3D point clouds are data points defining the morphology of environments, and completion refers to the reconstruction of missing points. 6G Internet of Things (6G-IoT) connected with 3D mapping devices needs reliable, consistent, high-fidelity real-time point cloud completion for accurate environment registration. Trustworthy AI, modeled with dependable Deep Learning (DL), enables reliable and robust point completion with spatial-geometrical consistency for deployment with 6G-IoT devices. Although several DL-based completion techniques are integrated with 6G-IoT devices, they have reliability issues, limiting key trustworthy AI characteristics. This research focuses on the reliability and robustness aspects of trustworthy AI to propose Point-KAN, a dependable real-time 3D point cloud completion model for 6G IoT-connected 3D mapping devices. Point-KAN integrates multi-head attention and Kolmogorov-Arnold Networks (KAN) within the modules of Attention Enhanced-Embedded Feature Collector (AEFC) and KAN-Enhanced Feature Mapper (KEFM) for trustworthy point cloud completion. Empirical evaluations on the ShapeNet demonstrate the superiority of Point-KAN against state-of-the-art (SOTA). Results concrete Point-KAN’s evolution as a trustworthy AI framework ensures reliability and robustness for real-time deployment in 6G-IoT-connected devices, facilitating 3D environment mapping.

Ensuring clean water availability is critical for sustainability and health. Conventional water quality assessments are limited by manual sampling, poor temporal resolution, and centralized data processing. This study proposes HydroFedNet, a multisource water quality monitoring framework that uses Federated Learning (FL) to integrate diverse data sources, including LANDSAT satellite imagery, RGB pond images, and Internet of Things (IoT) sensor streams. The spatio-spectral transfer learning network (Spatio-Spectral TLNet), the color transfer learning network (Color TLNet) and the sensor convolutional neural network - temporal convolutional network (Sensor CNN - TCN) are fundamental models for HydroFedNet. Spatio-Spectral TLNet and Color TLNet leverage EfficientNetB3 for optimized, low-cost training, while Sensor CNN–TCN exploits improved temporal modeling. Models are trained locally and share weight updates with a central server, which builds a global model using the chosen FL strategy. FL strategies such as Federated Averaging (FedAvg), FL with Temporally Aware aggregation (FedLTA), and Federated Optimization (FedOpt) are evaluated with six objectives, including energy efficiency, fault tolerance, and handling of non-independent and identically distributed (non-IID) data. FedLTA surpasses the 90% accuracy across all three models with less communication overhead, whereas FedOpt effectively handles non-IID data. HydroFedNet allows an optimal selection of an intent-aware FL strategy, allowing robust, scalable, and efficient water quality monitoring across heterogeneous environments.

Electroencephalogram (EEG) signal processing is essential for achieving accurate and efficient real-time edge computing, particularly in resource-constrained smart wearables. They demand lightweight and optimized solutions for rapid analysis and decision-making. However, the higher dimensionality of EEG signals introduces challenges of latency and computational cost on edge devices. This article presents E-OptEEG, a hybrid ensemble metaheuristic framework designed to bring edge-optimized EEG processing and feature selection specifically for low-powered devices. The E-OptEEG framework integrates the strengths of multiple evolutionary feature selection algorithms, leveraging an ensemble threshold voting scheme to combine their outputs and identify the most relevant features. Further, E-OptEEG employs a fruit-fly optimization algorithm with deep metric feature transformation based on cosine similarity to transform the refined feature set to a minimal latent space. E-OptEEG demonstrates its ability to identify and select the most relevant features, enabling effective emotion and sentiment analysis from EEG signals captured through edge-powered wearable devices. Experimental evaluations against state-of-the-art feature selection techniques on three different EEG-based behavior modeling datasets highlight the framework’s effectiveness, achieving an average accuracy exceeding 98% with an average accuracy improvement of 2.55%. The E-OptEEG framework exemplifies the potential for lightweight artificial intelligence solutions to enable real-time, resource-efficient decision-making in wearable electronics.

In the era of digital intelligence, wearable devices act as pivotal tools for monitoring mental health data. These data facilitate the delivery of targeted support via mental health intervention platforms. As this integrated model gains traction, the selection of appropriate platforms has become essential to ensure effective mental health services for users. Given the involvement of large-scale users with conflicting opinions, this selection issue constitutes a large-scale group decision-making problem, underscoring the need for group consensus. Within this context, effectively addressing trust semantics and minority opinions emerges as two major challenges. To address these challenges, this article explores the cooperative mental health monitoring group consensus based on dynamic trust semantics and behavioral cultural modeling. First, to address trust propagation and evolution within dynamic trust semantics, an ordered trust propagation method and a trust evolution model are constructed to establish a complete and reliable social network among users. Second, a comprehensive index is designed based on opinion similarities and trust relationships to guide the Leiden community detection, thereby reducing the user dimensionality. Third, an objective minority opinions handling method is explored. Specifically, the user weight identified as having minority opinions is increased, while an adjustment strategy that considers the confidence level is applied to remaining users. Finally, the effectiveness and robustness of the proposed group consensus method for mental health monitoring are demonstrated via extensive experimental validation.

Accurate crop yield forecasting is essential for sustainable food production and effective agricultural decision-making. This paper presents TCAN-AgriCloud, a novel cloud-enabled deep learning framework that integrates IoT-based real-time data acquisition with spatio-temporal deep learning models for scalable and interpretable yield prediction. The system combines Temporal Convolutional Networks (TCN) for capturing long-range temporal dependencies and Kolmogorov-Arnold Networks (KAN) for adaptive non-linear feature modeling. To facilitate real-world deployment, we simulate the ingestion of data streams from edge IoT sensors (soil, weather, and farm management) into a scalable cloud computing platform that enables centralized training and real-time analytics. Evaluated on corn and soybean datasets from Illinois (1980–2018), the model demonstrates superior performance in RMSE, MAE, and MSE when compared to state-of-the-art ML/DL methods. Additionally, SHAP-based interpretability helps identify key yield-affecting features. The proposed TCAN-AgriCloud framework bridges edge IoT sensing and cloud-based intelligence, offering a robust and scalable decision-support system for precision agriculture.

Facial forgery technology is advancing rapidly, leading to significant social security concerns. In recent years, as forgery technologies and types continue to emerge, many methods struggle to strike a balance between accuracy and robustness. Most existing methods rely on CNN to extract high-quality forged face clues but often overlook inherent forgery traces. Consequently, they may overfit the training dataset and perform poorly on data from diverse sources or be subjected to various post-processing operations. To address this challenge, we propose leveraging multi-scale texture information to expose subtle artifacts in RGB space. To achieve this, we devise a two-stream detection architecture that integrates texture features and RGB features. We analyze forgery traces using global large texture information and local detailed texture information separately. Additionally, we design a feature pyramid to fuse these two texture features and employ an attention mechanism to enhance the features of both streams. By examining forgery traces from multiple perspectives, we have developed an adaptive feature fusion module to facilitate interactive feature fusion between the two streams. We conduct extensive experiments on various benchmark datasets and compare our method with recent state-of-the-art (SOTA) methods to demonstrate its effectiveness. Our code will be provided at https://github.com/hryyyy/MST .

