The digitalization of traffic networks has spurred the development of intelligent transportation systems. By utilizing reinforcement learning for dynamic traffic optimization, it efficiently handles real-world traffic complexities. However, as the demand for real-time, high-efficiency tasks increases, relying solely on reinforcement learning struggles to meet both goals. Integrating reinforcement learning with mobile communication technology offers a promising solution for efficient, low-overhead traffic networks. As an important physical layer technology for Integrated Sensing and Communications Systems, Spectrally Efficient Frequency Division Multiplexing (SEFDM) addresses the communication overhead challenge in reinforcement learning-enabled optimization. However, the main challenge of SEFDM is eliminating the inter-carrier interference (ICI) caused by non-orthogonal modulation. Considering that existing post-interference cancellation methods fail due to the ill-conditioning of the generalized channel matrix, which cannot be directly inverted, we propose a nonlinear precoding algorithm at the transmitter, instead of post-cancellation, that effectively eliminates interference and improves transmission reliability. We firstly use a nonlinear feedback structure to avoid power boost and error propagation. Besides that, Geometric Mean Decomposition (GMD) based interference matrix decomposition algorithm is used in the proposed precoding scheme to avoid matrix singularity and obtain diversity gain. Finally, the numerical results show that the proposed precoding method can achieve higher order QAM SEFDM signaling with higher spectral efficiency and get comparative BER performance.

The growing demand for computation-intensive artificial intelligence (AI) and machine learning (ML) applications necessitates carbon-neutral edge computing to enhance resource efficiency, reduce energy consumption, and promote sustainability in Industrial Internet of Things (IIoT) systems. However, reducing service latency and energy consumption while ensuring execution accuracy and a predictable carbon footprint and its associated cost remains a critical research challenge. Existing works in the literature experience significant challenges for task offloading due to a lack of edge collaboration and ineffective management of Carbon Emission Rights (CER) credits. In this paper, we have developed an optimization framework leveraging Mixed Integer Linear Programming (MILP), namely GRELMON, to jointly minimize service latency and energy consumption while maximizing task accuracy in carbon-neutral collaborative edge and cloud computing for IIoT environments. Moreover, a carbon emission forecasting model using a hybrid deep learning approach is also developed to prevent unnecessary CER purchases. The experimental results demonstrate that GRELMON outperforms state-of-the-art methods by reducing latency and energy consumption while improving the accuracy of the execution of ML tasks.

The Internet of Medical Things (IoMT) has emerged as a transformative technology platform in the healthcare sector, enabling real-time monitoring and intelligent decision-making through connected devices. However, prioritizing and offloading the massive volume of computational tasks generated by IoMT devices while minimizing latency and energy consumption poses significant challenges. Existing approaches often overlook dynamic real-time factors such as task urgency and data freshness, as well as the integration of local task processing via Device-to-Device (D2D) communication with offloading to Mobile Edge Computing (MEC) servers. In this paper, we develop a priority- and Age of Information (AoI)-Aware task offloading framework for latency and energy optimization in healthcare IoT systems, namely PRALEIT, exploiting Mixed Integer Linear Programming (MILP) problem. The developed PRALEIT system introduced probabilistic classification of IoMT tasks based on vital signs and AoI value by leveraging a Bayesian classifier. The experimental results depict that the PRALEIT system significantly reduces task execution delay and energy consumption compared to state-of-the-art models, ensuring reliable and sustainable healthcare services.

Abstract Emergency departments, crucial in managing patient emergencies, are often challenged by overcrowding and diagnostic errors during triage—the process that assesses the urgency of patients' conditions. Traditional triage systems, heavily dependent on human judgement, are prone to errors like under‐triage, where severe conditions are missed, delaying treatment, and over‐triage, where less severe conditions are overly prioritized, causing unnecessary resource use and morbidity. This thesis presents a novel multi‐model machine‐learning framework designed to enhance triage accuracy by evaluating multiple medical conditions concurrently, including chronic illnesses like heart disease. The framework employs various machine‐learning techniques—such as logistic regression, support vector machines, random forests, deep neural networks, and decision trees—to analyze different medical conditions in two comprehensive phases. In the first phase, patients' conditions are categorized, and a multi‐tiered analysis using multiple classifiers refines the assessment by considering probabilistic outcomes from each classifier. The second phase synthesizes these insights into a unified and precise triage decision, distinguishing between patients who require critical care and those needing less urgent hospitalization. This integration of diverse machine learning models allows for a fused and precise triage decision, overcoming traditional triage systems' limitations that usually focus on single conditions and rely on isolated model predictions. A hybrid feature selection method is also utilized to identify critical predictors, enhancing the decision‐making process. The framework is validated through a specially curated dataset that simulates multiple triage scenarios, evaluated by medical experts for efficacy. Comparative analysis with traditional triage methods demonstrates significant improvements in decision accuracy, as evidenced by higher area under curve (AUC) values—0.95 for critical care and 0.90 for hospitalization. The implementation of this data‐driven approach substantially reduces human error, boosts operational efficiency, and aids medical staff in making rapid, informed decisions. This research represents a significant advancement in emergency medical care, illustrating the benefits of integrating sophisticated machine learning techniques to improve patient outcomes and resource management.

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, significantly outperforming benchmark strategies.

The paradigm shift toward digital transformation is increasingly advancing toward cognitive decision discovery, particularly within the healthcare domain, where it has emerged as a critical area of research. Numerous researchers are actively contributing to this field. However, due to the sensitive nature of healthcare data, ensuring robust security within the cognitive decision-making process is paramount for Internet of Medical Things (IoMT). To address this concern, the present study proposes a comprehensive privacy-preserving authentication scheme associating aerial computing and knowledge discovery. This scheme leverages an elliptic curve-based cryptosystem to establish the authentication protocol and incorporates blockchain technology to ensure data storage security. Furthermore, the scheme facilitates secure knowledge discovery in data (KDD) within cognitive decision-making frameworks. The proposed authentication mechanism is evaluated across communication, computational efficiency, and security parameters to validate its functionality and robustness as well as to formally verify the developed scheme Scyther tool verification is done by authors. Additionally, to demonstrate the necessity and effectiveness of the proposed scheme, the authors conducted a KDD experiment using both a securely authenticated dataset and an insecure, compromised dataset. The results of these experiments are presented and thoroughly analyzed in the article.

Abstract In the past, most strategies were mainly designed to focus on stocks or futures as the trading target. However, due to the enormous number of companies in the market, it is not easy to select a set of stocks or futures for investment. By investigating each company’s financial situation and the trend of the overall financial market, people can invest precisely in the market and choose to go long or short. Moreover, how to determine the position size of the transaction is also a problematic issue. In the past, many money management theories were based on the Kelly criterion. And they put a certain percentage of their total funds into the market for trading. Nonetheless, three massive problems cannot be overcome. First, futures are leveraged transactions, and extra funds must be deposited as margin. It causes that the position size is hard to be estimated by the Kelly criterion. The second point is that the trading strategy is difficult to determine the winning rate in the financial market and cannot be brought into the Kelly criterion to calculate the optimal fraction. Last, the financial data are always massive. A big data technique should be applied to resolve this issue and enhance the performance of the framework to reveal knowledge in the financial data. Therefore, in this paper, a concept of converting the original futures trading strategy into options trading is proposed. An LSTM (long short-term memory)-based framework is proposed to predict the profit probability of the original futures strategy and convert the corresponding daily take-profit and stop-loss points according to the delta value of the options. Finally, the proposed framework brings the results into the Kelly criterion to get the optimal fraction of options trading. The final research results show that options trading is closer to the optimal fraction calculated by the Kelly criterion than futures trading. If the original futures trading strategy can profit, the benefits after converting to options trading can be further superior.

