Wireless Fidelity or Wi-Fi, has completely transfigured wireless networking by offering a smooth connection to the internet and networks, particularly when dealing with enclosed environments. As with the majority of wireless technology, it functions through radio communication. This makes it possible for Wi-Fi to operate effectively close to an Access Point. However, a device’s ability to receive Wi-Fi signals can vary greatly. These discrepancies arise because of impediments or motions between the device and the access point. We have creatively used these variances as unique opportunities for applications that can be used to detect movement in confined areas. As this approach makes use of the current wireless infrastructure, no additional hardware is required. These applications could potentially be leveraged to enable sophisticated robots or enhance security systems.

Critical infrastructure and industrial systems are both becoming more and more networked and equipped with computing and communications tools. To manage processes and automate them where possible, Industrial Control Systems (ICS) manage a variety of components, including monitoring tools and software platforms. More complicated data is now being run on the networks, including data(past), money(present), and brains (future). In order to predictably detect specific services and patterns (deep learning) and automatically check authenticity and transfer value (blockchain), deep learning and blockchain are integrated into the ICS network. Hence, we conducted a thorough examination of the models published in the literature in order to comprehend how to integrate machine learning and blockchain efficiently and successfully for intrusion detection services. We also provide useful guidance for future research in this area by noting significant issues that must be addressed before substantial deployments of IDS models in ICS.

Passwordless authentication has revolutionized secure merchant payments, discarding reliance on passwords and PINs. Utilizing W3C Web Authentication (WebAuthn) and Client to Authenticator Protocol (CTAP), it employed public key cryptography to uniquely verify a user’s device and identity, constituting the FIDO authentication standard. Its growing popularity prompts widespread adoption, yet device attestation ties it to a specific device, hindering user flexibility. FIDO Passkeys attempt to address this by synchronizing cryptographic keys, enabling passwordless authentication from any device. However, Passkeys face challenges like proprietary encryption algorithms, reliance on specific cloud providers, and suboptimal cross-platform key synchronization. To overcome these limitations, this paper introduces a novel private key management system—Transferable User Secret on Hardware Key (TUSH-Key). TUSH-Key facilitates seamless, cross-platform synchronization for passwordless logins, aligning with FIDO2 specifications, thereby addressing the drawbacks of FIDO Passkeys.

Sixth-generation (6G) mobile computing is a wireless, cutting-edge technology that is made possible by the digital interconnectedness of everything (IoE). 6G connection depends on mobile edge and fog computing integration. Due to the mobility of various end users, task offloading in these computing devices is hard and unpredictable. The unexpected mobility of users and dynamic switching of networks from 5G to 6G make Mobile Fog Computing (MFC) benchmarks poor for task offloading. To overcome these inefficient task offloading in dynamic unpredictable 6G mobile environments, this paper presents an optimized Cyber Twin (CT) based solution for task offloading with reduced offload latency in MFC. The proposed solution is built on the CT based Fog Computing (CyTFC) system where the computation node predicts server state with a DRL-based Multi-Agent Actor-Critic (MA3C) framework and provides the training dataset for task offloading prediction with a reward function. Furthermore, a delay-efficient offloading scheme is also proposed at the CT fog computing nodes. The Lyapunov optimization function was also modified to lower long-term migration costs. Finally, a deep reinforcement learning method called Multi-Agent Actor-Critic (MA3C) is suggested to solve the multi-objective dynamic optimization issue. The proposed CT based solution exceeds existing state-of-the-art benchmarks with network fairness up to 92% and reduces offloading time, offloading failure rate, and task migration cost by 30%, according to the performance assessment findings from the thorough simulation.

Detection of abnormal events in the traffic scene is very challenging and is a significant problem in video surveillance. The authors proposed a novel scheme called super orientation optical flow (SOOF)‐based clustering for identifying the abnormal activities. The key idea behind the proposed SOOF features is to efficiently reproduce the motion information of a moving vehicle with respect to superorientation motion descriptor within the sequence of the frame. Here, the authors adopt the mean absolute temporal difference to identify the anomalies by motion block (MB) selection and localisation. SOOF features obtained from MB are used as motion descriptor for both normal and abnormal events. Simple and efficient K‐means clustering is used to study the normal motion flow during the training. The abnormal events are identified using the nearest‐neighbour searching technique in the testing phase. The experimental outcome shows that the proposed work is effectively detecting anomalies and found to give results better than the state‐of‐the‐art techniques.