Rapid development of the Industrial Internet of Things (IIoT) is turning the management of machinery and decision making into a multitude of operational data. A key issue in such an environment would be the ability to predict the failure of assets well to reduce downtimes and maximize on performance. The role of Predictive Maintenance (PdM) is significant here, although the current literature usually focuses on the development of the algorithms and ignores the consideration of technological, organizational, and industrial aspects. The paper will provide an extensive overview of AI-driven PdM in the framework of Industry 4.0 and suggest a hybrid taxonomy that will combine AI paradigms, stages of maintenance lifecycle, and industrial deployment scenarios. The taxonomy offers a multidimensional viewpoint connecting data analytics, machine learning and operational readiness as a conceptual framework to tie together scholarly research and industrial practice. The paper critically examines the existing issues such as data heterogeneity, model interpretability, and scalability and presents research gaps that impede the adoption of PdM, in particular by small and medium-sized enterprises (SMEs). This work provides a systematic basis to the development of explainable, adaptive, and interoperable PdM systems by providing engineering and computer science viewpoints. The results highlight the necessity of multidisciplinary solutions that can make the AI innovation stay relevant to the real-world maintenance plans and create resilient and smart industrial ecosystems.

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\) – \(14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (namedMHDP$_{SVD\_{A}RIMA}$) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show thatMHDP$_{SVD\_{A}RIMA}$approach is effective and efficient in predicting missing healthcare data.

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or parties with zero trust, which impedes the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the trade-off between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy.

Summary With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user‐service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality‐sensitive hashing (LSH)‐based service recommendation method, that is, SRAmplified‐LSH, is proposed in the article. SRAmplified‐LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS‐DREAM dataset validate the feasibility of our proposed method.

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point-of-interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check-in. Faced with this challenge, we propose an attention-based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check-ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision-making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check-in records, which can improve the interpretability of the model. Considering the time impact on users' check-in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check-in data.

IoT connects a large number of physical objects with the Internet that capture and exchange real-time information for service provisioning. Traditional network management schemes face challenges to manage vast amounts of network traffic generated by IoT services. Software-defined networking (SDN) and information-centric networking (ICN) are two complementary technologies that could be integrated to solve the challenges of different aspects of IoT service provisioning. ICN offers a clean-slate design to accommodate continuously increasing network traffic by considering content as a network primitive. It provides a novel solution for information propagation and delivery for large-scale IoT services. On the other hand, SDN allocates overall network management responsibilities to a central controller, where network elements act merely as traffic forwarding components. An SDN-enabled network supports ICN without deploying ICN-capable hardware. Therefore, the integration of SDN and ICN provides benefits for large-scale IoT services. This article provides a comprehensive survey on software-defined information-centric Internet of Things (SDIC-IoT) for IoT service provisioning. We present critical enabling technologies of SDIC-IoT, discuss its architecture, and describe its benefits for IoT service provisioning. We elaborate on key IoT service provisioning requirements and discuss how SDIC-IoT supports different aspects of IoT services. We define different taxonomies of SDIC-IoT literature based on various performance parameters. Furthermore, we extensively discuss different use cases, synergies, and advances to realize the SDIC-IoT concept. Finally, we present current challenges and future research directions of IoT service provisioning using SDIC-IoT.

Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a threat to Internet security. Intrusion detection can intelligently detect anomalous activities and secure the Internet with the help of anomaly detection algorithms. Different from static data, intrusion detection data are a dynamic data form and have the following characteristics. First, it is multiaspect. Second, it contains point anomalies and group anomalies. Third, there are correlations between different attributes. Nevertheless, these properties pose a challenge on existing anomaly detection approaches. Thus, a novel anomaly detection approach MDS_AD is proposed in this article to deal with the challenges. It combines locality-sensitive hashing (LSH), isolation forest, and PCA techniques. MDS_AD has the following properties. 1) The introduced LSH can operate on multiaspect data. 2) MDS_AD can effectively catch group anomalies from the experimental results. 3) The PCA is utilized to reduce dimensionality for correlations between different attributes. 4) MDS_AD is a streaming approach, which can perform model update and process data in constant memory and time. To confirm the performance of MDS_AD, multiple experiments are designed and implemented on UNSW-NB15 dataset. Experimental results show that MDS_AD outperforms state-of-the-art baselines.