Computational offloading in mobile edge computing (MEC) systems provides an efficient solution for resource‐intensive applications on devices. However, the frequent communication between devices and edge servers increases the traffic within the network, thereby hindering significant improvements in latency. Furthermore, the benefits of MEC cannot be fully realized when the communication link utilized for offloading tasks experiences severe attenuation. Fortunately, reconfigurable intelligent surfaces (RISs) can mitigate propagation‐induced impairments by adjusting the phase shifts imposed on the incident signals using their passive reflecting elements. This paper investigates the performance gains achieved by deploying multiple RISs in MEC systems under energy‐constrained conditions to minimize the overall system latency. Considering the high coupling among variables such as the selection of multiple RISs, optimization of their phase shifts, transmit power, and MEC offloading volume, the problem is formulated as a nonconvex problem. We propose two approaches to address this problem. First, we employ an alternating optimization approach based on semidefinite relaxation (AO‐SDR) to decompose the original problem into two subproblems, enabling the alternating optimization of multi‐RIS communication and MEC offloading volume. Second, due to its capability to model and learn the optimal phase adjustment strategies adaptively in dynamic and uncertain environments, deep reinforcement learning (DRL) offers a promising approach to enhance the performance of phase optimization strategies. We leverage DRL to address the joint design of MEC‐offloading volume and multi‐RIS communication. Extensive simulations and numerical analysis results demonstrate that compared to conventional MEC systems without RIS assistance, the multi‐RIS‐assisted schemes based on the AO‐SDR and DRL methods achieve a reduction in latency by 23.5% and 29.6%, respectively.

The widespread use of social media platforms has led to an increase in the dissemination of fake news with the intention of manipulating public opinion and causing chaos and panic among the population. To address this issue, we focus on detecting the organized groups that participate together in fake news campaigns without prior knowledge of the news content or the profiles of social accounts. To this end, we propose aspatial–temporal similarity graph, a novel graph structure that connects social accounts that participate in the early stage of similar fake news campaigns. A community detection algorithm is applied on the similarity graph to cluster the users into communities. We propose acommunity labeling algorithmto label the communities as benign or malicious based on the output of a fake news classifier. Evaluation results show that the community labeling algorithm can correctly label the communities with an accuracy of$99.61\%$. In addition, we perform a statistical comparison analysis to identify the structural community features that are statistically significant between benign and malicious communities.

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers ofAnt Colony,Whale,Sine-Cosine, andHenry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

Football or Soccer is one of the most popular games in the world. “Handball” event in the game is one of the most controversial and important decisions by a single referee. This paper proposes a method to define a “handball event” in a football game by recognizing the hand, ball, and their interaction from a single camera. We trained a model to identify hands and balls using detectron2, then used the vertices of two objects to find out the overlapping situation between these two objects to determine the handball event in a football game. The train and test result of detection was satisfactory with 96% for hand and 100% for the ball respectively.

The traditional healthcare is overwhelmed by the processing and storage of massive medical data. The emergence and gradual maturation of Internet-of-Things (IoT) technologies bring the traditional healthcare an excellent opportunity to evolve into the IoT-enabled healthcare of massive data storage and extraordinary data processing capability. However, in IoT-enabled healthcare, sensitive medical data are subject to both privacy leakage and data tampering caused by unauthorized users. In this article, an attribute-based secure access control mechanism, coined (SACM), is proposed for IoT-Health utilizing the federated deep learning (FDL). Specifically, we manage to discover the relationship between users’ social attributes and their trusts, which is the trustworthiness of users rely on their social influences. By applying graph convolutional networks to the social graph with the susceptible–infected–recovered model-based loss function, users’ influences are obtained and then are transformed to their trusts. For each occupation, users’ trusts allow them to access specific medical data only if their trusts are higher than the corresponding threshold. Then, the FDL is applied to obtain the optimal threshold and relevant access control parameters for the improvement of access control accuracy and the enhancement of privacy preservation. The experimental results show that the proposed SACM achieves accurate access control in IoT-enabled healthcare with high data integrity and low privacy leakage.

Abstract Human conversational styles are measured by the sense of humor, personality, and tone of voice. These characteristics have become essential for conversational intelligent virtual assistants. However, most of the state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret the affective semantics of human voices. This research proposes an anthropomorphic intelligent system that can hold a proper human-like conversation with emotion and personality. A voice style transfer method is also proposed to map the attributes of a specific emotion. Initially, the frequency domain data (Mel-Spectrogram) is created by converting the temporal audio wave data, which comprises discrete patterns for audio features such as notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used to predict seven different affective states from voice. The voice is also fed parallelly to the deep-speech, an RNN model that generates the text transcription from the spectrogram. Then the transcripted text is transferred to the multi-domain conversation agent using blended skill talk, transformer-based retrieve-and-generate generation strategy, and beam-search decoding, and an appropriate textual response is generated. The system learns an invertible mapping of data to a latent space that can be manipulated and generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to voice synthesize and style transfer. Finally, the waveform is generated using WaveGlow from the spectrogram. The outcomes of the studies we conducted on individual models were auspicious. Furthermore, users who interacted with the system provided positive feedback, demonstrating the system’s effectiveness.

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. It takes a lot of effort to learn people’s food preferences and make recommendations based on their affects and nutrition. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social affect has some significant benefits over the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person’s affect is detected using electroencephalography (EEG) signals. EEG allows to capture the brain signals and analyze them to anticipate affective state toward a food. In this study, we have used a 14-channel wireless Emotiv Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person’s energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters.

The communication among smart meters (SMs) and neighborhood area network (NAN) gateways is a fundamental requisite for managing the energy consumption at the consumer site. The bidirectional communication among SMs and NANs over the insecure public channel is vulnerable to impersonation, SM traceability, and SM physical capturing attacks. Many existing schemes’ insecurities and/or inefficiencies call for an efficient and secure authentication scheme for smart grid infrastructure. In this article, we present a privacy preserving and lightweight authentication scheme for smart grid (LAS-SG) using elliptic curve cryptography. The proposedLAS-SGis proved as secure under the standard model. Moreover, the efficiency of the LAS-SG is extracted through a real-time experiment, which attests that proposedLAS-SGcompletes a round of authentication in 20.331 ms by exchanging only two messages and 192 B. Due to the adequate efficiency and ample security, the proposedLAS-SGis more appropriate for SG environments.

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms.

Artificial intelligence-driven automation has gradually become the technical trend of the new automation era. At present, many artificial intelligence technologies have been applied to improve the intelligence level in the field of automation. Among them, convolutional neural network (CNN) technology is one of the most representative, which is used in the detection of defective products in industrial automation, robot human tracking has been widely used in the field of machine vision driven automation. However, the high dependence of the current neural network application leads to the potential failure of the defective product detection system. In this article, we model the learning and decision-making process of CNN with a statistical physical percolation model. Based on the differentiation degree and vulnerability of percolation, we propose the concept of CNN differentiation degree and summarize the empirical formula to quantify it. The relationship between the differentiation degree and vulnerability is analyzed from both adversarial attack and adversarial training perspectives to explain the decision-making mechanism of CNN and classification reliability. The physical model can approach the essence of things and finally guide the reliable CNN for industrial automation.

In this article, we propose and analyze a group of adversarial backdoor attack methods on neural-architecture-search (NAS) enabled edge AI systems in industrial Internet of Things (IIoT) domain. NAS is a new popular way to generate scale-adaptive deep neural networks which can meet the respective requirements of cloud, edge, and terminal AI computing in IIoT domain. However, since most users in NAS-enabled edge side are not the generators of AI models, the deployed edge AI models may have some vulnerabilities such as backdoors. These might pose serious security issues in IIoT. We propose some effective policies to attack such edge AI systems and provide advice about how to defend them. The most significant attack through third-party pretrained NAS in IIoT may occur by backdoor attacks while the third party might introduce vulnerability in the training dataset. The article designs backdoor attack processes to NAS-enabled edge devices to identify NAS’s vulnerability to adversarial trojaning attacks and interpret the backdoor attacks. It shows that the existence of high impact nodes greatly weakens the robustness of the network. A malicious attacker can quickly paralyze the network by only selecting a few high impact nodes. Finally, it provides advice and possible solution on defending the adversarial backdoor attacks to NAS.

The smartphone-based sensors (including accelerometer, proximity, and gyroscope sensors) are ubiquitous and emerging mobility data sources that could be used for transportation modes (i.e. bus, train, car, walking, and stationary) detection. One of the important challenges in transportation modes detection is to build an appropriate model that can extract useful data from the sensor outputs and that can reduce misclassifications. Several factors make the feature modeling difficult including inappropriate sampling frequency of input signals, wavering behavior of devices (e.g. the changing orientation of a device relative to the human body), and continuous base vibration causing similar sensor outputs for both stationary and non-stationary states and related threshold values of velocity. This paper proposes novel approaches to address these challenges by developing a robust transportation mode detector based on a convolution neural network (CNN). The proposed robust detector develops a feature modeling technique by novel feature fusion and cloning techniques. Pre-trained features are constructed using a separate vanilla neural network (VNN) framework to extract the distinguishing components from the original features that are combined with the original and cloned features. The proposed feature fusion technique is successfully able to overcome the noise from the base vibration and the minimal informative outputs from the lower sampling frequency. This enables the CNN to be trained with more efficient and discriminative features that result in a better classification model. The proposed approaches have been validated using a large volume of mobile sensor data based on the movements of travelers. Different types of mobile sensors have been used to collect data including accelerometer, proximity, and gyroscope. Experimental results demonstrate that the proposed approaches can improve the performance of the detection engine significantly over conventional techniques and reduces the misclassification rate.

In recent years, intrusion detection systems (IDSs) are offering effective solutions to protect various types of cyber-attacks in different networks such as Internet of Vehicles (IoVs) network in Intelligent Transportation Systems (ITS). Deep learning models have largely been leveraged by these intrusion detection systems to achieve better effectiveness results. However, deep learning models are black boxes, which limits their acceptability in decision systems. Also, they require powerful processing capabilities such as GPU, which limit their deployments in resource-constrained devices in IoV environment. To deal with these issues, we propose a two-stage IDS in ITS to discover suspicious network activity of In-Vehicles Networks (IVN) and vehicles to everything (V2X) networks. Our proposed IDS system uses rule extraction methods from deep learning models, i.e., deep neural networks in two stages. In the first stage, we analyze network traffic to distinguish between normal and attack traffic. If the traffic is found malicious, the second stage is invoked to identify the type of attack. To this end, we propose three variants of rule extraction. The first and the second variants are homogeneous, and they apply$DeepRed$and$HypInv$rule extraction methods in both stages respectively. The third variant is heterogeneous, and it applies$HypInv$in the first stage to perform binary classification, and$DeepRed$in the second stage to perform attack classification. The key idea is to combine the advantages of rule extraction technique and two-stage IDS architecture to resource consumption and improve classification accuracy. The proposed IDS model was tested using four benchmark datasets, i.e. ISCXIDS2012, CIC-IDS2017, and CSE-CIC-IDS2018 datasets are used for external network communications and the car hacking dataset are used for in-vehicle communications. The evaluation results show that the homogeneous$DeepRed$is the optimal one in all cases of IDS system with an accuracy scores ranging between 92.43%-98.32% under CIC-IDS2017 dataset, between 91.32%-99.46% under CSE-CIC-IDS2018 dataset, and between 96.05%-99.21% under Car-hacking dataset.

The convergence of Maritime Transportation Systems (MTS) and Internet of Things (IoT) has led to the promising IoT-empowered MTS (IoT-MTS). However, abnormal trajectories of maritime transportation ships can have highly negative impacts on the management of IoT-MTS. Therefore, anomaly detection of trajectories is important for the successful deployment of IoT-MTS. In this paper, we propose a Transfer Learning based Trajectory Anomaly Detection strategy, named TLTAD, for IoT-MTS. Specifically, a variational autoencoder is used to discover the potential connections between each dimension of the normal trajectory, while a graph variational autoencoder is used to explore the spatial similarity between normal trajectories. Based on internal connection of trajectories, a deep reinforcement learning algorithm, Twin Delayed Deep Deterministic policy gradient (TD3), is employed to train the trajectory anomaly detection model. To reduce the model training time, transfer learning is used to migrate the trained anomaly detection model between different regions of an ocean area or between similar ocean areas. Moreover, an efficient data transformation module is designed to improve the efficiency of model transfer. The experiments were conducted on a real-world automatic identification system (AIS) dataset. The results indicate that the proposed TLTAD can provide accurate anomaly detection on ships’ trajectories in IoT-MTS with reduced model training times.

The recent burgeoning of Internet of Things (IoT) technologies in the maritime industry is successfully digitalizing Maritime Transportation Systems (MTS). In IoT-enabled MTS, the smart maritime objects, infrastructure associated with ship or port communicate wirelessly using an open channel Internet. The intercommunication and incorporation of heterogeneous technologies in IoT-enabled MTS brings opportunities not only for the industries that embrace it, but also for cyber-criminals. Cyber Threat Intelligence (CTI) is an effective security strategy that uses artificial intelligence models to understand cyber-attacks and can protect data of IoT-enabled MTS proficiently. Unsurprisingly, most of the existing CTI-based solutions uses manual analysis to extract relevant threat information, and has low detection and high false alarm rate. Therefore, to tackle aforementioned challenges, an automated framework called DLTIF is developed for modeling cyber threat intelligence and identifying threat types. The proposed DLTIF is based on three schemes: a deep feature extractor (DFE), CTI-driven detection (CTIDD) and CTI-attack type identification (CTIATI). The DFE scheme automatically extracts the hidden patterns of IoT-enabled MTS network and its output is used by CTIDD scheme for threat detection. The CTIATI scheme is designed to identify the exact threat types and to assist security analysts in giving early warning and adopt defensive strategies. The proposed framework has obtained upto 99% accuracy, and outperforms some traditional and recent state-of-the-art approaches.

Intelligent Transportation Systems (ITS) have gained popularity due to smart services and applications to facilitate the users on the roads. The increasing growth of users in these networks created new and complex data processing, storage, security, and privacy concerns. These networks are using centralized edge, fog, or cloud architecture for data management. User privacy is compromised in these networks due to the increasing demands and service provider’s services. To ensure the data privacy, the centralized architectures are used without privacy regulations. In this paper, we present a Blockchain-based Privacy-Preserving Authentication (BPPAU) model for ITS networks to ensures users privacy and security. The proposed model provides data storage, data accessing, and processing management by using a blockchain smartcontract system, access control policy and on demand based functions. The proposed model is tested in a simulation environment to check its performance in terms of transaction cost with data size, transaction per second analysis with block time, and computational time analysis with several transactions.

The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.

Edge AI technologies have been used for many Intelligent Transportation Systems, such as road traffic monitor systems. Neural Architecture Search (NAS) is a typcial way to search high-performance models for edge devices with limited computing resources. However, NAS is also vulnerable to adversarial attacks. In this paper, A One-Shot NAS is employed to realize derivative models with different scales. In order to study the relation between adversarial robustness and model scales, a graph-based method is designed to select best sub models generated from One-Shot NAS. Besides, an evaluation method is proposed to assess robustness of deep learning models under various scales of models. Experimental results shows an interesting phenomenon about the correlations between network sizes and model robustness, reducing model parameters will increase model robustness under maximum adversarial attacks, while, increasing model paremters will increase model robustness under minimum adversarial attacks. The phenomenon is analyzed, that is able to help understand the adversarial robustness of models with different scales for edge AI transportation systems.

Due to the privacy breach risks and data aggregation of traditional centralized machine learning (ML) approaches, applications, data and computing power are being pushed from centralized data centers to network edge nodes. Federated Learning (FL) is an emerging privacy-preserving distributed ML paradigm suitable for edge network applications, which is able to address the above two issues of traditional ML. However, the current FL methods cannot flexibly deal with the challenges of model personalization and communication overhead in the network applications. Inspired by the mixture of global and local models, we proposed a Communication-Efficient Personalized Federated Meta-Learning algorithm to obtain a novel personalized model by introducing the personalization parameter. We can improve model accuracy and accelerate its convergence by adjusting the size of the personalized parameter. Further, the local model to be uploaded is transformed into the latent space through autoencoder, thereby reducing the amount of communication data, and further reducing communication overhead. And local and task-global differential privacy are applied to provide privacy protection for model generation. Simulation experiments demonstrate that our method can obtain better personalized models at a lower communication overhead for edge network applications, while compared with several other algorithms.

Machine learning (ML) operations or MLOps advo-cates for integration of DevOps- related practices into the ML development and deployment process. Adoption of MLOps can be hampered due to a lack of knowledge related to how development tasks can be automated. A characterization of bot usage in ML projects can help practitioners on the types of tasks that can be automated with bots, and apply that knowledge into their ML development and deployment process. To that end, we conduct a preliminary empirical study with 135 issues reported mined from 3 libraries related to deep learning: Keras, PyTorch, and Tensorflow. From our empirical study we observe 9 categories of tasks that are automated with bots. We conclude our work-in-progress paper by providing a list of lessons that we learned from our empirical study.

