In cognitive heterogeneous networks, multiple secondary transmitters (STs) co-exist with primary users (PUs) on the same frequency band channel through spectrum sensing. Due to inaccurate sensing of whether the channel is occupied, STs can cause interference to PUs, thereby affecting the transmission performance of PUs. This paper proposes a constrained deep reinforcement learning-based joint adaptive modulation coding and power selection (CDRL-JAMCPS) algorithm. The proposed CDRL-JAMCPS learns the interference patterns of STs to PUs through interaction with the environment and selects the modulation coding scheme and transmit power for future frames of PUs based on the learned patterns, aiming to maximize the transmission rate while reducing energy consumption. Furthermore, addressing the issue where existing optimization algorithms solely consider network transmission rates while neglecting data transmission quality, this paper proposes a reward function in Lagrangian form based on frame error rate (FER) constraints. By optimizing this reward function in its dual domain, the problem of poor data transmission quality is resolved. The simulation results demonstrate that the proposed algorithm achieves better transmission performance compared to other reinforcement learning algorithms in environments where signal interference is difficult to perceive. Meanwhile, compared to algorithms that do not consider transmission quality, our algorithm exhibits significant advantages in meeting FER requirements and improving data transmission quality.

Internet of Things (IoT) consists of devices that generate, process, and exchange vast amounts of images. Unfortunately, these images always contain some kinds of noise, which significantly degrades data utility after the processing center receives the images. Image denoising plays a crucial role to recover the original image approximately from its noisy image according to the features of noise distribution and the structure information of the original image. However, the existing denoising algorithms are invalid when the original images are highly corrupted due to the high computational complexity. Therefore, this paper proposes a novel denoising algorithm based on the multi-low-rank model (MLR), which successively enforces similar blocks, dictionaries, and coefficient matrices approximating to low rank, thereby gradually removing noise. Extensive experimental simulations demonstrate that the MLR algorithm has the optimal denoising performance in terms of denoising quality and efficiency, especially in the case of strong salt noise.

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis.

Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while producing threats to the personal privacy. Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such that the adversary with arbitrary background knowledge cannot infer sensitive input from the noisy results. Nevertheless, existing differentially private mechanisms have poor utility and high-computation complexity on high-dimensional data because the necessary noise in queries is proportional to the size of the data domain, which is exponential to the dimensionality. To address these issues, we develop a compressed sensing mechanism (CSM) that enforces differential privacy on the basis of the compressed sensing (CS) framework while providing accurate results to linear queries. We derive the utility guarantee of CSM theoretically. An extensive experimental evaluation on real-world data sets over multiple fields demonstrates that our proposed mechanism consistently outperforms several state-of-the-art mechanisms under differential privacy.

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

The wireless video transmission environment of unmanned aerial vehicles (UAVs) is complex and unstable given the high mobility and changeable working conditions of UAVs, which lead to burst and consecutive errors and high error rates. A compressed video stream is extremely sensitive to transmission errors, such that even a single bit error sharply degrades the video quality. Hence, we propose an intraframe pixel-row-interleaved error concealment algorithm that interleaves pixel rows to generate high similarity in different parts of a frame, thereby achieving intraframe error resilience. Subsequently, we suggest an interframe time-field-interleaved alternative motion-compensated prediction that allows for automatic error elimination and recovers at least four consecutive frames in wireless video communications. The experiments demonstrate that the proposed algorithms recover frames with excellent subjective and objective effects. Moreover, these algorithms can provide reliable and robust video transmission for UAVs.