Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination

Shen, Yizhou; Shen, Shigen; Wu, Zongda; Zhou, Haiping; Yu, Shui

doi:10.1016/j.jisa.2022.103140

Cited by 21 publications

(12 citation statements)

References 39 publications

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“…First, the paramount problem that restricts the development of short video platforms is storage, in the case of drastic growth for users and videos. Therefore, we will introduce cost-efficient edge computing technologies with energy-saving [41][42][43][44] into video recommendations to overcome existing difficulties. Second, exploiting the rich story expressed in a short video will maximize the understanding of user's preference to improve the accuracy of recommendations.…”

Section: Conclusion and Further Discussionmentioning

confidence: 99%

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Liu¹,

Wan²,

Yan³

2023

Computer Modeling in Engineering &Amp; Sciences

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With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user's life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users' points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform recommendation. In this paper, we propose a collaborative filtering algorithm, integrating time context information and user context, which pours attention into expanding and discovering user interest. In the first place, we introduce the temporal context information into the typical collaborative filtering algorithm, and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos. There remains one more point. We also introduce the user situation into the traditional collaborative filtering recommendation algorithm, considering the context information of users in the generation recommendation stage, and weight the recommended short-form videos of candidates. At last, a diverse approach is used to generate a Top-K recommendation list for users. And through a case study, we illustrate the accuracy and diversity of the proposed method.

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Section: Conclusion and Further Discussionmentioning

confidence: 99%

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Liu¹,

Wan²,

Yan³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…x WSN malware periodically SI [8], SIS [13], SLIQRE [39] INF/D2D x malware periodically SIR [17], SADI [21],…”

Section: D2dmentioning

confidence: 99%

A Mobility-Based Epidemic Model for IoT Malware Spread

2022

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With the rapid advancement of technology, IoT has become inseparable from human lives. IoT is extensively used in transport, healthcare, manufacturing, among other sectors. However, this technology lacks sufficient security defense capabilities, thus becoming a highway for malicious actors. IoT networks use infrastructure-based (INF) and device-to-device (D2D) communications to propagate data. The INF communication utilizes technologies such as WLAN, LTE, GPRS, and GSM to relay information from source to destination. The D2D paradigm, on the other hand, is a close-proximity communication in which sensors exchange data in a multi-hop manner. Since malware can utilize both D2D and INF links to spread out, IoT networks are exceptionally vulnerable to attacks. Therefore, we propose Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model to examine the dynamics of IoT malware spread via INF and D2D communications.We analyze the impacts of mobility on infection propagation and illustrate that our model adequately captures IoT malware spread behaviors through mathematical analysis and simulations. We also compute the malware transmission threshold, which can be used as a guideline to mitigate and suppress an attack.

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Section: Introductionmentioning

confidence: 99%

“…With the rapid prevalence of smart devices 1 such as mobile phone and Internet‐of‐Things (IoT) devices, 2–4 a vast amount of data has been generated 5,6 and the demand of computation resources has been boosted 7,8 . Due to the limited computation, storage, and energy capacity of these smart devices, 9 the powerful cloud computing has been leveraged to provide elastic on‐demand services to cope with limitations of smart devices 10,11 . With the support of resource‐rich cloud servers, processing and storage‐intensive applications such as augmented reality (AR) 12 and virtual reality (VR) 13 become feasible.…”

Section: Introductionmentioning

confidence: 99%

“…7,8 Due to the limited computation, storage, and energy capacity of these smart devices, 9 the powerful cloud computing has been leveraged to provide elastic on-demand services to cope with limitations of smart devices. 10,11 With the support of resource-rich cloud servers, processing and storage-intensive applications such as augmented reality (AR) 12 and virtual reality (VR) 13 become feasible.However, the fast growing of computation demands pose severe burden on cloud centers, [14][15][16] and tremendous amount of data generated 5,17 congests the network with limited bandwidth, 15,16,18 hence causing bottlenecks for the cloud-based computing paradigm. To relieve the pressure on cloud centers, the edge computing concept has emerged, 15,19,20 which allows computation to be performed at the edge network.…”

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confidence: 99%

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PECCO: A profit and cost‐oriented computation offloading scheme in edge‐cloud environment with improved Moth‐flame optimization

Dai

Wang

et al. 2022

Concurrency and Computation

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With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource-rich cloud centres have been utilised to tackle these challenges. To relieve the burden on cloud centres, edge-cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and Quality of Service (QoS). Several optimisation models of edge-cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and costoriented computation offloading optimisation model PECCO proposed in this paper.Considering that the model is hard in nature and the optimisation objective is not differentiable, we propose an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of the original Moth-flame Optimiser and integrate it under the edge-cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimising the proposed task offloading model under the edge-cloud environment.

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Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination

Cited by 21 publications

References 39 publications

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

A Mobility-Based Epidemic Model for IoT Malware Spread

PECCO: A profit and cost‐oriented computation offloading scheme in edge‐cloud environment with improved Moth‐flame optimization

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