Infrastructure as code (IaC) scripts, such as Ansible scripts, are used to provision computing infrastructure at scale. Existence of bugs in IaC test scripts, such as, configuration and security bugs, can be consequential for the provisioned computing infrastructure. A characterization study of bugs in IaC test scripts is the first step to understand the quality concerns that arise during testing of IaC scripts, and also provide recommendations for practitioners on quality assurance. We conduct an empirical study with 4,831 Ansible test scripts mined from 104 open source software (OSS) repositories where we quantify bug frequency, and categorize bugs in test scripts. We further categorize testing patterns, i.e., recurring coding patterns in test scripts, which also correlate with appearance of bugs. From our empirical study, we observe 1.8% of 4,831 Ansible test scripts to include a bug, and 45.2% of the 104 repositories to contain at least one test script that includes bugs. We identify 7 categories of bugs, which includes security bugs and performance bugs that are related with metadata extraction. We also identify 3 testing patterns that correlate with appearance of bugs: 'assertion roulette’, 'local only testing’, and 'remote mystery guest‘. Based on our findings, we advocate for detection and mitigation of the 3 testing patterns as these patterns can have negative implications for troubleshooting failures, reproducible deployments of software, and provisioning of computing infrastructure.

The rapid development of the automotive Industrial Internet of Things requires secure networking infrastructure toward digitalization. Cybertwin (CT) is a next-generation networking architecture that serves as a communication, and digital asset owner, and can make the Vehicle-to-Everything (V2X) network flexible and secure. However, CT itself can publish end users’ digital assets to other entities as a service, making data security and privacy major obstacles in the realization of V2X applications. Motivated from the aforementioned discussion, this article presents BDTwin, a blockchain and deep-learning-based integrated framework to enhance security and privacy in CT-driven V2X applications. Specifically, a blockchain scheme is designed to ensure secure communication among vehicles, roadside units, CT-edge server, and cloud server using a smart contract-based enhance-Proof-of-Work (ePoW) and Zero Knowledge Proof (ZKP)-based verification process. Smart contracts are used to enforce rules and regulations that govern the behavior of V2X entities in a nondeniable and automated manner. In a deep-learning scheme, an autoregressive-deep variational autoencoder model is combined with attention-based bidirectional long short-term memory (A-BLSTM) for automatic feature extraction and attack detection by analyzing CT-edge servers data in a V2X environment. Security analysis and experimental results using two different sources, ToN-IoT and CICIDS-2017 show the superiority of the proposed BDTwin framework over some baseline and recent state-of-the-art techniques.

Critical infrastructures across many industries, such as smart water treatment and distribution networks (SWTDNs) and power generation and public transport networks, depend on the supervisory control and data acquisition (SCADA) system. However, being the core component of the critical infrastructures, it has made the SCADA-based SWTDN system an attractive target for cyberattacks. A successful attack on the SCADA will have a devastating impact on an SWTDN in terms of proper operations; therefore, safeguarding the SCADA from cyberattacks is of paramount. With the increasing cyberattacks on SWTDN, both in number and sophistication, the need to detect these attacks early has become a subject of great interest among practitioners and researchers. To this end, we propose a novel strategy, based on a semisupervised approach. Two semisupervised approaches, including unsupervised learning and deep learning-based approaches, have been proposed. The proposed approaches can involve learning dynamic cyberattack patterns from unlabeled data in an SWTDN. We validate the proposed semisupervised approach experimentally using an operational water treatment plant testbed. The proposed approach achieved almost 100% accuracy and substantially outperforms the existing baseline approaches used in this article. The outcome of the experiment is encouraging and demonstrates the potential use of the semisupervised approach for security control in smart water distribution.

Internet of Drones (IoD) is the interconnection of unmanned aerial vehicles or drones deployed for collecting sensitive data to be used in critical applications. The drones transmit the collected data to the control room (CR) for analysis, while CR sends control commands to the drone to monitor their operations. This exchange of information between the drones and CR takes place through a wireless communication channel, which is susceptible to various security risks. Therefore, it is vital to ensure the confidentiality and integrity of such information in the IoD environment. To this end, authenticated key management (AKM) protocols can be leveraged to provide reliable and secure communication. However, due to the peculiarities associated with IoD environments, it is challenging to devise a robust and resource-efficient AKM protocol. To tackle this challenge, in this article, we propose a robust AKM protocol for IoD (RAMP-IoD). RAMP-IoD uses lightweight cryptography-based authenticated encryption primitive and elliptic-curve cryptography along with a hash function to perform the AKM process. Moreover, RAMP-IoD verifies the user’s authenticity and then sets up a session key (SK) between the user and a specific drone for indecipherable communications. We verify the security of SK using the random oracle model. Scyther-based validation demonstrates that RAMP-IoD is protected against replay and man-in-the-middle attacks. Moreover, the informal analysis illustrates that RAMP-IoD is secure against various covert security attacks. Through a comparative study, we also demonstrate that RAMP-IoD provides enhanced security with low storage, communication, and computational overheads as compared to related AKM protocols.

Twitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.

Cryptocurrencies are the new form of trade that has revolutionized how we look into our financial institutions. Bitcoin dominates the industry with the highest market share among the hundreds of other cryptocurrencies. However, high energy consumption leading to increasing carbon emission, prioritizing high-value transactions, and long waiting times are some of the flaws preventing it from reaching its full potential. Owing to the block rewards getting halved every four years, miners and researchers are fearful that this would be the breaking point of Bitcoin’s success. This article proposes an Industry-4.0-compliant next-generation Bitcoin architecture by introducing a dynamic and sustainable block concept. Along with our modified knapsack algorithms, i.e., priority-based 0/1 knapsack and advanced-priority-based 0/1 knapsack, we can ensure a balanced transaction selection, quicker verification, higher transaction throughput, reduced carbon emission, and increased earnings for the miners. Moreover, with the addition of only one of our proposed sustainable blocks, we can cut down verification times by 50% and increase throughput by 39%. We can also reduce carbon emissions per transaction by 61.3%, which would help reduce Bitcoins’ large carbon footprint, enabling us to approach greener digital transactions.

With the proliferation of smart grid and deregulation of the energy market, a wide variety of peer-to-peer (P2P) energy trading systems have emerged. Common challenges for designing such systems include prosumers’ privacy and security threats. To this end, Blockchain-based solutions have gained a lot of attention, though most existing solutions have either employed permissionless blockchain, which is far from pragmatic for a P2P energy trading system with peers permitted to join or leave the network at their whim; or relatively secure yet inefficient permissioned blockchains. Hence, this article presents a flexible permissioned ascription (FPA) scheme that uses on-chain and off-chain permissioning scheme viaOrionandMetamaskwallet. It also employs contract permissioning through a JavaScript based chain code deployed over Hyperledger Besu (an Ethereum based permissioned Blockchain network) with istanbul byzantine fault tolerant (IBFT) 2.0 consensus algorithm. Additionally, the proposed framework is emulated for development of a working prototype for a P2P energy trading system. Its performance evaluation has been conducted and monitored with Grafana, Prometheus, Hyperledger Caliper, and Kibana for parameters such as latency, throughput, success rate, CPU time, block time, block behind time, memory usage, garbage collection (GC) time, and performance of the validator nodes. The latency of IBFT 2.0 was found five times lesser than that of Ethereum and two times lesser than HF RAFT and KAFKA under varying conditions. Also, the measured throughput was 1.5 times higher than RAFT and Kafka and three times higher than that of Ethereum. The average block confirmation time measured is 5–6 s. The GC usage measured very less, i.e., 0.5–0.8%, with the proposed framework. It has been observed that the proposed energy-trading framework provides an efficient performance for deploying, transferring, and querying the energy transaction to a P2P energy-trading Blockchain network when compared with other consensus mechanisms.

In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model.

5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5%, comparing with the best existing work.

A non-orthogonal multiple access (NOMA)-enabled double-layer airborne access vehicular ad hoc networks (DLAA-VANETs) architecture is designed in this paper, which consists of a high-altitude platform (HAP), multiple unmanned aerial vehicles (UAVs) and vehicles. For the designed DLAA-VANETs, we investigate the UAV deployment and network optimization problems. In particular, a UAV deployment scheme based on particle swarm optimization is presented. Then, the NOMA technique is introduced into the designed architecture, which can improve the transmission rate. Afterward, we take the information security into account and formulate a downlink total transmission rate maximization problem by optimizing UAV height and subcarrier allocation. For tackling this non-convex problem, we decouple this downlink total transmission rate maximization problem as two subproblems, where UAV height and subcarrier allocation problems are solved in turn. Moreover, the transmission performance of the designed DLAA-VANETs is analyzed, based on which the security outage probability (SOP) is derived. Finally, simulation results demonstrate that the presented UAV deployment scheme can maximize the relay coverage ratio. In addition, the proposed can achieve a higher downlink total transmission rate in comparison with the current works.

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs–RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

Works that use point cloud avoid wasting time and cost of collection, using simulators and datasets available in the literature. In this way, there is access to an unlimited and organized amount of point clouds, an ideal setting for deep learning networks and Vehicular ad hoc networks (VANETs). However, models trained with synthetic data present problems when applied to real-world data.This work proposes the use of deep learning in the recognition of 3D objects captured with a Light Detection and Ranging (LIDAR), including a pre-processing stage. In addition, it is proposed two datasets, a real-world and a syntetic; each dataset includes three classes. A method of pre-processing is proposed to circumvent the distribution discrepancies of the proposed datasets and the existing datasets from literature, such as ModelNet. We use deep learning with the PointNet method, as it supports raw data from point clouds as input to the network. We performed three evaluation approaches: training and testing steps with the proposed datasets using(1)Lidar3DNetV1, which is a proposed network in this paper,(2)PointNet, and (3) classification of ModelNet datasets using Lidar3DNetV1. The proposed network achieved 98.33% of accuracy and a testing time of$88~\mu \text{s}$in the synthetic dataset, while in the real-world dataset, the network reached 98.48% and$145~\mu \text{s}$in accuracy and testing time, respectively.

Deep learning-based auto-driving systems are vulnerable to adversarial examples attacks which may result in wrong decision making and accidents. An adversarial example can fool the well trained neural networks by adding barely imperceptible perturbations to clean data. In this paper, we explore the mechanism of adversarial examples and adversarial robustness from the perspective of statistical mechanics, and propose an statistical mechanics-based interpretation model of adversarial robustness. The state transition caused by adversarial training based on the theory of fluctuation dissipation disequilibrium in statistical mechanics is formally constructed. Besides, we fully study the adversarial example attacks and training process on system robustness, including the influence of different training processes on network robustness. Our work is helpful to understand and explain the adversarial examples problems and improve the robustness of deep learning-based auto-driving systems.

This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19 cases. The framework utilizes a multi-access edge computing technology such that end-user can access available resources as well the CNN on the cloud. Early detection of COVID-19 can improve treatment and mitigate transmission. During peaks of infection, hospitals worldwide have suffered from heavy patient loads, bed shortages, inadequate testing kits and short-staffing problems. Due to the time-consuming nature of the standard RT-PCR test, the lack of expert radiologists, and evaluation issues relating to poor quality images, patients with severe conditions are sometimes unable to receive timely treatment. It is thus recommended to incorporate computational intelligence methodologies, which provides highly accurate detection in a matter of minutes, alongside traditional testing as an emergency measure. CNN has achieved extraordinary performance in numerous computational intelligence tasks. However, finding a systematic, automatic and optimal set of hyperparameters for building an efficient CNN for complex tasks remains challenging. Moreover, due to advancement of technology, data are collected at sparse location and hence accumulation of data from such a diverse sparse location poses a challenge. In this article, we propose a framework of computational intelligence-based algorithm that utilize the recent 5G mobile technology of multi-access edge computing along with a new CNN-model for automatic COVID-19 detection using raw chest X-ray images. This algorithm suggests that anyone having a 5G device (e.g., 5G mobile phone) should be able to use the CNN-based automatic COVID-19 detection tool. As part of the proposed automated model, the model introduces a novel CNN structure with the genetic algorithm (GA) for hyperparameter tuning. One such combination of GA and CNN is new in the application of COVID-19 detection/classification. The experimental results show that the developed framework could classify COVID-19 X-ray images with 98.48% accuracy which is higher than any of the performances achieved by other studies.

Air pollution monitoring, finding the hazardous zone, and future air quality predictions have recently become a significant issue for many researchers. With the adverse effect of low air quality on human health, it has become necessary for predicting the air quality index (AQI) accurately and on time. The unmanned aerial vehicle (UAV) can collect air quality data with high spatial and temporal resolutions. Using a fleet of UAVs could be considered a good option. In the proposed work, we implement a distributed federated learning (FL) algorithm within a UAV swarm that collects air quality data using built-in sensors. A scheme for finding the area with the highest AQI value is proposed using swarm intelligence. The collected data are then fed to a CNN-LSTM model to predict the AQI. The trained local model is sent to the central server, and the server aggregates the received models from UAVs in the swarm. A global model is created and is transmitted to the UAV swarm again in the next iteration. The proposed architecture is compared with other time-series models. The results show that the proposed model predicts AQI daily with a minimal error rate on a real-time data set from Delhi.

In recent years, trust-boundary protection has become a challenging problem in Industrial Internet of Things (IIoT) environments. Trust boundaries separate IIoT processes and data stores in different groups based on user access privilege. Points where dataflow intersects with the trust boundary are becoming entry points for attackers. Attackers use various model skewing and intelligent techniques to generate adversarial/noisy examples that are indistinguishable from natural data. Many of the existing machine-learning (ML)-based approaches attempt to circumvent this problem. However, owing to an extremely large attack surface in the IIoT network, capturing a true distribution during training is difficult. The standard generative adversarial network (GAN) commonly generates adversarial examples for training using randomly sampled noise. However, the distribution of noisy inputs of GAN largely differs from actual distribution of data in IIoT networks and shows less robustness against adversarial attacks. Therefore, in this article, we propose a downsampler-encoder-based cooperative data generator that is trained using an algorithm to ensure better capture of the actual distribution of attack models for the large IIoT attack surface. The proposed downsampler-based data generator is alternatively updated and verified during training using a deep neural network discriminator to ensure robustness. This guarantees the performance of the generator against input sets with a high noise level at time of training and testing. Various experiments are conducted on a real IIoT testbed data set. Experimental results show that the proposed approach outperforms conventional deep learning and other ML techniques in terms of robustness against adversarial/noisy examples in the IIoT environment.

With the increase in natural gas consumption, distributed natural gas supply and transaction have become new development goals of the industrial Internet of Things (IoT) for natural gas. However, there are obvious disadvantages of the existing natural gas pipeline network in aspects of infrastructure warning, multilevel data transmission, automatic transaction, and security. Emerging technologies, such as blockchain, edge computing, and AI have been introduced to address these shortcomings. This article proposes Smart Micro-GaS, i.e., the concept of a cognitive micro natural gas industrial ecosystem based on mixed blockchain and edge computing. Three aspects, multilevel, multiview, and multidimension, are put forward for its design and deployment. Then, based on the most important smart contract algorithm in blockchain, a mixed transaction model for natural gas is established. Finally, a case analysis is conducted on a smart natural gas testbed for data prediction and the proposed smart contract algorithm. The framework proposed in this article makes the natural gas data have multilevel liquidity and realizes diversified transactions.

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.

The unified management of IoT devices with interoperability can be inspired by cloud computing. In addition, sinking the 5G core network to the edge brings chances for the deployment of end-to-end ultralow-latency services. However, the resource efficiency brought by heterogeneous computing devices in 5G spectrum multiplexing environments has encountered challenges. To discuss this issue from a comprehensive perspective, this article first proposes an ultralow-latency service deployment architecture in 5G heterogeneous networks, and three cognitive engines are the key components for efficient service communication across the terminal/edge/cloud computing structure. Then we give an analysis of application task model in the proposed architecture, and following the service response time models are established. In addition, it is efficient to deploy multiuser tasks with constraint resources when the differentiated user requirements are met. Finally, we conducted some experiments and the result statistics are up to our expectations. The first one is the system performance under two microcloud covered cells, and the second one is the performance comparison of the proposed solution with three single scenes of terminal computing, edge computing and cloud computing.

The rapid development of Internet of Things (IoT) platforms provides the industrial domain with many critical solutions, such as joint venture virtual production systems. However, the extensive interconnection of industrial systems with corporate systems in industrial Internet of Things (IIoT) networks exposes the industrial domain to severe cyber risks. Because of many proprietary multilevel protocols, limited upgrade opportunities, heterogeneous communication infrastructures, and a very large trust boundary, conventional IT security fails to prevent cyberattacks against IIoT networks. Recent secure protocols, such as secure distributed network protocol (DNP 3.0), are limited to weak hash functions for critical response time requirements. As a complementary, we propose an adaptive trust boundary protection for IIoT networks using a deep-learning, feature-extraction-based semisupervised model. Our proposed approach is novel in that it is compatible with multilevel protocols of IIoT. The proposed approach does not require any manual effort to update the attack databases and can learn the rapidly changing natures of unknown attack models using unsupervised learnings and unlabeled data from the wild. Therefore, the proposed approach is resilient to emerging cyberattacks and their dynamic nature. The proposed approach has been verified using a real IIoT testbed. Extensive experimental analysis of the attack models and results shows that the proposed approach significantly improves the identification of attacks over conventional security control techniques.

With the development of Internet of Things (IoT) and communication technology, the number of next-generation IoT devices has increased explosively, and the delay requirement for content requests is becoming progressively higher. Fortunately, the edge-caching scheme can satisfy users' demands for low latency of content. However, the existing caching schemes are not smart enough. To address these challenges, we propose a human-like hybrid caching architecture based on the software-defined edge cloud, which simultaneously considers the content popularity and the fine-grained user characteristics. Then, an optimization problem with a caching hit ratio as an optimization objective is formulated. To solve this problem, using reinforcement learning, we design a human-like hybrid caching algorithm. The extensive experiments show that compared with popular caching schemes, human-like hybrid caching schemes can improve the cache hit ratio by 20%.

The trustworthiness of an industrial Internet of Things (IIoT) network is an important stakeholder expectation. Maintaining the trustworthiness of such a network is crucial to void the loss of lives. A trustworthy IIoT system combines the security characteristics of IT trustworthiness-safety, security, privacy, reliability, and resilience. Conventional security tools and techniques are not enough to safeguard the IIoT platform due to the difference in protocols, limited upgrade opportunities, mismatch in protocols, and older versions of the operating system used in the industrial system. In this article, we propose to improve the trustworthiness of an IIoT network [i.e., supervisory control and data acquisition (SCADA) network] through a reliable and salable cyberattack detection model. In particular, an ensemble-learning model based on the combination of a random subspace (RS) learning method with random tree (RT) is proposed for detecting cyberattacks of SCADA by using the network traffics from the SCADA-based IIoT platform. The novelty of the proposed model is that it uses the industrial protocol-based network traffic and the RS to solve the sensitivity of irrelevant features and ensemble RT to reduce the overfitting problem, thereby constructs a detection engine based on industrial protocols and achieves high detection rates. The proposed model has been tested over 15 datasets of the SCADA network. Experimental results reveal that the proposed model outperforms conventional detection techniques and, thus, improves the security and related measure of the trustworthiness of the IIoT platform.

A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.

Recent advances in cooperative multiple-input-multiple-output (CMIMO) techniques have encouraged interest in the development of intelligent transportation systems (ITS). They have the potential for use in the infrastructure to vehicle (I2V) and infrastructure to infrastructure (I2I) communications in ITS networks where the energy consumption of wireless sensor nodes embedded on the road infrastructure is constraint. Therefore, how to reduce the energy consumption becomes a hot research topic . In this paper, applications of cooperative communications in ITS networks are proposed for reducing the total energy consumption . At first, the ITS model is established based on the cooperative multiple-input-multiple-output spatial modulation (CMIMO-SM). A detailed energy consumption analysis of the proposed scheme compared with the traditional single-input-single-output (SISO) based scheme is then presented. The comparison conducted between these communication schemes helps us select the optimal one for energy reduction in energy constrained ITS networks. Additionally, under the guidance of the proposed scheme we consider the multi-hop transmission scenario where the energy efficiency improvement is achieved by finding the optimal hop number with the equal hop-length scheme. As a result, we analyze the energy consumption in different situations, and discuss the requirements on the hop-length and hop number in ITS networks. It shows that the optimal results are dependent on the ITS scenarios and choosing the appropriate transmission scheme will provide a good energy consumption performance in ITS.

In a mobile crowdsourcing system (MCS), a platform outsources sensing tasks to numerous mobile worker devices. The collected data are analyzed and the processed information is shared among many other interested users. The platform pays the workers for the sensing data and earns money from the users receiving processed information services. Distributing the sensing workloads among the potential workers so as to maintain the required data quality and to make a reasonable amount of profit is a challenging problem for such a platform. In this paper, we develop a workload allocation policy that makes a reasonable tradeoff between worker utilities and platform profit. It quantifies the utility (i.e., the quality of sensed data) of a worker as a function of worker mobility, current location, and past sensing records. The workload allocation problem is formulated as a multiobjective nonlinear programming (MONLP) problem which aims to make the desired tradeoff between worker utilities and platform profit. The allocation problem is shown to be NP-hard and thus we develop two greedy algorithms with relaxed constraints to achieve polynomial time solutions. Performance of the proposed workload allocation policy is evaluated in a distributed computation environment using MATLAB. The results show its effectiveness compared to state-of-the-art methods in terms of platform profit, quality of sensing data, and request service satisfaction.

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients' health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT-based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is composed of four layers: 1) IoT network sensors/devices; 2) Fog layers; 3) cloud computing layer; and 4) healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: 1) light-weight field programmable gate array (FPGA) hardware-based cipher algorithm and 2) secret cipher share algorithm. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients' data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients' personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.

In this era of Internet of Things (IoT), most of the contemporary researches provide a new dimension of cooperation called Control Channel cooperation to eliminate hidden terminal problem, as well as deafness problem while using directional antenna in a multichannel environment for wireless networks. However, only using such concept of cooperation may increase the communication latency for iterative control channel negotiation. Therefore, cooperation may not only use in information sharing, can be used for relaying data frame to improve throughput and minimize the data transmission delay. In this paper, we have proposed a Two-Dimensional Cooperation-based Asynchronous Multichannel Directional MAC protocol (2D-CMD MAC) that combines both of these cooperation and multi-channel directional concepts of cooperation. This joint concept of cooperation solves the multichannel directional hidden terminal and deafness problems using cooperative information sharing. In addition, cooperation for relaying data in data channel increases the throughput by minimizing the data transmission delay and enhances the channel bandwidth utilization by obtaining parallel transmission in the same data channel. The simulation results show that 2D-CMD MAC improves the network performance in terms of throughput obtained from cooperative data transmission and parallel transmission in same data channel and packet delivery ratio.

Summary Online social networks (OSNs) have become popular and widely used by many people nowadays. As those OSNs increase, the virtual social interactions have resulted in increasing privacy and security concern. Many approaches have been proposed in order to provide users with “better” privacy protection, but none of those approaches have “fully succeeded,” due to their conflict with service providers' economic benefits or due to the suggestion of changing the OSNs architecture. In this paper, we propose a new model that would improve privacy and security in OSNs by preventing users from revealing private and or confidential information online as possible. We endeavor to create a balance between privacy and security improvement versus the service provider business model.

The concept of Social Internet of Things (SIoT) has emerged from the integration of social networking into the core of the Internet of Things (IoT). It envisions IoT objects and devices to have social interactions with each other autonomously, cooperate with other agents, and exchange information with human users and surrounding computing devices. These objects are able to sense/actuate, store, and interpret information in an opportunistic and loosely coupled fashion. The objects in the SIoT paradigm can exhibit multiple forms of social relationships derived from their collaborative activities or functional, temporal and spatial dependencies to meet a particular need of human users, which signify the difference between the SIoT domain to that of social-based mobile networks or sensor networks. The social interaction among the SIoT objects contribute a huge volume of data to be processed and used by various applications such as social VANET, social connected health, SIoT-based recommendation service, traffic service, policing, energy management etc, in the area of Smart Cities, Smart Homes, Smart Grid, and Smart Factories to satisfy human needs, interests, and objectives. Such a dynamic landscape with billions of social communities of objects and devices requires new models, theories, and approaches of interaction and collaboration, which could be established by referring to the experience that people have already gained in social networking domain over the past few years.

Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.

Electronic trading (e-trading) provides a virtual marketplace (e-Marketplace) where buyers and sellers can engage in business activities through electronic media rather than direct physical contact. Although negotiation is a fundamental component of e-trading, the critical risks of missing out on top utility offers that expire before client's negotiation deadline has not been addressed. In order to address these problems, we propose a mobile-agent based secure one-to-many bilateral e-trade negotiation framework that efficiently manages the risk of losing top utility offers and maximizes client's utility taking into account various temporal constraints. Theoretical and empirical analysis of the proposed approach is performed. We evaluated the performance of the proposed strategy in terms of client's utility and negotiation time and compared it with two baseline negotiation strategies. The experimental analysis shows that the proposed strategy maximizes client's utility, shortens negotiation time, and ensures adequate market search. Proofs of validity of the proposed utility function are presented. The security protocol is formally verified and the verification shows that the protocol is free of security flaws and hence, negotiation data are secured.

Summary Securing user identity from data breach in a web server is one of the major concerns for the users of the web applications. Similarly, protecting user access pattern from unauthorized access should be taken seriously, because the potential threats such attacks may pose, are huge. However, these security measures should not be adopted at the expense of user experience and convenience. Nevertheless, any extra overhead in the form of security measures introduced in a distributed system results in significant performance declination. The target of a secured framework for a distributed system like web application should be a reasonable trade‐off between security and user experience. Thus, in this work, we present a framework that ensures security for the user identity along with keeping the online activities of the users anonymous while ensuring scalability of the system. Our framework is designed in a scalable form that can work with other distributed architectures that provide security to user data and identities. To ensure all these measures, our proposal includes the implementation of Forward Secrecy using Diffie‐Hellman Key exchange protocol where the server cannot remember a user's history after a session ends. In addition, we present our own mechanism to hide logical data sharing strategies to protect users against selective DoS attacks. Moreover, we implemented a modified version of bloom filter to safeguard user access pattern in a compromised server. Our proposed implementation of bloom filter also ensures that the scalability of distributed system is preserved even with little infrequent overhead in the server because of security measures proposed in this work. Finally, we implemented different modules of our framework using both Web Socket and Long Polling transport protocols and recorded the time required to perform various tasks. Web socket protocol took less time to perform each task than the long polling protocol, which is convincing enough to suggest that web socket performs better than long polling in the given scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

Summary This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph‐based, considering the relationships among users to predict the spread information; human‐based, using human perspectives to determine reliable sources; or machine learning‐based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright © 2016 John Wiley & Sons, Ltd.

Summary With the advantages of extremely high access speed, low energy consumption, nonvolatility, and byte addressability, nonvolatile memory (NVM) device has already been setting off a revolution in storage field. Conventional storage architecture needs to be optimized or even redesigned from scratch to fully explore the performance potential of NVM device. However, most previous NVM‐related works only explore its low access latency and low energy consumption. Few works have been done to explore the appropriate way to use NVM device for improving virtual machine's storage performance. In this paper, we comprehensively evaluate and analyze conventional virtual machine's storage architecture. We find that, even with cutting‐edge optimization technologies, virtual machine can only achieve 30% of NVM device's original performance. Based on this observation, we propose a memory bus–based storage architecture, which we named MBSA. Memory bus–based storage architecture can greatly shorten the length of virtual machine's storage input/output stack and improve NVM device's use flexibility. In addition, an efficient wear‐leveling algorithm is proposed to prolong NVM device's lifespan. To evaluate the new architecture, we implement it as well as the wear‐leveling algorithm on real hardware and software platform. Experimental results show that MBSA can provide a big performance improvement, about 2.55X, and the wear‐leveling algorithm can efficiently balance write operations on NVM device with a negligible performance overhead (no more than 3%).

Summary Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning–based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 87%. However, because of the problems of spam drift and information fabrication, these machine learning–based methods cannot efficiently detect spam activities in real‐life scenarios. Meanwhile, the blacklisting method also cannot catch up with the variations of spamming activities, as manually inspecting suspicious URLs is extremely timeconsuming. In this paper, we proposed a novel technique based on deep‐learning technique to address the above challenges. The syntax of each tweet will be learned through WordVector and trained by deep learning. We then constructed a binary classifier to differentiate spam and regular tweets. In experiments, we collected and labeled a 10‐day real tweet dataset as ground truth to evaluate our proposed method. We first went for empirical analysis with a series of comparisons to other methods: (1) performance of different classifiers, (2) other existing text‐based methods, and (3) nontext‐based detection techniques. According to the experiment results, our proposed method largely outperformed previous methods. We further conducted principle component analysis on typical methods to theoretically justify the outperformance of our method. We extracted all kinds of features via dimensionality reduction. It was found that our features were most distinct among all the detection methods. This well demonstrated the outperformance of our method.

Sampling rate adaptation is a critical issue in many resource-constrained networked systems, including Wireless Sensor Networks (WSNs). Existing algorithms are primarily employed to detect events such as objects or physical changes at a high, low, or fixed frequency sampling usually adapted by a central unit or a sink, therefore requiring additional resource usage. Additionally, this algorithm potentially makes a network unable to capture a dynamic change or event of interest, which therefore affects monitoring quality. This article studies the problem of a fully autonomous adaptive sampling regarding the presence of a change or event. We propose a novel scheme, termed “event-sensitive adaptive sampling and low-cost monitoring (e-Sampling)” by addressing the problem in two stages, which leads to reduced resource usage (e.g., energy, radio bandwidth). First, e-Sampling provides the embedded algorithm to adaptive sampling that automatically switches between high- and low-frequency intervals to reduce the resource usage, while minimizing false negative detections. Second, by analyzing the frequency content, e-Sampling presents an event identification algorithm suitable for decentralized computing in resource-constrained networks. In the absence of an event, the “uninteresting” data is not transmitted to the sink. Thus, the energy cost is further reduced. e-Sampling can be useful in a broad range of applications. We apply e-Sampling to Structural Health Monitoring (SHM) and Fire Event Monitoring (FEM), which are typical applications of high-frequency events. Evaluation via both simulations and experiments validates the advantages of e-Sampling in low-cost event monitoring, and in effectively expanding the capacity of WSNs for high data rate applications.

Lately, Twitter has grown to be one of the most favored ways of disseminating information to people around the globe. However, the main challenge faced by the users is how to assess the credibility of information posted through this social network in real time. In this paper, we present a real-time content credibility assessment system named CredFinder, which is capable of measuring the trustworthiness of information through user analysis and content analysis. The proposed system is capable of providing a credibility score for each user's tweets. Hence, it provides users with the opportunity to judge the credibility of information faster. CredFinder consists of two parts: a frontend in the form of an extension to the Chrome browser that collects tweets in real time from a Twitter search or a user-timeline page and a backend that analyzes the collected tweets and assesses their credibility.

Summary This paper addresses the problem of efficient federation formation by the cloud providers (CPs) with an aim to fulfill the dynamic resource demands of users for supporting data‐intensive workloads. Existing works only focus on forming federations based on the highest profit gained by each of the CPs in a federation. Therefore, these approaches often suffer from the risk of selecting unreliable CPs in the federation resulting in additional penalty cost and loss of CPs's reputation due to service level agreement violation between the users and the federation. In contrast, we argue that a trust model is necessary to find the most promising cloud collaborators. Accordingly, we propose a novel cloud federation formation mechanism by utilizing a trust‐based cooperative game theory, which enables the CPs to dynamically form a federation based on profit maximization and penalty cost minimization as a result of selecting the trustworthy CPs. Simulation results show that the cloud federation formed by the proposed mechanism is stable, satisfies the fairness property, and yields higher profit for the participating CPs in the long run without incurring penalty cost as compared with the state‐of‐the‐art approaches. Copyright © 2015 John Wiley & Sons, Ltd.

