The number of fake products is increasing day by day, which creates many serious problems. These fake items are unsafe for brand reputation as breaks trust and value also unsafe for consumers. Traditional methods like holograms, barcodes, etc., are widely used, but at certain levels, counterfeiters misuse them and copy them, which is not fully transparent. To address these issues, in this paper, we suggest using a blockchain-based system to verify whether a product is real or fake. Leveraging blockchain’s immutability and integrity, the product information is securely recorded using unique block hashes and Quick Response (QR) codes, making unauthorized tampering more difficult once the data is recorded. The consumers can confirm the originality of the product by simply scanning or uploading QR codes, which makes it tough for counterfeiters to clone the QR codes. A key feature of our approach is the integration of multi-scan detection. This system helps to detect unusual or suspicious scanning behavior and creates more trust. The system uses smart contracts to automate secure product registration and verification processes. To demonstrate feasibility, we present the details of the prototype, including database design, flowcharts, and user interface, illustrating its practical deployment.

With the rapid development of industrial automation, few-shot object detection has emerged as a promising solution for recognizing novel categories using only limited annotated data. However, existing approaches often suffer from high computational complexity and limited adaptability when deployed in resource-constrained industrial environments. To achieve precise detection, efficiency, and security, this paper proposes a collaborative computing framework based on an Edge-Cloud Dual-Prototype Graph Convolutional Network (EC-DP-GCN) for few-shot object detection with hierarchical knowledge embedding. The framework comprises three key components: a device–edge–cloud architecture, a Positive-Negative Prototype (PNP) module, and a Class-Prototype-Sample Hierarchical Graph (CPS-HG) module. Specifically, the PNP module explicitly models intra-class diversity by constructing discriminative positive and negative prototypes from limited support samples, thereby enhancing prototype representativeness. In addition, we further introduce the CPS-HG module, which treats the dual prototypes as class-based prior knowledge and models the relationships among samples through a hierarchical graph structure encompassing class, prototype, and sample levels. This design effectively expands the semantic margins in the embedding space to improve knowledge-guided detection. Extensive experiments on the PASCAL VOC and MS COCO benchmarks demonstrate that EC-DP-GCN significantly outperforms strong baselines and previous state-of-the-art methods, achieving an average improvement of 1.1% in 10-shot detection scenarios.

Edge computing (EC), as a computing paradigm that mitigates cloud load and reduces task latency, has attracted widespread attention from both academia and industry. Current research on EC primarily focuses on edge task offloading problems, while effectively matching tasks with microservices after offloading is also crucial for reliable task processing. Therefore, considering the differentiated hardware resource requirements of various task types, this paper designs a heterogeneous computing model that enables precise matching between tasks and edge server (ES) computational capabilities. To ensure ESs proactively deploy effective microservices and maintain trustworthy operations, we introduce an incentive mechanism and an ES discriminator algorithm. Considering diverse demands in EC scenarios, where ESs pursue higher incentive returns while reducing energy consumption, and the system aims to minimize latency and maintain reliability under limited incentive budgets, we construct an interconnected satisfaction model between ESs and the system. Based on this, we propose a bilateral consensus service placement (BCSP) algorithm that balances incentive consensus between ESs and the system, achieving rational microservice deployment and optimized task processing efficiency. Experimental results show that compared with existing algorithms, the proposed BCSP algorithm better accommodates multi-party requirements and enhances both efficiency and reliability in microservice placement.

Efficient orchestration in the edge–cloud continuum is essential for reducing energy consumption and meeting latency requirements in large-scale IoT systems. This article presents a hybrid deep reinforcement learning (DRL) and federated learning (FL) framework that dynamically allocates computation across IoT, edge, fog, and cloud layers. The DRL agent learns energy-efficient scheduling strategies through a latency-aware reward design, while FL enables decentralized model training without exposing raw data. Experimental evaluation demonstrates up to 31.6% lower energy consumption and 28.4% latency reduction compared to existing heuristics. Results also show rapid learning convergence within 200 episodes, indicating strong adaptability under changing network and workload conditions. These findings confirm the effectiveness of the proposed framework in improving energy efficiency, latency performance, and scalability for next-generation IoT deployments.

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems.

Transportation Cyber-Physical Systems (T-CPS) integrate transportation information with physical elements, enabling advanced features such as real-time vehicle tracking, collision avoidance, and intelligent traffic management. However, this also increases the need for improved security protocols to safeguard the critical identity-based data they transmit through vulnerable wireless networks, such as Vehicle Identification Numbers (VINs) and live Global Positioning System (GPS) coordinates. These T-CPS, traditionally protected by classical encryption algorithms, are now vulnerable due to the rise in quantum computing. Existing solutions are hindered by their complex security features and high computational overhead. To address these challenges, we propose Quantum Key Agreement and Signature Verification (QKASV), a post-quantum protocol that integrates Quantum Key Distribution (QKD) and Quantum Signatures for T-CPS. QKASV uses identity-tied QKD-based keys for session creation, followed by either individual or group quantum signatures. Signatures are created by applying basic quantum gates sequentially rather than lattice-based or certificate-based schemes. Hence, this speeds up the overall key generation, distribution, and management process without compromising the enhanced security. Formal security analysis using standard security models and the Scyther cryptographic protocol verification tool proves that QKASV meets standard security requirements. Further, comparison with similar schemes shows that QKASV operates on the least communication rounds required per signature, and reduces the computational overhead by at least 12%. Therefore, QKASV offers a better security solution compared to existing schemes.

With the rapid development of digital technologies, a large range of real-world systems, spanning from cloud servers, IoT devices, to industrial control systems, continuously generate vast amounts of time series data. Time series anomaly detection (AD) plays a crucial role in maintaining system stability by identifying unusual patterns from normal distributions, with the primary challenge lies in learning effective anomaly-discriminative representations. Recently, diffusion models have been applied to time series AD due to their strong representational capabilities. However, existing diffusion-based methods typically rely on reconstruction errors, which not only fail to fully exploit the representational potential of diffusion models but also be computationally intensive. To address these limitations, through experimental observation and theoretical analysis, we show thatspecific regions of the diffusion noises exhibit stronger representation capabilitiesfor normal patterns, which can be leveraged to enhance AD performance and reduce computational costs. Building on these insights, we propose NoiseAD, a diffusion noise-guided anomaly detection method incorporating an optimal noise steps selection approach to identify diffusion steps with higher resolution. Extensive experiments on diverse benchmarks demonstrate the superiority of NoiseAD over state-of-the-art methods, further substantiated by insightful visualizations. Code could be available athttps://github.com/shiwang-Xing/NoiseAD.

The real-time processing of large-scale, heterogeneous tasks—including CPU-only, general-purpose GPU, and specialized GPU tasks—poses significant challenges in Internet of Things (IoT) systems, driven by severe GPU resource fragmentation, inefficient CPU and memory resource utilization on edge servers. These issues often compromise system processing performance and server stability. To address these issues, we formulate a multi-stage mixed-integer nonlinear programming (MINLP) model, to jointly optimize GPU fragmentation rate and system processing capability. We then introduce a novel deviation-based Lyapunov optimization framework that explicitly maintains memory utilization around a predefined optimal threshold, effectively balancing resource usage and system stability. Finally, to achieve real-time decision-making for massive tasks in dynamic systems with randomly arriving tasks, we propose the MA-LHTO algorithm, a multi-agent deep reinforcement learning approach that incorporates a multi-head architecture, entropy-based exploration, and a parameter reset mechanism. Experimental results confirm that our algorithm significantly improves resource utilization, and exhibits good performance under various working conditions.

With the rapid expansion of the Internet of Medical Things (IoMT) and cloud computing, ensuring secure communication in e-health systems has become increasingly critical. However, many existing authentication solutions suffer from excessive overhead and security vulnerabilities. To address these challenges, we present a lightweight, high-speed authentication protocol that relies on secure hash functions and XOR operations, facilitating efficient mutual authentication among users, trusted servers, and medical servers while establishing session keys for data exchange. We then rigorously assess our protocol's security against a comprehensive threat model, employing both informal methods and formal analyses, including Real-Or-Random (ROR) model, BAN logic, and automated verification via ProVerif. The results demonstrate that our protocol remains resilient against known attacks and satisfies e-health security standards. Furthermore, a detailed performance comparison reveals that our approach significantly reduces some costs compared to existing schemes, while reinforcing security and privacy protections.

