Intelligent Cooperative Edge Computing in Internet of Things

Gong, Chao; Lin, Fuhong; Gong, Xiaowen; Lu, Yueming

doi:10.1109/jiot.2020.2986015

Cited by 124 publications

(54 citation statements)

References 28 publications

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“…It should be noted that only the nodes with state S may be infected due to their vulnerability. [10]. Meanwhile, the numbers of nodes converting from state S to state R and converting from state S to state D are ρ SR n S ðtÞ and ρ SD n S ðtÞ, respectively.…”

Section: Security Risk Propagation Model Of Fog Computingmentioning

confidence: 99%

An Invocation Chain Test and Evaluation Method for Fog Computing

Zhao

Yang²,

Tian

et al. 2020

Wireless Communications and Mobile Computing

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In this paper, an invocation chain technology is used to test the security of fog computing systems. Atomic attacks based on the attack graph are combined according to the type, time sequence, and causal relationship. Different test sequences have gone through according to the principle of depth-first. In addition, the vulnerability assessment based on an invocation chain is evaluated to verify whether it can detect existing or unknown vulnerability in fog computing systems. Finally, the experimental results show that the invocation chain test and evaluation method based on the attack graph can evaluate the system risk quantitatively rather than qualitatively by calculating comprehensive probability on all test sequences.

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Section: Security Risk Propagation Model Of Fog Computingmentioning

confidence: 99%

An Invocation Chain Test and Evaluation Method for Fog Computing

Zhao

Yang²,

Tian

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm can efficiently utilize heterogeneous edge servers in a cooperative manner. [25] proposes a novel Intelligent Coopera-tive Edge (ICE) computing framework, which realizes the complementary integration of edge computing and artificial intelligence (AI) in the IoT environment from two aspects: edge-based AI and AI-enabled edge. [26] studies the joint optimization of the CPU frequencies, the offloading bits, the transmit power, and the UAV's trajectory of the UAV-enabled wireless powered cooperative MEC system.…”

Section: Related Workmentioning

confidence: 99%

Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

et al. 2020

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Edge computing can be widely used in unmanned aerial vehicle (UAV) inspection, field operation control, power consumption information collection and other businesses in the power Internet of Things scene. Edge computing offloads functions such as data processing and applications to network edge nodes near the terminals to provide low-latency services and ensure service quality. However, with the explosive growth of business terminals, the capacity of single edge node is limited and it is difficult to meet all business requirements at the same time. Therefore, this paper proposes a task allocation mechanism based on cooperative edge computing. Firstly, a task allocation model based on cooperation of two edge nodes is established to minimize the average task completion delay while meeting business requirements. Secondly, the Two-edge-node Cooperative-task Allocation based on Improved Particle Swarm Optimization (TCA-IPSO) algorithm is proposed, which applies the crossover and mutation strategy in genetic algorithm to improve the particle swarm optimization algorithm, and solves the problem that the task allocation scheme in cooperation is prone to fall into a local optimum. Finally the simulation results show that the proposed TCA-IPSO algorithm reduces the average task completion delay by 53.8% and 36.0% compared to the benchmark and QoS-based Task Distribution (QBTD) algorithm. INDEX TERMS Cooperative edge computing, Power Internet of Things, Task completion delay, Task allocation.

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“…When these data are transmitted to the cloud, network latency and bandwidth become a bottleneck [1]. To over-come these problems, edge computing (e.g., cloud-aware fog computing mechanism [2,3]) is one of the most promising solutions. In real applications, industrial enterprises could make some necessary computing tasks close to the machine in the industrial control system (ICS).…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Wang

Liu

et al. 2020

Wireless Communications and Mobile Computing

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As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency. The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT. ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives. ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years. The risk of being attacked increases as a result. In order to protect these assets, intrusion detection systems (IDS) have drawn much attention. As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS. In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack. In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset. The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.

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Intelligent Cooperative Edge Computing in Internet of Things

Cited by 124 publications

References 28 publications

An Invocation Chain Test and Evaluation Method for Fog Computing

An Invocation Chain Test and Evaluation Method for Fog Computing

Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

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