ABSTRACT Nowadays, mobile robot navigation is a crucial topic in the Industrial Internet of Things, and a number of sensors are arranged around robots to avoid obstacles on navigation paths. Obtaining optimal sensor readings that can be used to optimize the path planning of mobile robots is essential. Thus, an effective feature selection technique is explored in this study to select the optimal subset of sensor readings for mobile robot navigation. Feature selection can reasonably remove irrelevant and redundant features, reduce the dimensionality of data, and improve the learning accuracy and comprehensibility. A novel feature selection method based on 3D mutual information is studied. First, the proposed method defines symmetrical uncertainty with 3D mutual information to measure the correlation between candidate features and select features. Afterward, a merit function of feature sets is defined based on the symmetrical uncertainty to search for the optimal feature subset. Lastly, the proposed feature selection method is applied to a wall‐following robot navigation data set. Results show that the proposed method can obtain few sensor readings but with enhanced prediction accuracy of the robot's movements.

ABSTRACT Deepfake, as a popular form of visual forgery technique on the Internet, poses a serious threat to individuals' data privacy and security. In consumer electronics, fraudulent schemes leveraging Deepfake technology are widespread, making it urgent to safeguard users' data privacy and security. However, many Deepfake detection methods based on Convolutional Neural Networks (CNNs) struggle to achieve satisfactory performance on mainstream datasets, especially with heavily compressed images. Observing that tampered images leave traces in the frequency domain, which are imperceptible to the naked eye but detectable through spectrum analysis, this study proposes a novel face forgery detection framework integrating spatial and frequency domain features. The framework introduces three innovative modules: the cross‐attention fusion module (CAFM), the guided attention module (GAM), and the multi‐scale feature fusion module (MSFFM), Specifically, CAFM combines spatial and frequency‐domain features through cross‐attention to enhance feature interaction. GAM generates attention maps to refine the integration of spatial and frequency features, while MSFFM fuses multi‐scale hierarchical features to capture both global and local tampering artifacts. These modules collectively improve the richness and discrimination of the extracted features, contributing to the overall detection performance. The proposed method demonstrates its effectiveness and superiority in forgery detection tasks, achieving a 3.9% average improvement in AUC compared to the state‐of‐the‐art method GocNet [1] on FaceForensics++ (FF++) and WildDeepfake datasets. Extensive experiments further validate the effectiveness of our approach.

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing (CC) platform meets these stringent computation requirements, but it has a significant latency that fog computing (FC) effectively handles. Software defined network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network prerequisites in an agile manner. Task offloading (TO) is a crucial issue in software defined-IoV (SD-IoV), particularly when the vehicle’s resources are insufficient. Fog nodes schedule offloaded tasks; however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this article introduces two proactive intelligent TO (ITO’ and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO’ and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7–13.9)% more than the existing scheme. They also reduce average packet loss, delay, round trip time (RTT), and offloaded node time selection by (45.35–21.3)%, (50–33.3)%, (53.9–9.9)% and (43.2–26.06)% which demonstrates the efficacy of the proposed scheme.

Digital transformation is changing the entire landscape of the financial industry. The increasing customer demand to address, or at least have a positive impact on social problems, fuel the rapid growth of social fintech companies. However, as these companies scale significantly, they face critical challenges in managing employee stress, which can lead to decreased performance and high turnover rates. Following the rise of ChatGPT, large language models (LLMs) have been increasingly utilized in mental health-related applications and offering a promising solution for social fintech companies to support their employee’s mental health. Nevertheless, existing LLMs are predominantly English-focused, limiting their effectiveness in addressing mental health support across diverse linguistic groups. To address this gap, we propose a novel multilingual adaptation of widely used mental health datasets, translated from English into the two most widely spoken languages globally—Mandarin and Spanish. This adaptation enables a comprehensive evaluation of LLMs, such as GPT and Llama, in detecting and assessing mental health conditions across different languages. Initially, we used ChatGPT-4o-Mini to translate the original English dataset into Spanish and Mandarin. We then evaluate the performance of these translated datasets using various state-of-the-art LLMs. Additionally, we analyze the relationship between sentence length and prediction performance. Our experiments reveal significant variability in model performance, with language-specific nuances and disparities in mental health data coverage posing challenges to achieving consistent accuracy.

With the development of Internet technology, deepfake (DF) videos can spread rapidly through online platforms, providing a new way of cyberbullying by generating nude pictures of female victims and using their faces to generate pornographic movies, which bring potential harm to individuals, society, and the country. Recently, there have been some really impressive results with DF detection models. These models have shown excellent outstanding performance when they are trained and tested using data from the same dataset. However, detecting DF remains difficult when the data comes from challenging datasets. To address this issue, this article aims to enhance the model's generalization by taking full advantage of the learning and representation capabilities of convolutional neural networks (CNNs) to adaptively suppress image texture information and catch deeper and more universal forgery features. Specifically, we introduce the texture suppression module (TSM) as a first step to suppress image content while simultaneously revealing the differences between authentic and tampered regions. Then, we carefully designed the cross stream interaction module (CSIM) and the cross stream mix block (CSMB) module to fully exploit the extracted forgery traces. Our proposed model has demonstrated superior generalization performance in extensive experiments.

Predicting information cascades holds significant practical implications, including applications in public opinion analysis, rumor control, and product recommendation. Existing approaches have generally overlooked the significance of semantic topics in information cascades or disregarded the dissemination relations. Such models are inadequate in capturing the intricate diffusion process within an information network inundated with diverse topics. To address such problems, we propose a neural-based model using Topic-Aware Masked Attentive Network for Information Cascade Prediction (ICP-TMAN) to predict the next infected node of an information cascade. First, we encode the topical text into user representation to perceive the user-topic dependency. Next, we employ a masked attentive network to devise the diffusion context to capture the user-context dependency. Finally, we exploit a deep attention mechanism to model historical infected nodes for user embedding enhancement to capture user-history dependency. The results of extensive experiments conducted on three real-world datasets demonstrate the superiority of ICP-TMAN over existing state-of-the-art approaches.

The accessibility of mobile apps takes into account the rights and interests of various social groups, which is vital for the millions of smartphone users who are visually impaired given the variety of mobile applications available on Google Play and the App Store. Most application icons, however, lack natural language labels. It is challenging for these users to engage with mobile phones utilizing screen readers featured in mobile operating systems. Millions of visually impaired smartphone Internet users’ inability to communicate with mobile applications have become the socio-cyber world’s dark side. COALA is a pilot work that solves this issue by generating the textual label from the imaging icon automatically. However, most icon datasets have imbalance distributions in the real-world scenario that only a few categories have rich-resource labeled samples, and the major rest categories have very limited samples. To address the data imbalance problem in the icon label generation task, we provide an interconnected two-stream language model with mean teacher learning, which learns a generalized feature representation from divergent data distributions. Extensive experiments demonstrate the superiority of our two-stream language model over previous single-language models on different low-resource datasets. More experimental results reveal that our method outperforms the COALA model by a wide margin in decreasing the dark side of the socio-cyber world.

