Quantum-based machine learning (QML) is known to integrate quantum computing (QC) and machine learning (ML) to boost performance in data analysis and decision-making. The demand for professionals in QML has increased; however, QML is not part of the curricula for most colleges and universities. In this project, we create labware in Google Colab that encompasses knowledge of QC, ML, and QML, and demonstrates QML applications for analyzing scientific and engineering data. We integrate our learning modules into courses in Computer Science, Industrial Engineering, Physics, and other disciplines, and host a faculty workshop, a student camp, and a conference tutorial session to promote the use of our learning modules. The feedback from participating faculty and students is overwhelmingly positive.

Assessing data quality in multivariate time-series datasets is crucial for reliable analysis, particularly when dealing with missing values, inconsistent feature availability, and massive records in large-scale edge computing and IoT clusters. Existing methods often fall short of capturing intricate patterns of missingness and co-coverage, restricting the capacity to make well-informed decisions regarding the usability of the data. In order to systematically extract reliable data segments, this paper presents a comprehensive framework that combines a heuristic model with temporal coverage, period-specific missingness, and co-coverage metrics. By integrating these metrics with visualizations such as temporal coverage heatmaps and parallel coordinates plots, the framework reveals complex patterns of missingness while supporting human involvement in validating data subsets. Our approach effectively balances automation with expert judgment, enhancing the interpretability of data quality assessments. The findings show that the proposed methods satisfy the design specifications for revealing patterns, quantifying missingness impact, measuring feature availability, guiding feature selection, and facilitating scalable, multi-scale data summarization. The framework offers a solid way to improve the quality of data in multivariate time-series analysis, opening the door to more precise and trustworthy insights for assessing data gathered from edge computing infrastructures and large-scale, heterogeneous IoT deployments, where data consistency and completeness are frequently very variable.

Cloud-based large language models (LLMs) and their variants have significantly influenced real-world applications. Deploying smaller models (i.e., small language models (SLMs)) on edge devices offers additional advantages, such as reduced latency and independence from network connectivity. However, edge devices’ limited computing resources and constrained energy budgets challenge efficient deployment. This study evaluates the power efficiency of five representative SLMs — Llama 3.2, Phi-3 Mini, TinyLlama, and Gemma 2 on Raspberry Pi 5, Jetson Nano, and Jetson Orin Nano (CPU and GPU configurations). Results show that Jetson Orin Nano with GPU acceleration achieves the highest energy-to-performance ratio, significantly outperforming CPU-based setups. Llama 3.2 provides the best balance of accuracy and power efficiency, while TinyLlama is well-suited for low-power environments at the cost of reduced accuracy. In contrast, Phi-3 Mini consumes the most energy despite its high accuracy. In addition, GPU acceleration, memory bandwidth, and model architecture are key in optimizing inference energy efficiency. Our empirical analysis offers practical insights for AI, smart systems, and mobile ad-hoc platforms to leverage tradeoffs from accuracy, inference latency, and power efficiency in energy-constrained environments.

Quantum-based Machine Learning (QML) combines quantum computing (QC) with machine learning (ML), which can be applied in various sectors, and there is a high demand for QML professionals. However, QML is not yet in many schools' curricula. We design labware for the basic concepts of QC, ML, and QML and their applications in science and engineering fields in Google Colab, applying a three-stage learning strategy for efficient and effective student learning.

Haplotype inference with pure parsimony (HIPP) problem seeks to reconstruct a minimum set of haplotypes that explain a given set of genotypes observed from a population. This important problem is known to be NP-hard. In this paper, we explore the potential of quantum computing in retrieving optimal solutions for HIPP. We investigate several approaches to encode HIPP into quadratic unconstrained binary optimization (QUBO), which can be solved on quantum annealers. Further, we propose a new QUBO for HIPP, termed QHI, exploiting the structure of HIPP to reduce the QUBO size. Our comprehensive experiments on the state-of-the-art D-Wave annealer indicate comparable solution quality for quantum annealing approaches compared to classical simulated annealing. They also validate the effectiveness of our proposed QHI formulation in both solution quality and size.

