Krishnendu Guha

Multi-tier aerial communication networks (MACNs), integrating satellites, high-altitude platforms, and unmanned aerial vehicles, are emerging as a cornerstone of next-generation global connectivity. Their promise of resilient and ubiquitous coverage, however, is hindered by highly dynamic topologies, severe energy and computational constraints, environment-sensitive channels, diverse quality-of-service requirements, and limited real-world validation. Artificial intelligence (AI) has increasingly been explored as a flexible framework to address these challenges, enabling adaptive routing, distributed computing and task offloading, handover management, intelligent resource allocation, and large-scale network optimization. This survey provides a comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives. We critically evaluate representative approaches in terms of scalability, efficiency, data demands, and practical deployability, and identify emerging trends such as graph neural networks with reinforcement learning for dynamic routing, predictive learning for mobility management, and federated learning for distributed computation. Persistent challenges remain in lightweight edge intelligence, real-world testbeds, reproducible benchmarking, and simulation-to-deployment transfer. To address these issues, we offer a research roadmap emphasizing compressible and interpretable models, standardized benchmarks, realistic validation, and hybrid designs that balance adaptability with computational and energy overhead. Finally, we identify open challenges and future research directions, offering insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.

Polynomial multiplication is the most computationally demanding arithmetic operation used in many Post-Quantum Cryptographic (PQC) and Fully Homomorphic Encryption (FHE) algorithms. The Number Theoretic Transform (NTT) is the most efficient technique for performing polynomial multiplication in these schemes. However, at the implementation level, NTT designs used in PQC and FHE are vulnerable to information leakage due to intentional fault injection attacks. Preventing both intentional and unintentional faults has become a major concern for next-generation secure processors. In this regard, we introduce Full and Partial Recomputation–based Algebraic Invariant Checking (FR-AIC and PR-AIC) schemes to robustly safeguard the arithmetic operations of the NTT processing element (PE). The FR-AIC achieves a high fault detection rate, and the lightweight PR-AIC offers reduced hardware overhead at the cost of a slightly lower detection capability. The proposed architecture is scalable across different NTT variants, supporting arbitrary polynomial sizes ( n ), the number of polynomial coefficients) and data widths (log q ), thereby making it suitable for a wide range of PQC and FHE applications. Furthermore, the design is fully compatible with multi-PE parallel architectures, enabling efficient acceleration to meet high-throughput NTT performance requirements. Extensive simulations and fault-injection emulation of multiple NTT variants implemented on an Artix-7 FPGA for PQC and FHE applications show that the proposed fault detection mechanism efficiently detects nearly 100% of faults without introducing any latency overhead. The area and energy overheads of our schemes are tolerable for practical implementations of NTT.

In the present era, blockchain technology has been widely used to facilitate transparency and mitigate issues related to counterfeiting and piracy in the electronic supply chain (eSC). However, present state-of-the-art solutions do not provide sufficient information about tracking of the components in the eSC to users, who do not know about the technicalities of blockchain. We provide an approach to this using a token-based solution. Moreover, the existing models are expensive in terms of time, power, and energy, which we try to reduce by formulating a lightweight consensus algorithm. For this, we use a modified byzantine fault-tolerant mechanism that is more secure, less energy, and more power-consuming than the conventional RAFT consensus strategy, which most of the existing works adhere to. Finally, we introduce an open marketplace having a public possession record. This provides a one-stop solution for all queries of the users, related to the e-supply chain and its products. Such type of informative open marketplace is also not focused on existing works. For enacting these, we adhere to semi-fungible tokens, which are unique identification codes that are stored in a blockchain, and transferred between users via the blockchain to confirm ownership and ensure traceability. Experimentation has been performed on Hyperledger Fabric. During experimentation and result analysis, we obtain higher stabilization and fault tolerance than existing strategies. Additionally, we observe that fewer communication bits are required in our strategy, which implies that fewer messages need to be transferred between the nodes and an increase in throughput and reduction of latency of the network is obtained.