Part-of-speech (POS) taggers are the primary requisite of any natural language processing (NLP) mechanism. Conventional POS tagger and libraries are expert-made or static and concentrate on the literature domain. These POS taggers limit the performance of subsequent mechanisms like polarity detection, sentiment analysis, opinion mining, and so on. The unsuitability of a tagger for a new genre of literature makes famous libraries, such as Natural Language Toolkit (NLTK) and University Centre for Computer Corpus Research on Language (UCREL) Constituent Likelihood Automatic Word-tagging System Seven (CLAWS7) create the need for a neural POS tagger to serve Shakespearean literature. This article reports a preliminary study on the suitability of the neural taggers over static or manual taggers, supported by the accuracy of 97% achieved onHamlet. Furthermore, these neural networks are scalable over the literature domains irrespective of the stylistic variations, opening up this area to computer scientists to aid literary enthusiasts in contributing to domain of the social systems.

Video models on federated learning (FL) enable continual learning of the involved models for video tasks on end-user devices while protecting the privacy of end-user data. As a result, the security issues on FL, e.g., the backdoor attacks on FL and their defense have increasingly become the domains of extensive research in recent years. The backdoor attacks on FL are a class of poisoning attacks, in which an attacker, as one of the training participants, submits poisoned parameters and thus injects the backdoor into the global model after aggregation. Existing backdoor attacks against videos based on FL only poison RGB frames, which makes it that the attack could be easily mitigated by two-stream model neutralization. Therefore, it is a big challenge to manipulate the most advanced two-stream video model with a high success rate by poisoning only a small proportion of training data in the framework of FL. In this paper, a new backdoor attack scheme incorporating the rich spatial and temporal structures of video data is proposed, which injects the backdoor triggers into both the optical flow and RGB frames of video data through multiple rounds of model aggregations. In addition, the adversarial attack is utilized on the RGB frames to further boost the robustness of the attacks. Extensive experiments on real-world datasets verify that our methods outperform the state-of-the-art backdoor attacks and show better performance in terms of stealthiness and persistence.

Many countries worldwide aim to proactively conduct digital transformation in conventional industries as these businesses require a massive workforce to classify, inspect, and package, causing high costs. Moreover, manual approaches might lead to defective products due to human errors and productivity limits. This article develops an Internet of Things (IoT) system with an edge computing function to detect agricultural products' quality and classify them. The proposed system in this research detects the luster, weight, and appearance of agricultural products to judge the quality; hence, the equipment needs sensors and a camera that connects to the Artificial Intelligence (AI) module. The features of our research are as follows: 1. The system analyzes agricultural products' appearance, luster, and weight; when the equipment detects an object, the AI module will be activated to analyze the item; 2. The edge computing function in the system allows it to pass the task to the next step directly after the IoT development board completes the process without centralized operations on a server; 3. The 5G network used in this study ensures data transmission and reduces unnecessary circuits between devices; 4. The network safety mechanism built into the system also protects data transmission security. Additionally, this study creates a small-scale conveyor to experiment with the design and test the system's practical feasibility.

Abstract Today, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage.

Video capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to the high redundancy and subtle manifestation of anomalies among thousands of frames, the manual construal of VCE videos requires considerable patience, focus, and time. The automatic analysis of these videos using computational methods is a challenge as the capsule is untamed in motion and captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential of improving the VCE analysis. However, the clinical impact of these methods is yet to be investigated. This survey aimed to highlight the gaps between existing ML-based research methodologies and clinically significant rules recently established by gastroenterologists based on VCE. A framework for interpreting raw frames into contextually relevant frame-level findings and subsequently merging these findings with meta-data to obtain a disease-level diagnosis was formulated. Frame-level findings can be more intelligible for discriminative learning when organized in a taxonomical hierarchy. The proposed taxonomical hierarchy, which is formulated based on pathological and visual similarities, may yield better classification metrics by setting inference classes at a higher level than training classes. Mapping from the frame level to the disease level was structured in the form of a graph based on clinical relevance inspired by the recent international consensus developed by domain experts. Furthermore, existing methods for VCE summarization, classification, segmentation, detection, and localization were critically evaluated and compared based on aspects deemed significant by clinicians. Numerous studies pertain to single anomaly detection instead of a pragmatic approach in a clinical setting. The challenges and opportunities associated with VCE analysis were delineated. A focus on maximizing the discriminative power of features corresponding to various subtle lesions and anomalies may help cope with the diverse and mimicking nature of different VCE frames. Large multicenter datasets must be created to cope with data sparsity, bias, and class imbalance. Explainability, reliability, traceability, and transparency are important for an ML-based diagnostics system in a VCE. Existing ethical and legal bindings narrow the scope of possibilities where ML can potentially be leveraged in healthcare. Despite these limitations, ML based video capsule endoscopy will revolutionize clinical practice, aiding clinicians in rapid and accurate diagnosis.

Abstract Increasing amounts of data are being generated every year. Sustainable computing systems have become capable of extracting and learning information from the underlying data. Edge and AI (artificial intelligence) are expanding into industrial systems requiring new computing and networking infrastructure. Due to this, SLA computing is becoming increasingly challenging to handle in these emerging cloud environments. The cloud is a service that provides virtual resources to users. Qualitative and quantitative findings in market-oriented approaches are one of the most common methods for managing virtual and physical machines in a network. When allocating services, price is an important factor to consider. In this study, we aim to determine the initial price of VMs while considering the dynamic pricing model in a competitive, sustainable computing system. Besides negotiation-based trading, a multifactor architecture is used for trading in the marketplace. Based on the simulation results, it was found that the performance could be improved by categorizing the VMs based on regression. According to the simulation results, the cloud market system provides a better service-level agreement (SLA) and response time when assigning virtual machines to the market. Based on the results, we found that using the regression method for categorizing the VMs to manage the market improved the SLA.

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson’s disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions for FOG detection in PD from the perspective of uncertain information processing abilities, decision risks, and bounded rationality.

The wide area measurement system (WAMS) based on synchronous phasor measurement technology plays an increasingly important role in dynamic monitoring and wide area protection of modern power systems. If the phasor measurement unit (PMU) is placed on all buses of the power system, the voltage and branch current of all buses can be directly observed. However, due to the high placement cost of PMU and its ability to measure the voltage phasor of the installed bus and the current of the associated branch, it is unrealistic and unnecessary to install PMU on all buses of the system. This article discusses the incomplete observability under single PMU loss (N-1) contingencies and its effect on PMUs placement. An improved two-archive algorithm is proposed to solve the five-objective placement optimization model. In addition, a fuzzy decision-making method combining subjective and objective is proposed to help power grid enterprises select the most appropriate solution. The proposed method is tested on several IEEE bus systems and Polish 2383-bus system, and the test results verify its effectiveness.

