On robust estimation of the location parameter

The automotive industry, a key part of industrial Internet of Things, is now converging with cognitive computing (CC) and leading to industrial cognitive Internet of Vehicles (CIoV). As the major data source of industrial CIoV, social media has a significant impact on the quality of service (QoS) of the automotive industry. To provide vehicular social media services with low latency and high reliability, edge computing is adopted to complement cloud computing by offloading CC tasks to the edge of the network. Generally, task offloading is implemented based on the premise that edge servers (ESs) are appropriately quantified and located. However, the quantification of ESs is often offered according to empirical knowledge, lacking analysis on real condition of intelligent transportation system (ITS). To address the abovementioned problem, a c ollaborative method for the q uantification and p lacement of ESs, named CQP, is developed for social media services in industrial CIoV. Technically, CQP begins with a population initializing strategy by Canopy and K-medoids clustering to estimate the approximate ES quantity. Then, nondominated sorting genetic algorithm III is adopted to achieve solutions with higher QoS. Finally, CQP is evaluated with a real-world ITS social media data set from China.

Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV

The fast development of Blockchain technology makes it widely applied in several fields of digital transactions, like e-government affairs and the protection of financial transactions. In this article, we propose a homomorphic encryption-based Blockchain for circuit copyright protection that effectively addresses the issues in the protection of circuit copyright transactions, such as low security of private data, low efficiency in transaction data storage, cooperation and supervision. First, we establish a homomorphic encryption-based mathematical model by utilizing Blockchain and intelligent contract, and next, the algorithms that include Blockchain generation, homomorphic chain encryption/decryption, and intelligent contract are designed. As the intelligent contract is correctly executed in Blockchain, a fully homomorphic encryption-based identity authentication protocol is tackled for Blockchain, given that it ensures the change operation of any third-party in Blockchain and realizes real-time verification. The system is apposite for circuit copyright protection in a blockchain network, due to the use of distributed identity authentication and real-time extensible storage improves the security and extensibility of blockchain-based circuit copyright protection. The experimental results show that the proposed algorithm has reduced the transmission cost and improved the efficiency of data storage and supervision. In addition, it is resilient to several common attacks (e.g., double-spending attacks), yet incurs low cost/overhead and has a higher level of security when compared to three other competing algorithms.

Circuit Copyright Blockchain: Blockchain-Based Homomorphic Encryption for IP Circuit Protection

Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment

Privacy-preserving quality prediction for edge-based IoT services

With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF’s recommendation performance obviously outperforms nine state-of-the-art service recommendation methods.

Location-Aware Deep Collaborative Filtering for Service Recommendation

Autonomous Vehicle Platoon (AVP) is the most anticipated application of 5G ultrareliable and low latency communications. By joining the preexisting platoon to form the real-time AVP (RAVP), individual vehicles could gain benefits such as fuel consumption reduction and traffic safety enhancement. Unlike the scheduled AVP which only contains fixed vehicles, members in RAVP change frequently since individual vehicles would want to join or leave the platoon any time. Besides, malicious vehicles may attempt to sneak into the RAVP and try to manipulate the platoon. Public key cryptography and access control mechanisms can be adopted to create a relatively isolated area for communication inside the platoon. Nevertheless, there exist few works that take into account the regulation of the dynamic change members in RAVP. In this paper, we propose a membership management scheme for 5G-enabled RAVP by integrating revocable attribute-based encryption (RABE) and blockchain, namely, GAP-MM. It realizes fine-grained access control of key distribution and malicious vehicle’s key revocation efficiently. The sufficient evaluations and security analysis indicate that GAP-MM is practical for RAVP scenario in terms of both efficiency and security.

GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

Aiming at the current situation of network embedding research focusing on dynamic homogeneous network embedding and static heterogeneous information network embedding but lack of dynamic information utilization, this paper proposes a dynamic heterogeneous information network embedding method based on the meta-path and improved Rotate model; this method first uses meta-paths to model the semantic relationships involved in the heterogeneous information network, then uses GCNs to get local node embedding, and finally uses meta-path-level aggression mechanisms to aggregate local representations of nodes, which can solve the heterogeneous information utilization issues. In addition, a temporal processing component based on a time decay function is designed, which can effectively handle temporal information. The experimental results on two real datasets show that the method has good performance in networks with different characteristics. Compared to current mainstream methods, the accuracy of downstream clustering and node classification tasks can be improved by 0.5~41.8%, which significantly improves the quality of embedding, and it also has a shorter running time than most comparison algorithms.

https://www.mdpi.com/2076-3417/12/21/10898/pdf?version=1666945480

A Dynamic Heterogeneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model

Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction.

/pdf/relationship-discovery-and-hierarchical-embedding-for-web-8775vwpq.pdf

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items’ scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model’s score prediction, which also proves the method’s universality.

Qilin Wu

Papers

Location-Aware Deep Collaborative Filtering for Service Recommendation

GAP-MM: 5G-Enabled Real-Time Autonomous Vehicle Platoon Membership Management Based on Blockchain

A Dynamic Heterogeneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration