Due to the prominent development of public transportation systems, the taxi flows could nowadays work as a reasonable reference to the trend of urban population. Being aware of this knowledge will significantly benefit regular individuals, city planners, and the taxi companies themselves. However, to mindlessly publish such contents will severely threaten the private information of taxi companies. Both their own market ratios and the sensitive information of passengers and drivers will be revealed. Consequently, we propose in this work a novel framework for privacy-preserved traffic sharing among taxi companies, which jointly considers the privacy, profits, and fairness for participants. The framework allows companies to share scales of their taxi flows, and common knowledge will be derived from these statistics. Two algorithms are proposed for the derivation of sharing schemes in different scenarios, depending on whether the common knowledge can be accessed by third parties like individuals and governments. The differential privacy is utilized in both cases to preserve the sensitive information for taxi companies. Finally, both algorithms are validated on realworld data traces under multiple market distributions.

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic extension to the boundary of commercialized data statistic analysis, owing to the pervasive availability of low-cost wireless network access and off-the-shelf mobile devices. In such cases, many data consumers post their queries for urban statistic analysis in the system, like the scales of traffics, and then data contributors in IoT networks upload their contents, which can be evaluated by data brokers and responded to data consumers. However, huge volumes of devices bring large scales of data, constituting heavy burdens for data exchange. Even worse, contents in IoT systems are also sensitive as they are usually linked to private physical status of data contributors. The previous studies for IoT data trading fail to provide comprehensive estimation and pricing towards these difficulties. Therefore, this paper proposes a novel framework for the range counting trading over IoT networks by jointly considering data utility, bandwidth consumption, and privacy preservation. The range counting accumulates the number of data items falling in a concerned range of value, providing important information on the underlying data distribution. This paper first proposes a novel sampling-based method with histogram sketching for range counting estimation. The estimator is proved to be unbiased and achieves advanced performance on variance. Then the framework adopts a perturbation mechanism that can further preserve the results under differential privacy. The theoretical analysis shows that the mechanism can guarantee the privacy preservation under a given size of samples and the accuracy requirement of results. Finally, two types of pricing strategies for range counting trading are introduced for different circumstances, providing holistic consideration on how the parameters given in the estimator should be used for data trading. The framework is evaluated by estimating the air pollution levels and the traffic levels with different ranges on the 2014 CityPulse Smart City datasets. The evaluation results demonstrate that our framework can provide more accurate and reliable statistical information, with reduced bandwidth consumption and strengthened privacy preservation.

Internet of Thing (IoT) systems have been treated as a novel platform for graph data acquisition. Contents like dynamic network topology, organization and control flows, and interactions among monitored objects all contribute to the huge volumes of graph data generated in IoT. These data are believed to brought significant benefits to both the operation and functionalities of IoT systems, especially when combined with cutting-edge Artificial Intelligence techniques. However, these graph data are usually locally collected by data contributors with sensing devices, which could be both partially overlapped as they record same environment, and sensitive as they can indicate private physical status of contributors. Considering all challenges, current solutions for graph data collection in IoT are incapable. Therefore, this article proposes a novel framework for privacy-preserving distributed graph data collection for IoT. The framework allows the graphs kept by data contributors to be partially overlapped, and can help the data broker to efficiently derive the universal view by combining these graphs. The differential privacy is applied for privacy preservation during data collection. The proposed problem aims at minimizing the total bandwidth consumption for graph collection, which is proved to be NP-complete. Then three algorithms are proposed for different circumstances, based on the diverse knowledge and purposes held by the data broker. Finally, both theoretical and numerical analysis have demonstrated the advancement of these methods.

The easy and pervasive involvement of devices in Industrial Internet of Things has greatly benefited the implementation and adoption of various smart services. One prominent prerequisite of such trends is the extensive and continuous support and sharing of data and resources among devices. However, previous efforts usually treat the data sharing as one-time task among devices, which are incapable when the data are applied for the distributed and iterative training task of machine learning models. Therefore, this article proposes a novel framework for continuous data sharing in Industrial Internet of Things. The system consists of different system owners, each brings devices and participate the distributed training of models. Specifically, system owners hold different scales of devices, data, and resources, while devices own heterogeneous availability in different time periods. In this case, the goal is to properly assign devices for qualified model training process in different rounds, such that no devices will devote unlimited resources and the overall efforts and consumptions among different owners are balanced. Accordingly, three algorithms for device allocation are proposed, based on whether the availability of devices in each training round are known at the beginning of the training procedure. The analysis shows that all algorithms can achieve a rational allocation for devices and balance the performance among system owners. Finally, evaluation results reveal that the proposed solutions outperform baseline methods in providing better data sharing plans.

In order to perform fast analysis and recognition of power equipment images. A residual attention mechanism based image classification model is proposed. First, the power equipment dataset is preprocessed and the power equipment dataset is randomly divided into a training set and a test set. Based on the improved DenseNet, the image features of the training set are extracted. The training set images are used for training, and the classification test is implemented on the test set. The experimental results show that the accuracy of the improved DenseNet algorithm is improved by 8.89%. It can be seen that the classification accuracy of the improved DenseNet algorithm is effectively improved.