Monitoring security and quality of service is essential, due to the rapid growth of the number of nodes in wireless networks. In healthcare/industrial environments, especially in wireless body area networks (WBANs), this is even more important. Because the delays and errors can directly affect patients'/scientists' health. To increase the Monitoring Quality of Services (MQoS) in WBANs, a secure medium access control (MAC) protocol needs to be developed to provide optimal services. This article provides a comprehensive review of MAC protocols in WBANs with a technical security analysis approach. Time-based, contention-based, and hybrid protocols are compared in this article, regarding MQoS and their security vulnerabilities. We have considered delay, packet loss, and energy consumption as performance evaluation criteria in WBANs, which may be degraded under a cyberattack. This work shows that there is a research gap in the literature, which is the failure of covering security and privacy issues in the MAC layer protocols.

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization.

With the vigorous development of Industry 4.0, industrial Big Data has turned into the core element of the Industrial Internet of Things. As one of the most fundamental and indispensable components in industrial cyber-physical systems (CPS), intelligent anomaly detection is still an essential and challenging issue. However, with the development of the network, there may exist unknown types of attacks, which are difficult to collect. Facing one-class industrial intrusion detection scenario that the collected training data only includes normal state, the one-class broad learning system (OCBLS) and the stacked OCBLS (ST-OCBLS) algorithms are developed. Benefiting from the characteristics of BLS, our proposed approaches retain the advantage of efficient training process. Moreover, the high-level hidden features of the network traffic data can be learned through the progressive encoding and decoding mechanism in ST-OCBLS. Extensive comparative experiments on several real-world intrusion detection tasks are carried out to demonstrate that our proposed methods have competitive performance and high efficiency in the face of complex network data and diversified types of intrusions. Overall, this article provides a new alternative solution for network intrusion detection in Industry 4.0.

All six papers in this special section engage in different streams but extremely relevant domain vectors of Cognitive Cyber-Physical Systems (CCPS) with artificial intelligence (AI) based solutions in medical informatics. Highlights recent trends in the scientific community and presents emergent technologies, implementations, applications concerning the CPSS. CPSS is witnessing rapid transformation as an interdisciplinary technology that blends physical components and computing devices to enable AI-based solutions. CCPS will be playing a significant role that integrates machine learning/AI techniques and resulting in dramatic improvements for medical informatics and the future of human-augmentation. CPHMS coordinates supervisory medical systems and medical resources everywhere; there is a great scope towards health consciousness and healthy society. Medical Cyber-Physical Systems (MCPS) in healthcare towards critical integration in network of medical devices. MCPS is the next generation computing that is comprised of tightly coupled computational and communication components of medical automation systems such as clinical decision, early detection of health infectious, disease prevention, rapid analysis of health hazards and so on. CCPS and MCPS research would be created new models, new design, and integration models for large scale systems in comprehensive, holistic medical automation systems. With recent enlargements in the big data processing, cognitive data science and AI, it is now possible to create even more realistic digital twins that properly model different operating situations and characteristics to process the medical intelligence systems.

In today’s world, interconnected Vehicular ad-hoc networks (VANET) and intelligent transportation systems have become more popular. Although IoV can bring many benefits for the smart cities and provide comforts for the passengers, however, the increasing needs for keeping the QoS and QoE at an acceptable level in time sensitive applications seems crucial and needs to be investigated deeply. Also, allocating the right number of resources to avoid congestions and fill the deficiencies in a distributed manner is a challenging issue. So, with the increase in users, attention must be given to Quality of Service (QoS) and resource allocation. As the vehicle network provides information to provide safety, comfort, and entertainment to drivers and passengers, they are one of the most compelling research topics in intelligent transportation systems. TDMA protocol is used in this study to increase the efficiency of the network and the quality of service it provides. To solve the synchronization problem, the Markov method predicts the size of slots and frames. The scenario field is used in the Markov application section to better predict TDMA gaps on solving the synchronization problem. Accordingly, the higher the quality of service, the lower the latency of the network, and the better the allocation of resources. Optimizing allocation and quality of service is further motivated by reducing collision between packets. In terms of its implementation, this method is divided into two components, the first being the database proposal for constructing the Markov matrix and the second being the simulation on VanetMobisim and implementation of the network in NS2. The proposed method performed better in different scenarios in terms of computational complexity, PDF, latency, and overhead, as shown in the results section.

Each group in a data-driven automobile network has its cluster head. A group can communicate with each other and members of other groups once it has been founded. Vehicles belonging to each group near the other group allow intergroup communication. Because nodes in automotive networks move so quickly, routing in these networks is a complex problem to solve. Each cluster in hierarchical clustering can be partitioned into multiple sub-clusters. Put another way, and the data is stored in a cluster, which is then divided into more clusters. The data is stored directly in separate clusters in non-hierarchical approaches. A dendrogram is a type of hierarchical tree. We anticipate increasing information sharing in clusters by properly clustering vehicles on the road and establishing clusters of the desired size in the relevant dendrogram. We can select clusters of the necessary extent and compare the Quality of Service (QoS) network’s outcomes by breaking the dendrogram at different levels. The findings reveal that the suggested method outperforms AIVISN in delay, PDR, overhead, and Drooped packets compared to AIVISN, 7.12%, 12.21%,8.32%, and 7.34%, respectively.

introduction Share on Guest Editors’ Introduction for Special Issue on Applications of Computational Linguistics in Multimedia IoT Services Authors: Quan Z. Sheng School of Computing, Macquarie University, Sydney, Australia School of Computing, Macquarie University, Sydney, Australia 0000-0002-3326-4147View Profile , Arun Kumar Sangaiah National Yunlin University of Science and Technology, Taiwan National Yunlin University of Science and Technology, Taiwan 0000-0002-0229-2460View Profile , Ankit Chaudhary Department of Computer Science, University of Missouri at Saint Louis, USA Department of Computer Science, University of Missouri at Saint Louis, USA 0000-0001-7510-1963View Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 23Issue 223 May 2023Article No.: 24pp 1–3https://doi.org/10.1145/3591355Published:23 May 2023Publication History 0citation28DownloadsMetricsTotal Citations0Total Downloads28Last 12 Months28Last 6 weeks28 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought unprecedented opportunities to meet massive behavioral data access and personalization requirements for Internet of Behavior (IoB). They facilitate the communication and computing resource allocation to guarantee low delay and energy consumption demands in healthcare. This article presents an improved offloading algorithm for Mobile Edge Computing (MEC) based on Deep Q Network (DQN) and Simulated Annealing (SA) for IoB. Firstly, we analyze the network model and establish a task cost function based on processing delay and energy consumption. Secondly, we define a Distributed Optimization Problem (DOP) to maximize individual utilities and system utility, which is proved to be a potential countermeasure. Thirdly, we conduct Markov modeling for the current offloading strategy-making scheme and define the objectives and constraints of the optimization function. At the same time, the SA is introduced into the DQN Algorithm, which improves the capacity of the algorithm by focusing on the exploration in the early stage and following the experience value in the later stage. From the simulation results, we can see that compared with the traditional scheme, the proposed strategy can maximize the utilization of the system and reduce processing delay and energy consumption.

Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user’s ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers–Jaccard–Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that.

