A study on channel and delay-based scheduling algorithms for live video streaming in the fifth generation long term evolution-advanced

This paper will investigate the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in the legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay, and maximizing network throughput are shown via simulations. As the M-LWDF having a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-

Autonomous underwater vehicle in internet of underwater things: A survey

Water, mostly oceans, covers over two-third of the earth. About 95% of these oceans are yet to be explored which includes 99% of the sea-beds. The introduction of the Internet of Underwater Things (IoUT) underwater has become a powerful technology necessary to the quest to develop a SMART Ocean. Autonomous Underwater Vehicles (AUVs) play a crucial role in this technology because of their mobility and longer energy storage. In order for AUV technologies to be effective, the challenges of AUVs must be adequately solved. This paper provides an overview of the challenges of IoUT, the contributions of AUVs in IoUT as well as the current challenges and opening in AUV. A summary and suggestion for future work was discussed

A study of channel and delay-based scheduling algorithms for live video streaming in the fifth generation long term evolution-advanced network

This paper investigates the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay and maximizing network throughput, are shown via simulations. As the M-LWDF has a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-A

Development of a near-infrared (NIR) forearm subcutaneous vein extraction using deep residual u-net

Impotence to locate the forearm subcutaneous vein leads to multiple intravenous (IV) attempts causing pain and injuries to patients such as bruise or vein damages. Various technologies and techniques were proposed and developed to overcome the multiple IV access problems. The standard techniques used in research and hospitals are Transillumination, Ultrasound Imaging, and Near-Infrared (NIR). Among those techniques, NIR is the most optimal way of locating the subcutaneous vein because of its non-invasive properties, low-cost implementation. The device can be assembled in a small size product. Nevertheless, the NIR forearm images contain noises that cause difficulties in extracting the vein features. Hence, the performance of NIR vein extraction is having the bottleneck of detecting the vein pixel accurately. Many research studies have been conducted to work on the NIR forearm subcutaneous vein detection due to such a limitation. Artificial intelligence is one of the powerful technology that would benefit this study. However, a limited number of articles were found on the patentability search, and thus we propose an automatic vein extraction algorithm using Deep Residual U-Net architecture. Our algorithm shows 75 percent of the accuracy in extracting the NIR vein from the experiments that tested. These results show the evidence that the Deep Residual U-Net can be applied to extract the NIR vein

Microsleep accident prevention for SMART vehicle via image processing integrated with artificial intelligent

Number of accidents caused by microsleep increases rapidly each day. This is due to the current trend of life, for example high workload, long working hours, traffic jams, having too much caffeine, drinking alcohol, age factor, and many others. This microsleep can lead to major accidents, higher number of deaths, injuries, demolition of property and permanent disability. The creation of SMART Vehicles in the Internet of Things (IoT) increases the technology capabilities in transportation sectors, in addition to reduce the number of crashes on the roads. An integration with Artificial Intelligent (AI) can be a perfect combination on development of a microsleep detection and prevention. While the image processing will be used as the method of detecting the face changes from normal to microsleep symptoms on detecting the eye degree, the head motion and the mouth yawning. This work presented a review of current research that supported the integration of IoT and AI. The analysis and discussion on the best solution and method to prevent microsleep accidents was shown. Lastly, recommendation on development of real sensors for SMART Vehicles will be discussed. A preliminary result on this work also will be shown

Development of IPv6 network with location assisted transfer for real time applications

An approach is presented to develop a system that has a location tracking mechanism to track the position of tile mobile unit (such as WiFi enabled devices or POA) so that a device switching can be done to switch session from one device to another in order to provide roaming in an IPv6 network. In this work an IPv6 framework will be formulated and set up as a read lest bed. Meanwhile a location tracking mechanism will be deve loped and Integrated into the IMS IPv6 network with real time application and soft switch module to ensure continuous muft im€dia communication in the Internet whIle roaming. The proposed location tracking will be based on Received Signal Strength Indicator (RSSI). An accurate Path Loss Exponent will be calculated based on the RSSI and accurate pos itioning will be de/ermined for soft switching of devices. The IMS based on IPv6 network will be developed and integrate with the location tracking system. The location tracking will be purely software based with minimum hardware dependen

Optimizing Blockchain Consensus: Incorporating Trust Value in the Practical Byzantine Fault Tolerance Algorithm with Boneh-Lynn-Shacham Aggregate Signature

The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen from among them based on which node has the highest trust value, it transforms the BLS signature process into the information interaction process between nodes. Consequently, communication complexity is reduced, and node-to-node information exchange remains secure. The simulation experiment findings demonstrate that the IBFT consensus method enhances transaction throughput rate by 61% and reduces latency by 13% when compared to the PBFT algorithm

Indoor location based tracking using Euclidean distance estimation (LTS-ED)

To provide location-based services like indoor navigation systems or indoor crowd monitoring systems in an indoor environment, indoor location tracking systems are required. The implementation of an indoor location tracking system employing Wi-Fi signal strength will be the main topic of this study. However, there are several methods for tracking an indoor location, including fingerprinting, triangulation, and trilateration. The proposed system was created using a fingerprinting technique. The server, which is based on Java, and the client, which is based on Android, make up the system's two primary parts. Regarding Wi-Fi RSSI scanning, the client's application oversees removing weak received signal strength (RSSI), more specifically RSSI lower than −85dBm, while the server provides the location ID with the highest likelihood of matching based on offline training data kept in a database. The software algorithm powered by MySQL will be the only foundation for the indoor location monitoring system. The findings demonstrate that smaller-area location tracking provides more accuracy than larger-area tracking

360-Degree Video Bandwidth Reduction: Technique and Approaches Comprehensive Review

Recently, the usage of 360-degree videos has prevailed in various sectors such as education, real estate, medical, entertainment and more. The development of the Virtual World “Metaverse” demanded a Virtual Reality (VR) environment with high immersion and a smooth user experience. However, various challenges are faced to provide real-time streaming due to the nature of highresolution 360-degree videos such as high bandwidth requirement, high computing power and low delay tolerance. To overcome these challenges, streaming methods such as Dynamic Adaptive Streaming over HTTP (DASH), Tiling, Viewport-Adaptive and Machine Learning (ML) are discussed. Moreover, the superiorities of the development of 5G and 6G networks, Mobile Edge Computing (MEC) and Caching and the Information-Centric Network (ICN) approaches to optimize the 360- degree video streaming are elaborated. All of these methods strike to improve the Quality of Experience (QoE) and Quality of Service (QoS) of VR services. Next, the challenges faced in QoE modeling and the existing objective and subjective QoE assessment methods of 360-degree video are presented. Lastly, potential future research that utilizes and further improves the existing methods substantially is discussed. With the efforts of various research studies and industries and the gradual development of the network in recent years, a deep fake virtual world, “Metaverse” with high immersion and conducive for daily life working, learning and socializing are around the corner