Intrusion detection in self-regulating manufacturing requires light yet precise modes to do real-time threat prevention. The performance of TinyML, 1D-CNN, GRU, and LSTM models is tested based on different metrics in this research. TinyML works better than all models with 97% accuracy and log-loss as low as 1.2, which is an indication of confident predictions. One of the significant aspects of TinyML is improved accuracy, and decreased sensitivity, with increased false negative and false positive rates also making it more useful in resource-constrained areas. For natural language generation tasks, Mixtral 8x7B 32768 shows the highest BLEU score of 0.60 and METEOR score of 0.86, reflecting its high similarity with reference outputs. It also scores the highest in ROUGE-1 (0.77) and ROUGE-2 (0.75), providing high-quality phrase-level recall. These results confirm that TinyML is the most effective for intrusion detection, while Mixtral 8x7B 32768 excels in text generation tasks.

Telesurgery is a new medical technology wherein the surgeon is situated remotely and operates through computers on surgically interactive robotic equipment connected through high speed communications networks. Optical fibers are necessary for this process as they have low latency and high capacity for realtime control of the process and exchange of data. Nevertheless, transmission is vulnerable to disruption by optical fiber faults which is disruptive to surgery safety and accuracy. This paper introduces TeleOps framework to enhance communication over optical fibers and fault management in telesurgery systems. Deep Learning (DL) models like Feedforward Neural Network (FFNN), 1D Convolutional Neural Network (1D-CNN), and Recurrent Neural Network (RNN) were implemented to detect and classify faults in Optical Time-Domain Reflectometer (OTDR) trace sequences. The RNN with Adam optimization achieved the highest detection accuracy. The proposed TeleOps framework also includes a blockchain-based smart contract to ensure transparent and encrypted tracking of faults, decentralized storage, and entity management in terms of surgical outcomes. This dual strategy provides reliable communication and efficient fault monitoring to minimize downtime of the remote surgical action. Thus the proposed TeleOps framework promises to enable safer and more effective solutions for remote healthcare by significantly enhancing the security and reliability of telesurgery systems.

The study develops an analysis framework built with deep learning techniques that extensively tests various architectures of modern Convolutional Neural Network (CNN) structures. The ResNet-18 model demonstrated the most successful implementation by reaching a training accuracy of 97.56% and validation accuracy of 86.92% at the same time. The corresponding training and validation losses amounted to 0.0754 and 0.6071, respectively. The LIME and Grad-CAM techniques of the XAI, and Occlusion were used to enhance the transparency of the model while providing components to better understand model decisions. The combination of accurate CNN models with interpretability tools produces successful explainable and robust classification in real-world application scenarios.

Communication in the green Internet of Vehicles (IoV) demands significant energy, encompassing both communication and computation costs, along with fuel and electricity for vehicle operation. The rise of quantum computing threatens the security of existing IoV frameworks, particularly those relying on conventional public-key cryptosystems (PKC) like integer factorization and elliptic curve cryptography, which are vulnerable to quantum attacks. This paper proposes a lightweight, postquantum security protocol for electric vehicles (EVs) in IoV, aimed at reducing computation and communication costs while enhancing energy efficiency. We conduct a comprehensive security analysis and compare our protocol with existing solutions, demonstrating its superior security, scalability, and practical effectiveness. Network simulations using NS3 further validate the robustness and efficiency of the proposed scheme for green IoV applications.

With the advent of technology, the transfer of sensitive information has become more prone to misuse, especially through unsecured platforms like social media. To address this challenge, UAV communication, particularly drones, has emerged as an alternative for document delivery in highly sensitive areas. But these devices can also get vulnerable to attacks which can cause a serious issue when the information is sensitive. Therefore, we propose a UAV-based secure document delivery framework that leverages AI models to detect potential attacks on the UAVs. The system ensures security throughout the document transfer process by evaluating various drone parameters. We employ the Decision Tree Classifier, which uses entropy to classify the potential threats. The result of the classification is used to guide the decision-making process for a secure delivery process. We further compare the various models based on metrics like accuracy, precision, recall, and$\mathbf{F - 1}$score, demonstrating the effectiveness of our framework, which enhances the security of UAV-based communication systems.

Moving computing components from headsets to cloud servers is a promising approach to increasing headsets' lifespan and comfort for Extended Reality (XR) users. However, latency is an unavoidable challenge for XR services due to longdistance transmission, especially when haptic feedback (usually requires 1 ms latency) is involved. To address this challenge, we leverage the Latent Diffusion Model (LDM) to propose a novel transmission architecture, which can accurately generate the future potential haptic feedback from current or previous video frames. Moreover, we also introduce an acceleration architecture to accelerate the haptic feedback generation process. The simulations indicate that the lifespan of headsets can be tripled by moving computing resources to the cloud. In addition, our proposed architecture can accurately generate future potential haptic feedback at least 170 ms before contact, which can satisfy the 1 ms latency requirement of haptic feedback.

With the rise in industries and population, electricity demand is increasing daily. Microgrids play a crucial role in providing green energy by utilizing renewable energy resources. Microgrids not only help meet the growing electricity demand but also reduce global warming and greenhouse effects. However, many homeowners are hesitant or reluctant to share their excess energy resources with other Microgrid or traditional electric grid users. In this article, we propose a hybrid deep learning and machine learning stacking model named GreenTrust. GreenTrust consists of three evaluation deep learning models at the base level and a single machine learning model at the meta-level. GreenTrust first establishes trust among home users using trust parameters. Once trust is buildup, a Microgrid can share its resources with other grid users. Results show that the hybrid model outperforms than other standalone machine learning schemes, such as Random Forest, XGBoost, and AdaBoost, in terms of accuracy, precision, recall, and F1 score.

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment.

With the growth of the Internet of Things (IoT), medical and multimedia data increasingly rely on visuals. Thus, in the Internet of Medical Things (IoMT), encryption safeguards sensitive images but also introduces noise, which makes them harder to recognize. Consequently, this decreases availability and hinders rapid image identification. Many researchers use Thumbnail Preserving Encryption (TPE) for visibility needs, but most schemes suffer from less efficiency, high data expansion, and low security. This work addresses these by proposing a lightweight cryptographic method that builds strong S-boxes using the 2D Henon map and Grey Wolf Optimization (GWO). These optimized S-boxes present robust cryptographic features like balanceness and nonlinearity, which are difficult to achieve. The first S-box has high nonlinearity value 110. The proposed TPE scheme uses bitwise swapping and substitution-permutation with the Rank(.) and Rank-1(.) functions utilizing optimized S-boxes to secure the image while preserving the thumbnail. This method meets the need for fast processing, ideal thumbnails, full decryption, and strong protection where current TPE methods fail. It ensures that the encrypted image is difficult to understand and resists various attacks, including statistical, chosen-plaintext, and differential attacks. Simulation reveals good image quality, speed, and security, making it fit for secure IoMT healthcare use.

The Internet of Things (IoT) has become a revolutionary concept that connects various devices and systems to enable smooth communication and data exchange. In this vast network, unmanned aerial agents (UAAs)-assisted mobile edge computing (MEC) communication plays a crucial role in facilitating direct interaction between edge devices. This aspect of IoT goes beyond traditional interactions between humans and machines. It creates a dynamic environment where devices collaborate autonomously, share information, and perform tasks. UAA-assisted MEC network offers several benefits, such as supports short range communication, reduced delay, improved scalability, and enhanced energy efficiency. Furthermore, for the purpose of enhancing the widespread interconnection and exceptionally dependable minimal delay in the fifth generation (5G) and beyond network, the utilization of nonorthogonal multiple access (NOMA) can be considered. Within this context, the impact of federated learning (FL) on NOMA-based UAV-assisted MEC network in wirelesspowered communication networks is examined. Initially, the transmitters extract energy from the radio frequency signals emitted by the MEC server. Subsequently, the transmitters utilize NOMA to establish communication with the receivers by utilizing the stored harvested energy. The formulation of a stochastic optimization problem is proposed with the aim of improving energy consumption (EC) and minimizing delay. Results indicate that the proposed scheme exhibit superior accuracy compared to baseline schemes, achieving an accuracy 98.37% after 59 communication rounds. The FL is employed to attain the objective and accelerate the local training data across the UAA-assisted MEC network.

Mobile edge computing (MEC) is a highly promising approach for achieving low-latency and high-performance computing services for mobile users. However, traditional MEC systems face challenges in meeting the increasing demands of mobile users due to the limited coverage and flexibility of fixed MEC servers. Integrating unmanned aerial vehicles (UAVs) with MEC has gained significant attention as a promising way to improve the MEC networks’ performances and meet the demands of next-generation networks. UAVs can act as flying edge servers, providing mobile users with flexible and on-demand computing resources. This article shows a new way to use the grey wolf optimizer (JDTS-GWO) algorithm to improve both the placement of UAVs and the scheduling of tasks in a multi-UAV MEC system. The objective is to minimize the overall system’s energy consumption while meeting various constraints, such as UAV coverage, collision avoidance, and task execution requirements. The proposed approach formulates the joint optimization approach, considering the deployment of UAVs, offloading decisions, and resource allocation. An encoding scheme is proposed to represent UAV deployment and task allocation within the JDTS-GWO framework. Simulations demonstrate significant improvements in energy efficiency and task completion compared to existing benchmarks, with up to 35% energy savings and a 98% task completion rate. Sensitivity analysis confirms the approach’s scalability and robustness. The problem is modeled as a mixed-integer nonlinear programming (MINLP) problem, taking into account the consumed energy of mobile nodes, UAVs, and the MEC system. The JDTS-GWO algorithm is adapted to solve the optimization problem efficiently.

