In prior research, spatial coverage has been the primary metric for assessing the quality of mobile network data, with various participant selection strategies developed for data brokers operating within budget constraints. This paper introduces a novel data participant service quality model that prioritizes participant privacy while ensuring creditworthiness and participation willingness. This paper proposes a selection strategy for mobile network data participants that operates under the principles of local differential privacy, designed to maximize the marginal gain under a cost budget constraint. Drawing upon the principle of marginal gains from economics, our strategy advocates for the selection of participants whose marginal gains exceed their associated costs, thereby optimizing the selection process based on relative marginal costs. This paper formalizes this approach as the marginal problem of data participant selection in mobile network data markets, constructing a model and providing theoretical analysis to establish its NP-hard. To address this problem, we develop the Random Response Adaptive Algorithm (RRAA), designed to select participant groups that maximize mobile network data gains. Extensive experiments on both real and simulated datasets demonstrate the algorithm's efficiency and effectiveness. Additionally, to safeguard participant privacy, we propose the Random Response Self-Adaptive Algorithm (RRSAA). Our experimental evaluation on real datasets not only validates the algorithm's privacy-preserving capabilities but also elucidates the impact of privacy preservation parameters on marginal gains.

The generation and defense of text adversarial samples are crucial for improving the robustness and security of social Internet of Things (SIoT) systems, as the exchange of information between devices in SIoT relies heavily on NLP technology. However, the discrete nature of text data leads to a lack of contextual integration in current mainstream adversarial sample generation methods based on text replacement. This results in poor stealth of the generated samples and inefficiencies due to excessive queries to the target model. Meanwhile, defense methods like adversarial training are insufficiently universal and generalizable to handle the diverse and complex range of adversarial attack strategies. This article proposes a contrastive learning-based method for generating text adversarial samples and a mutual information regularization-based method for defending against text adversarial samples, tailored to the characteristics of devices in SIoT systems and the challenges mentioned above. The proposed method leverage keyword localization, optimal perturbation, and candidate set evaluation to enhance the effectiveness of adversarial samples. Additionally, by combining mutual information measures, statistical estimation functions, and idempotent constraints, the model itself is equipped with effective defenses against adversarial samples. Compared to the baseline, the proposed method significantly reduce the magnitude of perturbations and the number of access attempts to the original samples, while greatly increasing the attack success rate. When applying the proposed adversarial sample defense method, the model’s accuracy showed a significant improvement after being subjected to adversarial attacks.

The Social Internet of Things (SIoT) system enables connectivity among smart devices by integrating social networks with the Internet of Things. This integration is essential for advancing intelligent services and applications, as well as enhancing the commercial value of data. Rational task and data deployment strategies allow different types of devices to perform optimally in their areas of expertise, reducing network load, improving the timeliness of data processing, and ensuring efficient collaboration across the entire system. However, the limited computational capacity and network bandwidth of SIoT devices result in communication delays that significantly impact job responsiveness and energy consumption. The heterogeneity in computational power and bandwidth across different devices leads to resource overload or underutilization with traditional data partitioning methods, further impacting the performance of SIoT systems. This article proposes an intelligent task and data deployment method to address these issues. The proposed method abstracts the job execution process as JobGraph instance model and optimizes the mapping relationship of operators within TaskProcess. Additionally, an improved linear programming model is used to optimize the data distribution ratio among operators in heterogeneous computing environments. The proposed task deployment method achieves an average improvement of 19.6%–30.2% in job efficiency while reducing interdevice data transmission by 34.2%. In heterogeneous computing environments, the combination of the two deployment optimization methods further reduces job execution time, achieving efficiency improvements of over two times in optimal scenarios.

Label-constrained reachability (LCR) has been ex-tensively studied. However, these studies have neglected two aspects: the label sequence and time-dependent properties. When processing reachability queries, not only label presence but also label sequence and time-dependent properties should be considered. Various real-world scenarios, including vehicular networks, computing networks, and biological networks, require such queries. In this paper, we present a formal definition of time-dependent label-constrained reachability (TDLCR) queries based on LCR. These queries require both label sequence and time-dependent constraints to be considered, thus introducing a higher level of complexity. To address this challenge, we propose two indexing algorithms that are optimized for the label constraint: OneL and TD2H. OneL builds a single-label index for each vertex and provides a baseline for solving the TDLCR problem. TD2H is based on classical 2-hop index with excellent query efficiency, while innovative pruning rules and vertex order strategies are proposed to reduce indexing overhead. To further balance indexing overhead and query efficiency and to optimize the time-dependent constraint, we introduce a BII algorithm. It effectively improves index construction efficiency by building only a local index instead of a global one. Finally, experiments on many real datasets demonstrate that although the BII has a slightly inferior query time to TD2H, it has a significant advantage in the index construction.