Without physical boundaries, a wireless network faces many more vulnerabilities than a wired network does. IEEE 802.16 provides a security sublayer in the MAC layer to address the privacy issues across the fixed BWA (broadband wireless access). Several articles have been published to address the flaws in IEEE 802.16 security after IEEE 802.16-2001 was released. However, even the enhanced version IEEE 802.16-2004 does not settle all the problems and additional flaws emerge. In addition, we found that PKM (privacy and key management) protocols version 2 (PKMv2), proposed by recently released IEEE 802.16e, is also vulnerable to new attacks. In this paper, we first overview the IEEE 802.16 standard, especially the security sublayer, and then investigate possible attacks on the basic PKM protocol in IEEE 802.16 as well as in its other versions from related works and the newest PKMv2. We also give possible solutions to counter those attacks and verify our analysis using formal (BAN) logic.

IEEE 802.16 is the standard for broadband wireless access. The security sublayer is provided within IEEE 802.16 MAC layer for privacy and access control, in which the Privacy and Key Management (PKM) protocols are specified. This paper models the PKM protocols using Casper and analyzes the CSP output with FDR, which are formal analysis tools based on the model checker. Later versions of PKM protocols are also modeled and analyzed. Attacks are found in each version and the results are discussed.

Regional traffic flow forecasting is the key to the realization of intelligent transportation system. The existing traffic flow forecasting methods have problems such as insufficient Spatio-temporal Correlation modeling and ignoring the impact of weather factors, which lead to high prediction errors. Therefore, in this study, a multi-channel graph convolutional neural network (MGCN) was first established to analyze and express the spatial correlation of traffic flow on different dimensions, and a self-attention mechanism was used to weight the spatial correlation features of MGCN output. Next, an LSTM is built after the MGCN network layer to obtain temporal features, an Embedding layer is added to embed traffic flow temporal periodic features, and the Whale Algorithm (WOA) is introduced to find the global optimal LSTM network parameter combination, which is then applied to the prediction model. The performance of the model was tested using the public dataset PeMSD4 and corresponding weather data. Compared with prediction models that were not optimized by WOA and did not consider the influence of weather factors (GCN, LSTM, ASTGCN, etc.), the prediction errors RMSE, MAE, and MAPE of the final constructed prediction model were reduced, indicating that the MGCN WOALSTM model has better traffic flow prediction performance.

Multicast enables efficient large-scale content distribution and has become more and more popular in network service. Security is a critical issue for multicast because many applications require access control and privacy. This issue is more sensitive to wireless network, which is lack of physical boundaries. IEEE 802.16 is the standard for next generation wireless network, which aims to provide the last mile access for Wireless Metropolitan Area Network (WirelessMAN). Multicast is also supported in IEEE 802.16, and a Multicast and Broadcast Rekeying Algorithm (MBRA) was proposed in the standard as an optional function for secure multicast. However, this algorithm does not provide backward and forward secrecy. It is not scalable to a large group either. This paper reviews the above two deficiencies of MBRA and proposes revision to address these problems for Intra-BS multicast. We also propose algorithms for secure multicast in different scenarios of WirelessMAN besides its basic schema, including Inter-BS multicast, multicast for mesh network, and multicast during handover.

Crop diseases not only severely affect crop quality but also lead to significant reductions in yield. To address the challenge of accurate crop disease detection in complex environments, we propose a novel detection method based on the YOLO11 model, termed YOLO-MSCM. To enhance the extraction of small-object features, we introduce the MCSA module, which improves the model’s spatial feature perception. Additionally, a SimRepHMS module is designed to leverage local contextual information and strengthen multi-scale feature fusion. To improve the model’s adaptability and generalization capability, we employ DynamicConv, which dynamically adjusts channel weights based on input-specific patterns. For more accurate bounding box localization, we incorporate the WIoUv3 loss function to optimize box regression. Experimental results demonstrate that YOLO-MSCM achieves improvements of 6.1% in precision, 9.1% in recall, 6.2% in mAP@50, and 3.7% in mAP@50:95 compared to the baseline YOLO11n model. Comparative evaluations with several mainstream and state-of-the-art models further validate the superior detection performance of YOLO-MSCM, offering a reliable and effective solution for accurate crop disease detection in complex scenarios.