The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), have transformed sectors such as home automation, healthcare, and manufacturing by enhancing data management through advanced networking. However, the rapid growth of IIoT has introduced significant cybersecurity challenges, necessitating a comprehensive approach to securing data across the TCP/IP model. This paper presents a novel cybersecurity investment strategy formulated as a bi-objective optimization problem, validated through genetic and iterative algorithms. The strategy effectively balances security and cost, achieving nearly 50% efficiency in solution effectiveness. By utilizing these optimization techniques, the approach provides a practical and cost-effective solution to improve IIoT security within budget constraints, offering valuable insights for cybersecurity professionals seeking robust and economically viable solutions.

There is an explosive growth of intelligent devices in the IoT ecosystem over the years. Owing to the massive multiple access at the network edge, there is increased latency and transmission overhead. Multiaccess edge computing (MEC) is a key technology used to offload the wireless devices from the computational tasks. But the wireless signal propagation is subject to fading, attenuation, obstructions, and other disturbances thereby affecting the performance of edge network. Reconfigurable intelligent surface (RIS) technology improves the quality of wireless propagation links through controlled reflection. This article presents an RIS-aided framework for a heterogenous edge network to offload the computation tasks of the resource constraint user equipment to the small access points (APs). A resource control algorithm is proposed which enables selection of an RIS-AP pair for each node in the edge network. The proposed algorithm selects the RIS-AP pair using maximum channel gain criteria such that the system sum throughput is maximized. Also, enabling reflection through the multiple RISs, the shortest path is selected using the graph theory to obtain the tradeoff between latency and reflection loss. It is observed that the proposed approach improves the achieved sum throughput of the system by 21.7% and the latency is reduced by 13.8%. The network performance is evaluated for varied RIS size and number of reflecting elements under different RIS phase shift design. It is shown that RIS with 1000 reflecting elements each of size${}({\lambda }/{2})\times {}({\lambda }/{2})$with equal phase shifts achieve sum throughput gain of 25.2% over randomly chosen phase shifts. Further, the comparison of intelligent reflecting the surface-aided MEC system with the conventional MEC system and the clustered MEC system is performed.

Cloud-based Intelligence of Things is significant for Augmented Enterprise Management Systems. Data integrity auditing is challenging in the intelligence of things environment, mainly when the newer versions in the public cloud environment update existing encrypted data. The related literature on cloud-based intelligence relies on encrypted data uploading or locally handling encryption and decryption using user keys. Considering the security risk, storage constraints at the edge, and realtime environment, both approaches have limited applicability in the intelligence of things environment. This paper presents the Privacy-Aware Secure Data Auditing (PASDA) framework at the cluster head for online data integrity verification. Specifically, the users hide data files by the blinding process with a generation of their corresponding signatures, which achieves data auditing by utilizing homomorphic techniques. A novel automated self-triggering/ Self-auditing-based data integrity auditing system is proposed, which detects the changes made in the cloud-stored data and sends alert messages to the trusted primary cloud server and users. A data dynamics method is developed containing a timestamp with a pointer to store multiple versions of the same file without signatures re-generation for the whole same file. The user is revoked due to prolonged absence or detection of the missed behaviour with system or service expiry. With these data dynamics, the proposed PASDA framework allows CH to regenerate signatures of the revoked user using its membership key for cloud-based stored data access and data integrity auditing. In-depth security analysis and extensive simulations based on comparative performance evaluation attest to the benefits of the proposed PA

With the rapid advancement of Internet of Things (IoT) applications, efficient and secure communication is considered a challenging task. Symmetric authenticated key exchange (AKE) is a promising solution due to its lightweight design. However, existing studies have demonstrated that traditional symmetric AKE schemes are unable to achieve perfect forward secrecy (PFS). Although some improved schemes were proposed based on the evolution of long-term secrets, the analysis indicates that there exists a zero-sum trade-off between PFS and self-synchronization. In addition, privacy preservation remains a critical issue. In response, this paper proposes a novel three-party symmetric AKE scheme. Specifically, the secret evolving mechanism to prevent the reverse inference of crucial secret values is constructed in the scheme. Meanwhile, any reachable session state can be self-transferred to the synchronization state at the end of a complete session. The proposed scheme provides anonymity and pseudonym unlinkability to the required party, while also improving the robustness of conditional identity traceability, avoiding false accusations caused by misdirected requests. The formal analysis, heuristic analysis based on the state transition and secrecy dependency, and performance comparison indicate that the proposed scheme achieves essential properties while maintaining manageable overhead.

Connected Autonomous Vehicles (CAVs) need a reliable communication structure which enables the complete evolution of transportation systems. Our proposed trust management strategy implements Quantum Key Distribution (QKD) and blockchain technology for solving CAV network security and coherence problems. The system applies QKD to produce unbreakable quantum keys which defend vehicle communication networks and blockchain systems strengthen governing networks by decentralizing operations and ensuring transparency and credibility. This framework employs QKD together with blockchain technology through a structure that demonstrates adaptability to changing networking conditions and cyber dangers within CAV environments. The independent operational mindset allows for automatic security rule updates that result in steady improvements to the encryption standards and system protocols. The framework demonstrates its functionality through simulations performed on multiple traffic conditions featuring different speeds together with density levels. The analysis included evaluation of energy efficiency together with overhead ratio performance as well as QKD success rates and blockchain verification duration. The testing results demonstrate that this system performs effectively under different operational scenarios while demonstrating strong defense capabilities against Sybil and Wormhole security attacks. Under dense traffic scenarios, Quantum-Secured Trust Management Framework (QSTMF) delivered improved network throughput reaching 15% above current models while high-speed traffic circumstances led to 20% diminished blockchain verification times. Attack detection rate performance of the framework exceeded 90% throughout multiple attack simulations indicating its robust capabilities for vehicle communication protection.

In Vehicular Edge Computing (VEC) environments, the increasingly complicated functional and non-functional requirements from vehicular applications such as MetaVehicles usually incur larger sizes of task-input data, which not only increase the transmission delay of task-input data via the front-haul links but also degrade the quality of experience for users, even if computation tasks can be offloaded and executed at the network edge. In this article, we put forward a caching-enabled task offloading strategy, by caching and reusing the universal context data at the edge server, to avoid duplicated data transmission in VEC systems. The goal is to minimize the overall response latency for all the tasks, by jointly optimizing task offloading, content caching, and resource allocation decisions in VEC. The optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem. To efficiently solve this problem, we decompose this problem into two subproblems, namely, the computing Resource Allocation (RA) problem and the Joint Offloading and Caching (JOC) problem. The corresponding algorithms are put forward to solve the content caching and task offloading problems, respectively. Numeric evaluation reveals that our strategies and algorithms can achieve better performance in minimizing the overall response latency, in comparison with other approaches.

Vehicular Ad-hoc Networks (VANETs) face significant security and privacy challenges in modern intelligent transportation systems. This paper analyzes vulnerabilities in Al-Shareeda et al.'s vehicle authentication protocol (doi: 10.1109/TDSC.2025.3553868) and proposes an enhanced ECC-based scheme using short-lived pseudonymous certificates. We identify two critical weaknesses in Al-Shareeda et al.'s protocol—a desynchronization attack causing potential denial-of-service and an identity linking attack compromising vehicle privacy. Our protocol establishes mutual authentication between vehicles and roadside units, ensuring message integrity, anonymity, and perfect forward secrecy. Unlike existing approaches, it eliminates the need for online third-party authenticators. Formal security proofs demonstrate that the scheme's security is reducible to the hardness of the ECDLP and CDH problems. Performance analysis shows our approach achieves an optimal security-efficiency balance with competitive communication overhead (4608 bits) and computation costs (5.02 ms) compared to state-of-the-art alternatives, while uniquely satisfying all twelve evaluated security properties.

Autonomous aerial vehicle (AAV)-assisted mobile edge computing systems have high mobility and can be deployed in various rugged terrain and emergency scenarios for communication and monitoring. However, the malicious use of AAV swarms poses a potential threat to key areas. Therefore, accurate positioning of AAV swarms is crucial for the security of high-value civilian facilities and equipment. This article investigates angle estimation of coherent signals from AAV swarms in bistatic multiple-input multiple-output radar under nonuniform noise. The nonuniform noise powers are iteratively estimated based on the structural characteristics of the covariance matrix and subsequently removed from the observations. Transmission-reception diversity smoothing is then applied to the signal subspace, obtained through higher order singular value decomposition, to recover the rank deficiency. Furthermore, a block sparse reconstruction method is proposed, utilizing the reweighted smoothed$\ell _{0}$-norm, to obtain angle estimates. This method automatically pairs the direction-of-arrivals and direction-of-departures of AAVs. Experimental results demonstrate the superiority of our approach over existing solutions.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model.

