Efficient deployment of <scp>multi‐UAV</scp> assisted mobile edge computing: A cost and energy perspective

Xu, Fei; Zhang, Zhuoya; Feng, Jianqiang; Qin, Zengshi; Xie, Yue

doi:10.1002/ett.4453

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…F. Xu et al proposed an improved mean shift (IMS) algorithm to find the optimum location of UAVs where they provide computing services for the ground users. They showed the trade-off between the cost of service providers and energy consumption in such networks 26 . An adaptive discrete particle swarm optimization (ADPSO) algorithm and power calibration were employed to optimally localize the UAVs in order to minimize the security threats of other UAVs 27 .…”

Section: Related Workmentioning

confidence: 99%

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Abdollahi

Azarhava

Niya

2023

Preprint

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In this paper, we investigate the successful transmission probability of an aerial cellular network in which an unmanned aerial vehicle (UAV) as a Macrocell Base Station (UAV-BS) serves other UAVs as aerial users. The beamforming capable antennas are mounted on the UAVs, to increase the throughput of the network. The random effects of inner forces such as controlling errors or outer forces like the air conditions result in the random fluctuations. We assume Rician fading distribution over the links between the UAVs, then, we calculate the distribution of the channels under hovering fluctuations. Also, we derive the closed form expressions for successful transmission probability. Defining an optimization problem on the average successful transmission probability of the network, we obtain the best placement of UAV-BS along with the resource allocation. The problem turns out to be a non-convex problem and time consuming via numerical exhaustive search methods. Instead, we solve the optimization problem for its lower bound. Maximization problem for the achieved lower bound is equivalent to maximize the main problem. Then, we use some approximations to convert it to a low complex problem to find the solution. We use the entity of the low complex problem to obtain the allocated power for each UAV and in the following, the problem becomes convex which is solved by KKT conditions to obtain the location of UAV-BS. The theoretical results show that optimizing the lower bound probability achieves the suboptimal solution for power assignment and placement problem, which is verified by simulation results.

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Section: Related Workmentioning

confidence: 99%

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Abdollahi

Azarhava

Niya

2023

Preprint

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“…17,18 Due to energy constraints, a compromise must be maintained between the quality of information collected and the amount of energy consumed by IoT devices in an edge computing environment. 19,20 Furthermore, the availability of energy determines the lifespan of any IoT resource. The loss of power has an impact on the entire environment being observed.…”

Section: Research Domainmentioning

confidence: 99%

“…The more information acquired and evaluated, the more accuracy is achieved, but at the same time, the more energy consumed 17,18 . Due to energy constraints, a compromise must be maintained between the quality of information collected and the amount of energy consumed by IoT devices in an edge computing environment 19,20 . Furthermore, the availability of energy determines the lifespan of any IoT resource.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient IoT‐based informative analysis for edge computing environment

Bhatia

2022

Trans Emerging Tel Tech

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Internet of Things (IoT) is an intelligent technology that interconnection of everything at any time over an edge computing network. Because of pervasiveness and ubiquity, IoT technology is exhausting energy resources over edge computing platforms. As a result, IoT energy efficiency has become a prominent research domain. Conspicuously, an energy-efficient IoT framework for the edge computing paradigm has been proposed in the present research. Specifically, the proposed framework comprises three layers including perception and control layer, data processing layer, and application and visualization layer.The presented framework predicts inactive sensor intervals based on respective battery percentage, historical utility, and data accuracy needed for edge-based service delivery. Specifically, when the sensing nodes are in inactivity mode, the predicted value increases edge resource usage by re-provisioning the allotted resources. Moreover, the presented technique enables energy-efficient utilization of IoT resources. For validation purposes, the performance comparison is performed with state-of-the-art techniques. It shows that the presented framework is significantly enhanced in terms of energy consumption efficacy (30.45 mJ/K nodes), packet loss ratio (0.51%), statistical performance, and system stability (84.69%).

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“…Subsquently, a two-layer joint optimization method was proposed in [43] in order to minimize the average task response time by by jointly optimizing UAV deployment and computation offloading. In addition, the authors of [44] proposed an improved mean shift algorithm, which jointly optimizing the location and number of UAV edge servers, to optimize the total energy consumption and deployment cost of UAVs. Furthermore, the authors of [45] proposed an effective multi-agent collaborative environment learning algorithm to realize the dynamic resource allocation of UAV networks to optimize the coverage and utility.…”

Section: Introductionmentioning

confidence: 99%

Content-Aware Transmission in UAV-Assisted Multicast Communication

Zhang

Xiong

et al. 2023

IEEE Trans. Wireless Commun.

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To alleviate the explosive growth of data traffic caused by the increased use of smart devices, new transmission techniques are needed to increase the utilization of limited bandwidth resources and for providing high transmission rates in future wireless networks. On the other hand, due to their flexibility and autonomy, unmanned aerial vehicles (UAVs) are considered as a potential candidate to support ubiquitous connectivity and operate as flying base stations, where the deployment of UAVs can affect the quality of experience (QoE) of users. Hence, in this paper, we employ UAVs as aerial base stations to transmit data to ground users (GUs) via air to ground (A2G) communication links, where we show how content awareness can help improve the data rate. Specifically, we design two contentsharing (CS) data transmission schemes to improve the average data rate of the GUs. Additionally, two UAV deployment strategies, namely the fixed-point deployment scheme and traversesearch deployment scheme, are proposed based on the proposed CS transmission schemes. The simulation results demonstrate that our proposed data transmission schemes combined with their proposed deployment schemes outperform the traditional transmission scheme by 26 bits/s/Hz and 51 bits/s/Hz, respectively.

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Efficient deployment of multi‐UAV assisted mobile edge computing: A cost and energy perspective

Cited by 10 publications

References 34 publications

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Placement and Power Assignment for Hierarchical UAV Networks under Hovering Fluctuations in mmWave Communications

Energy efficient IoT‐based informative analysis for edge computing environment

Content-Aware Transmission in UAV-Assisted Multicast Communication

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