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Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes. A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection. Recent investigations have explored cutting-edge methods, such as leveraging blockchain for transaction recording to enhance security and privacy, along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Moreover, the analysis indicates that encryption and hashing techniques are prevalent in the data plane, whereas access control and certificate authorization are prominently considered in the control plane, and authentication is commonly employed within the application plane. Additionally, this paper outlines future directions, offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem. [ABSTRACT FROM AUTHOR]

This study explores the efficacy of vehicle-assisted monitoring for bridge damage assessment, emphasizing the integration of diverse sensor data sources. A novel method utilizing a deep neural network is proposed, enabling the fusion of fixed sensors on bridges and onboard vehicle sensors for damage assessment. The network offers scalability, robustness, and implementability, accommodating various measurement types while handling noise and dynamic loading conditions. The main novel aspect of our work is its ability to extract damage-sensitive features without signal preprocessing for future bridge health monitoring systems. Through numerical evaluations, considering realistic operational conditions, the proposed method demonstrates the capability to detect subtle damage under varying traffic conditions. Findings underscore the importance of integrating vehicle and bridge sensor data for reliable damage assessment, recommending strategies for optimal monitoring implementation by road authorities and bridge owners. [ABSTRACT FROM AUTHOR]

Differential modulation has recently been proposed for wireless relay networks. However, it suffers from a 3-dB performance loss as in point-to-point communication systems. In this paper, we propose a new differential modulation scheme, which is referred as generalized differential modulation (GDM) for wireless networks with amplify-and-forward relaying. In the proposed scheme, each frame transmitted by the source is divided into several blocks, and each block consists of one reference symbol and several normal symbols. The reference symbol in each block is differentially encoded based on the previous reference symbol, whereas the normal symbols are differentially encoded based on the reference symbol in the same block. At the destination, the signals from the source and the relays are combined to improve communication reliability. The proposed scheme has the advantage of allowing power allocation among the reference symbol and the normal symbols to improve the system performance. Numerical results show that the proposed GDM scheme can perform very close to the coherent receiver with perfect channel state information (CSI) in slow-fading channels. Index Terms--Amplify-and-forward (AF), differential modulation, power allocation, wireless relay networks.