The integration of Electric Vehicles (EVs) into the energynet, the network from power generation to EV charging station, presents a symbiotic relationship with potential benefits for sustainable and efficient transportation. However, the existing research has revealed challenges in maintaining an equilibrium between energy supply and demand, often resulting in underutilization or overutilization of energy networks. Blockchain technology has emerged as a promising solution to enhance transparency and secure decentralized energy distribution; however fails to connect the equilibrium in the presence of uncertainty of demand-supply and/or handling information cascading. In this paper, we introduce TAURITE (sTAckelberg eqUilibRium in blockchaIned energyneT with Evs), a novel blockchain-based energynet framework that explicitly leverages the Stackelberg model for energy flow equilibrium within EV interfaces. TAURITE employs Subgame Perfect Nash Equilibrium (SPNE) to address demand uncertainty in dynamic vehicular environments. It also tackles information cascades’ impact on energy distribution, demonstrating its ability to maintain equilibrium even in such scenarios. TAURITE introduces a multi-variate polynomial-based key generation process through the smart contractAVTALand incorporatesProof-of-Energy-Equilibrium (PoEE)as an energy sector consensus mechanism. Experimental results show that TAURITE significantly improves throughput, latency, and energy efficiency, with an average$30\%$enhancement in these metrics. Notably, TAURITE ensures$100\%$allocation stability, even in the presence of information cascades, marking a substantial advancement in sustainable and efficient energy management within the evolving energynet-EV ecosystem.

With the gradual increase in market share of electric vehicle (EV), Vehicle to Grid(V2G) has become a new research hotspot in the field of intelligent transportation systems. Its goal is to avoid overloading the power grid due to the simultaneous charging of a large number of electric vehicles. However, when EV is connected to the power grid, V2G will involve a large amount of privacy data exchange. Once these data are leaked, the privacy and security of users will be threatened. Ensuring the secure transmission of user privacy information in V2G is crucial. Therefore, this paper proposes a practical and secure authentication protocol for V2G in intelligent transportation systems. This protocol ensures user login security through three-factor authentication mechanism and then implements authentication based on Chebyshev chaotic maps. Finally, secure communication is carried out through the established key. Security analysis shows that this protocol is secure and can ensure the privacy and security of V2G. Informal security analysis shows that this protocol can meet various security attributes. Functional comparison and performance analysis indicate that the protocol not only has high security but also has low computation and communication overhead.

Autonomous Vehicles (AVs) are redefining the transportation sector through their ability to navigate, make decisions, and complete autonomous tasks. For accurate perception and comprehension of the surroundings, the AVs heavily rely on segmenting high-resolution 3D point cloud data provided by Light Detection and Ranging (LiDAR) sensors for discerning objects and other environmental features. However, the current vehicular segmentation approaches experience shortcomings in data insufficiency, computational performance, and precision concerns. Hence, to counteract these limitations, the paper proposes a Semantic Segmentation approach using Ball-Pivoting Algorithm and U-Net (SSBU) that harmoniously combines the Ball-Pivoting surface reconstruction algorithm and 3D U-Net to enhance image characteristics, leading to highly accurate outcomes with optimal cost efficiency. This SSBU integration involves carrying out augmentations and pre-processing of the raw LiDAR data to transform them into voxels through the process of Voxelization. The voxels are further improved through a surface reconstruction technique that utilizes the Ball Pivoting Algorithm (BPA). The resulting 3D model is analyzed using 3D U-Net deep learning architecture for robust and real-time interpretation. The implementation has produced a mean Intersection Over Union (IoU) of 83.3 over the NuScenes data and 69.7 on the KITTI dataset, outperforming the state-of-the-art.

The increasing integration of Internet of Things (IoT) devices in Wireless Local Area Networks (WLANs) necessitates robust and efficient authentication mechanisms. While existing IoT authentication protocols address certain security concerns, they often fail to provide comprehensive protection against threats such as perfect forward secrecy violations, insider attacks, and key compromise impersonation, or impose significant computational and communication overhead on resource-constrained IoT systems. This paper presents a novel Extensible Authentication Protocol (EAP) based scheme for IoT-WLAN environments that addresses these security challenges while maintaining cost-effectiveness. Our approach utilizes elliptic curve cryptography and incorporates advanced features including perfect forward secrecy, strong identity protection, and explicit key confirmation. We provide a thorough security analysis using informal heuristics, formal methods (Random Oracle Model and BAN Logic), and automated verification with ProVerif. Performance evaluations demonstrate that our protocol achieves lower communication, storage, and computational costs compared to state-of-the-art solutions, with an average 79.6% reduction in computation time. A detailed comparison with existing schemes highlights the efficiency and enhanced security features of our proposed authentication mechanism for IoT-WLAN deployments.

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%.

Data stream classification is widely used in Internet of Things (IoT) scenarios such as health monitoring, anomaly detection and online diagnosis. Due to the continuous data stream changing dynamically over time, it is impossible to classify all the data simultaneously. Moreover, labeling each sample in practical data stream applications is time-and resource-consuming. The realistic situation is that only a few instances in a data stream are labeled. Therefore, classifying data streams with limited labels has become challenging in IoT scenarios. In this paper, we propose an incremental dynamic weighted semi-supervised method for classifying IoT data streams. Considering the dynamics and continuity in data streams, we use a chunk-based approach to learn the features in the data stream and assign weights to the classifier dynamically. Moreover, we deploy incremental learning methods to continuously learn from the sampled labeled data stream to update the classifier model, which can take advantage of newly incoming labeled data to improve learning performance. Experimental evaluations on seven IoT datasets show that the proposed method outperforms semi-supervised methods in accuracy, precision, and geometric mean (Gmean) by 10% and 5% over supervised methods, respectively.

Integration of the Internet of Things (IoT), intelligent sensor networks, and patient-centric modules has successfully revitalized the way we pursue healthcare services. Seamless online doctor-patient communication facilities have vanished the partial line between traditional physical on-site treatment and current remote monitoring. In the Healthcare 4.0 environment, patients can connect with doctors via video conference, send audio transcripts as responses, and share pictorial or text-based vital information. One of the major concerns related to remote healthcare treatment applications is the efficient utilization of networks for data transmission. To mitigate this paramount challenge, we propose SDN care. It is a Deep Learning(DL)-based SDN-enabled network classification approach to facilitate seamless and secured communication between doctors and patients in the Healthcare 4.0 ecosystem. SDN care relies on one-dimensional Convolutional Neural Network (CNN) architecture to efficiently classify the type of data under communication and make adjustments in SDN parameters. The latency, bandwidth, jitter, etc., are adjusted based on prediction from the CNN model for effective utilization. Proposed SDN-care is further compared with Artificial Neural Network (ANN), and it also has been examined using different types of optimizers. The performance evaluation of SDN-care has been done through various metrics such as accuracy, loss convergence, precision, recall, f1 score, Receiver Operating Characteristic (ROC) curve, precision-recall trade-off curve and compared with different optimizers such as Adam, SGD, RMSprop, and Adadelta. Thus, SDN care introduces significant advancements in SDN-enabled remote patient-doctor communication environments using various modes of data exchange.

Parkinson’s disease (PD) is a neurodegenerative disease which is the second most common neurological disease. Early diagnosis of PD poses significant challenges as in earlier stages of PD, symptoms can’t be clinically recognized. This paper presents a framework called XSH-ParK with integrated deep learning (DL) models and XAI techniques to assist in early PD diagnosis using MRI scans. Pre-trained models VGG16, InceptionV3, ResNet50 and a custom CNN are used to analyze the NTUA dataset, which consists of MRI scans of 78 individuals. Through rigorous evaluation considering accuracy, precision, recall, and F1-score metrics, it is evident that the fine-tuned VGG16 model achieves the highest efficiency with an accuracy rate of 97.56% in the XSH-ParK framework. Additionally, LIME and integrated gradient are the XAI methods used on the top-performing VGG16 model to provide transparent and interpretable diagnostic insights, Enabling healthcare professionals to understand the reasons behind the models’ decisions.

