The convergence of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) is expected to enhance sensing coverage, connectivity, and resilience in distributed smart grids, especially in remote or infrastructure-sparse regions. In this UAV-assisted, IoT-enabled paradigm, UAVs act as aerial relays that collect, aggregate, and forward sensing data between ground devices and control centers. However, privacy-preserving data aggregation (PPDA) in such settings still faces key-escrow vulnerabilities, certificate management overhead, incomplete privacy protection, and high computational and energy costs, particularly for signature verification at UAV relays and decryption at control centers. To address these challenges, we propose LCE-PPDA, a lightweight, certificateless, and escrow-free PPDA scheme tailored for UAV-assisted, IoT-enabled smart grids. LCE-PPDA eliminates key escrow through joint key generation, adopts a hierarchical timing structure with macro-interval rekeying and micro-interval reporting, and supports ciphertext-level in-network aggregation with both individual and batch authentication at UAV relays. To ensure privacy with accountability, it integrates certificateless signatures, dynamic pseudonyms, and session-bound key masking, achieving end-to-end confidentiality, conditional anonymity, unlinkability, and accountable traceability. Formal analysis shows that LCE-PPDA achieves correctness and EUF-CMA security in the random-oracle model under the ECDLP assumption against both Type-I and Type-II adversaries. Performance evaluation further demonstrates that LCE-PPDA reduces computational, communication, and energy overheads compared with representative schemes, providing a scalable and lightweight foundation for secure, privacy-preserving data aggregation in UAV-assisted, IoT-enabled smart grids.

Timely and secure emergency message delivery is critical to resilient smart-grid operation and rapid disturbance response. However, existing schemes remain inadequate, leaving smart grids vulnerable to security and privacy threats and causing verification bottlenecks, particularly when nonlinear emergency measurements cannot be homomorphically aggregated, which prevents bandwidth-efficient in-network aggregation and scalable batch verification. We propose EF-CPPA, an escrow-free, conditional privacy-preserving authentication scheme for real-time emergency messaging in smart grids. EF-CPPA enables smart meters to deliver authenticated emergency messages to the CC via power gateways verifiable as legitimate relays, while ensuring the confidentiality, integrity, and unlinkability of embedded nonlinear measurements. EF-CPPA further provides conditional anonymity with accountable tracing, as well as origin authentication, intra-domain verification, and scalable batch verification under bursty multi-meter messaging. An ECDLP-based escrow-free key-generation mechanism reduces reliance on the CC and enables efficient node joining and revocation. Security analysis shows that EF-CPPA achieves existential unforgeability under chosen-message attacks (EUF-CMA) and satisfies the stated security and privacy requirements. Performance evaluation demonstrates low computational, communication, energy, and node-management overhead, making EF-CPPA suitable for security-critical, time-sensitive smart-grid emergency messaging.

The emergence of unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) represents a new paradigm of intelligent monitoring that transcends geographical limitations. However, UAVs are highly sensitive to disruptive factors, and their failure can easily trigger cascading failures due to a surge in traffic. To address this, this article proposes a cascading failure model to help understand cascading risks, incorporating the unique online and offline modes of UAVs. For the issue of IoT devices not being fully covered after a cascading failure, a topology-control-based recovery scheme is proposed. Surviving UAVs are divided into two categories, where backbone UAVs (BUAVs) hover at specific positions to form a reliable backbone network spanning the service area, and cruising UAVs patrol nearby to achieve extensive coverage. To provide a solution, the optimization model is transformed into two stages, and approximate algorithms are developed separately. The improved combination of cuckoo search and a density-based clustering algorithm is employed to determine the positions for BUAVs. Meanwhile, a multistep approach utilizing the nondominated sorting genetic algorithm II is introduced to optimize the cruising paths of UAVs. Case study shows that the proposed cascading failure model is reasonable; the proposed recovery scheme can achieve up to 91% coverage, with most IoT devices covered by cruising UAVs maintaining more than half of their service time; the recovered network can still maintain 50% of IoT devices within coverage under up to 20% link failure. These findings offer network managers practical insights for enhancing network resilience.

The advancement of space technology has positioned LEO satellite networks as a pivotal solution for global digitalization. However, satellites can fail due to various internal and external threats. Their limited buffer capacity and processing ability can exacerbate failures, posing a risk of cascading failures to the entire network. To delve deeper into the cascading process of LEO satellite networks, we have developed a dynamic cascading failure model. We propose a method to calculate the minimum time required for satellite movement to cover discrete zone units on Earth. By considering the dynamic changes in satellite coverage, we model the probability of user data reaching satellites using a Poisson process. Additionally, we have established models for satellites’ processing ability and buffer capacity to depict a more realistic satellite state transition. Satellites can become congested or even overloaded when they receive an excessive amount of data and require a corrective phase before returning to standard operations. We present a dynamic cascading algorithm, ensuring that the sequence of observations doesn’t impact the final cascading process outcome. Through detailed case studies involving three constellations, we found that cascading failures tend to propagate mainly to satellites within two hops. Smaller constellations are more susceptible to periodic cascading failures, while mega-constellations might experience rapid, severe avalanche effects. These findings offer practical insights for satellite network managers aiming to mitigate cascading risks.