Anomaly detection plays a critical role in ensuring safe, smooth, and efficient operation of machinery and equipment in industrial environments. With the wide deployment of multimodal sensors and the rapid development of Internet of Things (IoT), the data generated in modern industrial production has become increasingly diverse and complex. However, traditional methods for anomaly detection based on a single data source cannot fully utilize multimodal data to capture anomalies in industrial systems. To address this challenge, we propose a new model for anomaly detection in industrial environments using multimodal temporal data. This model integrates an attention-based autoencoder (AAE) and a generative adversarial network (GAN) to capture and fuse rich information from different data sources. Specifically, the AAE captures time-series dependencies and relevant features in each modality, and the GAN introduces adversarial regularization to enhance the model’s ability to reconstruct normal time-series data. We conduct extensive experiments on real industrial data containing both measurements from a distributed control system (DCS) and acoustic signals, and the results demonstrate the performance superiority of the proposed model over the state-of-the-art TimesNet for anomaly detection, with an improvement of 5.6% in F1 score.

Blockchain is a revolutionary technology that has reshaped the trust model among mutually distrustful peers in a distributed network. While blockchain is well-known for its initial usage in a public manner, such as the cryptocurrency of Bitcoin, consortium blockchain, which requires authentication of all involved participants, has also been widely adopted in various domains. Nevertheless, there is a lack of comprehensive study of consortium blockchain in terms of its architecture design, consensus mechanisms, comparative performance, etc. In this study, we aim to fill this gap by surveying the most popular consortium blockchain platforms and assessing their core designs in a layered fashion. Particularly, Byzantine fault tolerant (BFT) state machine replication (SMR) is introduced to act as a basic computational model of consortium blockchain. Then the consortium blockchain is split into the hardware layer, layer-0 (network layer), layer-I (data layer, consensus layer and contract layer), layer-II protocols, and application layer. Each layer is presented with closely related discussion and analysis. Furthermore, with the extraction of the core functionalities, i.e., robust storage and guaranteed execution, that a consortium blockchain can provide, several typical consortium blockchain-empowered decentralized application scenarios are introduced. With these thorough studies and analyses, this work aims to systematize the knowledge dispersed in the consortium blockchain, highlight the unsolved challenges, and also indicate the propitious avenues of future work.

Blockchain technology has been increasingly used for decentralizing cloud-based Internet of Things (IoT) architectures to address limitations faced by centralized systems. While many existing efforts are successful in decentralization with multiple servers (i.e., full nodes) to handle faulty nodes, an important issue has arisen that external clients have to rely on a relay node to communicate with the full nodes in the blockchain. Compromization of such relay nodes may result in a security breach and even a blockage of IoT sensors from the network. We propose blockchain-based decentralized IoT management systems for censorship resistance, which include a “diffusion” function to deliver all messages from sensors to all full nodes and an augmented consensus protocol to check data losses, replicate processing outcome, and facilitate opportunistic outcome delivery. We also leverage public key aggregation to reduce communication complexity and signature verification. The experimental results from proof-of-concept implementation and deployment in a real distributed environment show the feasibility and effectiveness in achieving censorship resistance.

Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical class clusters. However, DPC relies heavily on the calculation methods of the cutoff distance threshold and local density and cannot analyze complex manifold data, especially datasets with uneven density distribution and multiple peaks in the same cluster. To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC). First, we redefine the local density calculation method using the layered k-nearest neighbors. To adapt to datasets with different densities, the k-nearest neighbors are divided into multiple layers. Second, for the multiple peaks in the same cluster problem, we design a new mechanism to calculate the similarity of subclusters based on the idea of shared neighbors and Newton’s law of gravitation, and a subcluster merging strategy is proposed. To prove the effectiveness of our algorithm, we compare the LKSM_DPC with K-means, DBSCAN, DPC, and DPC derivatives for 24 datasets. A large number of experiments demonstrate that our algorithm can often outperform other algorithms.

Peer-to-peer (P2P) content delivery is up-and-coming to provide benefits comprising cost-saving and scalable peak-demand handling compared with centralized content delivery networks (CDNs), and also complementary to the popular decentralized storage networks such as Filecoin. However, reliable P2P delivery demands proper enforcement of delivery fairness, i.e., the deliverers should be rewarded in line with their in-time delivery. Unfortunately, most existing studies on delivery fairness are on the basis of non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc P2P setting. We propose an expressive yet still minimalist security requirement for desired fair P2P content delivery, and give two efficient blockchain-enabled and monetary-incentivized solutions ${\mathsf {FairDownload}}$ and ${\mathsf {FairStream}}$ for P2P downloading and P2P streaming scenarios, respectively. Our designs not only ensure delivery fairness where deliverers are paid (nearly) proportional to their in-time delivery, but also guarantee exchange fairness where content consumers and content providers are also fairly treated. The fairness of each party can be assured even when other two parties collude to arbitrarily misbehave. Our protocols provide a general design of fetching content chunk from any specific position so the delivery can be resumed in the presence of unexpected interruption. Further, our systems are efficient in the sense of achieving asymptotically optimal on-chain costs and optimal delivery communication. We implement the prototype and deploy on the Ethereum Ropsten network. Extensive experiments in both LAN and WAN settings are conducted to evaluate the on-chain costs as well as the efficiency of downloading and streaming. Experimental results show the practicality and efficiency of our protocols.