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance. The single update focuses on processing one update event at a time,i.e., updating a set of flows from their initial routes to target routes, but it fails to handle continuously arriving update events in time incurred by high-frequency network changes. On the contrary, the continuous update proposed in “Update Algebra” can handle multiple update events concurrently and respond to the network condition changes at all times. However, “Update Algebra” only guarantees the blackhole-free and loop-free update. The congestion-free property cannot be respected. In this paper, we propose Coeus to achieve the continuous update while maintaining consistency,i.e., ensuring the blackhole-free, loop-free, and congestion-free properties simultaneously. Firstly, we establish the continuous update model based on the update operations in update events. With the update model, we dynamically reconstruct the operation dependency graph (ODG) to capture the relationship between update operations and link utilization variations. Then, we develop a composition algorithm to eliminate redundant operations in update events. To further speed up the update procedure, we present a partition algorithm to split the operation nodes of the ODG into a series of suboperation nodes that can be executed independently. The partition algorithm is proven to be optimal. Finally, extensive evaluations show that Coeus can improve the update speed by at least 179% and reduce redundant operations by at least 52% compared with state-of-the-art approaches when the arrival rate of update events equals three times per second.

A new class of link flooding attacks (LFA) can cut off internet connections of target links by employing legitimate flows to congest these without being detected. LFA is especially powerful in disrupting traffic in software-defined networks if the control channel is targeted. Most of the existing solutions work by conducting a deep packet-level inspection of the physical network links. Therefore these techniques incur a significant performance overhead, are reactive, and result in damage to the network before a delayed defense is mounted. Machine learning (ML) of captured network statistics is emerging as a promising, lightweight, and proactive solution to defend against LFA. In this paper, we propose a ML-based security framework, CyberPulse++, that utilizes a pretrained ML repository to test captured network statistics in real-time to detect abnormal path performance on network links. It effectively tackles several challenges faced by network security solutions such as the practicality of large-scale network-level monitoring and collection of network status information. The framework can use a wide variety of algorithms for training the ML repository and allows the analyst a birds-eye view by generating interactive graphs to investigate an attack in its ramp-up stage. An extensive evaluation demonstrates that the framework offers limited bandwidth and computational overhead in proactively detecting and defending against LFA in real-time.

The ever-increasing popularity of the recommender system provides a convenient way for users to find their interesting items among plenty of candidate services. However, on account of the enhancement of user privacy protection consciousness in recent years, users tend to conceal their evaluation information from the public. Thus, a large number of users with little explicit rating information are generated (i.e., cold-start users), which makes it challenging to implement high-quality recommendations. It has become a serious barrier to further and broader applications of the recommender system. In response to this issue, we take social network information into account and first propose TeCF (Trust-enhanced Collaborative Filtering). Our proposal integrates user-based, item-based, and trust-based collaborative filtering methods harmoniously and achieves a good trade-off between privacy preservation and service recommendation accuracy. A case study is conducted to validate the feasibility and comprehensiveness of our research.

Network update enables Software-Defined Networks (SDNs) to optimize the data plane performance via southbound APIs. The single update between the initial and the final network states fail to handle high-frequency changes or the burst event during the update procedure in time, leading to prolonged update time and inefficiency. On the contrary, the continuous update can respond to the network condition changes at all times. However, existing work, especially "Update Algebra" can only guarantee blackhole- and loop-free. The congestion-free property cannot be respected during the update procedure. In this paper, we propose Coeus, a continuous network update system while maintaining blackhole-, loop- and congestion-free simultaneously. Firstly, we establish an operation-based continuous update model. Based on this model, we dynamically reconstruct an operation dependency graph to capture unexecuted update operations and the link utilization variations. Subsequently, we develop an operation composition algorithm to eliminate redundant update commands and an operation node partition algorithm to speed up the update procedure. We prove that the partition algorithm is optimal and can guarantee the consistency. Finally, extensive evaluations show that Coeus can improve the makespan by at least 179% compared with state-of-the-art approaches when the arrival rate of update events equals to three times per second.

Application development for the Internet of Things (IoT) poses immense challenges due to the lack of standard development frameworks, tools, and techniques to assist end users in dealing with the complexity of IoT systems during application development. These challenges invoke the use of model-driven development (MDD) along with the representational state transfer (REST) architecture to develop IoT applications, supporting model generation at different abstraction levels while generating software implementation artifacts for heterogeneous platforms and ensuring loose coupling in complex IoT systems. This article proposes an IoT application development framework, named IADev, which uses attribute-driven design and MDD to address the above-mentioned challenges. This framework is composed of two major steps, including iterative architecture development using attribute-driven design and generating models to guide the transformation using MDD. IADev uses attribute-driven design to transform the requirements into a solution architecture by considering the concerns of all involved stakeholders, and then, MDD metamodels are generated to hierarchically transform the design components into the software artifacts. We evaluate IADev for a smart vehicle scenario in an intelligent transportation system to generate an executable implementation code for a real-world system. The case study experiments proclaim that IADev achieves higher satisfaction of the participants for the IoT application development and service orchestration, as compared to conventional approaches. Finally, we propose an architecture that uses IADev with the Siemens IoT cloud platform for service orchestration in industrial IoT.