Environmental monitoring systems generate large volumes of multivariate time series data from heterogeneous sensors, including those measuring soil, weather, and air quality parameters. However, sensor malfunctions and transmission failures frequently lead to missing values, compromising the performance of downstream analytical and predictive models. To address this challenge, this study presents a comprehensive and systematic evaluation of previously proposed hybrid architecture that interleaves Long Short-Term Memory (LSTM) layers with a Multi-Head Attention mechanism in a “sandwiched” setting (LSTM–Attention–LSTM) for robust multivariate data imputation in environmental IoT datasets. The first LSTM layer captures short-term temporal dependencies, the attention layer emphasises long-range relationships among correlated features, and the second LSTM layer re-integrates these enriched representations into a coherent temporal sequence. The model is evaluated using multiple environmental datasets of soil temperature, meteorological (precipitation, temperature, wind speed, humidity), and air quality data across missingness levels ranging from 10% to 90%. Performance is compared against baseline methods, including K-Nearest Neighbour (KNN) and Bidirectional Recurrent Imputation for Time Series (BRITS). Across all datasets, the Hybrid model consistently outperforms baseline methods, achieving MAE reductions exceeding 50% and reaching over 80% in several scenarios, along with RMSE reductions of up to approximately 85%, particularly under moderate to high missingness conditions. An ablation study further examines the contribution of each layer to overall model performance. Results demonstrate that the proposed Hybrid model achieves superior accuracy and robustness across datasets, confirming its effectiveness for environmental sensor data imputation under varying missing data conditions.

A Software‐Defined Network (SDN)‐based Vehicular Ad Hoc Network (VANET) plays a crucial role in Intelligent Transport Systems (ITS) by enhancing road safety for drivers and vehicles through the periodic exchange of messages and data related to traffic, vehicle status, and weather conditions. Additionally, it offers entertainment services for passengers. However, SDN‐based VANETs face security challenges, particularly in the central control unit, making them vulnerable to Distributed Denial‐of‐Service (DDoS) attacks, which can disrupt the entire network. Moreover, due to the programmability of SDN infrastructure, injection attacks can manipulate traffic or generate false crisis events. The network is also susceptible to various cyber threats, including man‐in‐the‐middle (MITM), tracking, and replay attacks, necessitating robust security measures. Several security frameworks have been proposed to mitigate these risks, but many authentication mechanisms suffer from high computational and communication costs or provide protection against specific attacks while remaining ineffective against others. To address these limitations, we introduce a hybrid security framework integrating an authentication system between the trusted authority (TA), lead vehicle (LV), and other vehicles, along with an intrusion detection system (IDS). The authentication process involves key generation by the TA, mutual authentication between the TA and LV, as well as between the LV and other vehicles, while ensuring secure encryption using the AES‐ECDH algorithm. To enhance security further, the proposed IDS utilises Fuzzy C‐Means clustering to detect malicious activities and network threats. Performance analysis demonstrates that our approach effectively improves security, privacy, and efficiency while maintaining a low computational overhead, outperforming existing solutions.

In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the “grid effect” caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the “grid effect”. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE.

Vehicular ad hoc networks (VANETs) are fundamental to intelligent transportation systems (ITS), enabling secure and low‐latency vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communication. Conditional privacy‐preserving authentication (CPPA) is essential for safeguarding message integrity and anonymity, yet traditional ECC‐ and pairing‐based CPPA schemes are both computationally intensive and vulnerable to quantum attacks. Although lattice‐based CPPA (L‐CPPA) schemes offer post‐quantum resistance and batch verification, their reliance on a single roadside unit (RSU) introduces verification bottlenecks and a single point of failure in dense traffic scenarios. To overcome these limitations, we propose a multi‐aggregator lattice‐based CPPA (MA‐LCPPA) framework that distributes verification tasks across cooperating RSUs and integrates a ( k , n )‐threshold traceability mechanism. This design significantly reduces verification delay, improves scalability and enhances fault tolerance while maintaining conditional privacy and post‐quantum security. Formal analysis demonstrates unforgeability, traceability and resilience against replay, impersonation, and collusion attacks under the hardness of the ISIS problem. Simulation results confirm that MA‐LCPPA reduces verification delay by over 50% and lowers RSU computation costs, with minimal communication overhead, making it a scalable and quantum‐secure solution for next‐generation vehicular networks.