In recent years, significant advancements in computing power, data richness, algorithmic development, and the growing demand for applications have catalyzed the rapid emergence and proliferation of large language models (LLMs) across various scenarios. Concurrently, factors such as computing resource limitations, cost considerations, real-time application requirements, task-specific customization, and privacy concerns have also driven the development and deployment of small language models (SLMs). Unlike extensively researched and widely deployed LLMs in the cloud, the performance of SLM workloads and their resource impact on edge environments remain poorly understood. More detailed studies will have to be carried out to understand the advantages, constraints, performances, and resource consumption in different settings of the edge.This paper addresses this gap by comprehensively analyzing representative SLMs on edge platforms. Initially, we provide a summary of contemporary edge hardware and popular SLMs. Subsequently, we quantitatively evaluate several widely used SLMs, including TinyLlama, Phi-3, Llama-3, etc., on popular edge platforms such as Raspberry Pi, Nvidia Jetson Orin, and Mac mini. Our findings reveal that the interaction between different hardware and SLMs can significantly impact edge AI workloads while introducing non-negligible overhead. Our experiments demonstrate that variations in performance and resource usage might constrain the workload capabilities of specific models and their feasibility on edge platforms. Therefore, users must judiciously match appropriate hardware and models based on the requirements and characteristics of the edge environment to avoid performance bottlenecks and optimize the utility of edge computing capabilities.

Beyond 5G and future 6G aim to address rising energy demands as the world becomes more interconnected. LoRaWAN network is an energy-efficient IoT solution, but frequent retransmissions can quickly deplete sensor batteries. Efficient traffic management and collision avoidance are crucial. Initially, LoRaWAN used ALOHA for multi-access, causing increased network collisions. The recent innovation of Channel Activity Detection (CAD) has emerged to tackle these issues. CAD enhances multi-access by sensing channel activity before transmission. Although an improvement, CAD is not foolproof. Our paper introduces enhancements to CAD through the Deep Deterministic Policy Gradient-based Algorithm (DDPG_CAD). To assess CAD functionality, we develop LoRaCAD, a dedicated simulator. We also conduct a thorough comparative analysis of scheduling strategies, considering energy efficiency, latency, and packet delivery ratio.

As the growing trends in technology continue to drive massive transformation throughout the automotive sector, connected and autonomous transportation has become the future vision. Many researchers and practitioners wonder how connected, and autonomous vehicles will affect future transportation. This Special Issue explores some issues in the transition towards autonomous vehicles and their future trends and developments with artificial intelligence (AI) and robotics.

Bayesian inference is one method of statistical inference in machine learning. It predicts the probability that a given test belongs to a certain class and is widely used in various applications such as medical diagnosis, spam classification and fraud detection. The conventional binary architecture of computing the posterior probability is inefficient in practical implementation, which is involved in multiplication, addition and division operations. Recently, it has been shown that simple Muller C-elements, the asynchronous logic units, can perform stochastic Bayesian inference motivated by its truth table when the data is encoded as the bit-stream. The Bayesian inference machine is therefore implemented with low hardware cost. However, such an architecture is employed to compute the posterior probability of two classes only. This brief presents two stochastic circuit designs for computing the weighted ratio with multiple weights for generalized multi-class Bayesian machines. The first design is mainly based on the JK flip flop and multiplexers. The second approach is to construct the finite state machine (FSM) by manipulating the correlation between the input bit-streams. The FSM-based design requires fewer random number sources (RNSs) as compared to the JK flip flop-based implementation. These facts lead to a reduction of hardware area and energy. Simulation results show that the accuracy of the proposed JK flip flop-based and FSM-based designs is almost the same in the tested data sets. As compared to the traditional binary design, the circuit area of the proposed stochastic design is improved by$96\%$at least in the cases of three and four classes. The consumed energy per operation is reduced by$58.1\%$at least in the cases of three and four classes.

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD worsens over time and greatly impacts patients' life and affects their important mental functions, including thinking, the ability to carry on a conversation, and judgment and response to environment. Clinically, there is no single test to effectively diagnose Alzheimer disease. However, computed tomography (CT) and magnetic resonance imaging (MRI) scans can be used to help in AD diagnosis by observing critical changes in the size of different brain areas, typically parietal and temporal lobes areas. In this work, an integrative mulitresolutional ensemble deep learning-based framework is proposed to achieve better predictive performance for the diagnosis of Alzheimer disease. Unlike ResNet, DenseNet and their variants proposed pipeline utilizes PartialNet in a hierarchical design tailored to AD detection using brain MRIs. The advantage of the proposed analysis system is that PartialNet diversified the depth and deep supervision. Additionally, it also incorporates the properties of identity mappings which makes it powerful in better learning due to feature reuse. Besides, the proposed ensemble PartialNet is better in vanishing gradient, diminishing forward-flow with low number of parameters and better training time in comparison to its counter network. The proposed analysis pipeline has been tested and evaluated on benchmark ADNI dataset collected from 379 subjects patients. Quantitative validation of the obtained results documented our framework's capability, outperforming state-of-the-art learning approaches for both multi-and binary-class AD detection.

Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks engendering interruption of services, such as node and link failures. Thus, resiliency is one of the requirements in NFV-enabled network design for recovering network services once occurring failures. Therefore, in addition to a primary chain of virtual network functions (VNFs) for a service, one typically allocates the corresponding backup VNFs to satisfy the resiliency requirement. Nevertheless, this approach consumes network resources that can be inherently employed to deploy more services. Moreover, one can hardly recover all interrupted services due to the limitation of network backup resources. In this context, the importance of the services is one of the factors employed to judge the recovery priority. In this paper, we first assign each service a weight expressing its importance, then seek to retrieve interrupted services such that the total weight of the recovered services is maximum. Hence, we also call this issue the VNF restoration for recovering weighted services (VRRWS) problem. We next demonstrate the difficulty of the VRRWS problem is NP-hard and propose an effective technique, termed online recovery algorithm (ORA), to address the problem without necessitating the backup resources. Eventually, we conduct extensive simulations to evaluate the performance of the proposed algorithm as well as the factors affecting the recovery. The experiment shows that the available VNFs should be migrated to appropriate nodes during the recovery process to achieve better results.

This special issue comprises 12 articles, showcasing the latest advances in computational social systems research.

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

5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate’s resources into a set of self-contained programmable RAN slices. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on substrate nodes. In this work, we focus on the following fundamental tasks: i) establishing the theoretical foundation for constructing a VNF mapping plan for RAN slice recovery optimization and ii) developing algorithms needed to map/embed VNFs efficiently. Specifically, we propose four efficient algorithms, including Resource-based Algorithm (RBA), Connectivity-based Algorithm (CBA), Group-based Algorithm (GBA), and Group-Connectivity-based Algorithm (GCBA) to solve the resource allocation and VNF mapping problem. Extensive experiments are also conducted to validate the robustness of RAN slicing via the proposed algorithms.

Quantum computing has paved a new way for faster and more efficient solutions to large-scale, real-world optimization problems that are challenging for classical computing systems. For instance, selective traveling salesman problem (sTSP) that is famous in such fields as logistic optimization and has attracted increasing attention from the research community, however, is known as an NP-Hard problem. Solving the sTSP is, therefore, extremely complex because the optimization function potentially comes with an exponential number of variables that cannot be solved in polynomial time in general. To this end, we propose a quantum annealing framework for time-bounded and near-optimal solutions for the sTSP, overcoming hardware limits of near-term quantum devices. In particular, we put forth an efficient Hamiltonian (QUBO) to encode the complex decision-making for the sTSP on noisy intermediate-scale quantum (NISQ) annealer. Furthermore, experimental results we obtained on the D-Wave 2000$Q$quantum hardware demonstrate that the optimal solutions for several instances can be attained.

Network-in-box (NIB) architectures are designed to improve communication information sharing in an ad hoc manner with limited infrastructure support. These architectures are interoperable, and hence it is capable of providing services based on sixth-generation (6G) communication technologies. Resource allocation for massive machine-type communications in the 6G platform aided for NIB architectures is challenging due to the terahertz and high throughput features. Then it comes to resource allocation, and the most difficult part is figuring out what capacity is to check the availability of work on a project is difficult to determine. In this manuscript, the attuned slicing-dependent concurrent resource allocation (AS-CRA) method is formulated for improving the service reliability of the 6G users in NIB architecture. Learning assisted slicing and concurrent resource allocation process is jointly exploited in this proposed method to improve the users’ service reliability. The output of the learning process is useful in classifying resource allocation and user mapping irrespective of the limited NIB infrastructure support. Virtualization and concurrency in resource allocation are balanced based on the user capacity and network blocking rate to achieve optimality in service responses. The performance of the proposed resource allocation method is verified using simulations, and the performance is verified using the metrics capacity 89.726%, latency 81.32%, resource utilization rate 0.963%, response ratio 92.309, and blocking rate 0.047%.

With the increasingly humanized and intelligent operation of Industrial Internet of Things (IIoT) systems in Industry 5.0, delay-sensitive and compute-intensive (DSCI) devices have proliferated, and their demand for low latency and low power consumption has become more and more eager. In order to extend the battery life and improve the quality of user experience, we can offload DSCI-type workloads to mobile edge computing (MEC) servers for processing. However, offloading massive amounts of tasks will incur higher energy consumption, which is a severe test for the limited battery capacity of devices. In addition, the delay caused by frequent communication between IIoT devices and MEC cannot be ignored. In this article, we first formulate the stochastic computation offloading problem to minimize long-term energy consumption. Then, we construct a virtual queue using perturbed Lyapunov optimization techniques to transform the problem of guaranteeing task deadlines into a stable control problem for the virtual queue. Based on this, a novel delay-aware energy-efficient (DAEE) online offloading algorithm is proposed, which can adaptively offload more tasks when the network quality is good. Meanwhile, it delays transmission in the case of poor connectivity but ensures that the deadline is not violated. Moreover, we theoretically demonstrated that DAEE can enable the system to achieve an energy-delay tradeoff, and analyzed the feasibility of constructing virtual queues to assist the actual queue offloading tasks. Finally, simulation results show that DAEE performs well in minimizing energy consumption and maintaining low latency, especially for DSCI-type tasks.

Internet of Medical Things (IoMT) connects different medical devices, health sensors and hospital records to data platforms using wireless communications. Federated Learning (FL) is an emerging collaborative learning technique that can be beneficial for IoMT due to reduced communication overhead and enhanced security. This paper provides an overview of different architectures used in FL and potential approaches for FL based IoMT. We also discuss how Physical Layer Security (PLS) can be used for efficient privacy preservation of data in FL based IoMT. We highlight the recent work in this area and major research challenges related to PLS assisted FL in IoMT. We also provide a case study demonstrating that clustering of IoMT devices (such that a single device in each cluster acts as a cluster head) enhances the secrecy rate of the FL based IoMT network as compared to its non-clustered counterpart. Finally, we also discuss future opportunities and open research questions related to PLS assisted FL in IoMT.

The papers presented in this special issue focus on technological innovations in wearable, implantable, mobile, and remote healthcare that include Internet of Things (IoT), sensor informatics, and patient monitoring applications. These new technologies are used to track the key signs of people’s health to improve their lifestyle and health disorders. Innovations in IoT devices play a vital role in assisting patients in managing their health conditions. Thanks to the advent of modern communication technologies and Internet of Things (IoT) paradigms that have made the implementation of biomedical devices nearly universal. Now with the evolving industrial revolution, patients and healthcare providers are expecting something more. Practically speaking, healthcare wearables have experienced tremendous growth in the past few years, and it is expected to grow even more shortly, making it an ideal space for the biomedical informatics research community to solve complex healthcare problems and more informed decision making to improve human health.

Next generation networks will involve huge number of industrial internet of things (IIoT) sensors which require reliable connectivity with low latency to manage the data transmission and processing. The design of these networks entails a lot of challenges. This article describes the 3D placement of multiple unmanned aerial vehicles (UAVs) in an IIoT network that supports non-orthogonal multiple access (NOMA). UAVs act as decode and forward (DF) relays. The 3D UAV placement problem is formulated which is highly non-convex in the coordinates. Therefore, we employ an improved adaptive whale optimization algorithm (IAWOA) to handle the problem. Even with its improved performance, IAWOA is not suitable for real-time application. Hence, we propose path aggregation network (PANet) to handle the 3D UAV placement. The simulation results show that PANet is more suitable for the online-learning.

An increase in the number of Maritime Intelligent Transport Systems (MITSs) also means an increase in the number of information security risks. Usually, the administration and operation of MITSs are done through web servers that are frequently targeted by hackers. In marine transportation industry, malicious code injection attacks (webshell) has been widely exploited by hackers to take full control of Web servers. Traditional webshell detection methods based on pattern matching that are no longer effective against new types of webshell. This motivates us to investigate the problem of detecting obfuscation or unknown webshells, termed OUW problem. In this work, we propose a pattern-matching-deep-learning hybrid ASP.NET webshell detection method (H-DLPMWD) to address the OUW problem. H-DLPMWD is based on Yara-based pattern matching to clean dataset; modeling ASP.NET code files as an operation code index (OCI) vectors; and applying CNN method to train and predict webshell in OCI vectors. To validate H-DLPMWD, our rigorous experimentation demonstrates that H-DLPMWD achieves an excellent accuracy of 98.49%, F1-score of 99.01%, and a low false positive rate of 1.75%.

Intelligent Transportation Systems provide ubiquitous communication for the driving users through heterogeneous interconnections. The heterogeneous interconnections are required for uninterrupted resource sharing. Spontaneous resource availability due to vehicle speed and infrastructure connectivity disturb prompt service utilization. In this manuscript, a Permissible Service Selection and Allocation (PSSA) method is proposed to address spontaneous issues in vehicular communication and connection. This method considers vehicle displacement and minimum interconnection factors in accessing a cloud service. Both factors and their balancing impact are analyzed throughout the vehicle’s service requesting interval. In this process, random forest learning is induced to identify the balancing factors’ adjustments. The service access is probed through the active infrastructure based on the balancing factor. The ordering process of the learning intervals provides ease of service selection and allocation. In this process, reallocation is not preferred due to the random displacement of the vehicles. Therefore, the interval dropouts are reduced in both handoff and non-handoff communication scenarios. Further metrics such as service ratio, delay, and connectivity are used in validating the proposed method’s performance.

6G vehicular networks will require massive connectivity among vehicles and Road Side Units (RSUs) to enable number of safety and non-safety applications. Intelligent Reflecting Surface (IRS) will be a vital part of 6G vehicular networks, providing features such as enhanced line of sight communication, extended coverage range and high data rate transmission. In this paper, we consider a multiple IRSs assisted multi-vehicle Multiple-Input Multiple-Output (MIMO) communication system and optimize the transmit beamforming vectors at the transmitter and reflect beamforming (phase shifts) at each IRS to achieve max-min Quality of Service (QoS) in the form of weighted Signal-to-Interference-Plus-Noise Ratio (SINR). In particular, we divide the optimization problem into two Semidefinite Relaxation (SDR) sub-problems and solve them iteratively using the alternate optimization technique. Simulation results show that the proposed technique significantly increases the sum-rate and SINR of the vehicular network.

Current Intrusion Detection Systems (IDSs), which rely on signature-based detection using a set of rules formed by the past inspection of traffic flows and their signature, fail to detect new threats and malware. In this paper, we advocate for a hybrid algorithm combining signature and deep learning, dubbed signature, and deep analysis-based intrusion detection (SDAID) algorithm. In particular, the SDAID algorithm is constituted by an ensemble learning model of Deep Neural Network and Extreme Gradient Boosting. In addition, we also handle the im-balanced training dataset to improve the prediction performance of SDAID. To validate the feasibility of the proposed SDAID, well-known datasets, including CSE-CIC-IDS2018 and NSL-KDD datasets, are employed to conduct rigorous experiments. The results indicate that the proposed deep learning analysis achieves an excellent F1-score of 99.93% and 99.62% with the CSE-CIC-IDS2018 and NSL-KDD datasets. It also performs better than related models using the same datasets.

There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot be done at once, especially on the aspect of quantum routing. In this paper, we study the maximizing entangled routing rate (MERR) problem. In particular, given a set of demands, we try to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network’s fidelity. We first formulate the MERR problem using an integer linear programming (ILP) model to capture the traffic patent for all demands in the network. We then leverage the theory of relaxation of ILP to devise two efficient algorithms including HBRA and RRA with provable approximation ratios for the objective function. To deal with the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Using both simulations and an open quantum network simulator platform to conduct experiments with real-world topologies and traffic matrices, we evaluate the performance of our algorithms and show up the success of maximizing entangled routing rate.

Quantum communications are gaining momentum in finding applications in a wide range of domains, especially those require high-security data transmissions. On the other hand, machine learning has achieved numerous breakthrough successes in various application domains including networking. However, currently, machine learning is not as much utilized in quantum networking as in other areas. With such motivation, we propose a machine-learning-powered entanglement routing scheme for quantum networks that aims to accommodate maximum numbers of demands (source-destination pairs) within a time window. More specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to schedule the network’s demands which are then routed by a qubit-preserved shortest path algorithm. DQRA is trained towards the goal of maximizing the number of resolved requests in each routing window by using an explicitly designed reward function. Our experiment study shows that, on averse, DQRA is able to maintain a rate of successfully routed requests above 80% in a qubit-limited grid network, and about 60% in extreme conditions i.e. each node can act as a repeater exactly once within a window. Furthermore, we show that the complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.

Recently, with the advent of the Internet of everything and 5G network, the amount of data generated by various edge scenarios such as autonomous vehicles, smart industry, 4K/8K, virtual reality (VR), augmented reality (AR), etc., has greatly exploded. All these trends significantly brought real-time, hardware dependence, low power consumption, and security requirements to the facilities, and rapidly popularized edge computing. Meanwhile, artificial intelligence (AI) workloads also changed the computing paradigm from cloud services to mobile applications dramatically. Different from wide deployment and sufficient study of AI in the cloud or mobile platforms, AI workload performance and their resource impact on edges have not been well understood yet. There lacks an in-depth analysis and comparison of their advantages, limitations, performance, and resource consumptions in an edge environment. In this paper, we perform a comprehensive study of representative AI workloads on edge platforms. We first conduct a summary of modern edge hardware and popular AI workloads. Then we quantitatively evaluate three categories (i.e., classification, image-to-image, and segmentation) of the most popular and widely used AI applications in realistic edge environments based on Raspberry Pi, Nvidia TX2, etc. We find that interaction between hardware and neural network models incurs non-negligible impact and overhead on AI workloads at edges. Our experiments show that performance variation and difference in resource footprint limit availability of certain types of workloads and their algorithms for edge platforms, and users need to select appropriate workload, model, and algorithm based on requirements and characteristics of edge environments.

