In the fire scene investigation, the firefighting Internet of Things (IoT) data is the key electronic evidence for event analysis and responsibility determination. However, the traditional centralized storage method leads to data easy to be tampered with and damaged. To solve these problems, this paper designs and implements a secure, reliable and low-cost distributed firefighting IoT data storage scheme based on the Fabric framework, combining blockchain technology, Interplanetary File System (IPFS) and Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to provide a strong support for fire accident traceability. This scheme mainly includes the storage model, key algorithms and Fabric construction and improvement. IPFS stores the complete firefighting IoT data, as the off-chain storage system of the blockchain, and the blockchain only stores the storage address (IPFS hash) of data returned by IPFS, thus reducing the storage space overhead of the blockchain and ensuring data security. Further, we adopt the Fabric framework as the blockchain platform for firefighting IoT data, and embed the PBFT consensus algorithm into the framework to ensure the reliability of consensus nodes in Fabric, thus improving the availability of the blockchain. In addition, we use the AES and RSA algorithms to ensure the security of firefighting IoT data storage and transmission. Through system analysis and experimental testing, the proposed scheme meets the need for secure storage and traceability of firefighting IoT data. Compared with the storage scheme using only blockchain, the blockchain combined with IPFS technology has advantages in storage space occupation, significantly improved throughput, and lower latency overhead. Meanwhile, compared with the official Fabric, the improved Fabric supports Byzantine fault tolerance and has better security.

Recognizing students’ behaviors in classes based on videos of their activity plays an important role in improving teaching quality and paying attention to the healthy growth of students. The existing student behavior recognition methods mainly focus on the behavior of the single student and have low performance and efficiency. To recognize the behaviors of multiple students in the classroom at the same time, we propose a fast and effective solution, called ET-YOLOv5s, which is an improved YOLOv5s with ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) and a tiny object detection module in this paper. First, the ESRGAN is adopted to generate high-definition images from original images of real classroom environments in colleges to improve the recognition accuracy. Then a tiny object detection module is added to the YOLOv5s to detect the smaller objects on high-definition images, such as phones, books, and students sitting at the back of the classroom. Finally, the experimental results show that the proposed solution can detect 11 kinds of students’ behaviors in classrooms, including playing mobile phones, having class, sitting with hands on face, sitting turn right, sitting turn left, bowing, sleeping, and so on. Compared with YOLOv4, YOLOv5s, and other tiny target detection algorithms, it has better detection performance and can effectively deal with the behavior recognition of multiple students.

In the Distributed Parallel Stream Processing Systems (DPSPS), elastic resource allocation allows applications to dynamically response to workload fluctuations. However, resource provisioning can be particularly challenging, due to the unpredictability of the workload. In addition, unlike CPU resources, bandwidth resources are often ignored in resource allocation. Moreover, resource allocation and resource placement are considered separately. In this paper, we investigate the proactive elastic resource scheduling problem for computation-intensive and communication-intensive applications, which aims at meeting the latency requirement with the minimal energy cost, and propose a dynamic collaborative strategy from the systemic perspective. Specifically, we first model a collaborative workload prediction pattern to accurately predict the upcoming workload, and construct a latency estimation model to estimate the latency of the application. Then, we design an energy-efficient resource pre-allocation method, in which the CPU frequency adjustment and the stability of resource reconfigurations are both considered. Finally, we present a communication-aware resource placement approach. Simulation results show that, compared with the reactive strategies, our strategy achieves an obviously better latency performance, and effectively avoids unnecessary resource adjustments. Meanwhile, the energy consumption is about saved by 50 percent on average, and the communication cost is maintained at a very low level of 4 percent.

Smart contract is the core of blockchain operation, and contract vulnerability will cause huge economic losses. Therefore, effective smart contract vulnerability detection is of vital importance and attracts more and more attention. In this paper, we propose a vulnerability detection model (VDM-AEI) based on automatic feature extraction and feature interaction. For the first time, this model converts smart contracts into gray images and uses VGG16 and GRU models to automatically extract vulnerability features and filter effective features, respectively. Then, a contract graph and an expert knowledge feature vector are constructed by using commonly used methods as part of feature construction. Next, AutoInt and DCN networks are used to build a dual feature interaction network to obtain more abundant vulnerability feature information, which extracts high-dimensional nonlinear features from the low and sparse features of the contract graph feature vector and the expert knowledge-defined feature vector. Finally, all output features of GRU, AutoInt and DCN networks are integrated to obtain vulnerability classification results through fully connected neural networks. We conducted extensive experiments on the ESC and VSC datasets for reentrancy vulnerabilities, timestamp dependency vulnerabilities, and infinite loop vulnerabilities. The experimental results prove the effectiveness and accuracy of the VDM-AEI model. Compared with the latest vulnerability detection model CGE, the accuracy rates of the 3 types of vulnerability detection are improved by 10.85%, 6.18%, and 12.34%, respectively. In addition, the predicted F1 scores of VDM-AEI are all greater than 95%, and the recall rate is no less than 94%.

Due to the time variability and bursty of data, accurate and lag-free time series prediction is difficult and challenging. To address these problems, we propose an online attention enhanced differential and decomposed LSTM (Long Short Term Memory) model called OADDL, which can better capture the comprehensive core features and important structures of time series. In this model, the core features of the time series are first generated through differential and decomposition methods to reduce data complexity and remove noisy data. Then, the self-attention module and LSTM capture the full time core features and important structures of time series. Finally, FCN (Fully Connected Network) fuses the omnidirectional features of time series. Meanwhile, we design an online two-stage training mode for this model, in which attention enhanced LSTM and FCN models are sequentially trained, and the training set and model hyper-parameters are continuously updated over time, thus further capturing the time-varying and burst characteristics of time series. We conduct tests on three typical datasets, and the experimental results show that compared with latest typical deep learning models, OADDL can more accurately predict time series data and effectively alleviate the problem of prediction lag.