A Proposed Architecture for Traffic Monitoring &amp; Control System via LiFi Technology in Smart Homes

Asif, Muhammad; Khan, Tahir Abbas; Taleb, Nasser; Said, Raed A.; Siddiqui, Shahan Yamin; Batool, Ghanwa

doi:10.1109/icbats54253.2022.9759069

Cited by 46 publications

(3 citation statements)

References 13 publications

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“…The system employs LiFi technology to transmit vital signs data from patients in the ICU to a cloud IoT platform at any time [8]. Through this platform, doctors and nurses can view patientspecific vital sign data online anytime to monitor and assess the patient's status.…”

Section: Smart Healthcare Monitoringmentioning

confidence: 99%

LiFi Technology Application and Prospect in the Field of Medical

Shen

2024

HSET

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Light Fidelity (LiFi) technology utilizes visible light as a transmission medium with a large spectrum and resistance to electromagnetic interference. Due to its unique characteristics, the use of light emitted by light-emitting diodes (LED) to transmit data makes it very safe when transmitting data. LiFi technology plays an integral role in many fields, such as aerospace, underwater communications, defense and security, and medicine. This paper introduces the basic working principle of LiFi. LiFi technology is used as a modulation technique for data transmission, and the current status of LiFi technology in the medical field is briefly analyzed, along with the challenges and difficulties encountered in this field. Examples include a medical monitoring system based on LiFi technology and the transmission of images in the operating theater using LiFi technology. In the medical field, LiFi technology also faces challenges and difficulties, such as ensuring the stability and reliability of communication and achieving efficient signal processing and data transmission. The potential of multiple-input multiple-output (MIMO) technology for in-depth research is described.

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Section: Smart Healthcare Monitoringmentioning

confidence: 99%

LiFi Technology Application and Prospect in the Field of Medical

Shen

2024

HSET

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“…Due to the relatively small scale of smart home device traffic data compared to network traffic and the high accuracy and real-time nature of packet-level features [28], most of the related research utilizes classification algorithms based on packet-level features for traffic classification by attackers. Therefore, this study designs attack models that infer user privacy information based on packet-level traffic fingerprints of smart home devices to demonstrate the feasibility and potential harm of smart home traffic classification attacks.…”

Section: Goals and Challengesmentioning

confidence: 99%

A Novel Traffic Obfuscation Technology for Smart Home

Zhang,

Shen,

Liu

et al. 2023

Electronics

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With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.

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“…Overall, by leveraging AI technologies, Koçtaş is able to provide a better customer experience and improve sales across all shopping channels [10]. To enhance security and risk mitigation in home improvement applications, control systems and convolutional neural networks can be used for threat detection and prevention [32][33][34]. Secure communication [35], mobile agent protocol [36], machine learning [37], artificial neural networks [38], blockchain technology [39,57], electronic health records [40], novel secret key generation techniques [41], secret sharing schemes [42], and secure information management [43] can be applied in home improvement to enhance security and privacy of personal and financial data [44].…”

Section: Introductionmentioning

confidence: 99%

Applications in Home Improvement Retailer, Koctas

Mourad

Raza

Amjad

et al. 2023

2023 International Conference on Business Analytics for Technology and Security (ICBATS)

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It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like experience. Finally, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective marketing. Overall, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry.

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A Proposed Architecture for Traffic Monitoring & Control System via LiFi Technology in Smart Homes

Cited by 46 publications

References 13 publications

LiFi Technology Application and Prospect in the Field of Medical

LiFi Technology Application and Prospect in the Field of Medical

A Novel Traffic Obfuscation Technology for Smart Home

Applications in Home Improvement Retailer, Koctas

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