Optimized Visual Internet of Things in Video Processing for Video Streaming

The global expansion of the Visual Internet of Things (VIoT) has enabled various new applications during the last decade through the interconnection of a wide range of devices and sensors.Frame freezing and buffering are the major artefacts in broad area of multimedia networking applications occurring due to significant packet loss and network congestion. Numerous studies have been carried out in order to understand the impact of packet loss on QoE for a wide range of applications. This paper improves the video streaming quality by using the proposed framework Lossy Video Transmission (LVT)  for simulating the effect of network congestion on the performance of  encrypted static images sent over wireless sensor networks.The simulations are intended for analysing video quality and determining packet drop resilience during video conversations.The assessment of emerging trends in quality measurement, including picture preference, visual attention, and audio visual quality is checked. To appropriately quantify the video quality loss caused by the encoding system, various encoders compress video sequences at various data rates.Simulation results for different QoE metrics with respect to user developed videos have been demonstrated which outperforms the existing metrics

Secure Multi-Level Privacy-Protection Scheme for Securing Private Data over 5G-Enabled Hybrid Cloud IoT Networks

The hybrid cloud is a secure alternative for enterprises to exploit the benefits of cloud computing to overcome the privacy and security concerns of data in IoT networks. However, in hybrid cloud IoT, sensitive items such as keys in the private cloud can become compromised due to internal attacks. Once these keys are compromised, the encrypted data in the public cloud are no longer secure. This work proposes a secure multilevel privacy-protection scheme based on Generative Adversarial Networks (GAN) for hybrid cloud IoT. The scheme secures sensitive information in the private cloud against internal compromises. GAN is used to generate a mask with the input of sensory data-transformation values and a trapdoor key. GAN’s effectiveness is thoroughly assessed using Peak Signal-to-Noise Ratio (PSNR), computation time, retrieval time, and storage overhead frameworks. The obtained results reveal that the security scheme being proposed is found to require a negligible storage overhead and a 4% overhead for upload/retrieval compared to the existing works

Sustainable Non-Cooperative User Detection Techniques in 5G Communications for Smart City Users

The 4G network is not sufficient for achieving the high data requirements of smart city users. The 5G network intends to meet these requirements and overcome other application issues, such as fast data transmission, video buffering, and coverage issues, providing excellent mobile data services to smart city users. To allocate a channel or spectrum to a smart city user for error-free transmission with low latency, the accurate information of the spectrum should be detected. In this study, we determined the range of non-cooperative detection techniques, such as matched filter detection with inverse covariance approach (MFDI), cyclostationary feature detection with inverse covariance approach (CFDI), and hybrid filter detection with inverse covariance approach (HFDI); based on the results of these methods, we provided highly accurate spectrum information for smart city users, enabling sustainable development. To evaluate the performance of the proposed detection techniques, the following parameters are used: probability of detection (PD), probability of false alarms (Pfa), probability of miss detection (Pmd), sensing time, and throughput. The simulation results revealed that the HFDI detection method provided sustainable results at low signal-to-noise ratio ranges and improved channel detection and throughput of approximately 17% and 10%, respectively