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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.

Age of Information (AoI) has been proposed to measure the freshness of the sensory data for IoT applications. In Battery-free WSNs (BF-WSNs), The AoI minimization data collection problem has been extensively studied. Apart from data collection, target monitoring is also an important application for BF-WSNs. To capture the freshness of the sensory data, the AoI related to targets (The AoI of targets) needs to be considered. To guarantee the performance of the target monitoring system, the maximum AoI of all targets should be minimized. However, existing works mainly investigated the AoI related to nodes (The AoI of nodes), which is different from the AoI of targets. Also, existing energy models are not practical enough for battery-free nodes. To deal with those problems, in this paper, we first propose a more practical energy model. Then the problem of Maximum AoI minimization for Target monitoring in Battery-free WSNs (MTB) is formally defined based on the proposed energy model. A two-stage algorithm is proposed to solve MTB optimally, in which all nodes in the network are scheduled collaboratively to monitor all the targets in the monitoring field. Extensive simulations and real-world experiments verify the high performance of our algorithm and energy model.