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.

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy.

To mitigate various challenges in the edge-cloud ecosystem, such as global monitoring, flow control, and policy modification of legacy networking paradigms, software-defined networks (SDN) have evolved as a major technology. However, the dependency on a single centralized controller is challenging due to the scalability and resilience issues. Thus, deploying multiple controllers becomes inevitable to process the data with maximum throughput and minimum delay. Controller placement problem (CPP) is a major issue that needs to be addressed by designing efficient solutions. To address the CPP, two parameters, i) number of controllers and ii) location of controllers, need to be handled optimally. Thus, an Optimal COntroller Placement Scheme (COPS) using the multi-objective evolutionary approach for SDN is proposed in this paper. The results prove its effectiveness in terms of various evaluation parameters.

This research introduces the Density-Clustering based Aggregation for Personalized Federated Learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for UAVs. The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in automatically determining cluster numbers using EPS neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning forward in complex and dynamic settings.

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.

Federated learning (FL) is a novel paradigm for distribute edge intelligence for the Internet-of-Vehicles (IoV) application, which can enable superior performance in model training without the need to share local data. However, in the actual architecture of FL, the existence of nonindependent and identically distributed (non-IID) data at the edge device, along with the involvement of randomly participating distributed nodes, can result in model bias and a subsequent decrease in overall performance. To solve this problem, a new federated self-supervised learning method based on prototypes clustering contrastive learning (FedPCC) is proposed, which can effectively addresses the issue of asynchronous edge training and global model bias by introducing an unsupervised prototypes layer. The prototypes layer maps edge features to a global space and performs clustering, facilitating the new aggregation method of global prototypes on the server. Then, models from other components are aggregated based on data weight. Besides that, during the parameter deployment phase, we replace the prototype layer to acquire global knowledge, while employing momentum updates to preserve the local knowledge of the other components. Finally, to assess the efficacy of our proposed approach, we carried out comprehensive experiments across the various data sets. The findings show that our method gains state-of-the-art performance, which also validates its effectiveness.

Rapid iterations and updates in both software and hardware, along with significant advancements in communication technology, have given rise to the concepts of symbiotic Internet of Things (IoT) and ubiquitous interconnectivity, providing strong evidence for the flourishing development of the Internet of Things. However, the limited resources and computing capabilities, along with the heterogeneity of deployment environments, make symbiotic IoT devices more susceptible to security threats and operational issues. Intermittent failures are especially prevalent in the symbiotic IoT, leading to more significant risks for devices. In this paper, we present an IFDGAT-LSTM (Intermittent Fault Diagnosis Based on Long Short-Term Memory and Graph Attention Network) framework for diagnosing intermittent failures in wireless sensing devices within the symbiotic IoT. The framework is based on a graph neural network and takes into account not only the time series characteristics of symbiotic IoT devices but also their deployment topology. By incorporating both aspects, we achieve more accurate diagnostics of intermittent failures in the symbiotic IoT, thus enhancing its reliability. Firstly, we propose the concept of a quasi-dynamic graph based on the variations in the topology within the symbiotic IoT. Subsequently, we introduce an intermittent failure diagnosis framework that combines a graph neural network to identify intermittent failure nodes within the quasi-dynamic graph. Finally, we performed experiments on the WADI symbiotic IoT dataset to evaluate the performance of our model in diagnosing intermittent failure nodes. We used the precision, recall, and F1 score metrics for assessment. The experimental outcomes show that our proposed model, IFDGAT-LSTM, achieves an Precision of 99.58% in diagnosing intermittent failure nodes. This highlights the strong performance and efficacy of the IFDGAT-LSTM model.

The rapid proliferation of Internet of Things (IoT) systems has introduced complex networks of interconnected devices, computational resources, and web-based communication infrastructure. Privacy protection in IoT data routing is critical to enabling secure deployment of large language models (LLMs) for processing distributed sensor data, user queries, and device-generated content. However, IoT environments inherently involve heterogeneous devices, dynamic network topologies, and resource-constrained nodes, complicating the design of privacy-preserving routing mechanisms that simultaneously ensure reliability across diverse communication layers. To address these challenges, we propose an innovative FtPR (Fault-tolerant Privacy Routing) model based on secure multiparty computing mechanism, which enables secure and efficient data fusion and transmission in IoT networks. FtPR establishes a novel connection between IoT device clusters and data center network architecture AQDNn routers, leveraging the hierarchical architecture of AQDNn to construct completely independent spanning trees (CIST). By exploiting the non-overlapping paths between nodes in distinct CISTs, FtPR achieves fault-tolerant routing while maintaining privacy guarantees. Building on this framework, we introduce a secure multiparty computing mechanism to perturb link weights in the AQDNn. This ensures that link weights across different CISTs adhere to constrained ranges, preventing adversarial inference of routing paths. Each node operates with localized knowledge of its connected link weights, eliminating the need for global network visibility. Consequently, even if malicious actors compromise one or multiple nodes, they cannot reconstruct end-to-end communication paths, thereby preserving route anonymity. Experimental results demonstrate that FtPR improves IoT network performance and security, reducing misclassification rates and marginal release score compared to state-of-the-art methods.

Digital twin (DT) is a promising technology for responding to Industry 4.0 and realizing comprehensive automation and virtualization. In the Web3.0-powered 5G/6G era, the expansion of the industrial data and closer interaction among cross-industrial entities pose new security challenges for DT industrial systems. As a prevalent computing paradigm, Graph Anomaly Detection provides an effective solution to ensure the security of DT industrial systems. However, the existing unsupervised graph anomaly detection methods tend to treat multiple graph attributes in isolation during the reconstruction process, resulting in insufficient semantics and suboptimal reconstruction performance. To overcome these challenges, we propose a multiattribute contrastive learning framework, which realizes graph anomaly detection by capturing both graph attribute patterns and their hidden relationships. First, we use an improved multiattribute aligned reconstruction approach to represent the anomaly information effectively. Besides that, the positive instance aggregation-based contrastive constraints are proposed, which can reduce the loss generated by mappings between different data dimension in feature representation space. Finally, to verify our proposal, extensive experiments have been conducted on five benchmark datasets, and the results show that our method obtains the state-of-the-art performance.

Interest in Intelligent Transportation Systems (ITS) has increased significantly with the development of 6G. Owning an extremely high transmission speed, 6G is able to support low-latency service for edge-intelligence applications by Machine Learning(ML) techniques. However, traditional centralized learning is not suitable for this scenario due to the requirement for users to upload local data to the server, which can compromise data privacy. To overcome this challenge, Federated Learning (FL) and Split Learning (SL), as progressive distributed learning techniques, have been proposed as a solution. They enable the training of ML models while preserving data privacy. However, conventional FL has poor convergence when data heterogeneity occurs, also fails to meet personalized demands. To address these issues, We propose a novel personalized Federated Learning(pFL) framework, which trains models in SL and collaborates in FL. It offers a personalized solution for each client while retaining a global solution for newcomers. Experimental results demonstrate that our method outperforms other advanced baselines on benchmark datasets.

The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.

Recently, there has been a growing interest in leveraging pre-trained large language models (LLMs) for various time series applications. However, the semantic space of LLMs, established through the pre-training, is still underexplored and may help yield more distinctive and informative representations to facilitate time series forecasting. To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embedding space and perform time series forecasting based on learned prompts from the joint space. We first design a tokenization module tailored for cross-modality alignment, which explicitly concatenates patches of decomposed time series components to create embeddings that effectively encode the temporal dynamics. Next, we leverage the pre-trained word token embeddings to derive semantic anchors and align selected anchors with time series embeddings by maximizing the cosine similarity in the joint space. This way, $S^2$IP-LLM can retrieve relevant semantic anchors as prompts to provide strong indicators (context) for time series that exhibit different temporal dynamics. With thorough empirical studies on multiple benchmark datasets, we demonstrate that the proposed $S^2$IP-LLM can achieve superior forecasting performance over state-of-the-art baselines. Furthermore, our ablation studies and visualizations verify the necessity of prompt learning informed by semantic space.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) can greatly enhance the capabilities of IoT networks by providing massive connectivity and timely computation. The paper presents a model and analysis of the performance for a large-scale grant-free (GF) SCMA aided MEC network. Firstly, stochastic geometry is used to derive closed-form solutions for offloading probability and SCMA ergodic rate. Then, the impact of SCMA on task completion time and energy cost in MEC networks is studied using queueing theory. Simulation results verify the validity of the theoretical expression and demonstrate that SCMA has advantages over orthogonal multiple access (OMA) in improving the offloading probability and ergodic rate, and reducing task latency and energy cost.

The performance of integrated satellite-aerial-terrestrial relay networks (ISATRNs) faces two main challenges, severe signal strength degradation over long transmission distances and limited spectrum resources. To address these issues, we consider the introduction of high altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) carrying reconfigurable intelligent surface (RIS) as relays during transmission from satellites to the ground. Additionally, we employ rate splitting multiple access (RSMA) at HAPs to improve signal transmission robustness. To optimize system energy efficiency, we formulate a multi-objective problem that considers the active transmit beamforming vector, RIS phase shift, power splitting ratio, and UAV trajectory. To tackle the non-convex problem involving both discrete and continuous variables, we introduce a novel approach called access-free federated deep reinforcement learning (AF-DRL). The optimal transmit beamforming and power splitting ratio are obtained by allowing the UAV to plan its path and locally train, reducing computational overhead caused by high-dimensional UAV movement. Simulation results demonstrate that the proposed RSMA-based enhancement scheme achieves higher energy efficiency compared to the comparison scheme.

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.

Industrial cyber–physical systems (ICPSs) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, artificial intelligence (AI) has had a significant impact on wireless sensor networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based green routing for ICPS. While following the cluster-based routing, the election of cluster head (CH) is executed using our proposed AI-inspired extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.

As the Augmented Intelligence of Things (AIoT) advances within vehicle-road coordination systems, challenges related to road traffic data transmission and processing are being increasingly addressed. However, this progress also brings significant risks of privacy data leakage. Federated learning (FL), a distributed machine learning paradigm, effectively safeguards client data privacy by allowing multiple participants to collaboratively train models while keeping their data localized. Despite its benefits, FL faces challenges, such as model parameter leakage and Byzantine attacks. To tackle these issues, this article introduces an anonymous and efficient FL framework for vehicle-road coordination systems (AEFL), designed to ensure a secure and reliable vehicle data transmission process. This architecture incorporates a novel group pairing onion routing protocol, which leverages pairing cryptography principles for hierarchical data encryption. During the routing process, relay group nodes decrypt the corresponding layer, ensuring both data confidentiality and node anonymity. Additionally, a sampling method is proposed to accurately identify Byzantine vehicle nodes, enhancing the precision of FL aggregation without compromising overall model performance. Experimental results show that AEFL outperforms the classic TOR anonymous routing protocol, achieving a 100% message delivery rate more quickly. Under the same conditions, the anonymity of the source node and the destination node improves by 3.9% and 1.9%, respectively. When half of the nodes are compromised, path anonymity can be increased by 24.8%. Furthermore, our framework excels in FL aggregation efficiency, with a Byzantine adversary detection accuracy of up to 99%.

