An Adaptive Resource Allocation Model With Anti-Jamming in IoT Network

Dou, Zheng; Si, Guangzhen; Lin, Yun

doi:10.1109/access.2019.2903207

Cited by 31 publications

(19 citation statements)

References 38 publications

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“…Based on the type of the job, a global CPU allocates resources by considering communication and computational cost. Dou et al [48] proposed a new method which improved a RA algorithm with anti-jamming for the IoT nodes. In this method a novel automatic control allocation (ACA) model has been invented in order to provide an adaptive allocation and anti-jamming transmission.…”

Section: Methods Based On Heuristic Algorithmmentioning

confidence: 99%

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sangaiah

Hosseinabadi

Shareh

et al. 2020

Sensors

141

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

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Section: Methods Based On Heuristic Algorithmmentioning

confidence: 99%

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sangaiah

Hosseinabadi

Shareh

et al. 2020

Sensors

141

View full text Add to dashboard Cite

show abstract

“…With the continuous development of artificial intelligence technology, machine learning and deep learning gradually occupy all aspects of people's lives. In the field of communication, the development of artificial intelligence is also very rapid, including wireless sensor networks [8][9][10][11], network resource allocation [12], modulation signal recognition [13][14][15] communication equipment individual recognition [16], abnormal information identification and detection [17,18], and others. Many scholars have conducted studies in these aspects.…”

Section: Introductionmentioning

confidence: 99%

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Tian

Lin

Guo

et al. 2020

Sensors

Self Cite

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With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

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“…Even if the data generated from same sources while they can still represent different forms [1]- [7]. Therefore, data processing techniques become particularly important in many areas, such as data transmission [8]- [12], restoration [13],image fusion [14],and feature extraction [2]- [4], [15]- [19] and so on. In the real world, an object…”

Section: Introductionmentioning

confidence: 99%

Cross-View Feature Learning via Structures Unlocking Based on Robust Low-Rank Constraint

Ding

Chen

et al. 2020

IEEE Access

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The cross-view multimedia are widely existed and attract many attentions in recent years. Nevertheless, it is noted that the phenomenon, that data in different classes from same view are more similar than that in same class from different views, is usually presented for cross-view multimedia data. The intrinsic imperfection leads disappointing performance for cross-view multimedia recognition or classification. To solve this problem, in this paper, we propose a novel discriminative learning framework with low-rank constraint, which can be applied for view-invariant low-dimensional subspace learning. The advantages of our framework include three aspects. Firstly, to unlock the latent class structure and view structure, a self-expressed model by dual low-rank constraints are presented, which can separate the two manifold structures in the learned subspace. Secondly, two effective discriminative graphs are constructed to guide the affinity relationship of data in the above two low-dimensional projected subspaces respectively. Finally, the joint semantic consensus constraint is designed to be integrated into the learning framework, which can explore the shared and view-specific information for enforcing the view-invariant character in semantic space. Experimental results on several public cross-view multimedia datasets demonstrate that our proposed method outperforms existing excellent subspace learning approaches. INDEX TERMS Multimedia analysis, multi-view discriminative analysis, cross-view feature learning, lowrank representation.

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An Adaptive Resource Allocation Model With Anti-Jamming in IoT Network

Cited by 31 publications

References 38 publications

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Cross-View Feature Learning via Structures Unlocking Based on Robust Low-Rank Constraint

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