Improving UAV base station energy efficiency for industrial IoT URLLC services by irregular repetition slotted-ALOHA

Salehi, Shavbo; Eslamnour, Behdis

doi:10.1016/j.comnet.2021.108415

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…One of the most common solutions to this problem is to optimize the trajectory and find the optimal positioning of the UAV to increase energy efficiency. In [18], a method for tracking an energy-efficient trajectory is proposed to minimize the energy consumption of UAV BSs. This solution improves UAV communication and mobility by meeting reliability and latency requirements for IoT applications and users in hard-to-reach areas.…”

Section: Related Workmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

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The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

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Section: Related Workmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

“…Researchers investigated several key issues, including adjusting the UAVs altitudes [1], energy consumption [3]- [5], and trajectory design [6], to facilitate the deployment of networked UAVs. A multi-UAV trajectory design is proposed in [7] by a combination of reinforcement learning and a deep learning algorithm to tackle resource allocation problems.…”

Section: Related Workmentioning

confidence: 99%

“…UAVs' limited flight duration hinders their capability to meet mobile users' requirements when users are distributed in larger areas, e.g., farmland, since a considerable percentage of UAVs energy is consumed for flying [1]. Then, utilizing Multi UAVs is suggested for meeting users' requirements.…”

Section: B Optimization Targets and Constraintsmentioning

confidence: 99%

“…Since various radio frequencies have different coverage areas [1], we calculate the WCSS for evaluating the signal strength based on the requested services.…”

Section: A Effects Of the Number Of Uavs' Used On Wcssmentioning

confidence: 99%

“…Next-generation networks utilize UAVs as aerial base stations (UAV-BSs) to provide services through dynamic wireless coverage in low bandwidth areas. These networks are to support the ever-increasing Internet of Things (IoT) applications [1], [2]. However, an efficient service delivery mechanism requires proper positioning of UAV-BSs to establish reliable communication channels with ground nodes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Optimal Multi-UAV Deployment Model for UAV-assisted Smart Farming

Salehi¹,

Hassan²,

Bokani³

2022

Preprint

Self Cite

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Next-generation wireless networks will deploy UAVs dynamically as aerial base stations (UAV-BSs) to boost the wireless network coverage in the out of reach areas. To provide an efficient service in stochastic environments, the optimal number of UAV-BSs, their locations, and trajectories must be specified appropriately for different scenarios. Such deployment requires an intelligent decision-making mechanism that can deal with various variables at different times. This paper proposes a multi UAV-BS deployment model for smart farming, formulated as a Multi-Criteria Decision Making (MCDM) method to find the optimal number of UAV-BSs to monitor animals' behavior. This model considers the effect of UAV-BSs' signal interference and path loss changes caused by users' mobility to maximize the system's efficiency. To avoid collision among UAV-BSs, we split the considered area into several clusters, each covered by a UAV-BS. Our simulation results suggest up to 11x higher deployment efficiency than the benchmark clustering algorithm.

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Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature review

2023

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Improving UAV base station energy efficiency for industrial IoT URLLC services by irregular repetition slotted-ALOHA

Cited by 9 publications

References 48 publications

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

An Optimal Multi-UAV Deployment Model for UAV-assisted Smart Farming

Energy-efficiency schemes for base stations in 5G heterogeneous networks: a systematic literature review

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