Satellite communications have been regarded as a promising solution to be incorporated in future Internet of Things (IoT) to support continuous and ubiquitous connectivity services. Constrained by limited contention resources, the conventional random access (RA) scheme will suffer from severe overload problem when applied to the emerging satellite-based IoT. Focusing on improving access efficiency in the massive and concurrent access scenarios, we propose a sample feature enhancement based collision detection and load estimation scheme with the aid of deep learning. Specifically, by leveraging the inherent characteristics of correlation results in case of preamble collision, a feature extraction method is first designed to precisely screen important sample features related to the current load information. Then, a multi-feature enhancement network with an adaptive neuron optimization strategy is further proposed to enable original 1D features mapped to a 2D domain, so as to improve the representation capability of the model while preventing overfitting. Simulation results validate the feasibility of our scheme in large-scale RA collision scenarios and demonstrate its remarkable performance superiority in terms of load estimation accuracy and collision detection probability over the state-of-the-art schemes.

This paper introduces a novel telehealth communication system, designed to enhance the security and integrity of medical data exchange. In the rapidly evolving digital healthcare landscape, the protection of sensitive patient information is paramount. To address this, our system uniquely combines quantum cryptography, specifically the BB84 protocol, with blockchain technology, offering a dual-layered security framework. The Quantum Layer, underpinned by the BB84 protocol, establishes quantum-secure communication channels, effectively encrypting data exchanges between patients, doctors, and hospitals. This layer guarantees that medical information remains confidential and safe from potential quantum-level eavesdropping threats. The subsequent Blockchain Layer further strengthens the system by storing these encrypted communications in an immutable blockchain ledger. This approach not only secures the data against unauthorized alterations but also provides a transparent and permanent record of all transactions, thereby enhancing the auditability of medical communications.

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc.

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

The expansion of intelligent transportation systems (ITS) is driven by the demand for cutting-edge cyber-physical systems, applications prioritizing comfort, and specialized services designed for utilization in smart vehicles. The internet of vehicles (IoV), a crucial element of ITS, supports data communication like vehicle-to-vehicle (V2V) and vehicle-to-anything (V2X). Ensuring the safety and security of passengers relies heavily on effective inter-vehicle communication. The network layer's routing protocols enable efficient data communication within the IoV, addressing its dynamic network topology. Existing literature has introduced various routing protocols, including topology-based, position-based, cluster-based, broadcast-based, and hybrid protocols, to navigate the dynamic network topology of IoV. The performance evaluation of these protocols considers parameters like security, throughput, packet delivery ratio, jitter, delay, and overhead. However, these protocols are susceptible to malicious attacks that compromise the integrity, confidentiality, and availability of network messages, potentially leading to accidents and degrading the system's performance. In this context, we propose a novel secure routing protocol called secure optimized link state routing protocol SecOLSR, which employs symmetric secret key exchange for connected vehicles, aiming to mitigate the aforementioned challenges. Our paper presents a comprehensive security analysis of SecOLsr in the IoV network and assesses its performance in terms of quality-of-service (QoS) parameters alongside existing state-of-the-art protocols.

In smart communities, Electric vehicles (EVs) have grown in popularity as a key component of the energy ecosystem where the focus has turned to the generation of clean, sustainable energy. The integration of EVs, charging stations (CS), and smart grids (SG), however, poses significant challenges in terms of energy trading (ET) optimization and profit maximization. Next, trust is another challenge in the ET ecosystem among the communicating entities (EVs, CS, and SG) to buy and sell energy. Recent studies have overlooked the fact of ET among CS and SG, and mostly have focused on ET by EVs. However, at peak loads, SG may experience bottlenecks in energy dissipation, and thus excess energy collected by CS from EVs might be traded to SG to manage loads during peak times. So, we propose a framework, StackGrid, that leverages the capabilities of Vehicle-to-Grid (V2G) systems over a blockchain network. We design a Stackelberg game between CS and SG for profit maximization of both parties and to obtain optimal payoff equilibria. The framework is powered over the 5G ultra-reliable low latency communications (uRLLC) service for real-time ET response and data exchange. To address blockchain scalability concerns, we incorporate Interplanetary File Systems (IPFS) as local off-chain ledgers, where only meta-information is stored on-chain to handle blockchain scaling issue. The framework is evaluated on metrics like 5G service latency, optimal payoff scenario, attack probability, and node throughput. The obtained results indicate StackGrid viability in real ET setups, with benefits for sustainable and efficient energy management.

The rise of vehicular networks has ushered in the era of vehicle-to-everything (V2X) communication aimed at bolstering driving safety. A pivotal aspect of V2X communication is its role in facilitating emergency warning systems. This study focuses on the propagation of emergency messages and the provisioning of emergency services through V2X communication while considering the efficient allocation of resources necessary for effective vehicle communication. The primary objective of resource allocation within the context of Cellular-Vehicle-to-Everything (C-V2X) is to optimize the utilization of available resources, amplify system capacity, and address the diverse communication requisites within the confines of system constraints. A notable challenge in C-V2X resource allocation resides in the judicious allocation of spectrum resources and broadcast opportunities to V2X users. Therefore, we present NeuroEvolution of Augmenting Topologies (NEAT)-based efficient channel allocation within the C-V2X framework. Our approach aims to maximize the sum rate and throughput of emergency service vehicles (ESV) while ensuring the attainment of a minimum threshold throughput for other vehicles. Subsequently, we conduct a comparative analysis between the average sum rate achieved by the NEAT algorithm and a random resource allocation scheme. Furthermore, we undertake a comparative assessment of the time complexity of NEAT in contrast to other state-of-the-art techniques employed for channel allocation. These techniques encompass the graph matching algorithm, the Hungarian, and the brute force method. Our proposed C-V2X model demonstrates superior performance across various evaluation metrics compared to various alternative algorithms.

The advent of healthcare 4.0 has catalyzed a paradigm shift in medical practices, ushering in innovative approaches such as telesurgery, a groundbreaking method for remote patient surgery and monitoring. This transformative technique extends beyond traditional surgeries, finding application in dermatological procedures. The success of telesurgery in skin-related surgeries hinges on accurate and efficient skin cancer detection using dermoscopic images. Recognizing the inherent complexities in interpreting deep learning models, especially in the context of healthcare, Explainable Artificial Intelligence (X-AI) becomes imperative. In this context, we propose a novel CNN-powered X-AI mechanism i.e., X-CaD, tailored for skin cancer detection in telesurgery environments, leveraging ResNet and MobileNet for feature extraction. To enhance interpretability and bridge the gap between model predictions and clinical decision-making, we employ X-AI techniques such as Local Interpretable Model-agnostic Explanations (LIME) and Integrated Gradient (IG). LIME provides granular insights into model predictions, elucidating decision-making processes, while IG offers a comprehensive view of feature attributions. X-CaD relies on the synergistic integration of advanced CNN architectures, i.e. ResNet and MobileNet along with X-AI techniques to identify skin cancer accurately and provide clinicians with clear insights. The effective impact of X-CaD is evaluated through the observed loss and accuracy values for the DL models, and heat map outputs for X-AI. This represents a significant advancement in the integration of state-of-the-art technology and healthcare, offering a dependable telesurgery solution for the diagnosis of skin cancer in surgical procedures.

The Internet of Vehicles (10V) brings significant economic benefits to countries. However, large-scale smart vehicle production planning remains challenging in the 10V. Currently, heuristic algorithms and solvers commonly used for these problems often lack scalability and fall into local optima. Moreover, security concerns about wireless data transfer arising from multi-factory manufacturing processes are garnering attention. To address these issues, this paper introduces an algorithm, TRL, which is a Transformer-based Reinforcement Learning for vehicle production planning problems. Furthermore, we propose a Transformer-based Federated Reinforcement Learning algorithm, named TFRL, tailored for large-scale manufacturing and secure wireless communication. Experimental results showcase the high performance and security of TFRL. It schedules 1000 orders in about 14 seconds and avoids exchanging plaintext during the interaction. Compared to Non-dominated Sorting Genetic Algorithm II(NSGA-II), the TFRL enhances computational speed by 95.12% and reduces constraint violation scores by 93.18%.

Abstract In a smart city, IoT devices are required to support monitoring of normal operations such as traffic, infrastructure, and the crowd of people. IoT‐enabled systems offered by many IoT devices are expected to achieve sustainable developments from the information collected by the smart city. Indeed, artificial intelligence (AI) and machine learning (ML) are well‐known methods for achieving this goal as long as the system framework and problem statement are well prepared. However, to better use AI/ML, the training data should be as global as possible, which can prevent the model from working only on local data. Such data can be obtained from different sources, but this induces the privacy issue where at least one party collects all data in the plain. The main focus of this article is on support vector machines (SVM). We aim to present a solution to the privacy issue and provide confidentiality to protect the data. We build a privacy‐preserving scheme for SVM (SecretSVM) based on the framework of federated learning and distributed consensus. In this scheme, data providers self‐organize and obtain training parameters of SVM without revealing their own models. Finally, experiments with real data analysis show the feasibility of potential applications in smart cities. This article is the extended version of that of Hsu et al. (Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ACM; 2020:904‐906).

