Equipment manufacturing industry plays a pivotal role in a nation's economy, national development, and technological innovation. Traditional equipment manufacturing enterprises often face the problem of untimely information sharing and inconsistent focus of various departments in the process of operation, which causes difficulties in analysis and conflict in decision-making. Considering the subjectivity of interdepartmental decisions, reinforcement learning is introduced to analyze the decisions of various departments and get the decisions that best meet the needs of the enterprise. In addition, due to the instability of the internal and external environment of the enterprise, the decision obtained only by the machine usually cannot meet the development requirements of the enterprise, so human–machine coordination is used to adapt to the environmental changes. Therefore, a decision conflict resolution for equipment manufacturing enterprises based on human–machine coordination is proposed in this article. The resolution of conflicts in the context of deep human–computer collaborative decision-making is achieved through the incorporation of expert knowledge twice within the framework of reinforcement learning, thereby expressing preferences for both production factors and decision model structures. The experiments indicate that our model achieves superior performance in resolving decision conflicts.

In the field of intelligent manufacturing, the vast and heterogeneous nature of data across different departments poses significant challenges for collaborative decision-making. Direct data sharing often leads to severe privacy and security concerns. Although federated learning offers a promising solution, issues such as participant selection and privacy protection remain inadequately addressed. During model training, it is crucial to minimize the influence of low-quality participants and prevent the inference of sensitive information through gradient analysis, which could threaten model performance or data privacy. To address these challenges, this paper proposes a robust federated learning model, Fed-RDP, based on participant contribution evaluation and personalized differential privacy. The model leverages blockchain technology for secure data flow and storage, implemented through smart contracts. Historical parameters are submitted to evaluate contributions based on the concept of the least core, with real-time updates to reputation scores. When participants upload their local models, the scores are used as weights to aggregate and update the global model. Additionally, personalized differential noise is added to the uploaded gradients based on participant scores, preserving privacy while maximizing the utility of the data. Experimental results demonstrate that this approach effectively identifies low-quality participants, optimizes evaluation time, and protects privacy through personalized differential noise.

The disadvantages of personnel tracking methods are such that traditional manual methods are inefficient, while internet-based tracking methods overload users with information. Manual viewing of surveillance videos is time-consuming and labor-intensive, and real-time search methods based on machine vision require excessive computation. Therefore, this paper proposes a specific pedestrian tracking method based on location prediction and edge computing. This method involves retrieving videos from relevant areas based on the given personnel image information, generating an image set containing the target and the target’s approximate trajectory, and performing personnel tracking. The proposed pedestrian tracking framework treats surveillance videos as three-dimensional spatiotemporal maps, transforming the pedestrian tracking task into a map search problem. The framework consists of two algorithms: a location prediction algorithm based on road network information and a multistep trajectory tracking algorithm for tracking specific pedestrians in an edge computing environment. The location prediction algorithm determines the target’s location and predicts the target’s next position, while the multistep trajectory tracking algorithm enables trajectory tracking and distributed training of the model in the context of an edge network. Experimental results demonstrate that the proposed solutions are highly practical and perform well in personnel tracking compared to previous approaches.

Federated Learning (FL) offers a distributed machine learning framework that enables collaborative model training across multiple data sources without the need to share raw data, thereby preserving data privacy. This framework is particularly well-suited for cross-departmental and cross-enterprise intelligent decision-making in smart manufacturing. However, challenges remain in selecting reliable participants and ensuring the secure transmission of parameters to defend against potential attacks. Malicious participants may upload low-quality data or compromise data privacy during model aggregation. To address these issues, we propose the Federated Reputation Evaluation Blockchain (FREB), which integrates a reputation evaluation mechanism with blockchain technology. By leveraging blockchain, FL tasks are executed through trusted transactions, with smart contracts ensuring transparency and accountability. In contrast to traditional contribution evaluation methods, FREB employs a multi-weight subjective logic model combined with Shapley values to assess participant reliability. Reputation scores are calculated based on factors such as activity, model contribution, stability, and data quality, guiding the selection of participants. Additionally, a PoR-based model aggregation method is implemented, and noise is added to the model parameters to protect sensitive data from potential attacks. Experimental results on real-world datasets demonstrate that FREB effectively mitigates malicious node attacks and encourages high-quality participants, while maintaining model accuracy and data privacy.

