Future Internet

A critical concern regarding the security vulnerabilities of Internet of Things (IoT) devices has been repeatedly highlighted in existing research. Considering resource limitations, the Internet-wide port scan (IWPS), a well-established vulnerability scan scheme, has recently gained attention for its applicability to IoT networks. There is an urgent need to develop Internet-wide scanning solutions that can achieve both high scanning rates and high reachability. In this paper, we focus on open scans and propose an Area-aware IWPS algorithm based on deep reinforcement learning (DRL). We first construct an average delay table based on the physical locations of the port scanners and the targets. To solve the problem efficiently, we formulate the problem as a Markov Decision Process (MDP) whose reward function is designed based on the average delay table. Then, a DRL-based algorithm is proposed to achieve efficient port scanning. Finally, we conducted a large number of experiments to verify the efficiency and reachability of the algorithm. Compared with the most popular open scan tool, Nmap, the scan rate of our proposed policy is 4–5 times faster, and the detection reachability increased by 6%.

Wireless sensor networks (WSNs) are integral components of smart environments. These allow monitoring and communication to take place autonomously across distributed sensor nodes. Nevertheless, they suffer from constrained resources that make them susceptible to routine-layer attacks. These specifically involve blackhole, flooding, selective forwarding attack traffic and normal traffic. The conventional machine learning and deep learning methods employed are effective in catering to these attacks, yet they have generalization issues when the network conditions are dynamic. The models are generally trained on the local features that make them more dependable and less interpretable. To overcome these issues, this paper proposes an attention-driven transformer encoder for tabular WSN traffic, designed for robust and interpretable intrusion detection in WSNs. The model represents the WSN features as sequential tokens and employs multi-head self-attention to capture global and local dependencies among sensor attributes and employs a multi-head self-attention for capturing the local and global dependencies among the sensor attributes. The framework integrated several components, including normalization, chi-square-based feature selection, and positional embedding. These are followed by multi-layer transformer encoding blocks for the feature fusion and subsequent classification. The framework has been evaluated on the publicly available WSN dataset. Results have been shown to attain an accuracy of 99.37%, which makes it outperform the traditional deep learning baseline models. The comparative analysis has shown the model to be superior in terms of generalization and reduced convergence time. It further offers enhanced interpretability that makes it a good fit to be deployed in real-world scenarios where resources can be constrained.

While 5G addresses extreme performance tiers, 3GPP Releases 17 and 18 RedCap fill critical mid-tier performance gaps for diverse applications like industrial sensors and consumer wearables. The existing academic literature remains fragmented, focusing on isolated metrics rather than a holistic synthesis. There is a significant need to integrate technical specifications with empirical industry data. This survey systematically reviews Release 17/18 specifications, integrating literature from 2021 to 2025. We consolidate academic simulations and industry empirical reports to facilitate a rigorous comparative analysis across critical performance indicators. Findings evaluate complexity reduction via bandwidth limitation, antenna reduction, and HD-FDD. We provide a comprehensive security threat matrix, mapping vulnerabilities like RACH spoofing and paging suppression to countermeasures. RedCap cannot match eMBB throughput or NB-IoT’s battery life. Consequently, legacy LPWA remains more suitable for simple, decade-long sensing tasks. This work contributes a novel use-case taxonomy and a security analysis. This study provides practitioners with actionable insights into complexity trade-offs and network security risks. Future research should prioritize AI-driven management and “zero-maintenance” IoT through advanced power-saving innovations.