In an Internet of Things (IoT)-based healthcare system, medical IoT devices gather and transmit critical patient data. Ensuring the security and privacy of medical data is paramount. One of the most critical challenges in this regard is the authentication of participating entities. The literature proposes specific authentication approaches for healthcare systems based on integer factorization and discrete logarithm problems. However, the advent of quantum computers would fundamentally break all of these protocols. In this study, we conducted an analysis of a recently proposed authentication and access control scheme for e-health systems, which is based on lattice-based cryptography and was developed by Gupta et al. Our analysis revealed that the scheme is vulnerable to several types of attacks, including impersonation, de-synchronization, and smart card stolen attacks, which could compromise the confidentiality and integrity of sensitive medical data. To address these security challenges, we propose an alternative authentication and access control scheme that uses Saber, a finalist lattice-based key encapsulation algorithm from round three of the NIST post-quantum cryptography standardization. One of the biggest advantages of Saber is its simplicity and efficiency. Our proposed scheme is designed specifically for e-health systems and provides robust protection against the vulnerabilities identified in Gupta et al.’s scheme. We believe that our proposed scheme represents a significant improvement over existing approaches and could help to enhance the security and privacy of e-health systems. Upon completion of our improved protocol, we proceeded to implement it within the Vivado 2018.3 environment for Zynq UltraScale FPGAs. To gather insight into its performance, we conducted a performance comparison study with various related protocols.

In the ever-evolving landscape of technology, healthcare continuously harnesses its benefits, propelling advancements in medical practices. Within intelligent healthcare, medical robots play a pivotal role, providing integral support to healthcare professionals, streamlining processes, and delivering efficient services. These robots securely transmit patient treatment plans, transferring them to cloud storage and subsequently storing them in blockchain systems. This innovative approach ensures the integrity and accessibility of patient data, introducing novel avenues for seamless interaction with medical information for hospitals and patients’ families. Despite these advantages, the looming privacy risks associated with sensitive patient data transmission pose a compelling challenge, demanding a comprehensive solution. In response to this challenge, we propose a mutual authentication and key agreement protocol designed to optimize healthcare services while prioritizing data security and patient privacy. To validate the robustness of our authentication protocol, we conduct thorough analyses based on both formal and informal models, establishing a foundational framework for evaluating the protocol’s security. Additionally, we perform a comprehensive comparative analysis, assessing the proposed protocol against existing counterparts across various dimensions. This comparative scrutiny reveals the superiority of our protocol in terms of security, as well as its efficiency in communication cost and computational overhead. These findings affirm the efficacy of our proposed solution in navigating the intricate interplay between medical robotics, blockchain, and data security.

The Internet of Vehicles (IoV) integrates wireless, mobile networking, cloud infrastructure, IoT, and wireless sensor networks, establishing an intelligent transportation system. While electric vehicles (EVs) contribute to environmental sustainability, they encounter challenges; battery-swapping technology emerges as a viable solution. Nevertheless, concerns arise regarding data security, privacy, and potential single points of failure. To address these issues, we propose a privacy-preserving authentication protocol based on intelligent blockchain. This protocol ensures the security and reliability of data storage and transaction verification processes, simultaneously upholding privacy, including user anonymity and untraceability. Additionally, leveraging the decentralized nature of intelligent blockchain, each participating node retains a copy of the data and verifies it through consensus algorithms to ensure its integrity and credibility. Verification using the Real-Oracle Random (ROR) model demonstrates both effectiveness and security, with informal analysis confirming resilience against known attacks. Comparative analysis underscores the proposed protocol’s security and performance advantages, placing emphasis on its reliability.

This paper explores the transformative potential of integrating Augmented Intelligence with Internet of Things (IoT) in autonomous vehicles, a concept we term AIoT. We begin by examining the critical roles of IoT and augmented intelligence in automotive technology, delineating their evolution and synergistic benefits when unified. The crux of our investigation lies in the intricate fusion of these technologies, addressing key elements such as data acquisition, processing, and real-time decision-making, particularly in enhancing traffic coordination, vehicle safety, and energy efficiency. We place a strong emphasis on the practical applications of AIoT in autonomous vehicles, underscoring advancements in sensor data integration and vehicle-to-environment communication. Our discussion also navigates through the challenges and limitations currently faced, including data privacy, real-time data processing demands, and technological constraints. A case study is presented, offering a quantitative and algorithmic perspective on AIoT implementation in modern autonomous vehicles. Concluding, the paper casts a vision for the future of AIoT in the automotive sector, pinpointing areas for potential breakthroughs and further research. This study asserts the indispensable role of AIoT in revolutionizing autonomous vehicle technology, setting a new benchmark in automotive innovation.

For Unmanned Aerial Vehicles (UAVs) with Tiny Machine Learning (TML), there is mutual exclusivity between the energy consumption for flight and the energy consumption to support their computation and processing. IoUAVs integrated with TML systems often consume substantial amounts of energy during flights, particularly when engaged in extended coverage and surveillance missions. The energy consumption of a UAV with TML performing long, wide-area coverage patrols and monitoring missions in complex areas is significant for the flight itself, and the energy required for the TML to perform calculations and processing is not guaranteed. Therefore, to better support TML computations, this study optimizes flight paths to reduce the energy consumption of UAVs while ensuring coverage. Specifically, in this study, the use of concave point elimination algorithms, enhanced convex decomposition algorithms, and determination of flight direction significantly reduced the frequency of UAV turns. The computational cost of obtaining a complete path is reduced by merging the subconvex regions and the weighted minimum traversal of the graph. This novel bidirectional forwarding path coverage path-planning (BFP-CPP) algorithm maximizes the reduction in the number of turns, reduces energy consumption, and achieves global coverage. The simulation experimental results show that compared with the existing methods without concave point elimination, the BFP-CPP algorithm can effectively reduce the number of subregions, minimize the number of drone turns, and lower energy consumption.

The unmanned aerial vehicle (UAV) array equipped with sensors is widely used for target localization owing to its superior maneuverability. Unfortunately, limited by the current manufacturing technology, sensor arrays usually exhibit inconsistent gain and phase responses across channels, i.e., gain–phase errors, which can seriously affect the target localization accuracy. Herein, we consider that the gain–phase consistency of all array channels is not been precalibrated. For accurate target localization, we develop a system architecture for bistatic multiple-input–multiple-output (MIMO) radar equipped with a UAV array at the receiver part to realize angle estimation. First, the UAV array is controlled to move near the transmitter to receive the transmitted signals directly. Therefore, the gain–phase consistency of the transmitter can be calibrated by using the data after matched filtering and combining the known relative position information of the transmitter and receiver. Second, we control UVAs away from the transmitter to form a bistatic MIMO radar and use the synthetic aperture technique introduced by the UAV array motion to convert the receive array into partially calibrated. Meanwhile, the array manifold matrices with unknown model errors can be obtained by parallel factor decomposition. Finally, the angle estimates, gain–phase errors, and position errors are estimated by the element-wise division of the steering vectors without iteration. Moreover, our method is insensitive to sensor position errors of the original UAV array while determining angles. Simulation results demonstrate that the proposed method can obtain accurate angle estimates under the aforementioned model errors.

The Internet of Medical Things (IoMT) is a compelling networking paradigm integrating wireless communications sensors, connected devices, and embedded computing technologies. The IoMT involves the collection of real-time health data using sophisticated medical sensors. In recent years, the IoMT has become increasingly significant within the broader context of the Internet of Things (IoT). It provides accessibility for health monitoring and poses security obstacles to safeguarding the confidentiality and privacy of patient data. Therefore, this article presents a blockchain-integrated quantum authentication scheme in sensor-assisted IoMT networks. The proposed concept utilizes blockchain technology to achieve efficient patient authentication without the need for third-party entities. In addition, a secure quantum authentication scheme is designed not to require patients to authenticate themselves when communicating with multiple doctors simultaneously. This protocol explicitly addresses how clinicians can misuse their professional roles toward patients in IoMT networks. An evaluation analysis assesses the proposed technique’s efficacy compared to existing authentication schemes. The performance analyses demonstrate that the proposed protocol is resilient against various security attacks. Also, the practical usability of the quantum authentication scheme proved its importance as a significant improvement in communication security for IoMT networks.

