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The Internet of Things (IoT) is the integration of all information and Internet technology in the information age, which can realize the collection and transmission of intelligent information. A large number of sensors are producing and collecting data involving various industries every day. The amount of stream data generated is huge, and a large number of abnormal data are also generated in the process. Due to the demands of business and life quality improvement, the application of IoT technology to real-time monitoring and correction of massive stream data, especially the correction of abnormal data, is a very valuable research direction, and also the key to ensure the credibility and fidelity of IoT data. This article proposes a recurrent neural network model based on long- and short-term memory network (LSTM) and LSTM+. LSTM+ model not only reduces the regression error compared with the traditional LSTM model, but also can detect abnormal data collected by IoT terminal nodes, and can correct the abnormal data in real time, so as to ensure that the network prediction can have good stability and robustness.

With the rapid development of the edge computing, Internet economy has become a new situation in economic development. What has brought development to the Internet economy is a variety of family-based platforms (i.e., edge computing). However, in the face of huge Internet users and different users’ shopping habits, reasonably personalized e-commerce platform allocations for users can improve the user’s website shopping experience. However, due to the huge user data and the diversity of SDN (software defined network) platforms, it is a huge challenge to reasonably allocate e-commerce resources/platforms to users. The quality of the SDN personalized resource allocations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can allocate the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized allocation faces the problem of too high user data dimensions. Therefore, we first conduct computational efficiency analysis toward each user. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that, in the edge computing context, our method can reduce the complexity of resource allocation toward complex data and improve the quality of the allocated data.

The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model's security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system's multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and 0.01~0.05 λ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.

Edge computing is the provision of cloud services and IT environment services to application developers and service providers on the edge of the network. Edge computing faces some challenges, such as dealing with randomly varying workloads, which is an important issue. Thus, a cloud-assisted edge computing system (CAECS) is studied. A replica placement strategy is proposed to satisfy the diversity of user demands and reduce the response time. A data migration strategy is proposed to guarantee data reliability if there exist the released instances. A heterogeneity-aware elastic provisioning strategy is proposed to rent the cloud instances. Finally, the performance of the proposed algorithms is evaluated via extensive experiments. The results imply that the total tenanted cost of the heterogeneity-aware elastic provisioning algorithm can averagely achieve up to 19.23% and 9.50% reduction over that of ARP algorithm and MADRP algorithm, respectively.

In recent years, edge computing becomes an increasingly popular alternative. Edge computing allows the computation is implemented in the edge of network, in which the data are stored in the edge of network, to improve the efficiency of data process. However, some resource management techniques in cloud or distributed system cannot better suit for edge computing. Therefore, there exist some challenges on the performance improvement of edge computing. In this paper, the main purpose is to combine the optimal placement of data blocks and the optimal scheduling of tasks to reduce the computation delay and response time for the submitted tasks and improve user experience in edge computing. In optimal placement of data blocks, the value of the data blocks considers not only the popularity of the data blocks, but the data storage capacity and replacement ratios of an edge server that will store those data blocks. Furthermore, the replacement cost for placed data blocks is regarded as an important component of data block placement. This optimal placement scheme can avoid replacing the placed data blocks repeatedly so that the bandwidth overhead is reduced. In optimal scheduling of tasks, the containers are taken as the lightweight resource unit for the services for user requests to make full use of data storage in edge servers and improve the services performance of edge servers. Finally, extensive experiments are conducted to value the performance of task scheduling strategy. The results show that the performance of the proposed task scheduling algorithm is better than that of the compared algorithms.

Recently, social network has been paid more and more attention by people. Inaccurate community detection in social network can provide better product designs, accurate information recommendation and public services. Thus, the community detection (CD) algorithm based on network topology and user interests is proposed in this paper. This paper mainly includes two parts. In first part, the focused crawler algorithm is used to acquire the personal tags from the tags posted by other users. Then, the tags are selected from the tag set based on the TFIDF weighting scheme, the semantic extension of tags and the user semantic model. In addition, the tag vector of user interests is derived with the respective tag weight calculated by the improved PageRank algorithm. In second part, for detecting communities, an initial social network, which consists of the direct and unweighted edges and the vertexes with interest vectors, is constructed by considering the following/follower relationship. Furthermore, initial social network is converted into a new social network including the undirected and weighted edges. Then, the weights are calculated by the direction and the interest vectors in the initial social network and the similarity between edges is calculated by the edge weights. The communities are detected by the hierarchical clustering algorithm based on the edge-weighted similarity. Finally, the number of detected communities is detected by the partition density. Also, the extensively experimental study shows that the performance of the proposed user interest detection (PUID) algorithm is better than that of CF algorithm and TFIDF algorithm with respect to F-measure, Precision and Recall. Moreover, Precision of the proposed community detection (PCD) algorithm is improved, on average, up to 8.21% comparing with that of Newman algorithm and up to 41.17% comparing with that of CPM algorithm.

In recent years, with the increase in users in social network, the social network has had the feature of big data. The large-scale social network has become an indispensable part in people’s life. However, the traditional data mining technology cannot suit the large-scale social network. Thus, it is urgent to develop a more suitable mining technology for the large-scale social network. In this section, a crawler model based on semantic analysis and spatial clustering is proposed firstly. Then, the content extraction model based on document object model tree is built to extract the target text information from the links fetched by the proposed crawler model. The similarities between textual information in different regions are computed to choose the important information. Moreover, a two-stage topic clustering model based on time information is presented. The time information is introduced into the similarity computation between two posts or clusters. The single-pass algorithm is improved and applied in different clustering stage to improve the clustering accuracy. Finally, the proposed algorithms are evaluated on Hadoop platform. The Hadoop platform can effectively reduce the computing time and improve the server quality of users in large-scale social network. Meanwhile, the experiments demonstrate that the proposed algorithms are suitable for the data processing in large-scale social network.

Heterogeneous wireless sensor network has the advantages of completing complex communication tasks and is widely applied to military, medical, environmental, industrial and commercial, family life and so on. But heterogeneous nodes are expensive, it is necessary to consider how to balance the energy consumption and prolong network lifetime by deploying minimum number of heterogeneous nodes. In this paper, a clustering routing algorithm for heterogeneous wireless sensor network (CHRA) is presented to balance energy and prolong the network lifetime. The proposed routing algorithm computes the optimal number of heterogeneous nodes and selects the cluster heads per round. In CHRA, all common nodes are divided into two kinds. The first kind includes the nodes which transmit data to Sink by heterogeneous node and the others belong to the second kind. Furthermore, the common nodes are clustered per round by LEACH-C. The performance analysis and numerical results show that the proposed routing algorithm can availably prolong network lifetime and stable period. And it also can balance the energy consumption significantly.