Integrating multiaccess edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this article, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC servers for execution. However, as the number of IDs increases dramatically, jointly optimizing the usage of sensing, communication, and computational resources becomes challenging due to the exponential growth in interactions among the IDs. In this article, we address the energy-efficient joint optimization problem for sensing and computation in the MEC-assisted IoT system, aiming to ensure the freshness of the status update and minimize the energy consumption of IDs. To reduce the computation complexity, we introduce the concept of the general mean-field N-player Markov game (GMFG), and reformulate it as a mean-field game (MFG) with teams, leveraging the network structure of states. Considering the advantages of reinforcement learning (RL) for solving dynamic problems, we propose an MFG-based actor-critic algorithm (MFGAC) to minimize the long-term average system cost. Through extensive simulations, we demonstrate that the proposed method is effective and can outperform other schemes under different scenarios.

Network slicing (NS) is a highly promising paradigm in 5G and forthcoming 6G communication networks. NS allows for the customization of multiple logically independent network slices to provide tailored service for vertical applications with diverse quality of service (QoS) requirements. However, current research on NS primarily relies on the traditional modeling methods such as service function chaining (SFC) and task offloading, which have limitations in adapting to the evolving scenarios in 5G/6G networks. To address this, our study introduces one novel Self-adaptive Network Slicing (SNS) modeling method. In this approach, each service is abstracted as multiple SFC replicas originating from diverse access points. Based on the SNS modeling, we investigate a VNF configuration and flow routing (VCFR) problem for service provisioning in a multi-tier system. With the objective of achieving load-balancing with minimal slice operational expenditure, we formulate the VCFR as a mixed-integer linear programming. However, deriving an exact solution via MILP is computationally expensive due to its NP-hardness. To reduce computational complexity, we propose one Load Balancing-considered Community Detection-based Heuristic (LBCD-Heu), our divide and conquer approach, to solve the problem. In LBCD-Heu, we first design a load balancing-considered community detection method to divide the substrate multi-tier network into multiple independent communities. Following this, the MILP is employed in each community to obtain a near-optimal solution. Extensive evaluations justify that LBCD-Heu can effectively reduce the service operational cost and algorithm run-time while ensuring the load balancing of substrate network. Additionally, our results verify that the SNS modeling enables the provision of services at lower expenditures compared with traditional modeling methods.

Satellite-terrestrial integrated networks (STINs) are troubled with the serious jamming threats in the counterwork environment. Non-orthogonal multiple access (NOMA) approach can not only improve the resource utilization by resource sharing, but also has the potential advantages to be used for anti-jamming. In this paper, under the threat of smart jammer with adaptive jamming policies, we investigate the NOMA-based anti-jamming problem in multi-cell STINs by jointly considering the NOMA-based user grouping in each cell and the beam allocation among multiple cells. Specifically, for each cell, the users can enhance anti-jamming performance and improve the sum rate by NOMA-based users grouping, which is formulated as the anti-jamming Stackelberg game and grouping game to obtain the equilibrium solutions. Then, an adaptive beam allocation algorithm with a low complexity is proposed to avoid allocation conflicts and achieve fairness among multiple cells. Finally, simulation results prove the performance of the proposed scheme.

Detecting out-of-distribution (OOD) samples is a problem of practical importance for a reliable use of deep neural networks (DNNs) in production settings. The corollary to this problem is the detection in-distribution (ID) samples, which is applicable to domain adaptation scenarios for augmenting a train set with ID samples from other data sets, or to continual learning for replay from the past. For both ID or OOD detection, we propose a principled yet simple approach of (empirically) estimating KL-Divergence, in its dual form, for a given test set w.r.t. a known set of ID samples in order to quantify the contribution of each test sample individually towards the divergence measure and accordingly detect it as OOD or ID. Our approach is compute-efficient and enjoys strong theoretical guarantees. For WideResnet101 and ViT-L-16, by considering ImageNet-1k dataset as the ID benchmark, we evaluate the proposed OOD detector on 51 test (OOD) datasets, and observe drastically and consistently lower false positive rates w.r.t. all the competitive methods. Moreover, the proposed ID detector is evaluated, using ECG and stock price datasets, for the task of data augmentation in domain adaptation and continual learning settings, and we observe higher efficacy compared to relevant baselines.

Information-theoretic clustering is one of the most promising and principled approaches to finding clusters with minimal apriori assumptions. The key criterion therein is to maximize the mutual information between the data points and their cluster labels. Such an approach, however, does not explicitly promote any type of inter-cluster behavior. We instead propose to maximize the Kullback-Leibler divergence between the underlying data distributions associated to clusters (referred to as cluster distributions). We show it to entail the mutual information criterion along with maximizing cross entropy between the cluster distributions. For practical efficiency, we propose to empirically estimate the objective of KL-D between clusters in its dual form leveraging deep neural nets as a dual function approximator. Remarkably, our theoretical analysis establishes that estimating the divergence measure in its dual form simplifies the problem of clustering to one of optimally finding k-1 cut points for k clusters in the 1-D dual functional space. Overall, our approach enables linear-time clustering algorithms with theoretical guarantees of near-optimality, owing to the submodularity of the objective. We show the empirical superiority of our approach w.r.t. current state-of-the-art methods on the challenging task of clustering noisy timeseries as observed in domains such as neuroscience, healthcare, financial markets, spatio-temporal environmental dynamics, etc.

The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet-of-Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this article, we propose an on-body device clustering (OBDC) scheme. First, the OBDC extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others’ devices incorrectly.

With the rapid progress of wireless communication technologies along with their digital revolutions, the quantity of the Internet of Things (IoT) has been increased by manifolds, resulting in a huge increase in data volume and network traffic. It became easier for an intruder to pretend as a valid service provider, and generate different types of network attacks. This becomes even more severe when the service involves digital financial transactions for possible urbanization. This article proposes an intrusion detection system (IDS) based on a stacked autoencoder (AE) and a deep neural network (DNN). The stacked AE learns the features of the input network record in an unsupervised manner to decrease the feature width. Then, the DNN is trained in a supervised manner to extract deep-learned features for the classifier. In the proposed system, the stacked AE has two latent layers and the DNN has two or three layers, where each layer has a fully connected layer, a batch normalization, and a dropout. The system was evaluated on three publicly available data sets: 1) KDDCup99; 2) NSL-KDD; and 3) aegean Wi-Fi intrusion data sets. Experimental results exhibited that the proposed IDS achieved 94.2%, 99.7%, and 99.9% accuracy, respectively, for multiclass classification.

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.

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.

The optimal transmit power that maximizes energy efficiency (EE) in Longe Range (LoRa) networks is investigated by using the deep learning (DL) approach. Particularly, the proposed artificial neural network (ANN) is trained two times; in the first phase, the ANN is trained by the model-based data which are generated from the simplified system model while in the second phase, the pre-trained ANN is re-trained by the practical data. Numerical results show that the proposed approach outperforms the conventional one which directly trains with the practical data. Moreover, the performance of the proposed ANN under both partial and full optimum architecture are studied. The results depict that the gap between these architectures is negligible. Finally, our findings also illustrate that instead of fully re-trained the ANN in the second training phase, freezing some layers is also feasible since it does not significantly decrease the performance of the ANN.

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.

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.

In Internet of Things (IoT)-based e-Health Systems (IoTEHS), medical devices form a large network that continuously sense and share the healthcare data with the nearby edge devices or cloud servers. The health data is subsequently made available to various IoTEHS stakeholders (such as doctors, nurses and patients) to track and monitor patients under observation. However, the entire IoTEHS stakeholders communicate with each other over a wireless unsecured public communication channel. This is a major security and privacy loophole wherein the attacker can exploit the vulnerability of the system and can launch various attacks on the ongoing communication. Motivated by the aforementioned challenges, a secure data dissemination scheme using AI and blockchain is proposed. In this scheme, the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices. The collected data is first filtered using AI-based intrusion detection system located at the edge of the network. Second, a secure health monitoring network is designed using blockchain. Specifically, the filtered or normal transactions are transmitted to centralized cloud servers where the smart contact-enabled consensus mechanism is used to validate the transactions. Once the transaction gets validated, it is stored on distributed InterPlanetary File System (IPFS) of cloud and returned transaction hash is stored on the blockchain ledger located at edge devices making data exchange faster. The detailed experimental investigation demonstrates that the proposed schemes are efficient (in terms of computing and processing time) as well as its resistance to a variety of security attacks.

Mobile Cloud Computing (MCC) also known as on-demand computing uses cloud computing to deliver applications to mobile devices. This new computational paradigm model which plays a big part in the Internet of Things (IoT), has increased its popularity even more during Covid-19 pandemic and became a necessity when schools, businesses and hospitals must work remotely. We can access and process remote data which are stored over the cloud server in real-time by connecting to a wireless network. For accessing any cloud server, a mutual authentication and key agreement between a mobile user and a cloud server provider is required. However, existing authentication schemes for MCC fail to provide user anonymity, server anonymity and user untraceability. Therefore, we propose a Lightweight Authentication Scheme with User Anonymity (LASUA) which artfully employs Elliptic Curve Cryptography (ECC), random number, time stamps, one-way hash functions, concatenation, XOR operations and fuzzy extractor for biometric to enable various security features including anonymity and resistance against various attacks. LASUA utilises the hardness of ECC to provide top-notch security with low computation and communication cost, a perfect solution for resource constrained devices.

By leveraging massive low-cost reconfigurable reflect array elements, intelligent reflecting surface (IRS) is recently proposed to exhibit the favorable wireless propagation environment of Internet of Things (IoT) systems. In this article, we provide a spectrum-efficiency approach by exploiting non-orthogonal multiple access (NOMA) as well as cognitive radio (CR) to form NOMA IRS-assisted CR system. In this IRS-based IoT, the active access point in secondary network transmits beamforming and the passive IRS elements are jointly operated to achieve different performance relying on demands of IoT devices, while maintaining the normal operation of primary network. Then, the exact closed-form formulas are introduced to evaluate outage probability at each IoT device. Moreover, to provide more insights of the system, a diversity order is considered aiming to look at limitation of outage performance when the system tries to increase average signal to noise ratio (SNR) at the secondary transmitter. Finally, we conduct numerical simulations to verify the superior performance of IoT systems with higher meta-surface elements at IRS over the necessary comparisons in practical scenarios.

