Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented.

Blockchain is a technology that has been gaining popularity in the recent years thanks to its immutability and decentralized features, various industries have started to integrate the technology with their business models, and this resulted in various types of blockchain networks being developed. This paper reviews the latest academic advancements of sharding techniques in the various types of sharded blockchain networks that have been implemented in the past 5 years to identify their strengths and weaknesses. The increasing volume of transactions in blockchain networks has caused issues in terms of their scalability, transaction efficiency, storage, and security which obstructs the global adoption of blockchain technology. Sharding in blockchain is a solution that aims to solve the issues through its various types of sharding techniques such as network, transaction, or state sharding along with the various types of networks and systems. The general definition of sharding is to partition the network into subsets known as shards which allows them to validate transactions parallelly and thus, resulting in performance improvement and scalability improvement of the existing network. The existing sharding techniques (before 2020) and latest sharding techniques (2020 – 2024) along with their characteristics, advantages, and disadvantages are also discussed. An analysis based on the performance evaluation of recent sharding techniques was done to further understand the impact brought by the techniques. The result of the review sheds critical insights on the advancements in sharding techniques and fosters innovation and facilitates the widespread adoption of sharding-based solutions in the blockchain ecosystem.

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 high-resolution 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.

The Fourth Industrial Revolution (4IR) has ushered in a new era of efficiency across various domains, including the management of Road Traffic Congestion (RTC) in metropolitan cities. Numerous studies have elucidated how smart urban traffic management systems (TMS), encompassing 4IR technologies like Artificial Intelligent (AI), Machine Learning (ML), Internet of Things, and even Software-Define Networks (SDN) or Blockchain can optimize traffic flow and mitigate congestion. Given these results, there is a pressing need to evaluate these approaches and innovations in terms of their performance and sustainability. This review aims to scrutinize current technologies predicated on intelligent traffic management. It will examine the methodologies employed, the benefits derived from these systems, and potential challenges that may arise. The study has identified and categorized research based on the TMS procedure from the sensing process, traffic data evaluation, and solution developments. By providing a comprehensive overview of state-of-the-art in intelligent traffic management, this review seeks to contribute to the ongoing discourse on leveraging 4IR technologies for sustainable urban development.

This paper investigates the adoption and impact of Blockchain decentralized applications (DApps) across various sectors in Malaysia, with a particular emphasis on their role in government agencies. The study aims to enhance transparency, efficiency, and security in both public and private sectors through the strategic implementation of Blockchain technology. Addressing the critical issue of underutilization in Malaysia, the research encompasses an extensive literature review from databases such as IEEE Xplore and Springer. This review integrates scholarly articles, conference papers, and technical reports on Blockchain across different Malaysian industries. The objective is to provide a holistic analysis of the current applications and explore the potential of Blockchain DApps to revolutionize industry operations, government functions, and drive national development. The research is expected to yield a comprehensive understanding of Blockchain's diverse applications in Malaysia, highlight key challenges hindering adoption, and suggest opportunities for sector-wide integration. The findings are anticipated to have significant implications for a range of stakeholders, including policymakers, technologists, entrepreneurs, and academics. By informing strategic decision-making and fostering dialogue on Blockchain technology, this study aims to contribute to a more efficient, transparent, and secure operational landscape across Malaysia's key sectors, thereby enhancing the nation's competitive edge in the digital economy.