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

This paper addresses polynomial computation using unipolar stochastic logic by exploiting correlation between the bit-streams. The AND-OR, double-NAND, OR-AND and double-NOR circuits are presented for polynomials with all positive coefficients whose sum is less than or equal to one by mathematically analyzing the joint probability distribution of coefficient bit-streams. The NAND-AND expansion is also developed for polynomials with alternatively positive and negative coefficients whose absolute values are decreasing by applying the same idea. Unlike the original methods with multiple uncorrelated random number sources (RNSs) for coefficient bit-stream generation, the presented methods only require a single RNS. Since the RNSs take up huge hardware resource in stochastic circuits, the proposed RNS-sharing techniques for polynomial computation result in a significant reduction of hardware complexity. For the factorization technique in the general polynomials, this paper enhances the original stochastic designs for the second-order polynomial and further presents the simple correlation-dependent circuits. Results show that the proposed architectures are superior to the previous ones by reducing the total number of RNSs.

The usage of cloud services is growing exponentially with the recent advancement of Internet of Things (IoT)-based applications. Advanced scheduling approaches are needed to successfully meet the application demands while harnessing cloud computing’s potential effectively to schedule the IoT services onto cloud resources optimally. This article proposes an alternative task scheduler approach for organizing IoT application tasks over the CCE. In particular, a novel hybrid swarm intelligence method, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing. This proposed method, called MRFOSSA, depends on using SSA to improve the local search ability of MRFO that typically enhances the rate of convergence towards the global solution. To validate the developed MRFOSSA, a set of experimental series is performed using different real-world and synthetic datasets with variant sizes. The performance of MRFOSSA is tested and compared with other metaheuristic techniques. Experiment results show the superiority of MRFOSSA over its competitors in terms of performance measures, such as makespan time and cloud throughput.

Cybertwin is the intelligent data analytics agent of the future that interacts with the Internet of Vehicles using 6G to make intelligent decisions for automotive application reliability. Intelligent reflecting surface (IRS) is a vital component of future cybertwin-driven 6G networks due to its ability to control unfavorable wireless propagation conditions and achieves higher data rates. In this article, we explain the four major advantages that can be gained by using IRS-enabled cybertwin 6G vehicular networks, including overcoming blockages, Doppler mitigation, enhanced localization, and enhanced security. We also provide a case study showing that IRSs can increase the percentage of vehicles with line-of-sight links by 25% in a high traffic density of 60 vehicles/km, compared to the scenario where no IRS is deployed. Finally, we discuss future opportunities and open challenges to realize IRS-enabled cybertwin 6G vehicular networks.

Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other intelligent transportation system entities. Nonorthogonal multiple access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this article, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multicell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex, which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.

In the last few years, attackers have been shifting aggressively to the IoT devices in industrial Internet of things (IIoT). Particularly, IoT botnet has been emerging as the most urgent issue in IoT security. The main approaches for IoT botnet detection are static, dynamic, and hybrid analysis. Static analysis is the process of parsing files without executing them, while dynamic analysis, in contrast, executes them in a controlled and monitored environment (i.e., sandbox, simulator, and emulator) to record system’s changes for further investigation. In this article, we present a novel and advanced method for IoT botnet detection using dynamic analysis to improve graph-based features, which are generated based on static analysis. Specifically, dynamic analysis is used to collect printable string information that appears during the execution of the samples. Then, we use the printable string information to traverse the graph, which is obtained based on the static analysis, effectively, and ultimately acquiring graph-based features that can distinguish benign and malicious samples. In order to estimate the efficacy and superiority of the proposed hybrid approach, we conduct the experiment on a dataset of 8330 executable samples, including 5531 IoT botnet samples and 2799 IoT benign samples. Our approach achieves an accuracy of 98.1% and 91.99% for detecting and classifying IoT botnet, respectively. These results show that our approach has outperformed other existing contemporary counterpart methods in the aspects of accuracy and complexity. In addition, our experiments also demonstrate that hybrid graph-based features for IoT botnet family classification can further improve static or dynamic features’ performance individually.

Internet of Things assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. Communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness invalidating multiple resources. IoT's open and ubiquitous nature results in proactive resource sharing, resulting in delayed transmissions. This manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme to address this issue. The available health data is accumulated on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves resource allocation, and reduces transmission complexity. The entire process is managed for transfer learning, data-to-capacity validation, and concurrent recommendation. The first validation knowledge base remains the same/shared for different data accumulation and sharing intervals.