Due to the constant penetration of various security attacks, it is highly important to secure the underlying communication networks between the IoT, Fog and Cloud in the next generation of mobile communication system (5G). Thus, secure authentication and key agreement protocol, namely 5G-AKA, has been proposed in the literature to safely and stably access the 5G mobile services. However, some recent findings reveal that 5G-AKA and its numerous versions based on symmetric or asymmetric encryption are either vulnerable to different attacks such as perfect forward secrecy violation, malicious Serving Network (SN), de-synchronization attack, privacy theft, stolen device, or are computationally intensive. Apart from that, these protocols use single-factor authentication. Considering the above demerits of these protocols and the necessity to provide enhanced security, we propose an Elliptic Curve-Cryptography (ECC)-based multi-factor 5G-AKA authentication protocol. It provides additional security and achieves cost-effectiveness in terms of computational, communication, storage costs and energy consumption. The formal security analysis using Real-Or-Random (ROR) logic has been done to confirm its security. Moreover, we evaluate the performance of the proposed protocol in terms of computational, communication, storage costs and energy consumption. The evaluation results show that the proposed protocol requires less cost than its counterparts, reducing computational cost by up to 57%, communication cost by up to 59%, storage cost by up to 52%, and energy consumption by up to 51%.

Wireless communication based on Unmanned aerial vehicle (UAV) is one of the important technologies in the future communication system. It is necessary to establish an accurate air-to-ground (A2G) wireless channel model. In this paper, a A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) is proposed. The internal vibration of the UAV is modeled as a sinusoidal random process, and the UAV wobble caused by the random air fluctuations is modeled as the uniform distribution random process. We derive the A2G channel temporal auto-correlation function (ACF) with UAV 3D wobbles, analyze the variation of the temporal ACF with different time instants, carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel ACF will decrease rapidly with the increase of the amplitudes of the wobble angles and the carrier frequency. This work contributes to the establishment of the next generation wireless channel model and the design of communication system.

In this paper, we study the coverage maximization problem in the aerial networks. Specifically, we propose a heterogeneous aerial network (HetAN) consisting of a high-altitude base station (HBS) acting as a hub to provide wireless backhaul and multiple low-altitude BSs (LBSs) acting as access points to provide on-demand wireless coverage. Besides, we adopt the non-orthogonal multiple access (NOMA) technique for the uplink transmissions of the terrestrial users so as to support massive connections. Then, we formulate a joint power control, channel assignment, and rate control problem with the objective to maximize user connectivity and network throughput. Based on the graph methods and theoretical analysis, we propose an efficient iterative algorithm to solve the formulated problem. Simulation results demonstrate that our algorithm outperforms the other schemes in terms of connectivity and throughput.

The growing usage of wireless devices has significantly increased the need for Wireless Local Area Network (WLAN) during the past two decades. However, security (most notably authentication) remains a major roadblock to WLAN adoption. Several authentication protocols exist for verifying a supplicant’s identity who attempts to connect his wireless device to an access point (AP) of an organization’s WLAN. Many of these protocols use the Extensible Authentication Protocol (EAP) framework. These protocols are either vulnerable to attacks such as violation of perfect forward secrecy, replay attack, synchronization attack, privileged insider attack, and identity theft or require high computational and communication costs. In this paper, a lightweight EAP-based authentication protocol for IEEE 802.11 WLAN is proposed that not only addresses the security issues in the existing WLAN authentication protocols but is also cost-effective. The security of the proposed protocol is verified using BAN logic and the Scyther tool. Our analysis shows that the proposed protocol is safe against all the above attacks and attacks defined in RFC-4017. A comparison of the computational and communication costs of the proposed protocol with other existing state-of-the-art protocols shows that the proposed protocol is lightweight than existing solutions.

Estimating Transfer Entropy (TE) between time series is a highly impactful problem in fields such as finance and neuroscience. The well-known nearest neighbor estimator of TE potentially fails if temporal dependencies are noisy and long ranged, primarily because it estimates TE indirectly relying on the estimation of joint entropy terms in high dimensions, which is a hard problem in itself. Other estimators, such as those based on Copula entropy or conditional mutual information have similar limitations. Leveraging the successes of modern discriminative models that operate in high dimensional (noisy) feature spaces, we express TE as a difference of two conditional entropy terms, which we directly estimate from conditional likelihoods computed in-sample from any discriminator (timeseries forecaster) trained per maximum likelihood principle. To ensure that the in-sample log likelihood estimates are not overfit to the data, we propose a novel perturbation model based on locality sensitive hash (LSH) functions, which regularizes a discriminative model to have smooth functional outputs within local neighborhoods of the input space. Our estimator is consistent, and its variance reduces linearly in sample size. We also demonstrate its superiority w.r.t. state-of-the-art estimators through empirical evaluations on a synthetic as well as real world datasets from the neuroscience and finance domains.

Surveillance drones, called as unmanned aerial vehicles (UAVs), are aircrafts that are utilized to collect video recordings, still images, or live video of the targets, such as vehicles, people or specific areas. Particularly in battlefield surveillance, there is high possibility of eavesdropping, inserting, modifying or deleting the messages during communications among the deployed drones and ground station server (GSS). This leads to launch several potential attacks by an adversary, such as main-in-middle, impersonation, drones hijacking, replay attacks, etc. Moreover, anonymity and untraceability are two crucial security properties that need to be maintained in battlefield surveillance communication environment. To deal with such a crucial security problem, we propose a new access control protocol for battlefield surveillance in drone-assisted Internet of Things (IoT) environment, called ACPBS-IoT. Through the detailed security analysis using formal and informal (nonmathematical), and also the formal security verification under automated software simulation tool, we show that the proposed ACPBS-IoT can resist several potential attacks needed in a battlefield surveillance scenario. Furthermore, the testbed experiments for various cryptographic primitives have been performed for measuring the execution time. Finally, a detailed comparative study on communication and computational overheads, and security, as well as functionality features, reveals that the proposed ACPBS-IoT provides superior security and more functionality features, and better or comparable overheads than other existing competing access control schemes.

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.

Industry 5.0 is the automation, digitization, and data communication of the industrial procedure that comprises industrial cyber–physical systems (I-CPSs), industrial Internet of Things (IIoT), and artificial intelligence (AI). In the I-CPS-enabled healthcare ecosystem, intelligent wearable devices have been extensively employed to sense body information and measure the health status of the patients. Besides other IIoT applications, the I-CPS-enabled healthcare ecosystem also bears various challenges. For instance, due to the communal communication mediums, the security of a patient’s physiological datum is becoming a significant challenge these days. In order to cope with this challenge, we presented a secure and lightweight key establishment protocol. To the best of our knowledge, this protocol is the first application of physically unclonable function (PUF) in the I-CPS-enabled healthcare. The security of the designed protocol is proved with the help of a widely recognized real-or-random (ROR) model. The practical demonstration of our protocol from the network perspective is also measured through broadly recognized NS3 simulator tool.

The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

For a number of years, due to an exponential increase in the demand for an eco-friendly environment, there has been a rapid increase in the green city revolution across the globe. Subsequently, load shifting of major energy consumers from conventional power grids to renewable energy sources (RES) has become inevitable. Towards this end, cloud data centers (DCs) have emerged as significant consumers of energy that solely rely on power grids to fuel their day-to-day operations. Nevertheless, their energy consumption has increased significantly which in turn has substantially raised the global carbon footprint rate. These challenges can be best addressed by the judicious utilization of RES which have well established advantages like reduced operational costs and carbon emissions. Keeping in view of the above facts, the ultimate goal of the proposed work is to design a comprehensive workload classification; and job scheduling and Vitual machine placement architecture for cloud DCs powered by RES and power grids. For this, a multi-objective optimization scheme is proposed which operates in two phases. In phase I,a random forest-based wrapper schemeknown as Boruta, is used for relevant feature set selection for the incoming workload. This is followed by classification of the workload using a locality sensitive hashing-based support vector machines approach. In phase II, a multi-objective optimization problem for job scheduling and VM placement is formulated with respect to parameters such as service level agreement (SLA), energy cost, carbon footprint rate (CFR), and availability of RES. It is further solved using an enhanced heuristic approach based on a greedy strategy. Our experimental evaluations show an average improvement of approximately 31 percent in energy utilization, 28 percent in energy cost, and 36 percent in CFR, with a slight degradation in SLA assurance (about 2 percent) compared with the existing schemes.

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

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.

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%.

Heterogeneous networks (HetNets) are becoming a promising solution for future wireless systems to satisfy the high data rate requirements. This paper introduces a stochastic geometry framework for the analysis of the downlink coverage probability in a multi-tier HetNet consisting of a macro-base station (MBS) operating at sub-6 GHz, millimeter wave (mmWave)-enabled unmanned aerial vehicles (UAVs) operating at 28 GHz, and small BSs operating both at mmWave and THz frequencies. The analytical expressions for the coverage probability for each tier have been derived in the paper. Monte Carlo simulations are then performed to validate the analytical expressions. The effectiveness of the HetNet is analyzed on various performance metrics including association and coverage probabilities for different network parameters. We show that the mmWave and THz-enabled cells provide significant improvement in the achievable data rates because of their high available bandwidths, however, they have a degrading effect on the coverage probability due to their high propagation losses.

Underwater optical wireless communications (UOWCs) are promising and potential wireless carriers to envisage underwater phenomenal activities for various applications towards the futuristic 5G and beyond (5GB) wireless systems. The main challenges to deploy underwater applications are the physicochemical properties and strong turbulence channel conditions. In this regard, the end-to-end (E2E) performance analysis of a dual-hop mixed FSO/UVLC system under the intensity modulation/direct detection (IM/DD) technique in consideration of pulse amplitude modulation (PAM) scheme is investigated. Throughout this study, to tackle the issues of moderate-to-strong turbulence channel conditions, this work deploys the Gamma-Gamma (GG) distribution fading model and the links are designed by unifying plane wave models in the corresponding links, respectively. This investigation outperforms higher achievable data rate with minimal delay response and enhance network connectivity in real-time monitoring scenarios as compared with the traditional underwater wireless communication technologies. In more contrast, the probability distribution function (PDF), cumulative distribution function (CDF), and closed-form expression of the system are derived and presented in terms of Meijer-G function as well as Extended Generalized Bivariate Meijer-G Function (EGBMGF). The significant E2E performance metrics are obtained by employing the decode-and-forward (DF) relay protocol in hostile channel conditions. In aggregating this work, we combine the analytical expressions that present an efficient tool to depict the impact of channel parameters on the system. The simulation results are plausible of the system performance metrics as average BER (ABER) and outage probability$(P_{out})$in the presence of pointing and without pointing error events. Finally, in this work, we use the Monte-Carlo approach for the best fitting curves and validate the numerical expression yields simulation results.