Access control is an important security parameter in industrial networks; a mismanaged access control system leads to security breaches. The existing security solutions significantly consider the access control methods in the Industrial Internet of Things (IIoT); however, falsified identity can bypass the secure access control system. Thus, a centralized access control method leads to risks for data security. We are the first to address the risk factors of granted access in an industrial environment and present a risk-adaptive access control framework for IIoTs. Our proposed solution framework uses blockchain to provide secure decentralized access control in the industrial environment with privacy-preserved multi-party data sharing. We name our framework “Adaptive Access controL for Multi-party data cOmputation in iNdustrial Decentralization (AALMOND)”. AALMOND uses lightweight cryptographic operations to reduce the complexity of the execution and loosen up the tight bounds on resource-constrained industrial devices. Further, the risk-adaptive access control in AALMOND provides a better security analysis of the multi-party sharing data. Our framework uses role-based, attribute-based, and organization-based access controls to map the assets for risk calculation. We put all the required policies in a smart contract for the ease of multi-party data sharing to obtain a transparent access control execution more suitably. We also pioneer in the calculation of the risk adaptivity of AALMOND considering the NIST recommendations of operation risk, security risk, and heuristic risk. We measure the performance of AALMOND with state-of-the-art frameworks based on throughput, latency, complexity analysis, and risk adaptivity factors. We find that AALMOND is efficient for IIoTs as it shows 24% reduced latency and 20% better throughput as compared to the other existing models.

Effective data management schemes have always been the major demand in universal industrial Internet of Things (IoT) systems, especially in resource-constrained scenarios. In realistic wastewater treatment process (WTP), only limited monitoring data resource can be available due to some digital constraint. Aiming at this practical issue, this work explores utilization of deep neural network to deal with such practical issue in the objective situation. Therefore, a deep learning-based data management scheme for intelligent control of WTP under resource-constrained IoT systems, is proposed in this paper. Firstly, a specific data encoding and preprocessing approach is developed for the objective business scenario. Then, the detailed workflow of a deep neural network structure is applied to predict key intermediate parameters which can further guide control decision. Finally, a comprehensive series of experiments are conducted on a real-world dataset which covers a range of one year. Both efficiency and robustness of the proposal are tested by introducing several performance metrics. The results show that it can have proper prediction effect in such resource-constrained environment, which can facilitate following intelligent control operations.

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.

Direct position determination (DPD) has received much attention in emitter localization, owing to its better accuracy than conventional two-step positioning. Most of the existing DPD algorithms are developed for circular signals (CS) by using uniform linear arrays (ULAs). However, these algorithms may ignore other characters of the signals, e.g., noncircularity. The use of ULAs limits the accuracy of source localization and the number of sources that can be estimated. In this article, a weighted$\ell_{0}$-norm sparse reconstruction algorithm for noncircular signals (NCS) is developed for DPD with a designed sparse array in motion. First, a sparse array configuration named extended nested array (ENA) is devised for NCS, which consists of three subarrays. Theoretical analysis proves that the designed array can obtain higher degrees of freedom (DOFs) effectively, and reduce the mutual coupling effects between antennas. Then, a weighted$\ell_{0}$-norm sparse reconstruction algorithm is developed to improve the accuracy of DPD. Finally, simulation results are provided to demonstrate the superiority of the proposed algorithm with the designed sparse array. Our scheme can provide better localization performance than the state-of-the-art methods.

The emergence of edge computing guarantees the combination of the Internet of Things (IoT) and artificial intelligence (AI). The vertical federated learning (VFL) framework, usually deployed by split learning, can analyze and integrate information on different features collected by different terminals in the IoT. The complete model is divided into a top model and multiple bottom models in a specific middle layer. Each passive party as a terminal with certain features owns a bottom model, and an active party as an edge server with labels holds the top model. Feature inference attack aims to infer the party’s features from the model predictions during prediction in VFL. Existing attacks considered the adversary an active party under the white-box or black-box model. However, an attacker usually is a passive party in practice because terminals are more vulnerable than edge servers. Therefore, this article discusses a practical feature inference attack in VFL during prediction in IoT under this setting. We design an adversary builds an inference model to minimize the distance between the predictions from the inferred features and target features. Because the information on the top model and other bottom models is unknown, the adversary cannot directly train the inference model. Therefore, we utilize the zeroth-order gradient estimation method to calculate the parameters’ gradients to train the inference model. Experimental results demonstrate that the performance of our attack is comparable to that of the white-box attacks while retaining apparent advantages over the existing black-box attacks.

Efficient communication in massive multiple-input-multiple-output (MIMO) systems relies on accurate channel estimation to optimize signal transmission efficiency, reliability, and minimize interference and power consumption. However, the presence of nonuniform array gain-phase perturbations among antenna elements poses practical challenges, degrading the precision of estimation. In response, this article introduces a parameterized joint angle and delay estimation (JADE) method tailored for multipath channel estimation in fully uncalibrated arrays within massive MIMO systems. Our innovative spatial and frequency-based co-smoothing method is proposed to construct a rank-recovered data covariance matrix, enhancing the system’s ability to distinguish coherent multipath signals. The JADE method employs a 1-D angular spectrum and delay spectrum search under the principle of rank reduction, providing a closed-form solution for array gain-phase perturbation estimates. The deterministic Cramér-Rao lower bound for the proposed model is derived. Numerical simulations affirm the method’s superior performance. In conclusion, our approach addresses the demand for precise channel estimation in low-signal-to-noise ratio scenarios, particularly benefiting Internet of Things (IoT) applications.

Artificial intelligence enhanced Internet of unmanned aerial vehicles (UAVs) is a promising network to achieve the complicated vehicular tasks and construct intelligent communication networks. One of the critical tasks is to guarantee a secure network access while achieving trade-off between accuracy and latency through lightweight deployment on resource-limited and hardware-constrained UAVs. To address this issue, a novel noise-tolerant radio frequency fingerprinting (NT-RFF) based on tiny machine learning (TinyML) scheme is proposed, which amalgamates contrastive learning and data augmentation, aiming to improve the generalization ability of unauthorized device identification (UDI). Particularly, we first exploit the augmentation technique to enhance the legitimate training datasets under the circumstance of varying signal-to-noise ratios, facilitating an enhanced and diversified datasets. Second, a synthesis of contrastive learning and supervised learning is employed to attain comprehensive global learning. We design a new contrastive loss criteria to capture relevant information from the samples collected over the air. Besides, we design a categorical cross-entropy loss criteria by which supervisory information can be leveraged from associated labels. Finally, quantification is utilized to enhance model efficiency and achieve an optimal balance between accuracy and latency within computing and energy resource-limited UAVs. Experimental results demonstrate that the proposed tiny NT-RFF which only contains about 25-30% quantitative parameters can maintain excellent performance and improve the UDI accuracy greatly compared with the traditional machine learning-based RFF schemes. Moreover, the remarkable results showcase that our proposed framework attains a substantial increase in identification accuracy compared to the DACL-RFF and DASL-RFF methods, exhibiting improvements of 14.16% and 5.17%, respectively.

Combining different heterogeneous components into a full HPC system results in combinatorial effects in their complexity.It is a huge challenge to design systems such that they can be used efficiently by the expected workloads, particularly, when the workload is very heterogeneous.Modular systems can help, deciding according to the user portfolio how much weight a particular module should get, and what connectivity is required within and between modules.To deal with these challenges, integrated projects that cover all levels of the HPC ecosystem are needed.Also, interoperability and exchangeability of components, both hardware and software, should be easier to give system designers and users, alike, more flexibility.This special issue calls for recent research which focused on the heterogeneous HPC for IDA, such as memory management, workload management for heterogeneous systems and so as the heterogeneity in storage technologies.

Abstract In recent era, the unmanned aerial vehicles (UAVs) commonly known as drones has emerged as a one of the most significant and promising tools which has demonstrated its wide range of implementations variating from commercial domain to the field of defense due to its distinct capabilities such as inspection, surveillance, precision and so forth. Internet connected drones provides a propitious trend that boosts the flying safety, and service qualities of the UAVs where numerous low‐altitude drones winged in different flying regions for executing a precise task such as gathering the real‐time information from the unuttered environment to be interpretated by users. Nevertheless, the open‐access insecure communications in hostile environment, the issues like safety and confidentiality threats, various security concerns such as the leakage of flying courses, identities, position, and gathered data by the drones are upstretched. To address these security concerns, access control mechanism provides a potential service in terms of authentication and key agreement for securing the communication between the individual drones within their respective flying regions. This article introduces a robust, efficient, lightweight, and privacy preserving ECC integrated access control approach by employing digital certificate with considering the high dynamicity and mobility of the drones. The designing of this proposed approach, that is, CLAASC‐IoD is influenced by the aim of inter‐drone and drone‐to‐ground station communication in the IoD paradigm. The detailed analysis of security using probabilistic random oracle model as well as simulation using well‐accepted security verification tool AVISPA and comparative performance evaluation supports the claim of robustness, effectiveness, and proficiency.

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.