Head pose estimation (HPE) has wide industrial applications, such as online education, human–robot interaction, and automatic manufacturing. In this article, we address two key problems in HPE based on label learning and asymmetric relation cues: 1) how to bridge the gap between the better prediction performance of networks and incorrectly label pose images in the HPE datasets and 2) how to take full advantage of the adjacent poses information around the centered pose image. We reconstruct all the incorrect labels as a two-dimensional Lorentz distribution to tackle the first problem. Instead of directly adopting the angle values ashardlabels, we assign part of the probability values (softlabels) to adjacent labels for learning discriminative feature representations. To address the second problem, we reveal the asymmetric relation nature of HPE datasets. The yaw direction and pitch direction are assigned different weights by introducing the half at half-maximum of the Lorentz distribution. Compared with the traditional end-to-end frameworks, the proposed one can leverage the asymmetric relation cues for predicting the head pose angle in the incorrect label scenarios. Extensive experiments on two public datasets and our infrared dataset demonstrate that the proposed ARHPE network significantly outperforms other state-of-the-art approaches.

Hong Kong Baptist University, Hong Kong

Recent studies in information computation technology (ICT) are focusing on Next-generation networks, SDN (Software-defined networking), 5G, and 6G. Optimal working mode for device-to-device (D2D) communication is aimed at improving the quality of service with the frequency spectrum structure is of research areas in 5G. D2D communication working modes are selected to meet both the predefined system conditions and provide maximum throughput for the network. Due to the complexity of the direct solutions, we formulated the problem as an optimization problem and found the optimal working modes under different parameters of the system through extensive simulations. After determining the links’ optimal modes, we calculated the network throughput; because of selecting the best working modes, we obtained the highest throughput. A major finding from this research is that D2D communication pairs are more inclined to use full-duplex (FD) mode in short distances to meet system requirements, and so most communications take place in FD mode at these distances. According to these results, using FD communication at short distances offers better conditions and Quality of service (QoS) than QoS-D2D method.

Summary Voice over Internet Protocol (VoIP) carries and transforms voice over the IP networks. The principles of VoIP calls are similar to traditional telephony that involves signalling, channel‐setup, digitization, and encoding of speech signal, but it transmits data over a packet‐switched network instead of circuit‐switched network. Factors which determine VoIP Quality of Service (QoS) include the choice of codec, packet loss, delay, jitter, and optimal hardware selection to handle different services. Hardware Calibration is a mechanism used for selecting an appropriate hardware for call manager to process and transmit different applications in real time. The widespread use of VoIP services in formal and informal sector produces a significant amount of data with variety of dimensions. This data can be used as leverage to analyze the system and predict various factors boosting the performance and cost effectiveness of the system. This paper proposes a predictive model that selects the best suitable hardware to handle particular offered load, which can support desired numbers of concurrent calls from wide array of processors available in the market today. This model would help the VoIP service providers in providing efficient services with QoS for different VoIP services like voice, data, video, chat, etc. This paper attempts to train and evaluate a model using various system parameters and system benchmark is predicted on an absolute scale. The results effectively demonstrate the selection of best call manager to handle offered load and hence provides QoS in overall network performance.

Summary The purpose of this paper is to compare the end results generated after usability assessment by two different ways. The assessment of top‐fifty academic websites is done by employing the tool and by involving end‐users. This research work is performed to assess latest heuristic guidelines for academic websites under three different categories i.e. {Social Media, Mobile and Security} by involving a hundred end‐users as well as by evaluating the websites using the tool. Association rule is implemented on the collected qualitative data to recognize common usability problem patterns pertaining to academic websites but from two uncommon perspectives. This study further compares these problematic patterns to provide more comprehensive analysis. Our findings investigate that most of the usability problems have been uncovered by the end‐users on academic websites that are not detected by tool. In other words, there are several key features that these tools are failed to evaluate. On the contrary, end‐users can help in evaluating these features and uncover remarkable usability problematic issues on any interactive system.

Summary In this paper, a multi‐carrier cognitive decision engine based on a binary particle swarm optimization with a non‐linear decreasing inertia‐weight (NDI‐BPSO) is presented. Our main goal is to solve the optimization problem of transmitter parameters in different wireless communication modes for cognitive radio systems (CRSs), especially for the transmitter in communication systems based on the environment sensing. In the new algorithm, the multi‐carrier cognitive decision engine based on an NDI‐BPSO algorithm can mitigate the local extreme points effectively and reduce the oscillation phenomenon in the process of optimization. We apply the NDI‐BPSO to the cognitive orthogonal frequency division multiplexing (OFDM) system to determine the best parameters to obtain good performances in different communication modes. The simulation results show that the proposed multi‐objective cognitive decision engine, which has a high fitness value and strong robustness for different communication modes, is better than the existing engines. The novel NDI‐BPSO algorithm achieves the objective of parameter optimization effectively.

Summary Outsourcing data to cloud servers is a popular service for data owners, however, how to check the integrity and freshness of the outsourced data is very challenge. Recently, Jin et al. proposed a cloud auditing protocol with full integrity and freshness support for cloud data, unfortunately in this article, we show their proposal is not secure. Concretely, the cloud servers can forge the authentication tag and thus has the ability to forge proof of data possession, which obviously invalidates their cloud auditing protocol. We also give a new cloud auditing protocol and analysis its security and performance. The results show our protocol is more efficient and secure.

Workforce monitoring is a vital activity in large factories in order to oversee the worker's concentration on their duty and increase productivity. Workforces are kind of moving targets which can be monitored via wireless sensor networks (WSNs). As sensor nodes have a limited source of energy, optimal energy consumption is of crucial importance in these networks. Several protocols for routing are designed in order to consider efficient energy consumption in conjunction with target tracking and coverage. In this article, a new energy-efficient routing algorithm geographic routing time transfer (GRTT) is proposed to use topological information of sensor nodes for target tracking and coverage applications. In this article, a weight called relay ability is defined for each node according to the sensor network topology. These weights are calculated and announced to sensor nodes by cluster heads (CHs). Once a target enters the area covered by sensor nodes, a signal is sent to the CH through the route having maximum predefined weights in the network. Simulations show better results than other tracking routing methods based on the metrics of energy consumption of the network, power consumption, and throughput for GRTT (proposed method), dynamic energy-efficient routing protocol (DEER), virtual force-based energy-hole mitigation (VFEM), nonequal-probability multicast routing protocol (MRP-NEP), and trace-announcing routing scheme (TARS) methods.

Elephant entry in human settlements causes a major threat to human–elephant conflict (HEC), but the rapid detection thereof while crossing boundary remains a difficult role, due to the lack of necessary boundary sensing infrastructure. A combination of wireless sensor networks and recent advances in deep learning has paved an effective way for elephant detection. This paper presents and evaluates a Boundary Sense Deep Learning architecture (BSDL) for automated detection of elephants, integrating, elephant image feature learning and elephant classification. A novelty in this approach enhances the deep learning Boundary Sense (BS) architecture to include a background subtraction module which highlights the visual region important for elephant recognition. The objective of the proposed BSDL architecture is validated by achieving better accuracy when compared with the raw image, time efficiency, simplifying and boosting up of Deep Convolution Neural Network (DCNN) by introducing preliminaries; elephant recognition with Multi view dataset dramatically improves the state of art and fine tuning using back-propagation. The dataset includes 1,28,069 images of various postures of elephants collected under different circumstances from different regions (Hosur, Bannerghatta, Shimoga (Sakrebailu) and Dubare). The trained model is designed for identifying only one target species, the elephants. The conclusions drawn from our work prove that the achievement percentage is 97% accuracy in the BSDL architecture, when compared with the existing deep learning algorithms using raw image.