The Internet of Things (IoT) networks are obstructed by security vulnerabilities that hackers can leverage to operate intrusions in many environments, such as smart homes, smart factories, and smart healthcare systems. To overcome this obstruction, researchers have come up with different intrusion detection and prevention systems (IDPSs). Out of all the implemented technologies, Machine Learning (ML) has emerged as the most promising approach. Therefore, to improve the detection accuracy, most ML-based intrusion detection solutions focus only on investigating appropriate ML algorithms. Yet, the limitations in terms of detection accuracy in various attacks are often caused by lack of appropriate detection features. Moreover, the majority of the previous works lack intrusion prevention mechanisms and deployment architectures. Thus, in this research, we study the properties of different smart home security attacks and the quality of the features that can be brought out and employed in ML algorithms to detect each of these attacks efficiently. Furthermore, this research proposes effective intrusion prevention mechanisms and a Software-Defined Networking (SDN) based deployment architecture of the IDPSs within home networks. Experimental evaluations of the proposed solution are provided using different feature sets and various ML models. The contributions and advancements discussed in this paper will upgrade future research and engineering works on IDPSs for IoT.

Self-attention mechanisms have recently been embraced for a broad range of text-matching applications. Self-attention model takes only one sentence as an input with no extra information, i.e., one can utilize the final hidden state or pooling. However, text-matching problems can be interpreted either in symmetrical or asymmetrical scopes. For instance, paraphrase detection is an asymmetrical task, while textual entailment classification and question-answer matching are considered asymmetrical tasks. In this article, we leverage attractive properties of self-attention mechanism and proposes an attention-based network that incorporates three key components for inter-sequence attention: global pointwise features, preceding attentive features, and contextual features while updating the rest of the components. Our model follows evaluation on two benchmark datasets cover tasks of textual entailment and question-answer matching. The proposed efficient Self-attention-driven Network for Text Matching outperforms the state of the art on the Stanford Natural Language Inference and WikiQA datasets with much fewer parameters.

Cloud computing has emerged as one of the popular technologies which provide on-demand services to the end users. Such services are hosted by massive geo-distributed data centers (DCs). Nowadays, connected vehicles in a smart city can also avail cloud services through Internet using cellular technologies. But, the advent of 5G technology has posed challenges for DCs such as-low latency and higher data rate requirements. To handle these challenges, edge-DCs (EDCs) can be deployed across a smart city to provide low latency services to the connected vehicles. In lieu of this, in this paper, EDCSuS: Sustainable EDC as a service framework in software defined vehicular environment is proposed. In EDCSuS, first, a software defined controller handles the incoming requests and suggest an optimal flow path. Second, a multi-leader multi-follower Stackelberg game is presented for resource allocation. Third, to improve the resource utilization, a cooperative resource sharing scheme is designed, thereby minimizing the energy consumption of servers in the EDCs. Lastly, a caching scheme is presented to avert excessive energy consumption for retracing the lost link due to vehicular mobility. The efficacy of the proposed scheme has been evaluated using extensive simulations with respect to various parameters. The results obtained prove the effectiveness of EDCSuS.

The effective combination of physical layer security communication and intelligent reflecting surface (IRS) technology has recently attracted extensive attention to improve the system security. Unlike existing works that mostly focus on single-carrier systems, we consider an IRS-assisted multi-carrier MIMO wireless physical layer security communication system, which consists of a legitimate transmitter, a legitimate receiver, an IRS node and an eavesdropper. With the aim of maximizing the sum secrecy rate, the precoding matrix and IRS reflecting coefficient matrix were jointly optimized under the constraints on the budget of the transmit power and unit modulus of IRS reflecting coefficients. An alternate optimization (AO) based inexact block coordinate descent (IBCD) algorithm was proposed to tackle the non-convexity of the formulated problem, where the Lagrange multiplier method and complex circle manifold (CCM) method were adopted to solve the subproblems and then closed-form solutions were obtained at each iteration. Finally, the simulation results validate the effectiveness of the proposed beamforming schemes.

With the advent of the Internet of Everything era, communication data has exploded, which requires more communication resources, such as frequency, time, and energy. In this context, this paper presents a machine learning-based data packet scheduling scheme to achieve efficient data packet transmission in the 5G/6G communication systems. To minimize the average number of packet overflows (APNO), we propose distributed deep deterministic policy gradient (DDPG)-based algorithm for multidimensional resource scheduling. To improve the algorithm stability and training efficiency, the strategy of centralized training and distributed execution is adopted, and an Action Adjuster is designed. The proposed algorithm enables the multidimensional resource management of the 5G/6G commu-nication systems without any information interaction between each agent. Simulation results show that the proposed Action Adjuster DDPG algorithm achieves faster convergence and less data overflow compared to other benchmark algorithms.

This paper investigates the long-term effectiveness and stability of an integrated unmanned aerial vehicles (UAV)-intelligent reflecting surface (IRS) relaying dynamic system in the context of time-varying system states. Consequently, a dynamic optimization problem is constructed to minimize the frame-average transmit power by joint active beamforming at the base station (BS) and passive beamforming at the IRS under frame-average rate constraints. The original problem as an infinite-horizon time-average one can be solved by introducing the drift-plus-penalty (DPP) algorithm and then the optimal active beamforming and passive beamforming can be obtained in an iterative manner. Simulation results demonstrate the theoretical analysis and assess the performance of the dynamic system.

As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices, the cloud server is no longer able to handle the large-scale datasets individually. Given the improved computational capabilities of the edge devices, coded distributed computing has become a promising approach given that it allows computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. Specifically, by using polynomial codes, computed results from only a subset of devices are needed to reconstruct the final result. However, there is no incentive for the edge devices to complete the computation tasks. In this paper, we present an all-pay auction to incentivize the edge devices to participate in the coded computation tasks. In this auction, the bids of the edge devices are represented by the allocation of their Central Processing Unit (CPU) power to the computation tasks. All edge devices submit their bids regardless of whether they win or lose in the auction. The all-pay auction is designed to maximize the utility of the cloud server by determining the reward allocation to the winners. Simulation results show that the edge devices are incentivized to allocate more CPU power when multiple rewards are offered instead of a single reward.

Large services depend on correct configuration to run efficiently and seamlessly. Checking such configuration for correctness is important because services use a large and continuously increasing number of configuration files and parameters. Yet, very few such tools exist because the permissible values for a configuration parameter are seldom specified or documented, existing at best as tribal knowledge among a few domain experts.In this paper, we address the problem of configuration pattern mining: learning configuration rules from examples. Using program synthesis and a novel string profiling algorithm, we show that we can use file contents and histories of commits to learn patterns in configuration. We have built a tool called ConfMiner that implements configuration pattern mining and have evaluated it on four large repositories containing configuration for a large-scale enterprise service. Our evaluation shows that ConfMiner learns a large variety of configuration rules with high precision and is very useful in flagging anomalous configuration.

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose the edge association strategies of the workers to be modelled using an evolutionary game. Then, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

This paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

Due to memory and processing limitations, Internet-of-Things (IoT) devices require external fog servers to perform some of their tasks. However, this offloading of tasks comes at the cost of more interactions whose security cannot be guaranteed without the authentication and key management scheme. Traditional prescriptions, such as those used for securing the Web, require referring to central agents, such as certificate authorities (CA) or online certificate status protocol (OCSP) responders, that sit in the cloud. This poses many challenges, including additional communication costs and repetitive delays which work against the low latency and energy efficiency goals of edge networking. In this article, we propose a novel semidecentralized public-key management scheme for smart IoT systems in which devices intelligently decide whether to look for the keying material locally at the edge or refer to the cloud for this purpose. The result is a security architecture that links IoT devices, fog servers, and cloud, but with minimal dependency on the latter. In the proposed solution, devices work collaboratively to deliver revocation lists and digital certificates of fog servers to each other. The decision to go for edge nodes or cloud CA/OCSP responders is made intelligently by each node upon learning its neighborhood and network statistics. The core idea is based on the Web of trust, but unlike that, whenever a material is not found locally, cloud servers are queried. Experiments show that through this intelligent approach, the cost of key management operations, e.g., delay, can be reduced by up to 50%.

Currently, the data collected by the Internet of Healthcare Things, i.e., healthcare oriented Internet of Things (IoT), still rely on cloud-based centralized data aggregation and processing. To reduce the need for transmission of data to the cloud, the edge computing architecture may be adopted to facilitate machine learning at the edge of the network through leveraging on the amassed computation resources of pervasive IoT devices. In this article, federated learning (FL) is proposed to enable privacy-preserving collaborative model training at the edge of the network across distributed IoT users. However, the users in the FL network may have different willingness to participate (WTP), a hidden information unknown to the model owner. Furthermore, the development of healthcare applications typically requires sustainable user participation, e.g., for the continuous collection of data during which a user’s WTP may change over time. As such, we leverage on the dynamic contract design to consider a two-period incentive mechanism that satisfies the intertemporal incentive compatibility (IIC), such that the self-revealing mechanism of the contract holds across both periods. The performance evaluation shows that our contract design satisfies the IIC constraints and derives greater profits than that of the uniform pricing scheme, thus validating its effectiveness in mitigating the adverse impacts of the information asymmetry.

With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people's willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming increasingly important. Furthermore, data collected by the edge device contain massive user's private data, which is challenging current detection approaches as user privacy has attracted more and more public concerns. With this focus, this article proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce an FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based convolutional neural network units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of the long short-term memory unit in predicting time-series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top- k selection to improve communication efficiency. Extensive experimental studies on four real-world data sets demonstrate that our framework accurately and timely detects anomalies and also reduces the communication overhead by 50% compared to the FL framework that does not use the gradient compression scheme.

Future generation communication systems, such as 5G and 6G wireless systems, exploit the combined satellite-terrestrial communication infrastructures to extend network coverage and data throughput for data-driven applications. These ground-breaking techniques have promoted the rapid development of Internet of Things (IoT) in maritime industries. In maritime IoT applications, intelligent vessel traffic services can be guaranteed by collecting and analyzing high volume of spatial data flows from automatic identification system (AIS). This AIS system includes a highly integrated automatic equipment, including functionalities of core communication, tracking, and sensing. The increased utilization of shipboard AIS devices allows the collection of massive trajectory data. However, the received raw AIS data often suffers from undesirable outliers (i.e., poorly tracked timestamped points for vessel trajectories) during signal acquisition and analog-to-digital conversion. The degraded AIS data will bring negative effects on vessel traffic services (e.g., maritime traffic monitoring, intelligent maritime navigation, vessel collision avoidance, etc.) in maritime IoT scenarios. To improve the quality of vessel trajectory records from AIS networks, we propose to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction. In particular, a density-based clustering method is introduced in the first phase to automatically recognize the undesirable outliers. The second phase proposes a bidirectional long short-term memory (BLSTM)-based supervised learning technique to restore the timestamped points degraded by random outliers in vessel trajectories. Comprehensive experiments on simulated and realistic data sets have verified the dominance of our two-phase vessel reconstruction framework compared to other competing methods. It thus has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.

Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users' sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback-Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU's reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.

The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined architectures and implementations, advanced machine learning and data analytics solutions, blockchain-based network services and applications, network function allocation, dependable and trustable solutions, energy efficient networks and services, and other enabling technologies for integrating softwarized networks into CPSs.