Vehicular Ad-Hoc Networks (VANETs) provide roadside communication for improving the ease of driving user information exchange. It interconnects hierarchical infrastructure units and other vehicles for real-time traffic and vehicle management applications. The growth of vehicle and information density requires concord information exchange for application responses. In this article, Vehicle-Consensus Routing Management Scheme (VCRMS) is proposed for achieving fair roadside assistance for driving users. The proposed scheme exploits the surrounding vehicle information for infrastructure selection and traffic management. The infrastructure and vehicle information are analyzed for their similarity using deep learning for extracting monotonous decisions. The current and previous decisions are used for succeeding in information selection and traffic information retrieval. This prevents unnecessary data from being congesting the traffic and vehicle management application during driver assistance. The performance shows that the proposed scheme achieves 10.7% high application response, 15.9% less information delay, and 10.7% less traffic for different vehicle velocities.

The timeless and efficient vehicle data transmission are the two common requirements for the Internet of Vehicles (IoV), especially the Unnamed Aircraft Vehicle (UAV)-aided Vehicular Edge Computing (VEC) network. Moreover, since the Age of Information (AoI) performance greatly influences these two indicators, data quality should be guaranteed in vehicle communication. However, few researchers pay attention to the AoI performance optimization issue regarding wireless resource constraint, transmission interference, and vehicle cooperation in recent years. To close this research gap, we propose an AoI-oriented channel access strategy in the UAV-aided VEC network from the game theory viewpoint. Firstly, the UAV-aided VEC network model and edge computing-based AoI expression are established and derived in the closed form, respectively. Subsequently, we transform the AoI minimization problem into an AoI-based channel access issue from the game theory viewpoint. Moreover, the stochastic learning-based algorithm is proposed to find the Nash Equilibrium (NE) solution of the formulated problem. Finally, simulation results evaluate the correctness and effectiveness of the proposed algorithms, where our scheme can achieve the better AoI value compared with baselines.

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework.

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution and significance, the optimization aspect of such networks has been overlooked due to their complexity and scale. Motivated by this discrepancy in the literature, this work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner. To evaluate the benefits of the proposed scheme, extensive simulation studies are conducted and a comparison is provided with benchmark techniques. The performance evaluation demonstrates the utility of the presented system architecture and learning-based optimization framework.

Intelligent Transportation Systems (ITS) is a smart-transportation system for road-side assistance and data exchange support by integrating cloud and wireless networks. ITS facilitates vehicle-to-vehicle and vehicle-to-anything (V2X) data exchanges for satisfying user demands. The rate of big data granting to the vehicular users is interrupted by the fundamental attributes such as mobility and link instability of the vehicles. To address the issues in vehicular data exchange big data, this article introduces displacement-aware service endowment scheme with the benefits of data offloading. Displacement-aware big data endowment ensures responsive availability of vehicle request information despite unfavorable location and density factors. The time congruency in V2V and V2X data exchanges are adopted for minimizing data exchange dropouts. In the data offloading phase, extraneous information and big data responses are detained based on data exchange relevance to improve congestion free big data endowment. The distinct methods work in a co-operative manner to improve big data quality of fast configuring smart vehicles to provide reliable big data in smart city environments.

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

In wireless sensor networks (WSNs), especially in underwater sensor networks, the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the article. The Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

Wireless sensor networks (WSNs) consist of nodes distributed in the region of interest (ROI) that forward collected data to the sink. The node’s location plays a vital role in data forwarding to enhance network efficiency by reducing the packet drop rate and energy consumption. WSN scenarios, such as tracking, smart cities, and agriculture applications, require location details to accomplish the objective. Assuming a 3D application space, a combination of received signal strength (RSS) and time of arrival (TOA) can be helpful for reliable range estimation of nodes. Notably, the anchor node can minimize localization error for non-line-of-sight (NLOS) signals. We proposed an error minimization protocol for localization of the sensor node, assuming that the anchor node’s location is known prior and can limit the receiving signal in LOS, single, or twice reflection. We start to exploit the sensor node’s geometrical relationship and the anchor node for LOS and NLOS signals and address misclassification. We started initially from the erroneous node position, bound its volume in 3D space, and reduced volume with each iteration following the constraint. Our simulation result outperforms the traditional methods on many occasions, such as boundary volume and computational complexity.

Tracking the concentration of students during online learning offers great benefits. For examples, distracted students can be suggested to do a brief exercise to refresh their brains; or the teacher can be notified when too many students have difficulties on concentration so the class could take a short break. Traditionally, mental states like concentration levels can be analyzed using Electroencephalogram (EEG) or Functional Near-Infrared Spectroscopy (fNIRS). However, methods that utilize these data require specialized equipment which is not feasible to deploy on a large scale. On the other hand, recent breakthroughs in deep learning provide possibilities of scalable solutions to detect concentration levels using only webcam. Leveraging this advancement, we investigate the task of tracking students’ concentration levels during online learning using facial data coupled with deep learning based computer vision technologies. More specifically, we examine the performances of different representations of facial data integrated with various deep architectures to empirically determine a solution balanced between prediction accuracy and time efficiency that is suitable for real-time application. Our experimental study shows that the proposed solution achieves over 91% accuracy while keeping execution time low enough for real-time deployment.

