The distribution of edge resources in the edge computing environment has an important impact on the Quality of Service (QoS) of edge services. Unreasonable server placement will inevitably lead to problems such as server overload or underload, deteriorating workload balancing and service wait time. Therefore, the key issue to be addressed in server placement is how to enhance the QoS of edge services through efficient edge server placement strategies under multiple requirements such as average task wait time and data privacy. Edge computing (EC) assisted with blockchain technology was argued to be the most potential solution. In this paper, we propose a blockchain-assisted secure ES placement algorithm named ETS_GA. ETS_GA is based on the elite-preserving genetic algorithm (EGA), which is proven to converge. The premature problem of traditional genetic algorithm (GA) is effectively solved by using tabu search (TS) and niche sharing (NS). In addition, we construct an adaptive state supervising machine (ASM) to realize real-time algorithm supervision and adaptively iterate the optimization strategy. Blockchain-based privacy protection methods are also deployed in the placed servers to provide real-time privacy protection. Finally, our proposed method is experimentally compared with four baselines using the real Shanghai Telecom base station data set, whose results demonstrate the superiority of ETS_GA in terms of convergence and global search capability.

Meteorological radar data is essential for meteorological monitoring, forecasting, and research, and it plays a crucial role in observing and warning of extreme weather risks. However, meteorological radars have some limitations, such as uneven distribution and severe topographical influence. Meteorological remote sensing satellites can partially overcome these limitations by providing larger observational scope and high spatial and temporal resolution. Using data from meteorological remote sensing satellites to train radar reflectivity factor (RF) retrieval models can effectively compensate for the missing and poor quality of radar data. However, there are still some challenges, such as extracting the features of intense convective weather with unclear coverage from complex multi-channel meteorological remote sensing satellite data and removing the interference caused by non-precipitation clouds on retrieval models. Moreover, the privacy and security of remote sensing data transmission need to be ensured. In this paper, we propose a novel method that combines the Advanced Encryption Standard (AES) method to protect the transmission of remote sensing data, a multi-scale feature fusion module (MFM) to extract multi-scale features from multi-channel meteorological remote sensing satellite data, and an attention technique to reduce the interference of non-precipitation clouds on retrieval models. We conduct comparison experiments with multiple indicators to demonstrate that our method has certain advantages in retrieving radar reflectivity values of different sizes. Our method achieves 0.63, 0.36, 0.49, 0.55 and 0.99 on POD, FAR, CSI, HSS and ACC scores respectively.

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data.