The new frontier research era and convergence of cognitive data science methods and models with reference to the Internet of Things (IoT) and big data systems have brought about various challenges in industrial systems that need to be addressed in the current scenario. Cognitive science will lead to a high level of fluidity to analytics. This special section aims to explore the domain knowledge and reasoning of data science technologies and cognitive methods with the IoT over the big data systems. Data science techniques have been adopted to improve the IoT in terms of data throughput, optimization, and management, and to have a major impact on the future of IoT networking systems. The main focus is the design of best cognitive embedded data science technologies to process and analyze the large amount of data collected through industrial IoT systems and help for good decision making.

Smart cities can manage assets and resources efficiently by using different types of electronic data collection sensors, devices and vehicles. However, growing complexity of systems and heterogeneous networking also enlarge the destructive effect of compromised or malicious sensor nodes. In this paper, we introduce electric vehicles to conduct trust evaluation for heterogeneous vehicle network in smart cities. Compared with traditional trust evaluation mechanism, mobility-based trust evaluation owns the advantages of low energy consumption and high evaluation accuracy. Meanwhile, we investigate the problem of minimizing transmission hops of trust evaluation and refers to this as the mobile trust evaluation problem (MTEP). We first formalize the MTEP into an optimization problem and present a heuristic moving strategy of single electric vehicle. Then, we consider the MTEP with multiple electric vehicles. By scheduling the electric vehicles to access the nodes on spanning tree with maximum neighbor distance ratio, the algorithm can improve the efficiency of trust evaluation. In experiments, we compare moving strategy of single electric vehicle and multiple electric vehicles with existing methods respectively. The results demonstrate that the proposed algorithms are able to effectively reduce the entire transmission hops of trust evaluation and thus prolong the life of the network.

Vehicle-to-vehicle communication assists road-side information exchange granting ease of access and sharing between users. The communication between the vehicles is short-lived due to interference and data congestion in the resource constraint medium. This manuscript introduces a linear adaptive congestion control (LACC) augmenting the benefits of greedy routing and data dissemination model (DDM). LACC focuses on selecting beneficiary vehicle by assessing its end-to-end service capacity and link stability preference. Different from the conventional greedy approach, routing is aided by a linear integer programming module for smart decisions on neighbor selection. The interrupts in data transmission and forwarding due to non-localized vehicles, congested routing paths and paused transmissions are addressed using LACC as a series of linear optimization. This helps to improve the performance of vehicular communication estimated using delay, message delivery, outage, and beacon messages.

Summary A systematic and profound reading of an electrocardiogram (ECG) is needed to identify the different kinds of cardiac diseases called Arrhythmia. The manual identification of the changes in the ECG pattern over a long period is challenging. This work can be automatized by developing algorithms that run perfectly on a computer or on a smartphone to identify the causes of arrhythmia. The proposed work includes three stages of analysis: (1) the ECG noise suppression, (2) RR and PR intervals extraction from the ECG signal, and the (3) ECG classification. The proposed methodology accurately identified the locations and amplitudes of P, Q, R, S, and T subwaves of the ECG signal using a dedicated wavelet design. Experimental results of the MIT‐BIH arrhythmia database records indicate the energy levels of the ECG signal at a decomposition level of 4 and 8 as 3.694e+09 and 7.148e+09, respectively. These energy levels are used in deciding the wavelet decomposition levels for feature extraction and classification of the ECG signal. A decomposition level of eight is proposed in this work for perfect feature extraction and classification of the ECG signal. An analysis of subband frequencies obtained in the decomposition of the ECG signal is also performed. The proposed methodology gives a sensitivity of 99.58% and positive predictive value of 95.92% in the ECG examination.

Summary Outsourcing datum to the cloud servers is more and more popular for most data owners and enterprises. However, how to ensure the outsourced datum to be kept secure is very important. Especially, how to check the outsourced datum's integrity is a very challenge problem. Until now, there are many cryptographic protocols proposed to solve this problem, such as (dynamic) provable data position protocol, (dynamic) proof of retrievability protocol, etc. Recently, Tian et al proposed a dynamic‐hash‐table‐based public auditing scheme for secure cloud storage, which aims at simultaneously supporting secure dynamic data updating and secure public auditing for cloud storage. However, we find a security flaw in this protocol; concretely, the signature algorithm for the data blocks in their protocol is not secure; the cloud servers can easily modify the outsourced data blocks without detecting. Finally, we give a new protocol by using homomorphic tags based on their protocol and roughly analysis its security.

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

It is generally accepted that the edge computing paradigm is regarded as capable of satisfying the resource requirements for the emerging mobile applications such as the Internet of Things (IoT) ones. Undoubtedly, the data collected by underlying sensor networks are the foundation of both the IoT systems and IoT applications. However, due to the weakness and vulnerability to attacks of underlying sensor networks, the data collected are usually untrustworthy, which may cause disastrous consequences. In this article, a new model is proposed to collect trustworthy data on the basis of edge computing in the IoT. In this model, the sensor nodes are evaluated from multiple dimensions to obtain accurately quantified trust values. Besides, by mapping the trust value of a node onto a force for the mobile data collector, the best mobility path is generated with high trust. Moreover, a mobile edge data collector is used to visit both the sensors with quantified trust values and collect trustworthy data. The extensive experiment validates that the IoT systems based on trustworthy data collection model gain a significant improvement in their performance, in terms of both system security and energy conservation.

Seam carving has been widely used in image resizing due to its superior performance in avoiding image distortion and deformation, which can maliciously be used on purpose, such as tampering contents of an image. As a result, seam-carving detection is becoming crucially important to recognize the image authenticity. However, existing methods do not perform well in the accuracy of seam-carving detection especially when the scaling ratio is low. In this paper, we propose an image forensic approach based on the cooccurrence of adjacent local binary patterns (LBPs), which employs LBP to better display texture information. Specifically, a total of 24 energy-based, seam-based, half-seam-based, and noise-based features in the LBP domain are applied to the seam-carving detection. Moreover, the cooccurrence features of adjacent LBPs are combined to highlight the local relationship between LBPs. Besides, SVM after training is adopted for feature classification to determine whether an image is seam-carved or not. Experimental results demonstrate the effectiveness in improving the detection accuracy with respect to different scaling ratios, especially under low scaling ratios.

Summary Sentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.

