Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

Ezzat, Mostafa; Ghany, Mohamed A. Abd El; Almotairi, Sultan; Salem, Mohammed A.-M.

doi:10.3390/s21093222

Cited by 22 publications

(17 citation statements)

References 134 publications

(100 reference statements)

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“…For video media streams, losing a small amount of data has little effect on video QoS. Therefore, the model is able to implement exception handling and is robust to a certain degree of object loss[19][20][21].4.3. Model Performance Analysis.To analyze the performance of the model, first analyze the synchronization quantification within the media, and then, analyze the synchronization quantification between the video information.…”

mentioning

confidence: 99%

Dynamic Synchronization Modeling and Simulation of Video Sensing Nodes Based on Internet of Things

Wang

2022

Wireless Communications and Mobile Computing

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In order to solve the dynamic synchronization problem of IoT video perception, the authors propose an abstract model DSAM based on IoT video perception. This method firstly establishes the abstract model DSAM of IoT video perception based on the π network and then analyzes the state evolution, model transition, and dynamic interaction of the model; finally, the model DSAM is used to analyze and simulate the example. The result shows that when the model DSAM is tested and verified and when the number of buffer media is 50, 100, and 200, the minimum number of underflows of the DSAM model is 2 and the minimum video data packet loss rate is 1.02; the results are better than those of the MTFSC and 3TFSC models; in addition, without synchronization control, the synchronization rate between video frames is lower than that with synchronization control. The test results show that the model proposed and established by the authors can correctly handle the dynamic synchronization of IoT video perception and has certain practical value.

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confidence: 99%

Dynamic Synchronization Modeling and Simulation of Video Sensing Nodes Based on Internet of Things

Wang

2022

Wireless Communications and Mobile Computing

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“…The intersection between AI and IoT has garnered significant academic interest, resulting in several transformative applications across multiple industries. The integration's potential to revolutionize various sectors has been emphasized, such as the healthcare sector [20], credit risk evaluation innovation [67], and the automation strategy in smart cities [68]. This integration provides an enhanced customer experience and more informed business decisions through intelligent data analysis [20,69].…”

Section: Discussionmentioning

confidence: 99%

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2024

Preprint

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The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

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“…The analysis of data based on big data has been evaluated by incorporating this into the system that is now being suggested. This article was motivated by technical elements for the development of the economy and blockchain based on the secrecy of flaws and constructing a system that improves vital component qualities [ 11 ]. Effective authentication and donation implementation have been suggested.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This increases the capacity of storage, robustness, and load balancing. It provides resources to users anytime and anywhere whenever a resource is required [ 11 ].…”

Section: Proposed Smart City Data Acquisitionmentioning

confidence: 99%

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

Thirumalaisamy

Basheer

Shitharth

et al. 2022

Sensors

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There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.

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Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends

Cited by 22 publications

References 134 publications

Dynamic Synchronization Modeling and Simulation of Video Sensing Nodes Based on Internet of Things

Dynamic Synchronization Modeling and Simulation of Video Sensing Nodes Based on Internet of Things

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

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