Going beyond the one-type-fits-all design philosophy, the future 5G radio access network (RAN) with network slicing methodology is employed to support widely diverse applications over the same physical network. RAN slicing aims to logically split an infrastructure into a set of self-contained programmable RAN slices in which each slice built on top of the underlying physical RAN (substrate) is a separate logical mobile network and delivers a set of services with similar characteristics. Each RAN slice is constituted by various virtual network functions (VNFs) distributed geographically in numerous substrate nodes. A RAN configuration scheme for the network is imperative to embed VNFs in substrate nodes. In this paper, we propose to design new algorithms to enhance the stability of RAN slicing by addressing the resources allocation and VNF embedding problem, referred to as RS-configuration. Specifically, we establish the theoretical foundation for using RS-configuration to construct a VNF mapping plan for all VNFs with two efficient algorithms, including Group-based Algorithm (GBA) and Group-Connectivity-based Algorithm (GCBA). Through rigorous analysis and experimentation, we demonstrate that the proposed algorithms perform well within reasonable bounds of computational complexity.

In wireless sensor networks (WSNs), the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address on using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or the noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the paper. In addition, the Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is therefore proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

In recent years, the effort of promoting versatile, easy to manage routing schemes, as a replacement to OSPF has gathered momentum particularly in the context of large-scale enterprise networks, data center networks and software-defined wide area networks (SD-WANs). Such routing schemes rely on embedding the network into a geometric/topological space (e.g. a binary tree) to facilitate multi-path routing with reduced state maintenance and quick recovery in localized failure scenarios. In this work, we propose a systematic framework to embed the network topology into a hierarchical binary virtual-identityspace that is particularly amenable to multi-path routing. Our methodology firstly involves a relaxed form of the connected graph bi-partitioning problem that exploits a geometric embedding of the network in an n-dimensional Euclidean space (n being the number of hosts in the network) based on the Moore-Penrose pseudo inverse of the Laplacian for the graph associated with the network. The edges of the network are mapped to a weight distribution that helps construct a spanning tree from the core of the network towards the periphery, thereby providing a point of symmetry in the network to facilitate balanced bipartitions. This, in turn, yields a (nearly) full balanced binary tree embedding of the network and consequently a good virtual-id space. We also explore the binary identity assignment problem in another point of view by using bi-connected graph as the input graph to introduce a recursive bipartition algorithm. Through rigorous theoretical analysis and experimentation, we demonstrate that our methods perform well within reasonable bounds of computational complexity.

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system, in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems, since each sensor's knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS.

There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency in wireless sensor networks (WSNs). Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system, in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems, since each sensor's knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. In this paper, we investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we conduct extensive simulations to demonstrate the performance of DCAS.

In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance of mobile sensors is minimized has received a great deal of attention. However, in reality, due to a limited budget or numerous targets, mobile sensors may be not enough to cover all targets or form a connected network. Therefore, targets must be weighted by their importance. The more important a target, the higher the weight of the target. A more general problem for target coverage and network connectivity, termed the Maximum Weighted Target Coverage and Sensor Connectivity with Limited Mobile Sensors (MWTCSCLMS) problem, is studied. In this paper, an approximation algorithm, termed the weighted-maximum-coverage-based algorithm (WMCBA), is proposed for the subproblem of the MWTCSCLMS problem. Based on the WMCBA, the Steiner-tree-based algorithm (STBA) is proposed for the MWTCSCLMS problem. Simulation results demonstrate that the STBA provides better performance than the other methods.

The cloud manufacturing yields insights of manufacturing services over cyber space based on integration of advanced manufacturing with cloud computing. However, the different communication standards between the different system levels are main challenges for the integration of them without a conflicting communication. Ethernet appears as the best solution to support all levels for industrial manufacturing systems. Since the synchronous integration is fulfilled, the prerequisite for deciding the overall performance of systems is adaptable real-time communication. This motivates us to study the problem of designing a new real-time communication based on Ethernet standard to enable real-time processing for factory networks, termed Real-Time Ethernet (RTEthernet). The objective is to schedule maximum number of machines transmitting data through a controller in a data transmission window (Coordinated Cycle) as well as guarantee the real-time communication. The problem is called the Maximum Number of Real-time Data Transmission (MNRDT) problem. Two relevant solutions are proposed for addressing the MNRDT problem. Simulation results are provided to show the performance of proposed solutions.