Federated learning is an emerging distributed machine learning paradigm, where multiple data sources collaborate to train a model without sharing the original data, safeguarding user data privacy. Nevertheless, users, whether intentionally or unintentionally, may contribute low-quality data that can impact model quality or infer user information from the model parameters, potentially leading to privacy breaches. Selecting appropriate participants for training is still a challenge needing resolution. This paper introduces a federated learning scheme called FREB, which leverages a reputation evaluation mechanism and blockchain technology. The foundation of this scheme is blockchain, and it incorporates reputation as an indicator of participant reliability. Reputation is computed using a multi-weight subjective logic model. Before the training process begins, participants are chosen based on their reputation. Furthermore, the paper presents a novel model aggregation approach. By injecting noise into the uploaded model parameters, the scheme ensures that potential attackers cannot glean sensitive data information from the parameters. To validate the efficacy of FREB, the authors conducted simulation experiments using real datasets. The results of these experiments demonstrate that FREB is capable of thwarting malicious node attacks and promoting high-quality collaborative learning. It achieves this while preserving data privacy and maintaining model accuracy.

Addressing expanded surveillance scopes, cross-device person re-identification emerges as a crucial issue. Traditional approaches to re-identification and tracking confront challenges in feature matching challenges, and fail to harness the potential of edge computing environments, which offer viable solutions to bandwidth and latency constraints. The paper introduces a novel, edge computing and shared cache-based method for person re-identification and tracking. In this framework, edge nodes exploit deep learning method to execute image recognition for person re-identification and trajectory tracking. Edge nodes are deployed within a tri-layered architecture, encompassing Hierarchical Edge Cloud (HEC), Mobile Edge Computing (MEC), and Central Cloud (CC). Emphasizing part feature significance for person re-identification, the approach employs a refined Part-based Convolutional Baseline model guided by the triplet loss function. Experimental outcomes validate the high re-identification precision and efficacious load balancing capability of this method in person re-identification and tracking operations. Achieving a harmonized trade-off between task accuracy and response time, the proposed framework offers a robust solution for person re-identification and tracking within edge computing contexts.

With the rapid development of embedded smart devices, a new data collection paradigm, mobile crowd-sensing (MCS), has been proposed. MCS allows individuals from the crowd to act as sensors and contribute their observation data. However, existing MCS systems are mostly based on third-party platforms, and there is no guarantee that a center is completely credible. In addition, security and privacy issues should not be ignored. During MCS’ execution, the participants’ various information and truth value are usually exposed, and the computation related to data privacy cannot be verified. In this paper, we integrate the blockchain into the MCS scenario to design a blockchain based privacy-preserving quality control mechanism, which prevents data from being tampered with, and denied, ensuring that the reward is distributed fairly. In the new system, we propose a privacy preserving participant selection scheme and the result can be verified (i.e., security against malicious node) without any third-party arbiter. Finally, considering the issues with sensing data privacy and efficiency in the truth discovery process, we propose a new privacy-aware crowdsensing design with iterative truth discovery based on rational secure multi-party computation. The experimental results show that compared to the prior result, the proposed solutions are highly practical and facilitate quality control without violating the participant’s privacy.

Recently with the popularity of embedded smart devices, mobile crowdsensing has become a new data collection paradigm. However, these traditional schemes rely on a central authority, leading to potential privacy disclosure, and there is no the guarantee that a center is completely credible in reality. In addition, the issues of security and privacy caused by the center platform should not be ignored, and the computation related to privacy data cannot be verified. In the paper, we propose a decentralized privacy-preserving model(PPNS) based on blockchain, which avoids data from being tampered with, and denied. In the prototype of PPNS, a privacy preserving node selection scheme is constructed and the result can be verified (i.e. security against malicious node) without any third-party arbiter. The experimental results show that compared with prior methods, our solution has a better performance and will facilitate node selection without violating participants' privacy.

