Blockchain is recognized for its robust security features, and its integration with Internet of Things (IoT) systems presents scalability and operational challenges. Deploying Artificial Intelligence (AI) within blockchain environments raises concerns about balancing rigorous security requirements with computational efficiency. The prime motivation resides in integrating AI with blockchain to strengthen IoT security and withstand multiple variants of lethal threats. With the increasing number of IoT devices, there has also been a spontaneous increase in security vulnerabilities. While conventional security methods are inadequate for the diversification of IoT devices, adopting AI can assist in identifying and mitigating such threats in real time, whereas integrating AI with blockchain can offer more intelligent decentralized security measures. The paper contributes to a three-layered architecture encompassing the device/sensory, edge, and cloud layers. This structure supports a novel method for assessing legitimacy scores and serves as an initial security measure. The proposed scheme also enhances the architecture by introducing an Ethereum-based data repositioning framework as a potential trapdoor function, ensuring maximal secrecy. To complement this, a simplified consensus module generates a conclusive evidence matrix, bolstering accountability. The model also incorporates an innovative AI-based security optimization utilizing an unconventional neural network model that operates faster and is enhanced with metaheuristic algorithms. Comparative benchmarks demonstrate that our approach results in a 48.5% improvement in threat detection accuracy and a 23.5% reduction in processing time relative to existing systems, marking significant advancements in IoT security for smart cities.

The Advanced Encryption Standard (AES) stands as a cornerstone in contemporary data protection, securing everything from enterprise storage and web traffic to Internet of Things (IoT) devices and high-performance cloud infrastructures. This paper offers a panoramic survey of AES, from its historical foundations and architecture through an exploration of its Substitution-Permutation Network (SPN) design, mathematical foundations in Galois Field arithmetic, and evolution since NIST’s public competition that selected Rijndael. Emphasis is placed on the algorithm’s resistance to classical cryptanalysis such as differential and linear attacks while recognizing that side-channel threats, including power analysis and timing leakage, can undermine even robust ciphers if implementations are flawed. Practical implementations and real-world applications are examined across software libraries, specialized hardware accelerators like Field-Programmable Gate Arrays (FPGAs), and lightweight adaptations for constrained environments, illustrating how factors such as performance, cost, and energy consumption must be balanced against potential vulnerabilities. Subsequently, we compare AES with alternative block and stream ciphers (e.g., 3DES, ChaCha20), underscoring AES’s continuing dominance in both regulated industries and modern cryptographic protocols. Key management emerges as a linchpin, where flawed lifecycle practices or insecure key storage can jeopardize AES’s theoretical security. Finally, the paper addresses looming challenges, chief among them the advent of quantum computing, which motivates hybrid cryptographic schemes that combine AES with post-quantum algorithms. By highlighting cutting-edge research on side-channel mitigations, post-quantum integration, and advanced masking techniques, this survey reaffirms AES’s pre-eminence while mapping out the critical avenues of innovation needed to preserve its resilience in an evolving threat landscape.

A typical Wireless Sensor Network (WSN) defines the usage of static sensors; however, the growing focus on smart cities has led to a rise in the adoption of mobile sensors to meet the varied demands of Internet of Things (IoT) applications. This results in significantly increasing dependencies towards secure storage and effective resource management. One way to address this issue is to harness the immutability property of the Ethereum blockchain. However, the existing challenges in IoT communication using blockchain are noted to eventually lead to symmetry issues in the network dynamics of Ethereum. The key issues related to this symmetry are scalability, resource disparities, and centralization risk, which offer sub-optimal opportunities for nodes to gain benefits, influence, or participate in the processes in the blockchain network. Therefore, this paper presents a novel blockchain-based computation model for optimizing resource utilization and offering secure data exchange during active communication among mobile sensors. An empirical method of trust computation was carried out to identify the degree of legitimacy of mobile sensor participation in the network. Finally, a novel cost model has been presented for cost estimation and to enhance the users’ quality of experience. With the aid of a simulation study, the benchmarked outcome of the study exhibited that the proposed scheme achieved a 40% reduced validation time, 28% reduced latency, 23% improved throughput, 38% minimized overhead, 27% reduced cost, and 38% reduced processing time, in contrast to the existing blockchain-based solutions reported in the literature. This outcome prominently exhibits fairer symmetry in the network dynamics of Ethereum presented in the proposed system.

In this paper, a Schottky Barrier MOSFET (SB-MOSFET) based on Impact Ionization mechanism is used to design a leaky integrate and fire (LIF) neuron with considerable enhancement in area, energy and cost is proposed. Using 2D calibrated simulation, we confirmed that SB-MOSFET LIF is able to replicate the neuron behavior precisely without using external circuitry. The proposed LIF neuron shows significantly lower energy per spike of 4 pJ/spike, which is lowest among the single transistor based neurons present in the literature. The recognition precision of 89.2% has been accomplished for Modified National Institute of Standards and Technology (MNIST) image. Besides this, SB-MOSFET doesn’t require any doped regions, therefore it can be fabricated with low thermal budget.