UAV-Assisted Privacy-Preserving Online Computation Offloading for Internet of Things

Wei, Dawei; Xi, Ning; Ma, Jianfeng; He, Lei

doi:10.3390/rs13234853

Cited by 13 publications

(12 citation statements)

References 35 publications

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“…In recent years, computation offloading has received widespread attention [8][9][10][11]. Offloading approaches in terrestrial networks utilize nearby edge servers or the cloud center to offload computation tasks, which can effectively reduce the task processing delay and energy consumption of end users [12].…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Lv,

Yang,

Ding

et al. 2023

Remote Sensing

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Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Lv,

Yang,

Ding

et al. 2023

Remote Sensing

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“…Specifically, adversaries may monitor the offloading decision-making process by capturing the status of the communication link and infer the value function of the learning algorithm, which in turn leads to an unprotected UAV's computation offloading preference. To preserve privacy during partial computation offloading, an online Differential Privacy (DP)-based Deep Q-Learning (DP-DQL) scheme for UAVaided MEC-enabled IoT networks was presented in [94]. In this scheme, the DQL represented the primal learning mechanism, a generated Gaussian noise safeguarded the offloading preference, and the Prioritized Experience Replay (PER) technique [95] expedited the learning process.…”

Section: Review Of Ml-inspired and Blockchain-based Security Solutionsmentioning

confidence: 99%

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

et al. 2022

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As the Internet of Things (IoT) ecosystem evolves, innovative applications with stringent demands with respect to latency will emerge. To handle computation-intensive tasks in a timely manner, data offloading to Mobile Edge Computing (MEC) servers has been suggested. On the other hand, prospective IoT networks are expected to include Unmanned Aerial Vehicles (UAVs) to enhance coverage and connectivity, while retaining reliable communication links with ground nodes in urban, suburban, and rural terrain. Nevertheless, the evolution of UAV-aided MEC-enabled IoT presupposes the mitigation of security threats through the implementation of efficient and robust countermeasures. As UAVs inherently have certain limitations in terms of energy, computational, and memory resources, designing lightweight security solutions is required. This paper provides an overview of the UAV-aided MEC-enabled IoT and a detailed presentation of use cases and application scenarios, where security is of utmost importance. Subsequently, up-to-date research works on security solutions for the UAV-aided MEC-enabled IoT are comprehensively presented. To this end, the adoption of information-theoretic techniques that ensure adequate Physical-Layer Security (PLS) is discussed along with sophisticated security approaches based on emerging technologies, such as Blockchain and Machine Learning (ML). In addition, research studies on software-and hardware-based methods for the identification and authentication of network nodes are presented. Finally, this paper provides future perspectives in this research domain, stimulating further work.INDEX TERMS Blockchain, Internet of Things (IoT), Machine Learning (ML), Mobile Edge Computing (MEC), Physical-Layer Security (PLS), Unmanned Aerial Vehicle (UAV).

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“…In view of the wide application of traditional Hanfu in modern society, advanced virtual simulation technology is applied in the product design and development stage under the background of huge demand, and an immersive display system is built to visually present the product model or process technology, so as to design [16], study, analyze, review, evaluate, and modify [17] Hanfu.…”

Section: D Simulation Designmentioning

confidence: 99%

Implementation of Intelligent Decision Model of Traditional Hanfu 3D Design Based on Internet of Things

Wang

2022

Mathematical Problems in Engineering

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In view of the problems existing in the design and application of traditional Hanfu, in order to better promote the application prospect of the Internet of Things in traditional Hanfu, the adaptive estimation model is adopted to analyze the relevant algorithms. Based on the Internet of Things theory, the optimized intelligent decision model is obtained, and the relevant parameters of the model are modified so as to obtain the application of traditional Hanfu in 3D simulation design based on the Internet of Things theory. Finally, the optimized model is used to predict and analyze the design and application of traditional Hanfu. The results show that the value of the Internet of Things in 2021 is 880, an increase of about 8.6 times compared with 102 in 2011, indicating that the industry shows a strong growth trend. In addition, the growth rate of the Internet of Things from 2018 to 2021 is low, indicating that the output value of the Internet of Things in this stage is relatively stable, and it also reflects from the side that the Internet of Things has entered a stable growth period. With the increase in the number of iterations, the corresponding curves of the two original trust degrees show little change, and the original trust degree curves are mainly divided into the stage of slow rise and the stage of steady increase. The influence of the optimization model on the willingness to trust is obvious, showing varying degrees of increase. Different calculation methods describe the test data differently. The mathematical estimation method shows an approximate linear trend. The result estimation method shows the change in the quadratic function. The key decision rules have the characteristic of subsection. Data method has strong fluctuation in the early stage. Objective function method can better describe the characteristics of traditional Hanfu. This study realizes the design and application of traditional Hanfu in the field of 3D simulation based on the Internet of Things theory so as to provide a theoretical basis for the application and research of the Internet of Things in other fields.

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UAV-Assisted Privacy-Preserving Online Computation Offloading for Internet of Things

Cited by 13 publications

References 35 publications

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Secure UAV-Aided Mobile Edge Computing for IoT: A Review

Implementation of Intelligent Decision Model of Traditional Hanfu 3D Design Based on Internet of Things

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