In recent years, with the popularity of smartphones, the indoor positioning systems based on mobile crowdsensing (MCS) have gained considerable interest and exploit. However, it is still challenging to construct a largescale indoor positioning system. 1) In indoor positioning model, storage and computing resources are very important. 2) The calibration operation of data label and selection of model parameters require the operation of professionals. 3) User location privacy may be compromise, which greatly affects participant safety and enthusiasm. To solve these problems, our model firstly provides an edge-crowdsourcing indoor localization architecture to improve storage, computing power and response speed. Then, based on manifold regularization, a semi-supervised indoor localization model is determined by an adaptive manner in terms of both similarity and manifold structure, which reduces the workload of the positioning model and improve localization accuracy. In addition, we propose a new privacy-aware indoor localization algorithm based on secure multi-party computation to protect location privacy. Experimental results on real-world datasets show that, compared with the previous methods, our method improves accuracy by 0.87m, and in terms of time overhead of privacy protection, our method reduces the running time of the thousand seconds level.

By allocating tasks to participants, crowdsensing has shown large potential in addressing large-scale data sensing problems. Considering the problem of unfair payment, negative work of participants, and cooperative cheating, how to assess data quality of tasks reliably is an important problem in crowdsensing. Therefore, a lightweight blockchain-based model for data quality assessment is proposed in this article. First, there are two data quality assessment processes in the model. One is implemented in the selection of participants and the other is implemented in data quality assessment. Second, consensus mechanism and smart contracts are redesigned to be suitable for crowdsensing. The lightweight consensus mechanism delegated proof of reputation (DPoR) is proposed in the blockchain-based model instead of proof of work (PoW). Furthermore, three smart contracts, verifiers selection contract (VSC), participants employment contract (PEC), and data verify contract (DVC), are generated to constrain the behaviors of the involved parties. Finally, expectation-maximization (EM) algorithm with multiverifiers is proposed to evaluate the performance of task participants. Experiments on the open data sets Wine Quality show that our new method outperforms the existing methods in improving the quality of sensing task.

With the popularization of intelligent terminals, crowdsensing has become increasingly prominent because of its advantages, such as low cost, high convenience, and fast speed in conducting tasks. However, the quality of the data collected through crowdsensing is varied and is difficult to evaluate. Furthermore, the existing crowdsensing quality control methods are mostly based on a central platform, which is not completely trusted in reality and results in the existence of fraud and other problems. To solve these two questions, a crowdsensing quality control model based on a two-consensus blockchain is proposed in this paper. First, the idea of a blockchain is introduced into this model. The credit-based verifier selection mechanism and the two-consensus approach are proposed to realize the nonrepudiation and nontampering of information in crowdsensing. Then, to help task publishers obtain higher-quality sensing data, the methods of node matching and QGE are proposed. The former method uses the idea of the calculation of matching degree to select workers, and the latter uses the idea of clustering and fuzzy theories to evaluate the quality of the sensing data. Finally, the experiments show that the running time of the block generation in our model is acceptable, and comparing with the other methods, our model can acquire data of higher ioj.

With the rapid growth of smart terminals in recent years, crowdsensing which utilizes the human intelligence to solve complicated problems have gained considerable interest and exploit. The majority of the existing crowdsensing systems rely on a trusted third-party platform to complete sensing tasks and collect large-scale data. However, the platform cannot completely ensure trust in the real world. The issues of security and privacy caused by the center platform should not be ignored. In this paper, we propose a decentralized privacy-preserving model based on twice verifications and consensuses of blockchain (TCNS). In the prototype of TCNS, an anonymity strategy which can be verified based on the elliptic curve algorithm is proposed to protect the user identity privacy. Then, we propose a twice consensus mechanism, which ensures that the data can be traced and avoids data from being impersonated, tampered with, and denied. Moreover, we propose a user attribute protection scheme based on the lightweight homomorphic encryption algorithm. Finally, considering various influencing factors comprehensively, TCNS uses fuzzy theories to select the candidate mobile nodes. Further, we implement the prototype with real-world datasets, the experimental analysis of privacy protection and safety shows that TCNS can effectively prevent association analysis attacks and background knowledge attacks. More gratifying, the time overhead for generating a new block is acceptable.

