Worker recruitment in mobile crowdsensing aims to recruit the most suitable users to perform tasks with high quality and in real-time. Many worker recruitment or task matching mechanisms have been proposed, especially for crowdsourcing platforms, where content information of tasks from the implicit feedback of workers' attendance is extensively exploited. However, tasks in mobile crowdsensing are usually time-sensitive and location-dependent which also play a crucial role in worker recruitment but have not been effectively explored. In this paper, we propose a novel personalized task-oriented worker recruitment mechanism for mobile crowdsensing based on a careful characterization of workers' preference. In particular, we fully exploit the content information (e.g., task category, task description) together with the context information (e.g., task time, task location) from the implicit feedback of workers' attendance to accurately model workers' preference on tasks. Moreover, we regard the task-worker fitness prediction as a binary classification problem and utilize the Logit model to integrate the heterogeneous factors into a single framework to predict the matching probability of each task-worker pair. Finally, the workers with the highest matching probability are recruited proactively for each new task. Extensive experiments on real-world datasets demonstrate that the proposed mechanism achieves better performance than the benchmark.

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. Cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today’s network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model’s validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall.

The collaborative demand in the Internet of Things (IoT) is becoming stronger. One of the collaborative challenges is the security of interoperability between different management domains. Although cross-domain access control mechanisms exist in IoT, the majority of them are based on a trusted third party. In addition, the heterogeneity of multidomain policies makes it difficult for authority delegation to satisfy the principle of least authority. In this paper, we propose a blockchain-based IoT cross-domain delegation access control method (CDDAC). The delegation-trajectory-on-blockchain strategy proposed enhances the scalability of the cross-domain delegation system. The presented multidomain delegation trajectory aggregation scheme supports the forensic analysis of the cross-domain delegation system. The performance of CDDAC is evaluated in the Ropsten, which is the Ethereum’s official public blockchain test network. The experimental results show that CDDAC has faster delegation verification speed and higher decision-making efficiency than existing work, demonstrating the lightweight and scalability of the method.

Eagerly anticipated, 6G networks are attributed with a variety of characteristics by researchers. A pivotal characteristic of 6G networks is the deep integration of sensing and networking, along with intelligent network applications operating on top of this infrastructure. To optimally harness the data collected by sensors distributed across various locations, the training paradigm of the new generation of 6G intelligence applications aligns naturally with the federated-learning paradigm. The exposure of gradients in federated learning to inversion attacks is a critical concern. To address this, cryptography-based secure aggregation methods are commonly implemented to protect the privacy and confidentiality of gradients. However, the semantic meaninglessness of encrypted data makes it difficult to assess the correctness, availability, and source legitimacy of participants’ data. In this paper, we propose a data attack detection framework for cryptography-based secure aggregation methods in 6G intelligence applications that address the security vulnerabilities associated with encrypted data obscurity. We employ a suite of encrypted-data-auditing techniques to prevent data-aggregation errors, data poisoning, and illegal data sources. Additionally, we have compared a series of promising security methods, analyzed, and provided recommendations for the most suitable security approaches in specific 6G scenarios.