Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network

Mondal, Abhishek; Mishra, Deepak; Prasad, Ganesh; Hossain, Ashraf

doi:10.1109/jiot.2021.3128883

Cited by 29 publications

(8 citation statements)

References 37 publications

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“…This sub-section presents the training outcomes corresponding to the proposed SARSA algorithm for optimal trajectory and subsequently evaluates the energyefficient data collection. Here, we compare the effectiveness and superiority of the proposed design with the benchmark PSO technique [41], where 100 IoT devices are uniformly distributed within a square field of size 2000 Â 2000 m. Moreover, we adopt the required simulation parameters from [40] and [24] to implement the proposed algorithm.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent Wireless Communication Systems

Mondal¹,

Mishra²,

Prasad³

et al. 2023

Edge Computing - Technology, Management and Integration

Self Cite

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Maintaining reliable wireless connectivity is essential for the continuing growth of mobile devices and their massive access to the Internet of Things (IoT). However, terrestrial cellular networks often fail to meet their required quality of service (QoS) demand because of the limited spectrum capacity. Although the deployment of more base stations (BSs) in a concerned area is costly and requires regular maintenance. Alternatively, unmanned aerial vehicles (UAVs) could be a potential solution due to their ability of on-demand coverage and the high likelihood of strong line-of-sight (LoS) communication links. Therefore, this chapter focuses on a UAV’s deployment and movement design that supports existing BSs by reducing data traffic load and providing reliable wireless communication. Specifically, we design UAV’s deployment and trajectory under an efficient resource allocation strategy, i.e., assigning devices’ association indicators and transmitting power to maximize overall system’s throughput and minimize the total energy consumption of all devices. For these implementations, we adopt reinforcement learning framework because it does not require all information about the system environment. The proposed methodology finds optimal policy using the Markov decision process, exploiting the previous environment interactions. Our proposed technique significantly improves the system’s performance compared to the other benchmark schemes.

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Section: Simulation Resultsmentioning

confidence: 99%

Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent Wireless Communication Systems

Mondal¹,

Mishra²,

Prasad³

et al. 2023

Edge Computing - Technology, Management and Integration

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [21], the authors investigate the UAV edge computing to minimize the energy consumption and completion time for data computation, while satisfying the task requirements at each ground offloading users. In [22], the authors intend to minimize the energy consumption of the UAV-IoT network, a reinforcement learning approach is proposed to optimize the trajectory and resources. In [23], the authors minimize the energy consumption in UAV-aided edge computing system through rate splitting for optimized computing task allocation.…”

Section: Related Workmentioning

confidence: 99%

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Tang¹,

Zhang²,

Zhang³

et al. 2023

Preprint

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Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

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“…The scheme selects sampling points using a matrix-based approach and optimizes the trajectory using an optimized ant colony optimization algorithm. To minimize the total energy consumption of all devices during UAV data collection, literature [110] uses the SARSA algorithm to obtain the UAV trajectory, thus solving the joint problems of UAV trajectory, device association, and transmit power allocation while ensuring that each device should meet a given data rate constraint. For collecting data from massive machinelike communication mMTCs, it is necessary to find the best hovering position and flight strategy for the UAV within the cluster to minimize the UAV's energy consumption.…”

Section: Data Collectionmentioning

confidence: 99%

“…In addition to using algorithms to perform other energy-saving operations, such as trajectory planning for UAVs, to improve the energy efficiency of UAVs. For example, in the literature [110], energy consumption was reduced by optimizing the UAV trajectory and transmit power to improve the UAV data collection efficiency. In [121], resource allocation and task scheduling were jointly optimized to reduce energy consumption.…”

Section: Resource and Energy Constrained Issues For Uavmentioning

confidence: 99%

AI for UAV-Assisted IoT Applications : A Comprehensive Review

Chen¹,

Wu²,

Wang³

et al. 2022

Preprint

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With the rapid development of the Internet of Things (IoT), there are dramatic increasing number of devices in the network, which causes the challenge that only using infrastructure such as based station cannot provide service with all devices with high quality. Therefore, due to their flexibility and economy, unmanned aerial vehicles (UAV) are widely used to increase the performance of IoT networks. UAVs can not only provide communication services for IoT devices in the absence of a network, but they can also perform video surveillance, cargo transportation, pesticide spraying, and other specialized tasks. However, due to the complexity of the scenario and the need for real-time decision making, it is challenging to schedule UAVs in the network using traditional optimization methods, and growing attention has focused on using AI to optimize UAVs in the network. In this paper, we focus on the AI-enabled UAV optimization method in IoT networks and give a comprehensive scope on what and how to use AI-enabled methods to increase the performance of UAV-assisted IoT networks. Moreover, a brief analysis of the challenges of using AI methods in IoT networks and some potential research directions are given.

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Joint Optimization Framework for Minimization of Device Energy Consumption in Transmission Rate Constrained UAV-Assisted IoT Network

Cited by 29 publications

References 37 publications

Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent Wireless Communication Systems

Optimal Unmanned Aerial Vehicle Control and Designs for Load Balancing in Intelligent Wireless Communication Systems

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

AI for UAV-Assisted IoT Applications : A Comprehensive Review

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