The mobile smart devices are becoming more and more powerful, and they have been pervasively applied in crowdsensing as effective tools to solve large-scale sensing tasks in urban areas. However, some crowdsensing tasks may bring high network traffic costs to participants using 3G/4G network. In this paper, a novel data diffusing and transmission method is proposed in Crowdsensing based on city Public Traffic System (PTS). This method makes full use of the advantages that the bus has predictable trajectory, wide coverage area, fast moving speed and long contact duration among passengers, so as to realize the rapid transmission of large-scale sensed data. Firstly, we design a data diffusing and transmission model in PTS, and emphatically discuss the Multi-data diffusion and transmission in budget constraints. Secondly, we propose a new algorithm called BUI-BA (Backhaul Utility Increment Based Backhaul Algorithm), which is able to transmit multi- data at the same time. And the algorithm is explained in detail with an example. Finally, the performance of BUI-BA is evaluated with comparisons to Greedy and effSense from aspects like overall transmission utility, fairness, success rate of transmission and transmission redundancy. The result has proved that BUI-BA has a better overall performance and can achieve a tradeoff between overall transmission utility and transmission redundancy, and save more network traffic costs and some other resources for mobile nodes.

Mobile edge computing (MEC) has emerged as a promising technique to address the challenge arising from the exponentially increasing data traffic. It not only supports mobile users to offload computations but also caches and delivers popular contents to mobile users. In this paper, we aim at designing novel content caching strategies in MEC networks to reduce access latency and improve energy efficiency. First, the distributed content delivery network based on MECs is developed to support users' requests locally. Moreover, based on users' distribution characteristics and MECs' location, a grouping-based and hierarchical collaborative caching strategy is proposed. Simulation results prove that our caching strategy is more efficient than alternative benchmark strategies in terms of average access latency, total energy consumption and content diversity.

Mobile edge computing (MEC) is an emerging paradigm to support the proliferation of smart phones and recent outstanding mobile data traffic growth. The network edge devices with computation, communication and storage capabilities can be applied to cache popular resource in close proximity to end uses and directly distribute resource to them, which is a promising solution to provide enhanced service. Firstly, in order to respond to the users' access requests at the edge of network, the distributed resource distribution network (DRDN) is constructed to realize the quick query, positioning and distribution of resource. It mainly consists of network elements: NEF (Network Exposure Function) node for global management and MEC nodes for distribution service. Secondly, on the basis of DRDN, two kinds of MEC's service models of distributing resource are defined and classified, the total energy consumption and average service delay for distributing resource to end users are analyzed. Finally, for the heavy access load and high latency of root caching MEC node, a hierarchical caching scheme (HCS) is designed and a caching node selection algorithm based on fuzzy C-means clustering is proposed according to the location characteristics of end users. The simulation results show that the HCS can save a lot of energy and reduce the average service delay. Additionally, for the different total arrival rate of users' access requests, the change of average service delay is stable with good adaptability in the HCS.

Homomorphic encryption can protect user’s privacy when operating on user’s data in cloud computing. But it is not practical for wide using as the data and services types in cloud computing are diverse. Among these data types, digital image is an important personal data for users. There are also many image processing services in cloud computing. To protect user’s privacy in these services, this paper proposed a scheme using homomorphic encryption in image processing. Firstly, a secret key homomorphic encryption (IGHE) was constructed for encrypting image. IGHE can operate on encrypted floating numbers efficiently to adapt to the image processing service. Then, by translating the traditional image processing methods into the operations on encrypted pixels, the encrypted image can be processed homomorphically. That is, service can process the encrypted image directly, and the result after decryption is the same as processing the plain image. To illustrate our scheme, three common image processing instances were given in this paper. The experiments show that our scheme is secure, correct, and efficient enough to be used in practical image processing applications.