Summary Because of the highly dynamic topology and the unstable service status of nodes, services in vehicular ad hoc networks (VANETs) are not always reliable enough for users. Nodes in such a VANET incline to be selfish, which will even enhance this situation. In this work, we present an incentive model for VANETs to support more reliable services in network. We model the situation of service request and response in VANETs by using game theory. We consider the competitive and cooperative relationship between the nodes to formulate the game for VANETs. A contribution measurement is given in order to encourage cooperation during the game. Nodes are encouraged to provide more services to their neighbors in order to acquire more services from other nodes in our model. We also conduct a simulation for the proposed model and give detailed analysis in this work. From the results of the simulation, we argue that we could enable a VANET to support more reliable service by configuring suitable parameters for it. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian's locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.

Presents correcttions to the paper, “A performance evaluation of machine learning-based streaming spam tweets detection,” (Chen ], C.; et al) , IEEE Trans. Comput. Social Syst., vol. 2, no. 3, pp. 65–76, Sep. 2015.

Abstract With the widespread deployment of cloud services, data center networks are developing toward large‐scale, multi‐path networks. Conventional switching‐oriented data center network meets difficulties in terms of scalability and flexibility to support increasing bandwidth requirements for cloud services. To solve this problem, a simple and scalable architecture, MatrixDCN, is proposed in this paper. MatrixDCN is an approximate non‐blocking network, in which switches and servers are arranged in rows and columns that compose a matrix structure. A MatrixDCN network can accommodate up to hundreds of thousands of servers without bandwidth bottlenecks. Furthermore, the physical topology of a MatrixDCN network can be designed consistently with its logic topology, which helps to reduce the complexity of the management and maintenance of a data center. An efficient routing algorithm, named fault‐avoidance routing (FAR), is well designed for MatrixDCN to fully leverage the regularity in the topology. FAR builds two routing tables for a router. A BRT is built based on local topology, and a novel negative routing table (NRT) is increasingly built based on learned partial network failures, which really avoids the problem of network convergence and further shortens the calculating time of routing tables. FAR also greatly reduces the size of routing tables by introducing NRTs at routers. Theoretical analysis and simulations show that MatrixDCN has advantages on the scalability of topology, network throughput, and the performance of FAR. Copyright © 2015 John Wiley & Sons, Ltd.

A federation of cloud providers (CPs) consists of a set of self-interested CPs that cooperate in order to provide virtual machine (VM) resources requested by users. The CPs by virtue of being part of a federation can make some profit by selling their unused VM capacity. This paper presents an efficient mechanism of resource and revenue sharing in a cloud federation that motivates the CPs to cooperate. The proposed mechanism models the interactions among the CPs in a federation as a coalition game. In contrast to existing approaches, the game model aims at maximizing the social welfare or total profit of the CPs in a federation to promote their long-term individual profit. In addition, we present a comprehensive analysis of the related costs and revenue associated with the various decisions of the CPs related to their joining in a federation. Various simulations were carried out to validate and verify the effectiveness of the proposed cooperative capacity sharing mechanism. Simulation results demonstrated that the proposed mechanism can satisfy the fairness and stability properties, maximize the social welfare the CPs in a federation and achieve cost effective resource sharing.

Summary In a mobilead hocnetwork, the data packet may fail to be delivered for various reasons mostly for route failure, congestion, and battery energy drain. Hence, providing reliable and timely data delivery in this network in an energy‐efficient way is challenging. Although there exist several solutions to solve these problems, they can handle either route failure or congestion or energy‐efficient routing. Hence, to cope up with all the problems simultaneously, we propose a route failure and congestion‐aware energy‐efficient cross‐layer design that spans the transport and network layer. In the transport layer, we introduce the concept of local packet buffering during link failure and congestion. As a result, the packet dropping rate of the network and energy consumption decreases. In the network layer, a routing protocol is proposed for selecting the energy‐efficient path for data transmission. It uses the buffering mechanism in case of route maintenance. In addition, we employ a multilevel congestion detection and control mechanism at the source and intermediate nodes that can judiciously take the most appropriate decision for congestion control in the network proactively. The simulation results showed that the proposed cross‐layer design provided better performance as compared with the state‐of‐the‐art protocols. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Network reprogramming faces lots of threats from both external attackers and potentially compromised nodes. Security thus becomes a critical requirement for network reprogramming protocols. This paper describes a secure network reprogramming system called SecNRCC for dynamically reprogramable wireless sensor network. In SecNRCC, a light weight authentication method is firstly introduced for the reboot control command. Secondly, a program image preprocess method with security and loss‐tolerance consideration is proposed. Furthermore, a novel immediate packet authentication algorithm with confidentiality consideration is also presented to resist the denial of service attacks exploiting the authentication delay, and finally, a weak authentication operation is performed before the digital signature verification to mitigate denial of service attacks against signature packets. The experimental results show that SecNRCC can securely disseminate the program image to all of node in the wireless sensor networks with acceptable latency and message cost. Copyright © 2014 John Wiley & Sons, Ltd.

Data deduplication is a technique for eliminating duplicate copies of data, and has been widely used in cloud storage to reduce storage space and upload bandwidth. However, there is only one copy for each file stored in cloud even if such a file is owned by a huge number of users. As a result, deduplication system improves storage utilization while reducing reliability. Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced by users to cloud. Aiming to address the above security challenges, this paper makes the first attempt to formalize the notion of distributed reliable deduplication system. We propose new distributed deduplication systems with higher reliability in which the data chunks are distributed across multiple cloud servers. The security requirements of data confidentiality and tag consistency are also achieved by introducing a deterministic secret sharing scheme in distributed storage systems, instead of using convergent encryption as in previous deduplication systems. Security analysis demonstrates that our deduplication systems are secure in terms of the definitions specified in the proposed security model. As a proof of concept, we implement the proposed systems and demonstrate that the incurred overhead is very limited in realistic environments.

The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption.

Distributed resource allocation is a very important and complex problem in emerging horizontal dynamic cloud federation (HDCF) platform. The HDCF platform differs from the existing vertical supply chain federation (VSCF) models in terms of establishing federation and dynamic pricing. There is a need to develop algorithms that can capture this complexity yet can be easily implemented and used to solve distributed resource allocation problem in HDCF platform. In this paper, we propose a game-theoretic solution to this problem that ensures mutual benefits so that the cloud providers (CPs) are encouraged to form a HDCF platform. We study two resource allocation games - cooperative and non-cooperative games to analyze interaction among CPs in a HDCF environment. Also both centralized and distributed algorithms are presented to find optimal solutions which have low overhead and robust performance. Various simulations were carried out to validate and verify the effectiveness of the proposed resource allocation games.

This paper presents a Secured Wireless Sensor Network-integrated Cloud computing for u-Life Care (SC3). SC3 monitors human health, activities, and shares information among doctors, care-givers, clinics, and pharmacies in the Cloud, so that users can have better care with low cost. SC3 incorporates various technologies with novel ideas including; sensor networks, Cloud computing security, and activities recognition.

To address interoperability and scalability issues for cloud computing, in our previous paper, we presented a novel cloud market model called CACM that enables a dynamic collaboration (DC) platform among different Cloud providers. As the initiator of dynamic collaboration, primary Cloud provider (pCP) needs an efficient local task selection and allocation algorithm to partition the whole tasks and allocate those tasks to be executed locally. Existing task allocation algorithms cannot be directly applicable in a DC environment since they may cause low resource utilization of local resources. So in this paper we propose a general task selection and allocation framework to improve resource utilization for pCP. The framework utilizes an adaptive filter to select tasks and a modified heuristic algorithm to allocate tasks. Moreover, a trade-off metric is developed as the optimization goal of heuristic algorithm, so that it is able to manage and optimize the trade-off between QoS of tasks and utilization of resources.

In this paper, we propose a promising multi-objective (MO) optimization model for partner selection in a market-oriented dynamic collaboration (DC) platform of cloud providers (CPs) to minimize the conflicts among providers that may happen when negotiating among providers. The model not only uses their individual information (INI) but also past collaborative relationship information (PRI) for partner selection which is seldom considered in existing approaches. A multi-objective genetic algorithm (MOGA) called MOGA-IC is also proposed to solve the model as the model is NP-hard. The algorithm is developed using two popular MOGAs- NSGAII and SPEA2. The experimental results show that MOGAIC with NSGA-II outperformed the MOGA-IC with SPEA2 in finding useful Pareto optimal solution sets. In addition, other simulation experiments are conducted to verify the effectiveness of the MOGA-IC in terms of satisfactory partner selection and conflicts minimization.