Artificial Intelligence (AI) based technologies have deeply changed people's daily lives. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Although bringing AI into industrial scenarios could improve the performance in many aspects, new security and privacy issues are also introduced consequently. Subsequently, machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. As many machine learning and deep learning models are vulnerable against well-designed adversarial input samples, outsourcing data and algorithms for training will require the integrity of the training data. Also, data privacy of the end users must be protected. On the other hand, traditional solutions for industrial system security could also be enhanced by these AI schemes. The papers in this special section focus on emerging privacy and security issues brought by Artificial Intelligence in industrial informatics.

Adversary models have been fundamental to the various cryptographic protocols and methods. However, their use in most of the branches of research in computer science is comparatively restricted, primarily in case of the research in cyberphysical security (e.g., vulnerability studies, position confidentiality). In this article, we propose an energy-aware green adversary model for its use in smart industrial environment through achieving confidentiality. Even though, mutually the hardware and the software parts of cyberphysical systems can be improved to decrease its energy consumption, this article focuses on aspects of conserving position and information confidentiality. On the basis of our findings (assumptions, adversary goals, and capabilities) from the literature, we give some testimonials to help practitioners and researchers working in cyberphysical security. The proposed model that runs on real-time anticipatory position-based query scheduling in order to minimize the communication and computation cost for each query, thus, facilitating energy consumption minimization. Moreover, we calculate the transferring/acceptance slots required for each query to avoid deteriorating slots. The experimental results confirm that the proposed approach can diminish energy consumption up to five times in comparison to existing approaches.

With the advent of 5G, the industrial Internet of Things has developed rapidly. The industrial sensor-cloud system (SCS) has also received widespread attention. In the future, a large number of integrated sensors that simultaneously collect multifeature data will be added to industrial SCS. However, the collected big data are not trustworthy due to the harsh environment of the sensor. If the data collected at the bottom networks are directly uploaded to the cloud for processing, the query and data mining results will be inaccurate, which will seriously affect the judgment and feedback of the cloud. The traditional method of relying on sensor nodes for data cleaning is insufficient to deal with big data, whereas edge computing provides a good solution. In this article, a new data cleaning method is proposed based on the mobile edge node during data collection. An angle-based outlier detection method is applied at the edge node to obtain the training data of the cleaning model, which is then established through support vector machine. Besides, online learning is adopted for model optimization. Experimental results show that multidimensional data cleaning based on mobile edge nodes improves the efficiency of data cleaning while maintaining data reliability and integrity, and greatly reduces the bandwidth and energy consumption of the industrial SCS.

The tremendous growth of interconnected things/devices in the whole world advances to the new paradigm, i.e., Internet of Things (IoT). The IoT use sensor-based embedded systems to interact with others, providing a wide range of applications and services to upper-level users. Undoubtedly, the data collected by the underlying IoTs are the basis of the upper-layer decision and the foundation for all the applications, which requires efficient energy protocols. Moreover, if the collected data are erroneous and untrustworthy, the data protection and application becomes an unrealistic goal, which further leads to unnecessary energy cost. However, the traditional methods cannot solve this problem efficiently and trustworthily. To achieve this goal, in this paper we design a novel energy-efficient and trustworthy protocol based on mobile fog computing. By establishing a trust model on fog elements to evaluate the sensor nodes, the mobile data collection path with the largest utility value is generated, which can avoid visiting unnecessary sensors and collecting untrustworthy data. Theoretical analysis and experimental results validate that our proposed architecture and method outperform traditional data collection methods in both energy and delay.

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

SCADA (Supervisory Control and Data Acquisition) networks are built to efficiently provide supervisory and control of national and international critical infrastructures. SCADA networks represent a challenging domain for forensic investigators who have the responsibility to discover the main causes of the catastrophic incidents that could happen in these critical mission systems and provide precise and logical evidences supported with comprehensive technical reports to the legal organizations. They urgently need technological tools and frameworks that enable them to effectively do their mission without affecting the running state of SCADA networks which must be sustainable and robust against technical and disruptive incidents. This paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological approaches and paradigms that can be considered as promising for the realization of such a framework taking into account the efficient consumption of computational resources. Further, this paper proposes a conceptual framework for automated and secure forensic investigation in modern complex SCADA networks accompanied with a possible realization architecture based on the Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) promising technological paradigms. The proposed framework is intentionally designed to be compliant with the currently active motivation towards promoting green computing requirements.

Network function virtualization (NFV) is a new way to provide services to users in a network. Different from dedicated hardware that realizes the network functions for an IoT application, the network function of an NFV network is executed on general servers, and in order to achieve complete network functions, service function chaining (SFC) chains virtual network functions to work together to support an IoT application. In this paper, we focus on a main challenge in this domain, i.e., resource efficient provisioning for social IoT application oriented SFC requests. We propose an online SFC deployment algorithm based on the layered strategies of physical networks and an evaluation of physical network nodes, which can efficiently reduce bandwidth resource consumption (OSFCD-LSEM) and support the security and privacy of social IoT applications. The results of our simulation show that our proposed algorithm improves the bandwidth carrying rate, time efficiency, and acceptance rate by 50%, 60%, and 15%, respectively.

The smart grid solves the growing load demand of electrical customers through two-way real-time communication of electricity supply and demand sides and home energy management system (HEMS). However, these technical features also bring network security risks to the real-time price signal of the smart grid. The real-time price attack (RTPA) can maliciously raise the real-time price in smart meter, resulting in an increase in electrical customers load demand, causing the extensive damage to the power transmission lines due to overload. In this paper, we based on the behavioral relationship between load demand of electrical customers and real-time price of electricity suppliers (ES), defined the game relationship between RTPA, ES, and electrical customers, established a price elasticity of electricity demand (PEED) model, and proposed a defensive strategy of real-time price attack based on multiperson zero-determinant strategy (MPZDS). The experimental results show that the combination of MPZDS to some extent cut the expected load demand of electrical customers and protect the safety of power transmission lines.

An industrial mobile network is crucial for industrial production in the Internet of Things. It guarantees the normal function of machines and the normalization of industrial production. However, this characteristic can be utilized by spammers to attack others and influence industrial production. Users who only share spams, such as links to viruses and advertisements, are called spammers. With the growth of mobile network membership, spammers have organized into groups for the purpose of benefit maximization, which has caused confusion and heavy losses to industrial production. It is difficult to distinguish spammers from normal users owing to the characteristics of multidimensional data. To address this problem, this paper proposes a spammer identification scheme based on Gaussian mixture model (SIGMM) that utilizes machine learning for industrial mobile networks. It provides intelligent identification of spammers without relying on flexible and unreliable relationships. SIGMM combines the presentation of data, where each user node is classified into one class in the construction process of the model. We validate the SIGMM by comparing it with the reality mining algorithm and hybrid fuzzy c-means (FCM) clustering algorithm using a mobile network dataset from a cloud server. Simulation results show that SIGMM outperforms these previous schemes in terms of recall, precision, and time complexity.

Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs.

