Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boost their susceptibility to cyberattacks. Consequently, the cybercrime rate is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit researchers in identifying appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, and healthcare. The role of advanced technologies like artificial intelligence, machine learning, cloud computing, edge computing, fog computing, and blockchain technology in cybercrime investigation is also discussed. Lastly, various open research challenges in IoT to assist cybercrime investigation are explained to provide a new direction for further research.

The Internet of Things (IoT) is commonly utilized for intelligent energy control, industrial automation, and a host of other applications. IoT sensors are installed in various stages of the smart grid (SG) to track and manage network statistics for safe and efficient power delivery. The challenges in the integration of IoT-SG must be overcome for the network to function efficiently. An IoT-based smart grid energy monitoring system depending on neuro-fuzzy is proposed in this paper. At the core of the operator, a wireless sensor network (WSN) is employed to calculate and transfer the necessary parameters for the prediction model. This project revolves around an IoT-based energy monitor, which can track and analyze electrical parameters, including current, voltage, active power, and load power consumption. For the collection of real-time electrical data from users, the IoT-based program is used. Based on this data, consumers and electric power companies in the SG model can better control their usage to minimize billing costs. The results obtained show that the performance of hybridized solar/wind power plants will be improved with the help of ANFIS controller to a great extent. Results indicate efficiency of 99.74% in the proposed ANFIS control system.

The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

Cyber threats involve unauthorized access, alteration, or deletion of private information, extortion, and disruption of business operations. Traditional network security methods need more scalability, data protection, and difficulty detecting advanced threats. The Explainable Artificial Intelligence with Ridgelet Neural Network and Soft Gated Recurrent Units (XAI-RNN-SGRU) is introduced to address these challenges. This model enhances financial data prediction and cybersecurity through advanced preprocessing for handling missing values and data imbalance, and also normalizes numerical features and encodes categorical variables. Enhanced Principal Component Analysis (EPCA) and Improved Fast Random Opposition-based learning Aphid Ant Optimization (AAO) are utilized for feature extraction and desired feature selection. Then, it combines the Ridgelet Neural Network with Gated Recurrent Units for accurate predictions and uses the Improved Homomorphic Encryption (IHE) process to reinforce data protection during computations. Comprehensive testing on three data sets reveals outstanding predictive performance with accuracies of 99.55%, 99.99%, and 99.95%, respectively. The introduced method improves data security, scalability, and efficiency in real financial applications. It outperforms current techniques with a bit error rate of 0.00004 and a data protection score of 98.03%, providing secure and reliable financial operations. In addition, it enhances efficiency in encryption and decryption, with rates of 9.25 and 8.30 seconds, respectively, and is exceptionally scalable for mass-market cybersecurity use. Such improvements offer a real-world solution for financial institutions, cybersecurity companies, and digital payment systems, guaranteeing strong fraud detection, improved data security, and enhanced operational efficiency compared to the current methodologies.