Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things

Wang, Tian; Qiu, Lei; Sangaiah, Arun Kumar; Liu, Anfeng; Bhuiyan, Zakirul Alam; Ma, Ying

doi:10.1109/jiot.2020.2966870

Cited by 111 publications

(62 citation statements)

References 37 publications

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“…In order to provide low processing delay for devices, the MEC computing is proposed to improve the performance of the system [16][17][18][19]. The MEC servers can process many tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Different from these works, we propose a task offloading scheme for IoT sensing devices through vehicles in edge-cloud network. 3 The system model and problem statement…”

Section: Background and Related Workmentioning

confidence: 99%

“…Wireless sensor networks (WSNs) is widely deployed in the smart city. There exists a great amount of sensing devices in the urban environment, the sensing devices in WSNs can sense the environmental data [1][2][3]. The collected sensing data can be processed and sent to the remote server to make decisions, which can help improve the city management [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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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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“…In order to provide low processing delay for devices, the MEC computing is proposed to improve the performance of the system [16][17][18][19]. The MEC servers can process many tasks.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Different from these works, we propose a task offloading scheme for IoT sensing devices through vehicles in edge-cloud network. 3 The system model and problem statement…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Long

Luo

Zhu

et al. 2020

Preprint

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show abstract

“…Wireless Sensor Networks (WSNs) [1,2] could initially collect the data distributed, and edge computing will enable traditional sensor networks to be effective [3][4][5][6]. Wireless Multimedia Sensor Networks (WMSNs) are a new type of network that adds video, audio, image, and other multimedia information perception functions based on traditional WSNs [7,8]. WMSNs perceive various media information in the surrounding environment through multimedia sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Image Compression Scheme for Energy Harvesting Wireless Multimedia Sensor Networks

Han

Zhang

et al. 2020

Sensors

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As an emerging technology, edge computing will enable traditional sensor networks to be effective and motivate a series of new applications. Meanwhile, limited battery power directly affects the performance and survival time of sensor networks. As an extension application for traditional sensor networks, the energy consumption of Wireless Multimedia Sensor Networks (WMSNs) is more prominent. For the image compression and transmission in WMSNs, consider using solar energy as the replenishment of node energy; a distributed image compression scheme based on solar energy harvesting is proposed. Two level clustering management is adopted. The camera node-normal node cluster enables camera nodes to gather and send collected raw images to the corresponding normal nodes for compression, and the normal node cluster enables the normal nodes to send the compressed images to the corresponding cluster head node. The re-clustering and dynamic adjustment methods for normal nodes are proposed to adjust adaptively the operation mode in the working chain. Simulation results show that the proposed distributed image compression scheme can effectively balance the energy consumption of the network. Compared with the existing image transmission schemes, the proposed scheme can transmit more and higher quality images and ensure the survival of the network.

show abstract

“…Wireless sensor networks (WSNs) is widely deployed in the smart city. There exists a great amount of sensing devices in the urban environment, the sensing devices in WSNs can sense the environmental data [1][2][3][4]. The collected sensing data can be processed and sent to the remote server to make decisions, which can help improve the city management [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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

Long

Luo

Zhu

et al. 2020

Preprint

View full text Add to dashboard Cite

With the developing of Internet of Things (IoT) and mobile edge computing (MEC), 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.

show abstract

Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things

Cited by 111 publications

References 37 publications

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

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

A Distributed Image Compression Scheme for Energy Harvesting Wireless Multimedia Sensor Networks

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

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