Special Issue on Intelligent Edge Computing for Cyber Physical and Cloud SystemsCyber Physical Systems (CPS) and Cloud Computing have received tremendous research interest and efforts from both academia and industry.Cloud computing extends the computing and storage ability of CPS and leads to a new paradigm-Cyber Physical and Cloud Systems (CPCS), which is a product of combining CPS and Cloud Computing together.It enables a new breed of applications and services, such as industrial process control, video surveillance, structural health monitoring, and intelligent agriculture, and can fundamentally change the way that people interact with the physical world.However, CPCS face many important challenges.First, the Cloud can neither manage CPS devices directly nor satisfy requirements of real-time.Second, communication bottleneck exists between CPS and the Cloud.Third, new security challenges need to be overcome to accelerate the development of these integrated applications.In particular, edge computing, acting as a new computing scheme, is a promising technology to address these challenges.It extends the Cloud Computing paradigm to the edge of the network.For example, edge computing devices, which are capable of intelligent computing, can reduce the network latency by enabling computation and storage capacity at the edge network.These so-called edge devices can bridge the gap between CPS and Cloud.The intelligent computing and storage on edge devices offer the potential to solve the communication problem, real-time problem, and security problem.The accepted papers represent the urgent needs to be considered in developing an intelligent computing for edge devices and to fill the gap between CPS and Cloud.Moreover, the outcome of this special section exhibits the latest research achievements and state-of-art research results to solve intelligent computing issues for CPCS. INTELLIGENT COMPUTING FOR EDGE DEVICES IN CYBER PHYSICAL AND CLOUD SYSTEMSThrough a peer-review process, we have accepted 10 submissions, and each selected article has received at least two rounds of rigorous reviews.The accepted articles represent activities in areas around the world and propose various theoretical research results and applications on applying Intelligent Edge Computing for Cyber Physical and Cloud Systems in industrial informatics.A brief introduction is provided to each of the articles as follows:The first three articles introduce intelligent computing for edge devices in Cyber Physical and Cloud Systems.In "Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System," Zhaolong Ning et al. construct an intelligent offloading system for vehicular edge computing in the development of smart vehicles, bringing a comfortable and safe environment to drivers and passengers.In this research, the author has investigated two-sided matching scheme and a deep reinforcement learning to solve sub-optimization problems.Numerical results demonstrate that the matching algorithm in the first module can reach 95% of the exhaustive algorithm in different network scenarios and decrease the execution time by more than 90%.For the

Summary A time‐limited data access control scheme allows a user's access to the data files only for a specified time period. A cryptographic solution to the time‐limited access control problem is by encrypting each data group associated with a time period with a distinct key. The data is encrypted by the data owner. The respective decryption keys are then distributed to authorized users by the data owner. A user requires one secret decryption key storage for each authorized time period. To reduce the secret key storage with each user, time‐limited hierarchical key management schemes are generally used. Many such schemes are proposed in the recent years. The objective of these schemes is system efficiency and data security. Construction of such schemes become more challenging when data is outsourced to an untrusted third party service provider. In current work, an efficient and secure time‐limited hierarchical key assignment scheme is proposed for key management suitable for data outsourcing scenario. We compare it with the other recent similar schemes. The scheme is formally proved against the modern stronger security notion called key indistinguishability.

Summary Recently, microblog sites such as Twitter attract a great deal of attention as an information resource for topic detection task. Most of existing feature‐pivot topic detection algorithms in Twitter just take a single feature into account rather than multiple features. Thus, these methods always only detect the topics related to the single feature and miss some important topics, which causes a relatively low performance. In this paper, we build a flexible term representation framework for feature‐pivot topic detection based on four features. A Learning‐based Topic Detection using Multiple Features (LTDMF) method is proposed to improve the performance of topic detection. We define a correlation function based on a specific neural network to integrate various features. A Hierarchical Agglomerative Clustering (HAC) algorithm is applied to cluster terms as topics. Based on multiple features, LTDMF detects all types of topics and improves the accuracy of topic detection to solve the problem of missing topics. Experiments show that LTDMF gets a better performance compared with several baseline methods in terms of precision and recall.

A top view camera having wide range lens installed overhead of the objects contributes greatly toward resolving the tracking problem and also maintains comprehensive visual access of the environment. Video analytics becoming more important to Internet of Things applications including automatic people monitoring and surveillance systems. We followed an approach based on machine learning features-based person tracking algorithm in industrial environment. The algorithm implements simple motion detection framework through motion blobs. The algorithm, rHOG uses the history of already imaged/blobed population with the anticipated blob position of the person observed. We have compared our results, acquired through five varying test sequences, with established algorithms used for object tracking. The results highlight that our algorithm beats others tracking algorithms by greater margins. The accuracy depicted in our results shows 99% of accuracy compared to the last known best algorithm, the mean shift algorithm, yielding 48% accuracy in result. Furthermore, unlike other blob-based tracking algorithms, our algorithm has additional property to discriminate any blob as a person or no person. Our proposed tracking algorithm has the additional advantage of detecting stationary person for a long time, handling occlusion, abrupt change in the environment, and keeps performing the tracking by compensating for the gaps in data pertaining to all the frames.

Recently, Integrated computing (IC) provides a promising solution to the industry for building the Internet of Things (IoT) systems and make innovation at a rapid pace. The new era of IC with reference to IoT for Industrial applications has three main components: 1) intelligent devices; 2) intelligent system of systems; and 3) end-to-end analytics. This Special Issue is integrating computational intelligence (CI) paradigms, advanced data analytics optimization opportunities to bring more compute to the IoT. CI paradigms are more appropriate for handling uncertainty and complexity of solving real work problems compared to traditional statistical approaches and tools presently being utilized. In fact, recent literatures have addressed the inherent power of fusion of CI approaches. Moreover, it can provide the effective solutions for machine understanding of data (structured/semi structured), optimization problems, specifically, dealing with incomplete or inconsistent information, with limited computational capability related to IoT.

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated temporal change image, which obtains temporal difference in the pixel level, are built by three different metrics. Second, the simple linear iterative clustering algorithm is used to generate superpixels, which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Third, a sCAE network is trained with the superpixel samples as input to learn the change features in semantic. Then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on principal components analysis and Markov random fields. Experiment results show that our deep learning model can separate nonlinear noise efficiently from change features and obtain better performance in change detection for synthetic aperture radar images than conventional change detection algorithms.

Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

Massive data sets of continuous position-based queries (CPQs) in position monitoring applications offer a challenge of time ingestion while forwarding the position-based data within wireless search space area. As a result of recurrent modifications in network topology due to the mobility of users, processing of large CPQ data sets and roaming CPQs is one of the challenges in position monitoring. Therefore, the parallel algorithm is proposed in this paper for clustering and parallel processing of roaming CPQs which will recognize solid clusters in the wireless search space area. We present an algorithm which proposes the use of search space areas for clustering and introduce a parallel framework for parallel processing of CPQ data sets. The wireless search space area from wireless networks are used for scalable and effective calculation of clusters and dimensions in wireless networks. Present continuous query processing techniques cannot competently process roaming CPQs in the wireless search space area. The proposed algorithm is demonstrated to have practically best speedups in processing roaming CPQs. Results indicate that the proposed work determine improved ability of CPQs over existing mechanisms and attain small query latency and high precision in position monitoring applications.