Joint mobile vehicle–UAV scheme for secure data collection in a smart city

Huang, Shaobo; Gui, Jinsong; Wang, Tian; Li, Xiong

doi:10.1007/s12243-020-00798-9

Cited by 23 publications

(9 citation statements)

References 64 publications

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“…It is also worth pointing out that simply reducing energy consumption may not be able to effectively improve network lifetime. This is because the main work of sensor nodes is to sense and collect data [3], [6], [7], [49], [50], [58]. In the daily work of sensor nodes, the data flow is from the edge of the network to the sink [3], [6], [7].…”

Section: ) Lifeitmementioning

confidence: 99%

“…In the daily work of sensor nodes, the data flow is from the edge of the network to the sink [3], [6], [7]. This is a many-toone data collection method, so its energy consumption is that the energy consumption of nodes in the near sink area is large, and the energy consumption of nodes in the far sink area is small [49], [50], [58]. Code multicast in this case can only save the energy consumption of nodes in the far sink area and cannot extend the lifetime.…”

Section: ) Lifeitmementioning

confidence: 99%

“…In the cloud sensor system, sensor nodes self-organize into a network on the one hand, and on the other hand, their data collection methods are more flexible and diversified. It is not necessary to connect to the Internet through the sink node like traditional WSNs, but to collect data through emerging tools such as mobile vehicles, Unmanned Aerial Vehicles (UAVs), which further expands the scope of their applications [49]- [51]. Another technology that promotes the further development of IoT devices is software define [41], [42].…”

Section: Introductionmentioning

confidence: 99%

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Fast Multicast With Adjusting Transmission Power and Active Slots in Software Define IoT

Liu

Zhang

et al. 2020

IEEE Access

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Section: ) Lifeitmementioning

confidence: 99%

Section: ) Lifeitmementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Multicast With Adjusting Transmission Power and Active Slots in Software Define IoT

Liu

Zhang

et al. 2020

IEEE Access

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“…With the development of wireless communication technologies, an increasing number of vehicles are connected to the Internet through access points, which promotes the emergence of the Internet of vehicle (IoV) and smart city [1]. In the IoV systems, mobile vehicles can collect data and exert some intensive computational tasks to make intelligent decision [2,3]. However, due to the lack of computational capability at mobile vehicles, local computing may cause a significant latency and even lead to some serious consequences, e.g, traffic accidents [4].…”

Section: Introductionmentioning

confidence: 99%

DQN Based Mobile Edge Computing for Smart Internet of Vehicle

Zhang

Zhou

Xia

et al. 2022

Preprint

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In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart internet of vehicle (IoV) network, where one edge server can help accomplish the intensive computational tasks from the vehicular users. For the MEC networks, most of existing works mainly focus on minimizing the system latency to guarantee the user's quality of service (QoS) through designing some offloading strategies, which however fail to consider the pricing from the server and hence fail to take into account the the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC based IoV networks, and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then the Lagrange multiplier method is employed to allocate the computational capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperforms the conventional ones. In particular, the proposed scheme can effective reduce the system latency up to 56% compared to the conventional schemes.

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“…The proposed method can upgrade the planned schedule in the event of an unpredicted emergency. Huang et al [18] proposed the Active Trust Verification Data Collection (ATVDC) system to the secure, efficient, and cheap gathering of information. In this system, a UAV is adapted for collecting basic information from the sensor to estimate the trust of mobile vehicle (MV), and a high-trust MV priority recruitment (HTMPR) approach is presented for recruiting credible MV at a lower cost.…”

Section: Introductionmentioning

confidence: 99%

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

Ragab¹,

Altalbe²,

AL-Ghamdi³

et al. 2022

Computers, Materials &Amp; Continua

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The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.

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Joint mobile vehicle–UAV scheme for secure data collection in a smart city

Cited by 23 publications

References 64 publications

Fast Multicast With Adjusting Transmission Power and Active Slots in Software Define IoT

Fast Multicast With Adjusting Transmission Power and Active Slots in Software Define IoT

DQN Based Mobile Edge Computing for Smart Internet of Vehicle

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

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