Sixth generation (6G) network is a revolutionary technology to satisfy the ever-growing demands from the sustainable development of emerging industrial applications and services. Due to its high flexibility, convenient and rapid deployment, self-organization capability, and outstanding expansibility, network-in-box (NIB) represents a promising approach for future networks. The integration of NIB with 6G can lead to many new applications in geoscience, robotics, and industrial automation. For 6G-enabled NIB, services are deployed directly on the NIB, which increases the fault tolerance and reduces the traffic volume on the backhaul link. As more and more data are processed and shared in industrial applications and services, the security of data aggregation becomes a key challenge for 6G-enabled NIB. To address this challenge, in this article, we propose a blockchain based privacy-aware distributed collection (BPDC) oriented strategy for data aggregation. In BPDC, an improved blockchain with a new block header structure and two different block generation rules are designed and introduced, which restricts the task receivers to search and receive the tasks beyond their levels of security permission. While guaranteeing the data aggregation performance, BPDC can also achieve privacy protection by decomposing sensitive tasks and task receivers into multiple groups. Validation experiments show that the BPDC accomplishes low overhead, high throughput, and privacy preservation in various industrial applications.

The Internet of Things (IoT) is a group of millions of devices having sensors and actuators linked over wired or wireless channel for data transmission. IoT has grown rapidly over the past decade with more than 25 billion devices expected to be connected by 2020. The volume of data released from these devices will increase many-fold in the years to come. In addition to an increased volume, the IoT devices produces a large amount of data with a number of different modalities having varying data quality defined by its speed in terms of time and position dependency. In such an environment, machine learning (ML) algorithms can play an important role in ensuring security and authorization based on biotechnology, anomalous detection to improve the usability, and security of IoT systems. On the other hand, attackers often view learning algorithms to exploit the vulnerabilities in smart IoT-based systems. Motivated from these, in this article, we propose the security of the IoT devices by detecting spam using ML. To achieve this objective, Spam Detection in IoT using Machine Learning framework is proposed. In this framework, five ML models are evaluated using various metrics with a large collection of inputs features sets. Each model computes a spam score by considering the refined input features. This score depicts the trustworthiness of IoT device under various parameters. REFIT Smart Home data set is used for the validation of proposed technique. The results obtained proves the effectiveness of the proposed scheme in comparison to the other existing schemes.

In this article, we design a novel multipath communication framework for Industry 4.0 using deep Q-network [1] to achieve human-level intelligence in networking automation and orchestration. To elaborate, we first investigate the challenges and approaches in exploiting heterogeneous networks and multipath communication [e.g., using multipath transmission control protocol (MPTCP)] for the information technology cum operation technology (IT/OT) convergence in Industry 4.0. Based on the novel idea of intelligent and flexible manufacturing, we analyze the technical challenges of IT/OT convergence and then model network data traffics using MPTCP over the converged frameworks. It quantifies the adverse impact of network convergence on the performance for flexible manufacturing. We provide a few proof-of-concepts solutions; however, after a clear understanding of the tradeoffs, we discover the need for experience-driven MPTCP. The simulation result demonstrates that the proposed scheme significantly outperforms the baseline schemes.

Massive machine-type Internet of Things (IoT) communication (mMTIC) has the potential for high impact in the anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering routing protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight toward the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multihop communication among the nodes often causes such an energy hole problem. In this article, we develop an artificial-intelligence-based CRP framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.

Cloud envisioned cyber--physical systems (CCPS) is a practical technology that relies on the interaction among cyber elements like mobile users to transfer data in cloud computing. In CCPS, cloud storage applies data deduplication techniques aiming to save data storage and bandwidth for real-time services. In this infrastructure, data deduplication eliminates duplicate data to increase the performance of the CCPS application. However, it incurs security threats and privacy risks. For example, the encryption from independent users with different keys is not compatible with data deduplication. In this area, several types of research have been done. Nevertheless, they are suffering from a lack of security, high performance, and applicability. Motivated by this, in this article, we propose a message lock encryption with neVer-decrypt homomorphic encRyption (LEVER) protocol between the uploading CCPS user and cloud storage to reconcile the encryption and data deduplication. Interestingly, LEVER is the first brute-force resilient encrypted deduplication with only cryptographic two-party interactions. We perform several numerical analysis of LEVER and confirm that it provides high performance and practicality compared to the literature.

Cognitive computing, a revolutionary AI concept emulating human brain's reasoning process, is progressively flourishing in the Industry 4.0 automation. With the advancement of various AI and machine learning technologies the evolution toward improved decision making as well as data-driven intelligent manufacturing has already been evident. However, several emerging issues, including the poisoning attacks, performance, and inadequate data resources, etc., have to be resolved. Recent research works studied the problem lightly, which often leads to unreliable performance, inefficiency, and privacy leakage. In this article, we developed a decentralized paradigm for big data-driven cognitive computing (D2C), using federated learning and blockchain jointly. Federated learning can solve the problem of “data island” with privacy protection and efficient processing while blockchain provides incentive mechanism, fully decentralized fashion, and robust against poisoning attacks. Using blockchain-enabled federated learning help quick convergence with advanced verifications and member selections. Extensive evaluation and assessment findings demonstrate D2C's effectiveness relative to existing leading designs and models.

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.

Software-defined industrial network has emer-ged as an autonomous ecosystem where the network control relies on a centralized controller to provide seamless data transfer. However, the reliance on a centralized controller can lead to several challenges, such as single point of failure. An adversary can initiate a denial of service attack and limit the availability of the controller by projecting malicious or uncontrolled traffic flows. To overcome this, in this article, a deep-learning-based blockchain framework is designed for providing secure software-defined industrial network. In this framework, a blockchain mechanism is designed wherein all the switch are registered, verified (using zero-knowledge proof), and, thereafter, validated in the blockchain using a voting-based consensus mechanism. A deep Boltzmann machine based flow analyzer is deployed at the control plane to identify the anomalous switch requests. The evaluation is performed using a mininet emulator wherein the results obtained depict the superiority of the proposed framework.

Industrial Internet of Things (IIoT) is ushering in huge development opportunities in the era of Industry 4.0. However, there are significant data security and privacy challenges during automatic and real-time data collection, monitoring for industrial applications in IIoT. Data security and privacy in IIoT applications are closely related to the reliability of users, which is determined by user authentication that have been widely used as an effective approach. However, the existing user authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability with the rapid growth of the number of users and the diversity of user categories. To solve the aforementioned issues, this article proposes a novel Authentication mechanism based on Transfer Learning empowered Blockchain, coined ATLB. In ATLB, blockchains are applied to achieve the privacy preservation for industrial applications. In addition, by introducing the transfer learning based authentication mechanism, trustworthy blockchains are built such that the privacy preservation for industrial applications is further enhanced. Specifically, ATLB first employs a guiding deep deterministic policy gradient algorithm to train the user authentication model of a specific region, which is then transferred locally for foreign user authentication or cross-regionally for another region's user authentication such that the model training time is significantly reduced. Experimental results show that the proposed ATLB not only provides accurate authentications for IIoT applications but also achieves high throughput and low latency.

With the recent advances in wireless communications, the automotive industry is leading to evolution. To succeed in this emerging era of technology, the Internet of Connected Vehicles (IoCV) has emerged as one of the potential applications of the Internet of Things (IoT). It refers to the dynamic mobile communication systems that communicate between vehicles and public networks to enhance the connectivity between cars via technology. By offering a wide variety of infotainment services, fleet operations, and in-vehicle applications, IoCV has gained the tremendous capacity to provide a safer and sustainable transportation system to the society. According to Gartner Inc., “the connected car is already a reality, and in-vehicle wireless connectivity is expanding rapidly.” As a result, the evolution of cars into the IoT will keep on accelerating the global market which is expected to grow by 270% by 2022. Furthermore, the increasing deployment of sensors and ever-evolving cognitive technology opens up new opportunities for IoCV. Due to these significant developments, connected vehicles are receiving widespread attention from the major automotive giants such as Tesla, BMW, Waymo (Google), Uber, Volvo, and so on. Despite all the opportunities offered by the IoCV, their highly dynamic topology and the increasing number of vehicles pose challenges regarding delivering low-latency vehicle-to-everything (V2X) communications.

Internet of Vehicles (IoV) has escalated the movement of big data across moving vehicles which create a huge burden on the network infrastructure. In IoV environment, effective handling of streaming data has to face various challenges like; traffic monitoring, flow management, re-configuration and security. Software-defined networks (SDN) provides improved flexibility, and centralized control of the network to overcome (almost) the above-mentioned challenges. However, it can lead to an easy target (node or controller) for malicious agents. So, to detect the anomalous behaviour of the nodes in the IoV environment, a hybrid approach using probabilistic data structures is proposed which works in the following phases. In phase I, a traffic monitoring scheme using Count-Min-Sketch is designed to identify the suspicious nodes. In phase II, to detect an anomaly, a Bloom filter-based control scheme is used for signature verification of suspicious nodes. In phase III, a Quotient filter is used for fast and efficient storage of malicious nodes. In phase IV, to detect the super points (malicious hosts that are connected to a large number of destinations), a Hyperloglog counter is used to measure the cardinality of each flow passing through the switches. The proposed scheme has been evaluated in a simulated environment. The results obtained depict that the proposed scheme is faster, accurate, and efficient concerning detection ratio and false-positive ratio.

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user's preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

The exponential surge in the number of vehicles on the road has aggravated the traffic congestion problem across the globe. Several attempts have been made over the years to predict the traffic scenario accurately and consequently avoiding further congestion. Crowdsourcing has come forward as one of the most adopted methods for predicting traffic intensity using live data. However, the privacy concerns and the lack of motivation for the live users to help in the traffic prediction process have rendered existing crowdsourcing models inefficient. Towards this end, we present an advanced blockchain-based secure crowdsourcing model. Not only does our model ensure privacy preservation of the users, but by incorporating a revenue model, it also provides them with an incentive to participate in the traffic prediction process willingly. For accurate and efficient traffic jam probability estimation, our work proposes a neural network-based smart contract to be deployed onto the blockchain network. The results reveal that the proposed model is highly efficient in terms of attaining high participation and consequently obtaining highly accurate predictions.

Vehicle taillight detection is essential to analyze and predict driver intention in collision avoidance systems. In this article, we propose an end-to-end framework that locates the rear brake and turn signals from video stream in real-time. The system adopts the fast YOLOv3-tiny as the backbone model and three improvements have been made to increase the detection accuracy on taillight semantics, i.e., additional output layer for multi-scale detection, spatial pyramid pooling (SPP) module for richer deep features, and focal loss for alleviation of class imbalance and hard sample classification. Experimental results demonstrate that the integration of multi-scale features as well as hard examples mining greatly contributes to the turn light detection. The detection accuracy is significantly increased by 7.36%, 32.04% and 21.65% (absolute gain) for brake, left-turn and right-turn signals, respectively. In addition, we construct the taillight detection dataset, with brake and turn signals are specified with bounding boxes, which may help nourishing the development of this realm.