Crowd sensing (CS) is a new paradigm of collecting large amounts of sensing information from a crowd. Unfortunately, not everyone is willing to participate in sensing tasks or provide high-quality information. Offering incentives is a common method of increasing the success of CS. Existing incentive mechanisms based on auction models lack deep consideration regarding the effects of malicious competition behavior and the “free-riding” phenomenon in crowdsourcing services. The design proposed in this paper focuses on an incentive mechanism based on a reverse auction. First, the crowdsourcing system incentive model is built. Second, an incentive mechanism is proposed based on an auction which combines the concepts of reverse auctions and Vickrey auctions. Next, proof that the mechanism is computationally efficient, individually rational, budget-balanced, truthful, and honest is provided. Simulation results indicate that the proposed incentive mechanism can effectively improve fairness of the bids and the quality of the sensed data.

With the powerful sensing capability of mobile smart devices, users can easily obtained the crowd sensing services with smart devices in the Internet of Things (IoT). However, credible interaction issues between mobile users are still the hard problems in the past. In this paper, we focus on how to assign the crowdsourcing sensing tasks based on the credible interaction between users. First, a novel credible crowdsourcing assignment model is proposed based on social relationship cognition and community detection. Second, the service quality factor (SQF), link reliability factor (LRF), and region heat factor (RHF) are introduced to scientifically evaluate the user crowdsourcing preferences. Then, a crowdsourcing algorithm based on analytic hierarchy process (AHP) theory is proposed. Finally, the simulation experiments prove the correctness, effectiveness, and robustness of our method.

Abstract With the rapid growth of various data collected by companies and organizations, there is an increasing need for the data owners to share their data with the third party for the purpose of data mining. Therefore, protecting the privacy of the shared data has received considerable attention from academia and industry. However, existing methods do not work well for distance‐based mining algorithms. This paper proposes a novel privacy‐preserving approach to support distance‐based mining algorithms while reducing the shared data size. This approach transforms the shared data to discrete cosine transformation (DCT) coefficients, and adaptively selects DCT coefficients so as to form the compressed coefficient matrix. To enhance data privacy, this approach employs rotation‐based transformation with modified constraints to process the compressed coefficient matrix. Extensive experiments show that the proposed approach achieves the best tradeoff between data privacy and mining quality comparing with other existing ones. Copyright © 2015 John Wiley & Sons, Ltd.

We propose a new K-nearest neighbor (KNN) algorithm based on a nearest-neighbor self-contained criterion (NNscKNN) by utilizing the unlabeled data information. Our algorithm incorporates other discriminant information to train KNN classifier. This new KNN scheme is also applied in a community detection algorithm for mobile-aware service: First, as the edges of networks, the social relation between mobile nodes is quantified with social network theory; second, we would construct the mobile nodes optimal path tree and calculate the similarity index of adjacent nodes; finally, the community dispersion is defined to evaluate the clustering results and measure the quality of community structure. Promising experiments on benchmarks demonstrate the effectiveness of our approach for recognition and detection tasks.

The contours and segments of objects in digital images have many important applications. Contour extractions of gray images can be converted into contour extractions of binary images. This paper presents a novel contour-extraction algorithm for binary images and provides a deduction theory for this algorithm. First, we discuss the method used to construct convex hulls of regions of objects. The contour of an object evolves from a convex polygon until the exact boundary is obtained. Second, the projection methods from lines to objects are studied, in which, a polygon iteration method is presented using linear projection. The result of the iteration is the contour of the object region. Lastly, addressing the problem that direct projections probably cannot find correct projection points, an effective discrete ray-projection method is presented. Comparisons with other contour deformation algorithms show that the algorithm in the present paper is very robust with respect to the shapes of the object regions. Numerical tests show that time consumption is primarily concentrated on convex hull computation, and the implementation efficiency of the program can satisfy the requirement of interactive operations.