Edge-enabled Industrial Internet of Things (E-IIoT) has gained massive attention as a new type of IIoT for hosting emerging low-latency applications. However, due to device variations and complex ...communication environments, the edge servers and channel information are usually uncertain which poses significant challenges to the computational capacities in practical E-IIoT networks. To address these challenges, in this paper, we introduce a risk-sensitive task offloading scheme for the practical E-IIoT networks. Firstly, a distributed robust offloading optimization problem is formulated by jointly considering the high latency risks caused by network uncertainty and the average latency of the system. Then, by characterizing the latency risk as the conditional value, the formulated problem is transformed into a distributed robust mean conditional value-at-risk (CVaR) optimization problem. Further, we use a fuzzy set to capture the uncertainty of the first and second-order statistical information of the system to overcome the computational difficulties of the problem, and a distributionally robust optimization (DRO) method is employed to transform the mean-CVaR optimization problem into a computable semi-definite programming (SDP) problem. Finally, an improved DRO-based task offloading algorithm is proposed to obtain the optimal decision under uncertain E-IIoT networks. The simulation results show that the proposed offloading scheme not only improves the reliability in computation but also reduces the high latency risks. Therefore, our proposed model is more suitable for practical E-IIoT networks.
Over the past few years, fog radio access networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage ...functionalities toward the edge of networks, closer to users. In F-RANs, distributed edge caching among fog access points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatiotemporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyze the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a federated learning-based cooperative hierarchical caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbor F-APs and vertical cooperation between the baseband unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH.
Carbon emissions trading has become an increasingly hot topic nowadays, due to the fact that how to reduce carbon emissions has been a common effort of different countries. However, traditional ...methods are plagued by issues, such as inadequate privacy protection mechanisms and the challenge of representing data assets in a comprehensive form using blockchain data models. In this article, we propose carbon emissions trading scheme (CETS), a secure carbon emissions trading system using blockchain combined with digital assets transactions. The proposed CETS scheme enhances the performance of models for carbon emissions trading by prioritizing the efficiency, privacy, and traceability of carbon emissions trading. Simultaneously, it improves the consistency of digital asset trading throughout the chain. First, we propose a dual-blockchain-based method for storing and tracing carbon emission data, which ensures the privacy of the data. Next, we propose algorithms for transaction of digital assets in carbon emission trading scheme, which include digital asset uniqueness algorithm, serializable mechanism, and cross-chain algorithm of digital assets. Finally, we propose an automated machine learning pipeline approach based on the carbon trading price forecasting model construction method, which can provide efficient, automatic price forecasting model construction and training. The experimental results prove that our proposed carbon emission trading system can provide an efficient and stable carbon emission trading solution.
With the Industrial Internet of Things (IIoT) continuing to expand, lots of data collection, exchange, and authentication generated from an increasing number of access devices is required with ...heterogeneity, multidimension, and multiobjective networks as its characteristics. However, traditional IIoT systems are vulnerable to security challenges, such as data leakage, theft, and tampering. As one of the most promising solutions, blockchain has played an essential role in ensuring security and transparency in the IIoT. But there are still some challenges that prevent the secure and effective implementation of blockchain-based IIoT systems in consensus security, consensus efficiency, and consensus application. To address these problems, we propose an effective security blockchain consensus algorithm for heterogeneous IIoT nodes aiming to defend against the consensus attack and improve consensus efficiency. First, we design a blockchain-based IIoT system architecture. Then, we present an identity authentication and transformation protocol to defend against consensus attacks. Furthermore, we introduce a method for constructing communication directed acyclic graphs (DAGs) and transaction set DAGs to enhance transaction throughput. Based on these two DAGs, we propose an efficient and security consensus algorithm (DAG-D). DAG-D employs transaction sets instead of single transactions or blocks, leveraging communication DAG propagation to swiftly confirm transaction set DAGs based on parent transactions for associated confirmation. Experimental results show that our proposed DAG-D outperforms DAG-M, DAG-Avalanche, and DAG-CoDAG, regarding transaction throughput, transaction latency, and communication overhead.
The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the ...cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a data trading scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain smart contract and software guard extensions (SGX) to enable owners to fully control users' identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype comparing with the state-of-the-art schemes with real-world IIoT datasets, which demonstrates the efficiency of DTSI.