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns hashcodes as text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.

Recently, the use of unmanned aerial vehicles (UAVs) as a relay node has been envisaged as an enabling technology in the upcoming wireless communication era. Thus, in this paper, we consider a full-duplex (FD) cooperative communication system with a source and a destination, where UAV serves as a mobile relay. Here, the transmission power cost is debited to energy harvested using simultaneous wireless information and power transfer (SWIPT) and self-interference energy harvesting (EH) via power-splitting (PS) protocol. In poor channel conditions, UAV uses a soft angular modulation scheme to perceive the soft information. In this proposed system, we present the outage probability over the Nakagami-m fading channels. A closed-form solution for the outage probability is derived. In addition, we formulate an optimization problem to minimize end-to-end outage probability subject of the UAV's power profile. The KKT conditions have been used to obtain a closed-form solution of the proposed optimization problem. Finally, numerical results are provided to evaluate the proposed system under various setups.

In the smart healthcare domain, a number of mutual authentication and key agreement protocols have been suggested by the research fraternity. However, the majority of the existing protocols fail to provide the required level of security and fall for different attack vectors. Thus, in this paper, a robust, secure, and lightweight authentication and key agreement protocol is presented. The designed protocol exploits the enhanced security and reduced key size features of Elliptic Curve Cryptography (ECC) to establish mutual trust between the patients (equipped with mobile devices/sensors) and the central servers; followed by settlement on a common session key for further communication. Furthermore, the designed protocol also exploits one of the crucial features of the blockchain technology, i.e., maintaining the hash of the previous transaction. This feature, in turn, instills greater security and prevents impersonation attacks to a much larger extent. The formal and informal security assessments of the proposed protocol establish the fact that it more secure and resilient against different attack vectors than its existing counterpart. In addition to this, comparative evaluation in terms of communication and computational overhead also indicate the lightweight attribute of the proposed protocol.

In this work, we present a multi-objective Virtual Data Center Embedding (VDCE) scheme for multi-domain cloud computing setups. The primary focus of the proposed scheme is on -Energy minimization, SLA assurance, and reduced energy Prices; and is named as ESP-driven VDCE. In the preliminary phase of this work, we formulate the proposed scheme as an optimization problem. However, due to the intractability of the formulated problem, its is remodelled and divided into three sub-problems (SP), i.e., data center identification, virtual machine mapping, and virtual link embedding. The output of one SP serves as an input to the next SP, such that the search space can be significantly narrowed. Finally, the proposed approach for VDCE is extensively validated against other algorithms. The obtained results indicate that the proposed ESP-driven VDCE approach achieves almost 7.6% more energy-aware embeddings with 11.5% higher SLA levels and approximately 23% lower energy expenses.

With the recent advancements in wireless communications, Tactile Internet (TI) has witnessed a major blow. TI is considered the next big evolution that will provide real-time control in industrial setups, particularly in the domain of tele-surgery. However, in remote-surgery ecosystems the transmission of data is prone to different attack vectors. Thus, to realize the true potential of secure tele-surgery under the umbrella of TI, it is required to design a secure authentication and key agreement protocol for tele-surgery. In this paper, we present an effective and secure mutual authentication and session establishment protocol for TI-driven remote surgery setups. The designed protocol enables secure communications between the surgeon, robotic arm, and the trusted authority (TA); where the protocol leverages the advantages of Elliptic Curve Cryptography (ECC) and biometrics. The protocol operates along the following three phases: i) setup phase, ii) registration phase, and iii) mutual authentication and key agreement phase. During the third phase, the surgeon and the robotic arm mutually authenticate each other with the help of the TA. Further, the security features of the designed protocol have been established using formal and informal means. The obtained results indicate the resiliency of the protocol against offline password guessing attacks, replay attacks, impersonation attacks, man-in-the-middle attacks, denial of service attacks, etc.

With significant and continuing advances in information and communication technologies, the Internet of Things (IoT) will play an increasingly important role in domains, such as healthcare, transportation, finance, and energy. In an IoT system, billions of devices (e.g., sensors, wearables, and smart appliances) are connected to the global network infrastructure, and one associated phenomenon is the generation of a large volume of data. Apart from data volume, the velocity, variety, and veracity of these data will pose a significant burden on conventional networking infrastructures. However, as sensor and fifth-generation (5G) cellular technologies advance, so will the pervasiveness of IoT deployment. Parallel to this trend, cloud computing has been integrated with IoT in order to address limitations in existing IoT networks (e.g., storage and computing resources), and examples include Google cloud dataflow and Amazon IoT. However, cloud-centric IoT solutions may not be suited for delay-sensitive and computationally intensive applications, for example, due to resource availability, end-to-end latency, bandwidth, etc. Increasingly, large-scale IoT deployments demand high connectivity, interoperability, and orchestration which are necessary for minimizing latency and maximizing throughput. This highlights the importance of a distributed computing platform that can support the interactions between IoT and cloud computing systems.

The Internet of Things (IoT) has many applications, including Industry 4.0. There are a number of challenges when deploying IoT devices in the Industry 4.0 setting, partly due to the low-cost IoT devices/nodes with limited capacity to run/support security solutions. Hence, there is a need for a lightweight and efficient security solution to protect the environment. Thus, in this article, we present a robust, lightweight, and provably secure authentication and key agreement protocol specifically for the IoT environment based on a hierarchical approach. The proposed protocol relies on lightweight operations, such as elliptic curve cryptography, physically unclonable functions, hash functions, concatenation, and XOR operations. We then evaluate the security of the designed protocol, including the widely used automated validation of Internet security protocols and applications (AVISPA), and demonstrate that it supports mutual authentication between IoT nodes and server, and is resilient against a number of common security attacks [denial of service (DoS), replay, spoofing, etc.]. The computational and communication overhead analysis shows that the proposed protocol is comparatively less expensive than three other recently published, competing protocols.

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.

The Internet of Things (IoT) has emerged as a revolution for the design of smart applications like intelligent transportation systems, smart grid, healthcare 4.0, Industry 4.0, and many more. These smart applications are dependent on the faster delivery of data which can be used to extract their inherent patterns for further decision making. However, the enormous data generated by IoT devices are sufficient to choke the entire underlying network infrastructure. Most of the data attributes present little or no relevance to the prospective relationships and associations with the projected benefits foreseen. Therefore, order-based generalization mechanisms, known as tensors, can be used to represent these multidimensional data, thereby minimizing the flow table (FT) lookup time and reducing the storage occupancy. So, a novel IoT-train-deep approach for intelligent software-defined networking is designed in this article. The proposed approach works in four phases: 1) tensor representation; 2) deep Boltzmann machine-based classification; 3) subtensor-based flow matching process; and 4) incremental tensor train network for FT synchronization. The proposed model has been extensively tested, and it illustrates significant improvements with respect to delay, throughput, storage space, and accuracy.

With the advent of 5G, technologies such as Software-Defined Networks (SDNs) and Network Function Virtualization (NFV) have been developed to facilitate simple programmable control of Wireless Sensor Networks (WSNs). However, WSNs are typically deployed in potentially untrusted environments. Therefore, it is imperative to address the security challenges before they can be implemented. In this paper, we propose a software-defined security framework that combines intrusion prevention in conjunction with a collaborative anomaly detection systems. Initially, an IPS-based authentication process is designed to provide a lightweight intrusion prevention scheme in the data plane. Subsequently, a collaborative anomaly detection system is leveraged with the aim of supplying a cost-effective intrusion detection solution near the data plane. Moreover, to correlate the true positive alerts raised by the sensor nodes in the network edge, a Smart Monitoring System (SMS) is exploited in the control plane. The performance of the proposed model is evaluated under different security scenarios as well as compared with other methods, where the model's high security and reduction of false alarms are demonstrated.

The papers in this special section examine artificial intelligence (AI)-driven developments in 5G mobile communications for industrial automation applications from a Big Data perspective. With the recent advances in information and communication technologies, industrial automation is expanding at a rapid pace. This transition is characterized by “Industry 4.0”, the fourth revolution in the field of manufacturing. Industry 4.0, also called as “Industrial Internet of Things (IIoT)” or “Smart Factories”, is a reflection of new industrial revolution that is not only interconnected, but also communicate, analyze, and use the information to create a more holistic and better connected ecosystem for the industries.

In this article, a secure and lightweight authentication scheme, which provides trust, anonymity, and mutual authentication, with reduced energy, communicational, and computational overheads, is proposed for resource-constrained smart meters (SMs). The designed mutual authentication-based key agreement protocol leverages the advantages of fully hashed menezes-qu-vanstone key exchange mechanism along with Elliptic curve cryptography and one-way hash functions. Moreover, it allows to securely establish and verify the trust between the two communicating parties, i.e., SMs and neighbourhood area network gateway. These entities communicate over the insecure channel and form an important component of the smart metering infrastructure. Furthermore, extensive performance evaluation validates the supremacy of the designed protocol over the state-of-the-art in furnishing higher security features with minimal communicational and computational overheads. The obtained results also reflect that the proposed protocol is fit for implementation on resource-constrained SMs as it leads to minimal energy consumption.

The rapidly evolving industry standards and transformative advances in the field of Internet of Things are expected to create a tsunami of Big Data shortly. This, in turn, will demand real-time data analysis and processing from cloud computing platforms. A substantial part of the computing infrastructure is supported by large-scale and geographically distributed data centers (DCs). Nevertheless, these DCs impose a substantial cost in terms of rapidly growing energy consumption, which in turn adversely affects the environment. In this context, efficient resource utilization is seen as a potential candidate to enhance energy efficiency and minimize the load on the power sector. Nevertheless, in the majority of the public clouds, the resources are idle most of the time (i.e., under-utilized) as the load of the servers is unpredictable; thereby leading to a lofty increase in the energy utilization index and wastage of resources. Thus, it is highly essential to devise a precise and efficient resource management technique. Therefore, in this article, we leverage the advantages of software defined data centers (SDDCs) to minimize energy utilization levels. Precisely, SDDC refers to the process of programmatically abstracting the logical computing, network, and storage resources; and configuring them in real-time based on workload demands. In detail, we demonstrate the possibility of 1) designing a consolidated SDDC-based model to jointly optimize the process of virtual machine (VM) deployment and network bandwidth allocation for reduced energy consumption and guaranteed quality of service (QoS), particularly for heterogeneous computing infrastructures; 2) formulating a multiobjective optimization problem to deduce the optimal allocation of resources for both critical and noncritical applications; and 3) designing an efficient scheme based on heuristics to provide suboptimal results for the formulated multiobjective optimization problem. The proposed article presents a suboptimal approach based on first fit decreasing algorithm. Further, our empirical evaluations suggest that the proposed framework leads to almost 27.9% savings in terms of energy consumptions against the existing schemes with negligible QoS violations (approximately 0.33).

