor networks

Cited by 10 publications

References 25 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks

Jewel Beetle-Batteryless WSN Eco Monitoring System

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