This research work proposes a method for the securing and monitoring of petroleum product distribution records in a decentralized ledger database using blockchain technology. The aim of using this technique is to secure the transaction of distributed ledgers in a database and to protect records from tampering, fraudulent activity, and corruption by the chain participants. The blockchain technology approach offers an efficient security measure and novel advantages, such as in the transaction existence and distribution ledger management between the depot, transporter, and retailing filling station. Others advantages are transparency, immunity to fraud, insusceptibility to tampering, and maintaining record order. The technique adopted for this secure distributed ledger database is crypto hash algorithm-1 (SHA-1)-based public permissioned blockchain and telematics, while this telematics approach is an embedded system integrated into an in-vehicle model for remote tracking of geolocation (using Global Positioning System (GPS)), monitoring, and far-off data acquisition in a real-time. The scope of the data in the secure distributed ledger database (using blockchain) developed are identification (ID) of the tanker operator, Depot name, Source station ID, Destination station ID, Petroleum product volume, Transporter ID, and Geographic automobiles location. This system proved to be efficient, secure, and easy to maintain as it does not permit any individual for records tampering, but supports agreement of ~75% of participants in the chain to make changes.

The rapid migration of electrical grids towards Smart Grid (SG) architectures produces high-dimensional data streams that must be processed intelligently in real time. Traditional modelling, optimisation, and control approaches cannot handle the growing complexity of contemporary power systems. This comprehensive study aims to provide a synopsis of AI applications in smart grid settings for load forecasting, transient and voltage stability analysis, fault detection, and cybersecurity. This work provides a unified analytical framework that links supervised, unsupervised, reinforcement, and ensemble learning techniques to specific smart grid operational problems, in contrast to prior surveys that address these areas separately. The main contributions are (i) a structured AI-smart grid functional taxonomy; (ii) critical analysis of the challenges of data quality, model interpretability, computational scalability and adversarial security; and (iii) a roadmap for explainable, reliable and carbon-neutral intelligent energy systems. Compared with more conventional methods, AI-based solutions often outperform them in prediction accuracy, stability, categorisation speed, and defect identification accuracy. However, there are major challenges in real-world deployment, standardisation and regulatory compliance.

The fundamental goal of energy harvesting systems is to reduce the need for a wired power supply or battery replacements. Radio Frequency (RF) energy harvesting has been established as a viable alternative for powering mobile devices without increasing greenhouse gas (GHG) emission which is a threat to the environment. However, there are challenges facing the effective harvesting of appreciable energy for these devices. Low RF power harvestable from various sources and low radio frequency-direct current (RF-DC) conversion efficiency have made it a very difficult task to harvest sufficient power to drive mobile devices such as smartphones. Lower frequency RF sources could yield appreciable harvestable energy but this comes with the challenge of portable antennas that could match these frequencies. This paper presents various RF energy harvesting techniques in literature and discusses some of the difficulties encountered by researchers while designing RF energy harvesting circuits. The significance of adopting alternative renewable sources to power mobile devices in the face of the looming global energy crisis while avoiding global warming was highlighted. Additionally, suggestions for future work aimed at harvesting enough energy to power smartphones and other mobile devices were presented.