In this work, we devise a new 5-D hyperchaotic dynamo system by adding two feedback controllers to the Rikitake 2-disk dynamo system (1958). We show that the new 5-D hyperchaotic system does not possess any equilibrium point and deduce that the new 5-D system has a hidden hyperchaotic attractor. Using Multisim, we develop an electronic circuit design of the new 5-D hyperchaotic dynamo system for practical applications. We also exhibit the implementation of the new 5-D hyperchaotic dynamo system by using a field-programmable gate array (FPGA), which requires adders, subtractors and multipliers. The hardware resources are given for the application of three numerical methods, all of them providing results in good agreement with MATLAB simulations. As an application, we devise a dual core high speed hybrid true random number generator (TRNG) using Ring and Heun algorithm based on the new 5-D hyperchaotic oscillator on FPGA. Based on the hyperchaotic features of the proposed 5-D hyperchaotic dynamo system, we suggest a new encryption approach for colour images. Simulation outcomes of the presented encryption approach confirm that our chaotic system has good cryptographic properties and its usability in different cryptographic purposes.

Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.

In Wireless Sensor Networks (WSN), energy efficiency is a fundamental issue that requires attention in the design of communication protocols. Energy waste occurs as a result of collision and idle listening. The widely used mechanism for energy saving in WSN is duty cycling. Duty cycling coordinates the sleep/wake-up time of sensor nodes to maximize the network lifetime while achieving specific application goals such as high throughput or low latency. Most existing works focused more on static duty cycle, which cannot guarantee the desired end-to-end delay at varying network conditions. In applications with specified delay requirements, the duty cycle of every node should be adjusted separately at runtime depending on the network conditions to achieve the desired delay and energy efficiency. In this paper, we present an Energy Efficient and QoS-aware (EEQ) MAC protocol with a duty cycle scheme that adapts the node’s duty cycle to the queue size and priority class of a packet to reduce the delay of high priority packets and support time-bounded delivery of priority packets. By checking the queue size and the priority class of packets in the message queue of each node, the node determines whether or not to adjust its duty cycle. In this approach, a node increases the length of its active period in the event of high traffic which provides less waiting time for the packets in the queue. The sender node informs the receiver the duration for which it has to stay awake at the beginning of data transmission, both the sending and the receiver’s duty cycle is controlled based on the queue length and the priority of the packets. This approach saves energy and lessen packet latency. Finally, extensive simulation experiments were conducted to evaluate its energy performance within different network topologies. Comparisons with the existing energy-aware MAC protocol verified the effect of EEQ on improving the energy efficiency and extending the lifespan of WSNs.

In this work, we describe the model of a new 4-D hyperchaotic system with no balance point and deduce that the new hyperchaotic system has a hidden attractor. We present a detailed bifurcation analysis for the new hyperchaotic dynamo system with respect to the system parameters and also exhibit that the new hyperchaotic system displays multistability with coexisting attractors. Using NI Multisim 14.0, we design an electronic circuit for the implementation of the new 4-D hyperchaotic system and present the circuit simulation results. We also show the implementation of the new 4-D hyperchaotic system by using a field programmable gate array (FPGA). The hardware resources are reduced by designing single-constant multipliers, adders, subtractors and multipliers. The FPGA design is done for three numerical methods, namely: Forward-Euler, Backward-Euler and fourth-order Runge-Kutta. We demonstrate that experimental chaotic attractors are in good agreement with theoretical simulations. To verify the ability of the presented hyperchaotic system for designing robust cryptosystems, we suggest a novel image cryptosystem using the proposed hyperchaotic system. Simulation outcomes confirm the effectiveness of the proposed image cryptosystem, and consequently, the effectiveness of the proposed 4-D hyperchaotic system in designing diverse cryptographic purposes.

This article begins with a dynamical analysis of the Permanent Magnet Synchronous Generator (PMSG) in a wind turbine system with quadratic nonlinearities. The dynamical behaviors of the PMSG are analyzed and examined using Poincare map, bifurcation model, and Lyapunov spectrum. Finally, an adaptive type-2 fuzzy controller is designed for different flow configurations of the PMSG. An analysis of the performance for the proposed approach is evaluated for effectiveness by simulating the PMSG. In addition, the proposed controller uses advantages of adaptive type-2 fuzzy controller in handling dynamic uncertainties to approximate unknown non-linear actions.

The Internet of Things (IoT) has transformed various sectors by connecting devices, services, and applications, which boosts intelligence and operational efficiency. However, this growing connectivity has heightened concerns regarding data privacy and security, particularly for sensitive information. Intrusion detection systems (IDSs) have emerged as an effective strategy through incorporating machine learning (ML) methodologies to identify sophisticated cybersecurity threats. Traditional IDS solutions typically rely on centralized learning architectures, where data from edge nodes are transferred to central servers for analysis. This approach is effective but introduces major risks to the privacy of IoT data. Artificial intelligence (AI), specifically federated learning (FL), is a new frontier that offers an alternative option by enabling direct implementation of distributed ML on IoT devices without requiring sharing of raw data. FL supports decentralized and privacy-preserving learning, which makes it a critical instrument for enhancing the security of IoT ecosystems. Research on data security and privacy in FL is increasingly becoming popular at present. However, the development of FL for IoT remains in its infancy and requires exploration in various areas. Moreover, current survey studies are limited and overlook the vital aspects of FL, particularly the diversity of datasets and the most recent methodologies employed in FL-enabled IDSs. This study conducts a systematic literature review that examines 43 studies published between 2020 and 2024. The focus revolves on recent advancements in applying AI to intrusion detection within IoT environments. This study provides a comprehensive analysis of key ML models and datasets used to develop an IDS tailored for IoT systems. The review also explores emerging trends, challenges, and opportunities in implementing FL-based IDSs in IoT networks. The findings highlight the potential of FL to tackle pressing privacy and security challenges while enabling scalable and adaptive threat detection solutions. The study highlights important research gaps and proposes critical future directions to advance the field.