Xiaofeng Lian

To manage the rumors in social media to reduce the harm of rumors in society. Many studies used methods of deep learning to detect rumors in open networks. To comprehensively sort out the research status of rumor detection from multiple perspectives, this paper analyzes the highly focused work from three perspectives: Feature Selection, Model Structure, and Research Methods. From the perspective of feature selection, we divide methods into content feature, social feature, and propagation structure feature of the rumors. Then, this work divides deep learning models of rumor detection into CNN, RNN, GNN, Transformer based on the model structure, which is convenient for comparison. Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods, based knowledge graph, and other methods for the first time. And compare the advantages of different methods to detect rumors. In addition, this review enumerate datasets available and discusses the potential issues and future work to help researchers advance the development of field.

As the nodes of AWSN (Aerial Wireless Sensor Networks) fly around, the network topology changes frequently with high energy consumption and high cluster head mortality, and some sensor nodes may fly away from the original cluster and interrupt network communication. To ensure the normal communication of the network, this paper proposes an improved LEACH-M protocol for aerial wireless sensor networks. The protocol is improved based on the traditional LEACH-M protocol and MCR protocol. A Cluster head selection method based on maximum energy and an efficient solution for outlier nodes is proposed to ensure that cluster heads can be replaced prior to their death and ensure outlier nodes re-home quickly and efficiently. The experiments show that, compared with the LEACH-M protocol and MCR protocol, the improved LEACH-M protocol performance is significantly optimized, increasing network data transmission efficiency, improving energy utilization, and extending network lifetime.

The demand for Electronic Shelf Labels (ESL), according to the Internet of Things (IoT) paradigm, is expected to grow considerably in the immediate future. Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other. Low-Power Wide-Area Networks (LPWANs) are continuously gaining momentum among these standards, mainly thanks to their ability to provide long-range coverage to devices, exploiting license-free frequency bands. The main theme of this work is one of the most prominent LPWAN technologies, LoRa. The purpose of this research is to provide long-range, less intermediate node, less energy dissipation, and a cheaper ESL system. Much research has already been done on designing the LoRaWAN network, not capable to make a reliable network. LoRa is using different gateways to transmit the same data, collision, data jamming, and data repetition are expected. According to the transmission behavior of LoRa, 50% of data is lost. In this paper, the Improved Backoff Algorithm with synchronization technique is used to decrease overlapping and data loss. Besides, the improved Adaptive Data Rate algorithm (ADR) avoids the collision in concurrently transmitted data by using different Spreading Factors (SFs). The allocation of SF has the main role in designing LoRa based network to minimize the impact of the intra-interference, cost function, and Euclidean distance. For this purpose, the K- means machine learning algorithm is used for clustering. The data rate model is using an intra-slicing technique based on Maximum Likelihood Estimation (MLE). The data rate model includes three critical communication slices, High Critical Communication (HCC), Medium Critical Communication (MCC), and Low Critical Communication (LCC), having the specified number of End devices (EDs), payload budget delay, and data rate. Finally, different combinations of gateways are used to build ESL for 200 electronic shelf labels.

According to the characteristics of the nonlinear, long time-delays and time-variation in the MSG wastewater treatment system based on three-phase fluidized bed bioreactor (FBBR), a modified T-S model fuzzy adaptive control system based on genetic algorithm (GA) is presented in this paper. In the system, firstly using GA to optimize the membership functions, then reducing the dimension of fuzzy controller and simplifying the rules by an integral unit. Moreover, adopt a prediction method to compensate the time-delay of system, which based on the theory of fuzzy. Finally, the method is verified by experiments. Simulation experimental results show that the method is feasible and effective, which provides an effective approach to solve the problem of process control with long time-delays, large inertia and time-variation.