Real-time environment monitoring is a key application in Industrial Internet of Things, where sensors proactively collect and transmit environmental data to the controller. However, due to limited ...wireless resources, keeping sensors' sampled data fresh at the controller is critical. This work aims to investigate the trade-off between the sensor's data-sampling frequency and long-term data transmission energy consumption while maintaining information freshness. Leveraging the entropic risk measure (ERM), we jointly minimize the global transmission energy's mean and variance subject to probabilistic constraints on information freshness. Furthermore, while jointly saving the model training energy, we adopt the federated learning (FL) paradigm and propose an FL-based two-stage iterative optimization framework to optimize the aforementioned objective. Specifically, we iteratively learn the sampling frequency via Bayesian optimization and minimize the long-term ERM of the global energy consumption via Lyapunov optimization. Numerical results show that the proposed FL-based scheme saves substantial executing energy with less performance loss. Quantitatively, compared with the centralized learning baseline, the proposed FL-based framework saves up to 69% model training energy at the expense of a mere increased objective outcome, i.e., 6.3% in the global data transmission energy consumption (<inline-formula> <tex-math notation="LaTeX">9.936\times 10^{-5} </tex-math></inline-formula> in ERM) under 0.4% bias from the global optimal data-sampling frequency.
The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal ...vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.
Healthcare providers all over the world are faced with a single challenge: the need to improve patient outcomes while containing costs. Drivers include an increasing demand for chronic disease ...management for an aging population, technological advancements and empowered patients taking control of their health experience. The digital transformation in healthcare, through the creation of a rich health data foundation and integration of technologies like the Internet of Things (IoT), advanced analytics, Machine Learning (ML) and Artificial Intelligence (AI), is recognized as a key component to tackle these challenges. It can lead to improvements in diagnostics, prevention and patient therapy, ultimately empowering care givers to use an evidence-based approach to improve clinical decisions. Real-time interactions allow a physician to monitor a patient ‘live’, instead of interactions once every few weeks. Operational intelligence ensures efficient utilization of healthcare resources and services provided, thereby optimizing costs. However, procedure-based payments, legacy systems, disparate data sources with the limited adoption of data standards, technical debt, data security and privacy concerns impede the efficient usage of health information to maximize value creation for all healthcare stakeholders. This has led to a highly-regulated, constrained industry. Ultimately, the goal is to improve quality of life and saving people’s lives through the creation of the intelligent healthcare provider, fully enabled to deliver value-based healthcare and a seamless patient experience. Information technologies that enable this goal must be extensible, safe, reliable and affordable, and tailored to the digitalization maturity-level of the individual organization.
Human activity recognition (HAR) applications have received much attention due to their necessary implementations in various domains, including Industry 5.0 applications such as smart homes, ...e-health, and various Internet of Things applications. Deep learning (DL) techniques have shown impressive performance in different classification tasks, including HAR. Accordingly, in this article, we develop a comprehensive HAR system based on a novel DL architecture called Multi-ResAtt (multilevel residual network with attention). This model incorporates initial blocks and residual modules aligned in parallel. Multi-ResAtt learns data representations on the inertial measurement units level. Multi-ResAtt integrates a recurrent neural network with attention to extract time-series features and perform activity recognition. We consider complex human activities collected from wearable sensors to evaluate the Multi-ResAtt using three public datasets, Opportunity; UniMiB-SHAR; and PAMAP2. Additionally, we compared the proposed Multi-ResAtt to several DL models and existing HAR systems, and it achieved significant performance.
Traditional medical privacy data are at a serious risk of disclosure, and many related cases have occurred over the years. For example, personal medical privacy data can be easily leaked to insurance ...companies, which not only compromises the privacy of individuals, but also hinders the healthy development of the medical industry. With the continuous improvement of cloud computing and big data technologies, the Internet of Things technology has been rapidly developed. Radio frequency identification (RFID) is one of the core technologies of the Internet of Things. The application of the RFID system to the medical system can effectively solve this problem of medical privacy. RFID tags in the system can collect useful information and conduct data exchange and processing with a back-end server through the reader. The whole process of information interaction is mainly in the form of ciphertext. In the context of the Internet of Things, the paper presents a lightweight RFID medical privacy protection scheme. The scheme ensures security privacy of the collected data via secure authentication. The security analysis and evaluation of the scheme indicate that the protocol can effectively prevent the risk of medical privacy data being easily leaked.
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