For industrial Internet of Things (IIoT), data trustworthiness should be maintained both at the time of sensing and at the time of transmission. This article is concerned with trustworthiness during transmission, which is determined by transmission reliability. We present a low-complexity and flexible method via partial and recursive superposition to improve the transmission reliability of IIoT, resulting in an IIoT with high trustworthiness. In our method, a portion of the previously transmitted data are superimposed onto the current transmitted data to introduce memory among different transmissions, which are then exploited by the windowed decoder to obtain performance gain. The proposed method is referred to as partially recursive block Markov superposition transmission of low-density parity-check (PrBMST-LDPC) codes. This article is focused on the construction of low-complexity PrBMST-LDPC codes since IIoT is resource-limited in nature. The first construction is the memory-one PrBMST-LDPC code. We present a simplified density evolution algorithm to optimize the superposition ratio for memory-one PrBMST-LDPC code. Both the analytical and numerical results show that PrBMST with memory one can be used to reduce the packet loss ratio (PLR) of IIoT using LDPC codes. Particularly, around 1.0 dB performance gain is obtained by PrBMST. We then present a low-complexity construction for PrBMST-LDPC codes with encoding memory larger than one. Simulation results show that compared with memory-one PrBMST, a further PLR reduction of around one order of magnitude can be obtained.

Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-ofthe-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.

Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo Cecchi. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Smart metering infrastructure (SMI) is the core component of the smart grid (SG) which enables two-way communication between consumers and utility companies to control, monitor, and manage the energy consumption data. Despite their salient features, SMIs equipped with information and communication technology are associated with new threats due to their dependency on public communication networks. Therefore, the security of SMI communications raises the need for robust authentication and key agreement primitives that can satisfy the security requirements of the SG. Thus, in order to realize the aforementioned issues, this paper introduces a lightweight and secure authentication protocol, "LiSA", primarily to secure SMIs in SG setups. The protocol employs Elliptic Curve Cryptography at its core to provide various security features such as mutual authentication, anonymity, replay protection, session key security, and resistance against various attacks. Precisely, LiSA exploits the hardness of the Elliptic Curve Qu Vanstone (EVQV) certificate mechanism along with Elliptic Curve Diffie Hellman Problem (ECDHP) and Elliptic Curve Discrete Logarithm Problem (ECDLP). Additionally, LiSA is designed to provide the highest level of security relative to the existing schemes with least computational and communicational overheads. For instance, LiSA incurred barely 11.826 ms and 0.992 ms for executing different passes across the smart meter and the service providers. Further, it required a total of 544 bits for message transmission during each session.

With the advent of the Internet-of-Things (IoT), vehicular networks and cyber-physical systems, the need for real-time data processing and analysis has emerged as an essential pre-requite for customers' satisfaction. In this direction, Mobile Edge Computing (MEC) provides seamless services with reduced latency, enhanced mobility, and improved location awareness. Since MEC has evolved from Cloud Computing, it inherited numerous security and privacy issues from the latter. Further, decentralized architectures and diversified deployment environments used in MEC platforms also aggravate the problem; causing great concerns for the research fraternity. Thus, in this paper, we propose an efficient and lightweight mutual authentication protocol for MEC environments; based on Elliptic Curve Cryptography (ECC), one-way hash functions and concatenation operations. The designed protocol also leverages the advantages of discrete logarithm problems, computational Diffie- Hellman, random numbers and time-stamps to resist various attacks namely-impersonation attacks, replay attacks, man-in-the-middle attacks, etc. The paper also presents a comparative assessment of the proposed scheme relative to the current state-of-the-art schemes. The obtained results demonstrate that the proposed scheme incurs relatively less communication and computational overheads, and is appropriate to be adopted in resource constraint MEC environments.

With the rapid emergence of the Internet of Things (IoT) paradigm, the evolution of wireless senor networks (WSNs) is expected to witness a major blow. However, the accelerated upsurge of sensors in the future IoT networks will face significant challenges due to their limited battery life capacity. Thus, it is essential to devise efficient schemes to prolong the battery life of the connected sensors in order to derive their full potential in the future interconnected IoT networks. Towards this end, different energy harvesting (EH) techniques relying on wide array of sources namely solar, wind, thermal, coupled magnetic resonances and radio frequency have been proposed in the literature. Working in the similar direction, in this work, an EH system based on time-switching has been proposed for half-duplex bidirectional WSN with intermediate relay over a Hoyt fading channel. For its extensive performance analysis, exact closed-form expressions have been derived with respect to outage probability (OP) and achievable throughput of the system under the hardware impairment condition. Additionally, asymptotic analysis of high signal-to-noise-ratio (SNR) regime for these performance measures has also been provided. Further, an approach for the symbol-error-rate (SER) analysis is also presented in context of the observed system. In a nutshell, the work provides a detailed analysis of the effects of various parameters on the performances of energy harvesting applied in wireless sensor networks over a Hoyt fading channel.

The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed intelligent platform, Software-Defined Network (SDN) has emerged as a viable network technology in the Fog computing environment. However, uncertainties related to task demands and the different computing capacities of Fog nodes, inquire an effective load balancing algorithm. In this paper, the load balancing problem has been addressed under the constraint of achieving the minimum latency in Fog networks. To handle this problem, a reinforcement learning based decision-making process has been proposed to find the optimal offloading decision with unknown reward and transition functions. The proposed process allows Fog nodes to offload an optimal number of tasks among incoming tasks by selecting an available neighboring Fog node under their respective resource capabilities with the aim to minimize the processing time and the overall overloading probability. Compared with the traditional approaches, the proposed scheme not only simplifies the algorithmic framework without imposing any specific assumption on the network model but also guarantees convergence in polynomial time. The results show that, during average delays, the proposed reinforcement learning-based offloading method achieves significant performance improvements over the variation of service rate and traffic arrival rate. The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.

The growing interest in the Internet of Things (IoT) applications is associated with an augmented volume of security threats. In this vein, the Intrusion detection systems (IDS) have emerged as a viable solution for the detection and prevention of malicious activities. Unlike the signature-based detection approaches, machine learning-based solutions are a promising means for detecting unknown attacks. However, the machine learning models need to be accurate enough to reduce the number of false alarms. More importantly, they need to be trained and evaluated on realistic datasets such that their efficacy can be validated on real-time deployments. Many solutions proposed in the literature are reported to have high accuracy but are ineffective in real applications due to the non-representativity of the dataset used for training and evaluation of the underlying models. On the other hand, some of the existing solutions overcome these challenges but yield low accuracy which hampers their implementation for commercial tools. These solutions are majorly based on single learners and are therefore directly affected by the intrinsic limitations of each learning algorithm. The novelty of this paper is to use the most realistic dataset available for intrusion detection called NSL-KDD, and combine multiple learners to build ensemble learners that increase the accuracy of the detection. Furthermore, a deployment architecture in a fog-to-things environment that employs two levels of classifications is proposed. In such architecture, the first level performs an anomaly detection which reduces the latency of the classification substantially, while the second level, executes attack classifications, enabling precise prevention measures. Finally, the experimental results demonstrate the effectiveness of the proposed IDS in comparison with the other state-of-the-arts on the NSL-KDD dataset.

Improved quality of life has lead the healthcare industry to geographically expand and support real-time services. Following this trend, a surge of healthcare monitoring devices has substantially overgrown in the global market. These devices tend to generate data in humongous quantity that need real-time analysis with seamless and secure transmission to the computing nodes. The existing computing and networking infrastructures fall short to cater the services with desirable quality of service. Hence, to overcome these challenges, the proposed work presents a comprehensive platform referred as software defined network (SDN) Assisted Framework for Edge-Cloud Interplay in Secure Healthcare Ecosystem (SAFE). The objectives of SAFE include: first, an offloading scheme to support edge-cloud interplay, second, an SDN-assisted virtualized flow management scheme, and, third, a secure Lattice-based cryptosystem. Finally, the proposed scheme is validated on different performance parameters. Additionally, a security evaluation of the designed cryptosystem is also presented. The results obtained indicate the supremacy of the designed framework.

Cloud computing has emerged as one of the most popular technologies of the modern era for providing on-demand services to the end users. Most of the computing tasks in cloud data centers are performed by geodistributed data centers which may consume a hefty amount of energy for their operations. However, the usage of renewable energy resources with appropriate server selection and consolidation can mitigate the energy related issues in cloud environment. Hence, in this paper, we propose a renewable energy-aware multi-indexed job classification and scheduling scheme using container as-a-service for data centers sustainability. In the proposed scheme, incoming workloads from different devices are transferred to the data center which has sufficient amount of renewable energy available with it. For this purpose, a renewable energy-based host selection and container consolidation scheme is also designed. The proposed scheme has been evaluated using Google workload traces. The results obtained prove 15%, 28%, and 10.55% higher energy savings in comparison to the existing schemes of its category.

With the ongoing trend of smart and Internet-connected objects being deployed across a broad range of applications, there is also a corresponding increase in the amount of data movement across different geographical regions. This, in turn, poses a number of challenges with respect to big data storage across multiple locations, including cloud computing platform. For example, the underlying distributed file system has a large number of directories and files in the form of gigantic trees, which are difficult to parse in polynomial time. Moreover, with the exponential increase of big data streams (i.e., unbounded sets of continuous data flows), challenges associated with indexing and membership queries are compounded. The capability to process such significant amount of data with high accuracy can have significant impact on decision-making and formulation of business and risk-related strategies, particularly in our current Industrial Internet of Things environment (IIoT). However, existing storage solutions are deterministic in nature. In other words, they tend to consume considerable memory and CPU time to yield accurate results. This necessitates the design of efficient quality of service-aware IIoT applications that are able to deal with the challenges of data storage and retrieval in the cloud computing environment. In this paper, we present an effective space-effective strategy for massive data storage using bloom filter (BF). Specifically, in the proposed scheme, the standard BF is extended to incorporate fuzzy-enabled folding approach, hereafter referred to as fuzzy folded BF (FFBF). In FFBF, fuzzy operations are used to accommodate the hashed data of one BF into another to reduce storage requirements. Evaluations on UCI ML AReM and Facebook datasets demonstrate the efficacy of FFBF, in terms of dealing with approximately 1.9 times more data as compared to using the standard BF. This is also achieved without affecting the false positive rate and query time.

