Traffic Management with Deployment of Li-Fi Technology

Al_Barazanchi, Israa; Sahy, Seba Aziz; Jaaz, Zahraa A.; Abdulshaheed, Haider Rasheed

doi:10.1088/1742-6596/1804/1/012141

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…The suggested scheme's efficacy is evaluated using these metrics. The outcomes were compared to those obtained using conventional methods are offFog (OF) [17], context-federated deep reinforcement learning (C-fDRL) [18], hierarchical multi-agent deep reinforcement learning (H-MADRL) [19], and graph optimized algorithm (GOA) [20]. All the energy needed to carry out an activity, create something, or just occupy a structure is referred to as energy consumption.…”

Section: Resultsmentioning

confidence: 99%

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

et al. 2023

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The internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of data transmission at the original targeted location. Initially, healthcare data was collected. Preprocessing is achieved by the normalization method. An EEEF data offloading scheme is proposed. A fruit fly optimization (FFO) technique is utilized. The performance metrics such as energy consumption, delay, resource utilization, scalability, and packet loss are analyzed and compared with existing techniques. The future scope will make use of a revolutionary optimization approach for IoMT.

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Section: Resultsmentioning

confidence: 99%

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

et al. 2023

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“…The main goal of LOA is to achieve good class mean separation while maintaining limited variance around these means in the projection direction. Like PCA, LDA uses a linear combination of initial data to generate its features [24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Mehdy¹,

Qasim²,

A.Habeeb³

et al. 2023

PEN

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The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use.

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“…The number of mobile devices has grown at an exponential rate. According to the CISCO visual network index (CVNI) [24], by 2023, almost 70 % of the world's population will have access to a mobile phone. As the number of mobile phones increases, so does interest in new mobile/cellular network applications, such as the internet of things (IoT), to enable smart homes and cities, including vehicular communication [25].…”

Section: Hybrid Light Radio System (Hlr)mentioning

confidence: 99%

Load Balancing Performance Optimization for LI-Fi/Wi-Fi HLR Access Points Using Particle Swarm Optimization and DL Algorithms

2022

IJIES

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The increasing number of mobile devices challenges the current radio frequency (RF) networks. A hybrid RF/VLC network (HLRN) is proposed to mitigate the spatial fluctuation of data rate, offering a system throughput greater than that of standalone visible light communications (VLC) or radio frequency (RF) networks. In hybrid networks, the main problem is load balancing, which reduces network performance. Therefore, to solve this problem, the load balancing (LB) schemes in HLRNs are studied and focus on the AP assignment for the users. In that, efficient spectrum sensing is found essential to prevent the wrong holes detection in the band using five technologies divided into three stages, namely: Received signal strength (RSS), particle swarm optimization (PSO) and deep learning methods: Feed forward neural network (FFNN), convolutional neural network (CNN) and cascade back propagation neural network (CBPNN). An event such as high energy noise availability or primary user fluctuation (mobility) impact on the throughput is studied, which was carried out in three stages depending on MATLAB. In the first stage, the received signal strength (RSS) algorithm is proposed, which is used the SNR to calculate throughput for fixed and mobility user and user satisfaction. In stage two, the particle swarm optimization (PSO) algorithm is proposed to achieve a better performance than the RSS algorithm. At last, in stage three, the DL algorithms (FFNN, CNN, CBPNN) are used to enhance the performance's accuracy and compare them. The results in stage one show that throughput using RSS technique is decreasing when the number of users increases by achieving 390 Mbps approx. is detected with single user existence while 180 Mbps approx. is detected with 10 users' existence. And it can be noted that most of the users are connected to wireless fidelity (Wi-Fi) AP. Hence the Wi-Fi is overloaded. Further, only 34 % of the users in RSS based scheme will achieve the desired performance if a user satisfaction threshold of 0.5 is considered for the system. The results in stage two shown that throughput using the PSO technique is outperformed over the others by achieving high average throughput of 960 Mbps approx. Also, it has offered acceptable load balancing for the access points with good user satisfaction reach to 80 % of the users will achieve the desired performance when the user satisfaction threshold is considered to be 0.9 for the system. In stage three, deep learning methods for system performance enhancement are used (as compared with the standard RSS method). throughput is improved, and this improvement is lesser than that in the case of the PSO algorithm. 800 Mbps approx. is achieved using the CNN method, and it is realized that 70 % of the users will achieve full user satisfaction when the user satisfaction threshold is considered 1.2 for the system.

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Traffic Management with Deployment of Li-Fi Technology

Cited by 12 publications

References 13 publications

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

A novel data offloading scheme for QoS optimization in 5G based internet of medical things

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Load Balancing Performance Optimization for LI-Fi/Wi-Fi HLR Access Points Using Particle Swarm Optimization and DL Algorithms

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