Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices

Abstract

The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application

“…13, No. 2, June 2024: 1348-1357 1354 divided into three sections, i.e., propagation overhead, transmission overhead, and ARP study similar to work presented in [9], [12]. Moreover, for the ARP study, three scenarios have been considered, mainly, city, expressway and semi-urban [24], [28].…”

“…For calculating the 𝛼 given in the in (11), as given in [21], the path-loss and the log normal-shadowing component is evaluated in this work by considering the Signal-to-Noise Ratio (SNR) (𝑟) 𝑑𝐵 having the distance 𝑟 which separates the sender and receiver. Hence, from this the 𝛼 is evaluated using given as (12). 𝛼(𝑟) 𝑑𝐵 = 𝒫 𝑡 − 𝒫ℒ(𝑟 0 ) − 10 𝑛 log 10 ( 𝑟 𝑟 0 ⁄ ) − 𝒳 𝜎 − 𝒫 𝑛 (12) Where, 𝒫 𝑡 represents the power essential for processing the packets, 𝒫ℒ(𝑟 0 ) represents that path-loss for a given distance which is apart from 𝑟 0 , 𝒳 𝜎 represents the zero-mean Gaussian Randomized Parameter having the standard deviation 𝜎, and 𝒫 𝑛 represents the level of noise in decibel watts.…”

“…Hence, from this the 𝛼 is evaluated using given as (12). 𝛼(𝑟) 𝑑𝐵 = 𝒫 𝑡 − 𝒫ℒ(𝑟 0 ) − 10 𝑛 log 10 ( 𝑟 𝑟 0 ⁄ ) − 𝒳 𝜎 − 𝒫 𝑛 (12) Where, 𝒫 𝑡 represents the power essential for processing the packets, 𝒫ℒ(𝑟 0 ) represents that path-loss for a given distance which is apart from 𝑟 0 , 𝒳 𝜎 represents the zero-mean Gaussian Randomized Parameter having the standard deviation 𝜎, and 𝒫 𝑛 represents the level of noise in decibel watts. In this work, to solve the problem of multiple obstacles in the LoS, this work solves this issue by enhancing the shadowing component [22], [23] For developing an obstacle-aware RP model, in this work, we consider the adjacent MNs as the LoS obstacles.…”

“…These challenges can hinder reliable and timely video transmission, impacting communication and coordination efforts during disasters. Addressing these challenges requires innovative solutions such as adaptive transmission technique [11], optimized network infrastructure deployment [12], robust error correction mechanism [13], and efficient bandwidth management algorithm [14]. Overcoming these challenges is crucial to ensure effective video transmission that supports situational awareness, decision-making, and coordination in urban disaster management operations.…”

Cross layer protocol architecture for spectrum‐based routing in cognitive radio networks

“…Using novel and underutilised technologies from artificial intelligence, our approach partially answers the need to represent the layered protocol stack. We focus on BNs, a popular tool in machine learning that provides a visual depiction of the statistical correlations between random variables [18][19][20].…”