An edge intelligence empowered flooding process prediction using Internet of things in smart city

Chen, Chen; Jiang, Jiange; Zhou, Yang; Lv, Ning; Liang, Xiaoxu; Wan, Shaohua

doi:10.1016/j.jpdc.2022.03.010

Cited by 89 publications

(47 citation statements)

References 34 publications

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“…The impact of the epidemic on economic activities is mainly reflected in the following five aspects as shown in Figure 1 . Consumer demand shock: the new crown pneumonia epidemic has brought a serious impact on the consumer demand of residents [ 4 ]. Unemployment, shopping inconvenience, and wait-and-see mood will cause the consumer demand of residents to drop in the epidemic area Labor supply shock: similarly, a high unemployment rate brought on by an epidemic also aids in its further spread.…”

Section: Introductionmentioning

confidence: 99%

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

2022

BioMed Research International

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The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division’s economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management.

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Section: Introductionmentioning

confidence: 99%

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

2022

BioMed Research International

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“…Where 𝑻𝑵 𝑷𝑨𝑪𝑲𝑬𝑻 is the total number of packets, the average temperature (𝑨𝒗𝒈), and 𝑻𝑹 𝑷𝑨𝑪𝑲𝑬𝑻 is the temperature rise. From (3), the total thermal effect (𝑻𝑻𝑬) of the node can be calculated by adding the current node temperature (𝑪𝑻 𝑵𝑶𝑫𝑬 ) and 𝑹𝑻𝑬 𝑬𝑿𝑷𝑬𝑪𝑻𝑬𝑫 as computed below in (4).…”

Section: ) Energy Modelmentioning

confidence: 99%

“…The IoT vision extends traditional compute-based centralized systems to a dispersed environment by providing a seamless platform for connecting people and objects. It provides a wide range of everyday smart applications in industries [1], education [2], homes [3], cities [4], agriculture [5], and health [6]. The health sector is the most important, with everincreasing demands providing greater insight into the various platforms for conducting advanced research in this dynamic domain.…”

Section: Introductionmentioning

confidence: 99%

Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

et al. 2022

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The Internet of Healthcare Things has significantly altered traditional patient-doctor relationships by allowing essential healthcare treatment from the comfort of one's home. The wireless body area network is an IEEE 802.15.6 standard that focuses on healthcare data, necessitating various cross-layer and thermal-aware protocols. However, most cross-layer protocols have long convergence delays and a single failure point. Moreover, these protocols exploit excessive broadcasts and handshake acknowledgments, causing communication and processing overheads. Furthermore, thermal-aware protocols focus on thermal variations, disperse data collection, and do not support cross-layer techniques. To address these limitations, this study proposes an optimal distributive cross-layer and thermal-aware convergecast protocol. The proposed protocol enforces a novel hybrid convergecast using probability and both minimum attenuation strategies to collect data from leaf nodes to the root to improve data flow and adaptability in the network. In addition, it accelerates the convergence process by reducing recurrent broadcasts and unnecessary acknowledgments, resulting in improved energy efficiency and thermal control. The proposed protocol supports a distributive hierarchy by establishing multiple parent-child relationships to avoid a single root point failure. A multi-parameter maximum benefit-cost function calculates the next hop according to the extracted weights. Packet loss probability validates the number and sequence of packets received at the sink node. The simulation results demonstrate that cross-layer and thermal-aware protocols can coexist effectively. The proposed protocol reduces delays to 19.4%, improves throughput from 8% to 13.75%, and retains a packet loss probability of 0.3% by keeping the thermal rise within bounds.

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“…When multiple different versions of the same type of FOV video are stored in the MEC, it will take up too much storage space. erefore, the FOV video is stored in the MEC in a format of 360 °, and the optimized strategy is used to process it as a transmission of FOV video [23].…”

Section: Vr Network System Model Based On User Interest and Fovmentioning

confidence: 99%

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

2022

Scientific Programming

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To solve the application of Virtual Reality (VR) technology in the design of urban sculpture space, a VR network model is implemented based on Internet of Things (IoT) and Edge Computing. First, this work expounds and analyzes the theory of urban sculpture space and the interaction of urban sculpture space. Second, aiming at the problem that the development of IoT cannot meet the large amount of data and low delay of VR, a resource allocation algorithm, Joint Optimization of Resource Allocation and FOV (J-RAF) strategy is proposed based on the user interest and the resource allocation problem of (FOV), and simulation experiments are used to verify the effectiveness of the J-RAF algorithm. Finally, a VR network system is established based on cache cooperation and user interest, and a resource allocation algorithm Joint Cooperative Content Caching and FOV (J-CCF) is proposed, and its effectiveness is verified. Experiments show that compared with random strategy and random viewport strategy, algorithm J-CCF has 3% and 20% system gain. Compared with noncooperative cache scheme and random viewport strategy, algorithm J-CCF can improve the system gain by 15% and 25%, respectively. It is verified that the convergence of J-RAF algorithm and J-CCF algorithm and the effectiveness of improving the system gain. This work provides a technical basis for optimizing the design method of urban sculpture and contributes to the optimal design of urban space.

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An edge intelligence empowered flooding process prediction using Internet of things in smart city

Cited by 89 publications

References 34 publications

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

The Design of Urban Sculpture Space with User Behavior Based on Internet of Things and Edge Computing

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