As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

Industrial information integration engineering (IIIE) is a set of foundational concepts and techniques that facilitate the industrial information integration process. In recent years, many applicat...

A Survey on Industrial Information Integration 2016–2019

https://uwe-repository.worktribe.com/preview/4918315/Info%20Sci.pdf

Spatial-temporal data-driven service recommendation with privacy-preservation

For the issue of users’ sensibility to the QoS (Quality of Service) of multimedia applications, cloudlet has emerged as a novel paradigm which provides closer computing resources to users to improve the performance of multimedia applications and meet the QoS demands of users. However, the increasing users’ requirements of migrating tasks pose a challenge to preserve the security and integrity of offloaded data which are processed by cloudlets. In view of this challenge, a blockchain-based cloudlet management method for multimedia workflow, named MWSM, is proposed in this paper. Technically, we first model each multimedia application as a multimedia workflow and formulate the multimedia workflow scheduling problem. Then, blockchain is adopted to secure the data integrity during the offloading procedure. Besides, NSGA-III (Non-dominated Sorting Genetic Algorithm III) is employed to realize the QoS enhancement and ELECTRE (Elimination Et Choix Tradulsant la REaltite) is utilized to solve the decision-making problems of the most optimal scheduling strategies. Finally, experimental evaluations are conducted to demonstrate the efficiency and potential of our proposed scheduling method.

Blockchain-based cloudlet management for multimedia workflow in mobile cloud computing

LoRa wireless technology is a revolutionary wireless network access technology with a wide application prospect. An identification method for Lora devices based on physical layer fingerprinting is proposed to provide identities for authentication. Contrary to previous works, a differential constellation trace figure is established from the radio frequency (RF) fingerprinting features of LoRa devices, which transforms the feature matching to the image recognition. A classification method based on Euclidean distance of clustering center of LoRa signal is performed to analyze the differential constellation trace figure. The experimental results show that six LoRa transmission modules can be recognized accurately, and even in a low signal-to-noise ratio (SNR) environment, the different LoRa devices can still be distinguished and identified effectively.

/pdf/physical-layer-identification-of-lora-devices-using-4ehyxji9ml.pdf

Physical layer identification of LoRa devices using constellation trace figure

Item-based collaborative filtering (i.e., ICF) technique has been widely recruited to make service recommendations in the big data environment. However, the ICF technique only performs well when the data for service recommendation decision-making are stored in a physically centralized manner, while they often fail to recommend appropriate services to a target user in the distributed environment where the involved multiple parties are reluctant to release their data to each other due to privacy concerns. Considering this drawback, we improve the traditional ICF approach by integrating the locality-sensitive hashing (LSH) technique, to realize secure and reliable data publishing. Furthermore, through integrating the published data with little privacy across different platforms, appropriate services are recommended based on our suggested recommendation approach named ICF LSH . At last, simulated experiments are conducted on WS-DREAM data set. Experiment results show that ICF LSH performs better than the competitive approaches in terms of service recommendation accuracy, efficiency, and the capability of privacy-preservation.

Privacy-Aware Data Publishing and Integration for Collaborative Service Recommendation

The key to network virtualization technology is virtual network mapping, which has been proven to be an NP-hard problem. At present, the methods to solve the problem of virtual network mapping still have the following defects. Most of the existing literature is limited to static virtual network (VN) mapping and static linear resource pricing, which rely on peak allocation and don't meet the user dynamic resource requirements. Therefore, this paper proposes a virtual network resource allocation model based on dynamic resource pricing named GSO-RBFDM. Firstly, group search optimization (GSO) is used to optimize the node mapping scheme during the network mapping process to reduce the cost of network mapping. Secondly, a dynamic nonlinear resource pricing model is established, and genetic algorithm (GA) is used to more accurately search a low-cost network mapping path instead of the traditional Dijkstra algorithm. Finally, virtual network dynamic modeling is performed according to the user dynamic resource requirements, and radial basis function (RBF) is used to predict resource requirements to realize the dynamic resource allocation to users. Simulation results show that, compared with traditional virtual network mapping algorithms, GSO-RBFDM can not only realize dynamic resource allocation, but also show good performance in terms of acceptance rate, network cost, link pressure and average network revenue.

/pdf/a-virtual-network-resource-allocation-model-based-on-dynamic-47yw7cl635.pdf

Xin-Chun Cui

Papers

Privacy-Aware Data Publishing and Integration for Collaborative Service Recommendation

A Virtual Network Resource Allocation Model Based on Dynamic Resource Pricing