Data Collection Through Mobile Vehicles in Edge Network of Smart City

Luo, Yueyi; Zhu, Xiaoyu; Long, Jun

doi:10.1109/access.2019.2951587

Cited by 17 publications

(24 citation statements)

References 41 publications

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“…Luo et al [38] proposed a data collection scheme in smart city using vehicles, the sensing devices were widely deployed to detect the status of basic infrastructures, the vehicles sent sensing data from sensing devices to data center, then the data center could make scientific decisions. The authors of [39] proposed to collect data through unmanned aerial vehicle in a trust way, the energy consumption of system was decreased by optimizing the unmanned aerial vehicle's trajectory.…”

Section: Background and Related Workmentioning

confidence: 99%

Computation offloading through mobile vehicles in IoT-edge-cloud network

Long

Luo

Zhu

et al. 2020

Preprint

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With the developing of Wireless Sensor Networks (WSNs), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

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Section: Background and Related Workmentioning

confidence: 99%

Computation offloading through mobile vehicles in IoT-edge-cloud network

Long

Luo

Zhu

et al. 2020

Preprint

Self Cite

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“…With the development of Internet of Things (IoT), there will be more than 20 billion IoT devices by 2020 [1][2][3]. Most of these IoT devices are deployed at the edge of the network [4][5][6]. Due to the huge number of these IoT devices and the development of micro-processing technology, their computing and storage capabilities have greatly improved.…”

Section: Introductionmentioning

confidence: 99%

“…However, the Edge network is an open network with various IoT devices in various forms connected [19][20][21]. For example, Luo et al [4] gave a typical application of smart city as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…In smart city, a large number of IoT devices, most of which are wireless sensing devices, are deployed in various applications in the city to realize the perception of the surrounding environment [4,9,22,23]. These sensing devices are relatively simple and do not have the ability to communicate directly with the Internet [4,9,24]. They are often selforganized into networks [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…They are often selforganized into networks [25][26][27]. Sensing devices on the roadside act as gateway to collect the data of the entire network [4], and then send the collected data to the passing mobile vehicles [28][29][30]. Due to the advanced hardware of mobile vehicles, they can communicate directly with the Internet.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An active and verifiable trust evaluation approach for edge computing

Wang

Zhang

et al. 2020

J Cloud Comp

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Billions of Internet of Thing (IoT) devices are deployed in edge network. They are used to monitor specific event, process and to collect huge data to control center with smart decision based on the collected data. However, some malicious IoT devices may interrupt and interfere with normal nodes in data collection, causing damage to edge network. Due to the open character of the edge network, how to identify the credibility of these nodes, thereby identifying malicious IoT devices, and ensure reliable data collection in the edge network is a great challenge. In this paper, an Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost. The main innovations of the AVTE approach compared with the existing work are as follows: (1) In AVTE approach, the trust of the device is obtained by an actively initiated trusted detection routing method. It is fast, accurate and targeted. (2) The acquisition of trust in the AVTE approach is based on a verifiable method and it ensures that the trust degree has higher reliability. (3) The trust acquisition method proposed in this paper is low-cost. An encoding returned verification method is applied to obtain verification messages at a very low cost. This paper proposes an encoding returned verification method, which can obtain verification messages at a very low cost. In addition, the strategy of this paper adopts initiation and verification of adaptive active trust detection according to the different energy consumption of IoT devices, so as to reliably obtain the trust of device under the premise of ensuring network lifetime. Theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.

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Q‐learning based flexible task scheduling in a global view for the Internet of Things

Liu²,

Wang

et al. 2020

Trans Emerging Tel Tech

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Summary With billions of sensor‐based devices connected to the Internet of Things (IoT), it is a pivotal issue to design an effective task scheduling scheme when the resource of sensor nodes is limited. In the past, Q‐learning based task scheduling scheme which only focuses on the node angle led to poor performance of the whole network. Thus, a Q‐learning based flexible task scheduling with global view (QFTS‐GV) scheme is proposed to improve task scheduling success rate, reduce delay, and extend lifetime for the IoT. First, the Q‐learning framework, including state set, action set, and rewards function is defined in a global view so as to forms the basis of the QFTS‐GV scheme. Then, a task scheduling policy is established with distinguishing rewards for nodes in different areas of the network, so the energy‐strained nodes can be protected to ensure a high lifetime, and the energy‐relaxed nodes can increase their transmission power to promote the benefits of the whole network. Finally, experimental results demonstrate that the QFTS‐GV scheme can achieve a higher task scheduling success rate, lower delay, and less energy consumption. Compared with the Q‐learning based task scheduling scheme, the QFTS‐GV improves the task scheduling success rate by 1.42% to 7.13%, reduces the delay by 24.60% to 42.56%, and saves energy by 21.18% to 36.60%.

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Data Collection Through Mobile Vehicles in Edge Network of Smart City

Cited by 17 publications

References 41 publications

Computation offloading through mobile vehicles in IoT-edge-cloud network

Computation offloading through mobile vehicles in IoT-edge-cloud network

An active and verifiable trust evaluation approach for edge computing

Q‐learning based flexible task scheduling in a global view for the Internet of Things

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