The continuous development and usage of multi-media-based applications and services have contributed to the exponential growth of social multimedia traffic. In this context, secure transmission of data plays a critical role in realizing all of the key requirements of social multimedia networks such as reliability, scalability, quality of information, and quality of service (QoS). Thus, a trust-based paradigm for multimedia analytics is highly desired to meet the increasing user requirements and deliver more timely and actionable insights. In this regard, software-defined networks (SDNs) play a vital role; however, several factors such as as-runtime security, and energy-aware networking limit its capabilities to facilitate efficient network control and management. Thus, with the view to enhance the reliability of the SDN, a hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in the context of social multimedia is proposed. It consists of the following two modules: (1) an anomaly detection module that leverages improved restricted Boltzmann machine and gradient descent-based support vector machine to detect the abnormal activities, and (2) an end-to-end data delivery module to satisfy strict QoS requirements of the SDN, that is, high bandwidth and low latency. Finally, the proposed scheme has been experimentally evaluated on both real-time and benchmark datasets to prove its effectiveness and efficiency in terms of anomaly detection and data delivery essential for social multimedia. Further, a large-scale analysis over a Carnegie Mellon University (CMU)-based insider threat dataset has been conducted to identify its performance in terms of detecting malicious events such as-Identity theft, profile cloning, confidential data collection, etc.

With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the need to process streaming data on real-time basis has become essential. However, the existing data stream management systems are not efficient in analyzing the network log big data for real-time anomaly detection. Further, the existing anomaly detection approaches are not proficient because they cannot be applied to networks, are computationally complex, and suffer from high false positives. Thus, in this paper a hybrid data processing model for network anomaly detection is proposed that leverages grey wolf optimization (GWO) and convolutional neural network (CNN). To enhance the capabilities of the proposed model, GWO and CNN learning approaches were enhanced with: 1) improved exploration, exploitation, and initial population generation abilities and 2) revamped dropout functionality, respectively. These extended variants are referred to as Improved-GWO (ImGWO) and Improved-CNN (ImCNN). The proposed model works in two phases for efficient network anomaly detection. In the first phase, ImGWO is used for feature selection in order to obtain an optimal trade-off between two objectives, i.e., reduced error rate and feature-set minimization. In the second phase, ImCNN is used for network anomaly classification. The efficacy of the proposed model is validated on benchmark (DARPA'98 and KDD'99) and synthetic datasets. The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score. In average, the proposed model exhibits an overall improvement of 8.25%, 4.08%, and 3.62% in terms of detection rate, false positives, and accuracy, respectively; relative to standard GWO with CNN.

Network traffic analysis is one of the most important tasks in the era of on-demand Cloud Computing. However, increased resilience on computing needs, migration flexibility, and decreased costs, have made the security and privacy issues more challenging in the context of cloud computing. Although, there are several anomaly detection techniques available in literature, but due to the unbalanced nature of data, curse of dimensionality, noise in incoming data, and frequently changing anomalies, most of the existing solutions pose critical challenges in detection of aberrant patterns. Thus, in order to overcome these gaps, a new ensemble based anomaly detection scheme called "Hybrid Classification Model for Anomaly Detection (HyClass)" in cloud environment has been proposed. HyClass operates in two phases: feature selection and classification namely- (i) Boruta algorithm supported by scaling and normalization to identify important set of features and improve the accuracy and efficiency of subsequent classification and (ii) Chaotic Optimization and Differential evolution based Support Vector Machine to reduce the computational complexity by tuning the parameters of kernel function and perform classification with high accuracy. In order to evaluate the proposed anomaly detection model, two case-studies were conducted using real-time dataset from our University network and benchmark Knowledge Discovery and Data Mining (KDD'99) dataset. Experimental results in terms of detection rate, false positive rate and accuracy demonstrate the effectiveness and reliability of the proposed HyClass model.

With an exponential increase in the data generation from various Internet-enabled devices, end user's demand satisfaction with respect to Quality of experience (QoE) has become a prime concern over the past few years. However, to assure QoE to the end users, content delivery networks (CDNs) aim to provide the content close to the user's geographical location so as to decrease network congestion, and latency along with an optimal bandwidth consumption. This paper proposes a popular content storage at the edge nodes/gateways instead of a remote server for increasing the data availability. For efficient cache management at the edge nodes, data is stored using Quotient filters (QFs), where number of QFs considered are determined by the number of categories taken for data segregation. To improve the accuracy and reduce the effort in caching process, one extra bit called timer-based metabit has been used with the QF, which helps to implement least frequently used caching efficiently. It has been experimentally proved that the proposed scheme has an approximate gain of 8.9% in object hit ratio with respect to the existing CDN based techniques. Moreover, the search time complexity of the proposed edge-based CDN is independent of the number of incoming requests.

With the rapid proliferation of big data, real-time processing of huge datasets becomes a challenging task; primarily because of their heterogeneous nature. Due to this, one of the most serious concerns of the modern cloud data centers is massive energy consumption during job execution. Hence, energy-aware task scheduling with data placement are considered as two important parameters for enhanced energy efficiency of modern cloud data centers. Moreover, considering the ``pay-per-use" model of cloud computing infrastructure, it is important to maintain desirable service level agreement (SLA) while attaining improved data locality. Poor task scheduling decisions with limited focus of data locality are the prime reasons for escalated data communications and energy utilization levels. In order to deal with the aforementioned issues, data locality- aware energy-efficient (EnLoc) scheme for task scheduling and data placement has been proposed, particularly for MapReduce framework. The proposed EnLoc scheme is a multi-objective optimization problem (MOOP) and is solved using multi-objective evolutionary algorithm with ``Tchebycheff decomposition"; wherein the formulated MOOP is decomposed into theoretically finite number of subproblems to get optimal scheduling and placement decisions. The proposed scheme has been evaluated on real-time data traces acquired from OpenCloud Hadoop Cluster. The results obtained clearly demonstrate that the proposed EnLoc scheme outperforms the existing schemes in terms of energy efficiency, SLA assurance, and data locality.

The need to develop smart unmanned aerial vehicles (UAVs) which are capable of deciding their trajectories is increasing at a rapid pace. Due to their usage in wide range of applications, such as-military, security, communications, survey mapping, disaster management, etc., the provisioning of end-to-end quality of service (QoS) is a challenging task in UAV environment. Moreover, with limited power, the efficiency of the UAVs can be enhanced if adaptive decisions with respect to their itineraries is considered dynamically. However, most of the solutions reported in the literature are not efficient with respect to QoS preservations for various applications. Motivated by this, several recently proposed meta-heuristic optimization schemes for reactive path planning of UAVs have been explored while designing a UAV path planning problem using multiverse optimizer (MVO). By carrying out the simulations over 1000 iterations, it has been demonstrated that MVO algorithm performs better in majority of the cases with average fitness function value of 0.152 and average execution time of 33.686 s.

Industrial Internet of things (IIoT) is an emerging technology with a large number of smart connected devices having sensing, storage, and computing capabilities. IIoT is used in a wide range of applications such as transportation, healthcare, manufacturing, and energy management in smart grids. Most of the solutions reported in the literature for secure communications are not suitable for the aforementioned applications due to the usage of traditional TCP/IP-based network infrastructure. So, to handle this challenge, in this paper, a software-defined network (SDN) enabled multi-attribute secure communication model for an IIoT environment is designed. The proposed scheme works in three phases: 1) an SDN-IIoT communication model is designed using a cuckoo-filter-based fast-forwarding scheme, 2) an attribute-based encryption scheme is presented for secure data communication, and 3) a peer entity authentication scheme using a third party authenticator, Kerberos , is also presented. The proposed scheme has been evaluated using different parameters where the results obtained prove its effectiveness in comparison to the existing solutions.

With an exponential increase in the Internet traffic over the network, there are growing concerns of identification of legitimate users which are the bulk sources of Internet traffic generation. However, due to the occurrence of anomalies in the network traffic, normal operations or the functionalities (traffic classification, resource allocation, and service management) of network get affected. Thus, in a given time frame, there is a requirement of anomalies detection in the network. The efficiency of any anomaly detection model mainly depends on the selection of relevant features and the learning algorithms which are used for classification of the network traffic patterns. However, due to curse of dimensionality, imbalance between classes, and variations in the types of anomalies, most of the existing solutions reported in the literature fail to deal with problems that occurs while detecting anomalies in large-scale network data. So, to remove these gaps in the existing solutions, we propose a new hybrid anomaly detection scheme called as Ensemble-based Classification Model for Network Anomaly Detection (EnClass) to detect anomalies in real- world networking datasets. EnClass has three modules as (i) Hoeffding-bound based clustering to identify the optimal subset of features to be taken for classification of network traffic (ii) Eigenvalues computation module to refine the features set for removal of unnecessary attributes and (iii) Very-fast decision tree for network traffic classification. In order to validate the proposed anomaly detection model, experimental evaluation is performed using real-world Knowledge Discovery and Data Mining (KDD'99) dataset with respect to parameters such as-detection rate, false positive rate, and F-score. The comparison with existing approaches clearly demonstrates the effectiveness of the EnClass in terms of detection rate (98.58%), false positive rate (0.42%), and F-score (96.06%).

Internet is an integrated platform where the data is growing at an exponential rate. Since it incorporates numerous business and personal services, we need to protect the data from illegal access or modifications. In literature, a large number of techniques have been proposed to protect the data against the malicious intent of the intruders. However, one of the most important way for monitoring and analyzing network traffic against various attacks is by the deployment of Intrusion detection systems (IDS). This paper presents a novel IDS based on probabilistic data structures named as ProIDS. In the proposed ProIDS, a popular probabilistic data structure (PDS), Bloom filter has been used to store the information about the suspicious nodes. Using Bloom filter, the number of hits on suspicious nodes per unit time has been computed using the modified version of Count min sketch, i.e., MCMS, a PDS. The work also presents a detailed theoretical analysis backed by relevant technical description. Simulation results clearly depict that the proposed system is more reliable and scalable in comparison to the existing Count-min sketch method. The results obtained show that proposed system requires comparatively less computational time and storage in comparison to the existing Count-min sketch method.

We address the problem of online model adaptation when learning representations from non-stationary data streams. Specifically, we focus here on online dictionary learning (i.e. sparse linear autoencoder), and propose a simple but effective online model selection approach involving “birth” (addition) and “death” (removal) of hidden units representing dictionary elements, in response to changing inputs; we draw inspiration from the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with better adaptation to new environments. Empirical evaluation on real-life datasets (images and text), as well as on synthetic data, demonstrates that the proposed approach can considerably outperform the state-of-art non-adaptive online sparse coding of [Mairal et al., 2009] in the presence of non-stationary data. Moreover, we identify certain data- and model properties associated with such improvements.

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.

One of the primary aspects of sustainable development involves accurate understanding and modeling of environmental phenomena. Many of these phenomena exhibit variations in both space and time and it is imperative to develop a deeper understanding of techniques that can model space-time dynamics accurately. In this paper we propose NOSTILL-GP - NOn-stationary Space TIme variable Latent Length scale GP, a generic non-stationary, spatio-temporal Gaussian Process (GP) model. We present several strategies, for efficient training of our model, necessary for real-world applicability. Extensive empirical validation is performed using three real-world environmental monitoring datasets, with diverse dynamics across space and time. Results from the experiments clearly demonstrate general applicability and effectiveness of our approach for applications in environmental monitoring.