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

Owing to the breakthrough in mobile wireless communication technologies, almost everyone has been immersed into social networks, while fake news and misinformation are also being pushed into people’s minds with astonishing speed and breadth. The rising disparity between limited computing resources and the exploding news size necessitates innovative solutions to handle the challenge posed by booming data volume and make it more likely to differentiate fake news. In response to the aforementioned dilemma, the social-aware computation offloading system is analyzed, where the digital twin (DT) paradigm is used to simulate tasks offloading and assess the associated costs. Next, to obtain the best offloading choice, we fully consider the social relationship constraints and further propose an online task execution method that includes two stages of cluster selection and computing offloading, named TOFDS. Specifically, it exploits the technologies from multiobjective optimization and deep reinforcement learning (DRL) and realizes the joint optimization of resource utilization, load balancing, service latency, and energy consumption. Eventually, the comparative experiments demonstrate that TOFDS performs well when dealing with fake news data and can adapt to changes in dataset size and service clusters.

Intelligent detection techniques for online spamming have been a hot concern in academia. Although much technical progress has been achieved in recent years, two aspects of challenges are still confronted by scholars. For one thing, spamming activities are accompanied by multisource attributes, such as behaviors and semantics. For another, spamming is a cross-platform activity, where multiple platforms are exploited simultaneously to expand the influential reach. The above circumstances actually make spamming detection tend to become a fuzzy detection task. Existing works typically consider one-sided attribute and lack cross-platform multifeature fusion, which limiting the effectiveness of detection. To handle the current challenges, this article proposes a cross-field deep learning-based fuzzy spamming detection approach via the collaboration of behavior modeling and sentiment analysis. First of all, a cross-field deep learning-based technical framework is put forward to implement multisource feature fusion from mixed context. It first extracts multisource features from single fields and then integrates them into a hybrid-field feature space. In addition, three cross-field datasets based on real-world social network datasets are constructed, and utilized in the evaluation of our proposed approach. The findings demonstrate that our proposal improves the detection accuracy by about 7% to 12%, in comparison to five other baseline approaches.

Presently, the Vehicular Ad-hoc Network (VANET) is very important for the entire traffic management system. The 5th Generation (5G) enables VANET and Intelligent Transportation Systems (ITS) to connect devices in the million/sqkm range with improved performance, incredible transmission speed (terabit), and less cost to serve a vast, transformative, and diverse automobile sector. To ensure security and avoid the free flow of information in highly scalable, dynamic 5G, there are many challenges to restrict unauthenticated users access and proper key agreements with fine-grained access control. Here, a lightweight biometrics-based dynamic Anonymous Key Agreement Scheme (AKAS) with a fine-grained authentication feature is proposed to address challenges related to the restriction of unauthenticated users access and proper key agreement with fine-grained access control specifically for Vehicle-to-Vehicle (V2V) communication in VANET. In the proposed scheme, registered and authorized users can access services or information as per access privilege only. We have simulated our scheme using Automated Validation of Internet Security Protocols (AVISPA), Simulation of Urban MObility (SUMO), OMNET$++$(Objective Modular Network Testbed in C$++$), and performed formal security analysis using the Real-or-Random (ROR) oracle model. Analysis and simulation results show that our scheme is secured against various well-known attacks. Further, we have compared the security and efficiency of our scheme with the existing schemes and found that our proposed protocol is more secure, lighter, 5G-friendly, scalable, and even faster than the other related schemes.

As a component of the Intelligent Transportation System (ITS), Internet of Vehicles (IoV) is becoming increasingly important in the management and construction of urban transportation as it can provide users with a range of applications related to traffic accident warnings, entertainment information, collaborative driving and real-time road information through communication devices on vehicles. However, with the increasing variety of services in the IoV, the growing demand for user traffic and the advances in Unmanned Aerial Vehicle (UAV) technology, UAV is introduced into the IoV as a solution, which can relieve the pressure on the communication infrastructure in the network, provide emergency communication services and improve the performance of network services. Due to the openness of IoV and the high-speed movement of vehicles, authentication and privacy issues are among the most pressing issues in IoV. Therefore, the paper proposes a secure and effective authentication protocol for UAV-assisted IoV. The protocol utilises elliptic curve cryptography to assure the security of the authentication. The protocol undergoes proof of security, Burrows-Abadi-Needham (BAN) logic analysis and informal security analysis to ensure secure and mutual authentication, and have a good resistance to known attacks. Furthermore, performance analysis and comparison are conducted to evaluate the efficiency of our protocol. The results indicate that our protocol has superior advantages in overhead.

The proliferation of Autonomous Vehicles (AVs) in recent times has opened up new possibilities for effective and secure transportation. However, with the increasing adoption of AVs, guaranteeing the accuracy and security of their sensory systems is becoming paramount. Specifically, the vulnerability of these systems to malware and sensor faults can pose significant risks to the dependable and secure operation of the vehicle. To identify and combat these issues we propose a stream-based Blockchain-powered Malicious Node Detection (BMND) method to analyze and report any malicious activity of the AV operating as a node on the Internet of Autonomous Vehicles (IoAV) network, wherein the existing solutions are at lower latency. BMND involves the detection of sensor anomalies and defects post-production of the AV. In the case that malware or any other malicious software is detected on the onboard compute unit it is isolated and contained, and the AV will be classified as malicious until appropriate remedial measures are taken to deter any sharing of erroneous or malicious data. When the AV is deemed safe from malware and defects in the system then a block is mined by the AV node and a unique ID is assigned to allow data transfers on the blockchain with other nodes. Only active nodes with assigned IDs and available blocks for transactions on the blockchain influence AV decision-making. BMND would allow modern AVs on the road to effectively communicate with reliable information. Experimental analysis shows that the malware detection in BMND is 4.4% more accurate with an F1-score of ~0.99 as compared to previous and other current state-of-the-art methods, and the communication capabilities of BMND are also better regarding security and latency concerning proposed vanilla blockchain methods.

Advances in the connected vehicle and cloud computing technologies, Big data, and artificial intelligence techniques have opened new research opportunities. We can integrate them to work out the issues originating from transportation complexities and offer improved services. In this work, we present a seamless multi-module multi-layer vehicular cloud computing system developed using resources of parked vehicles, cloud computing facilities, and vehicular networking technologies. It can offer transportation-specific AI and Big data-empowered services to on-road vehicles. As use cases, we present two innovative and improved services, vehicular Big data mining and vehicular route optimization. A physical testbed is formed to show the feasibility of this work. Results analysis shows that the systems perform better than the standalone systems and servers under different scenarios. Relevant fundamental challenges and future outlooks are also highlighted in this work.

Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a Computation Offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.

In an era of 5 G smart cities, precise traffic prediction remains elusive due to limited real-world data. Our paper introduces a novel approach using Generative Adversarial Networks (GANs) to create synthetic traffic data that closely mimics real-world statistics. This artificial dataset enhances our new 5GT-GAN-NET-based prediction model. The result is a significant boost in prediction accuracy, with Mean Square Error (MSE) reduced to 0.000346 and Mean Absolute Error (MAE) to 0.00685. Compared to benchmarks, our model improves MSE and MAE by up to 95.45% and 87.31%, respectively. User privacy remains a cornerstone of our approach, crucial for smart city applications. Our predictive capabilities enable more efficient resource allocation by service providers, increasing communication infrastructure reliability. Although tailored for smart cities, the approach is adaptable to other fields facing data scarcity and privacy concerns. Our research highlights the potential of GANs in generating large, accurate datasets for traffic prediction in 5 G environments while prioritizing user privacy.

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving data throughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, the distributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technology represents the further integration of deep learning and wireless communication. In this article, we propose Argute Distributed Uplink Beamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamic distributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/access point (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BS adopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditional distributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structure ensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM) compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an signal-to-noise ratio (SNR) gain of about 5 dB to 5.3 dB over the single vehicle transmission mode.

Recently, three-dimensional (3D) point-cloud analysis has been extensively utilized in the domain of machine vision, encompassing tasks include shape classification and segmentation. However the inherent disorder in point clouds poses a challenge in capturing relationships among points, particularly when dealing with mutilated and occluded data. To this end, We propose the Point Geometry Transformation (PointGT) method for 3D point-cloud classification and part segmentation, by exploring the underlying geometric structure in the local and global of points. Specifically, the efficacy of PointGT arises from the integration of a local abstraction (LA) module and an optimization strategy. The LA module is tailored to address the localized features inherent to point clouds. This module encapsulates the multidimensional attributes of local edge and inside points. The bi-directional cross-attention mechanism amalgamates these two constituents into the native channel with the primary objective of optimizing the exploitation of edge and inside delineations, thereby judiciously mitigating noise artifacts. Ultimately, the channel residual connections disseminate the postdownsampling point attributes, thereby inheriting the edge and inside delineations gleaned via post bi-directional attention. The effectiveness of the proposed method was verified through the validation of point-cloud classification and segmentation datasets. The empirical findings confirmed the efficacy of PointGT; accuracies of 93.2% and 87.8% were achieved for the ModelNet40 and ScanObjectNN datasets, respectively.

The automotive manufacturing industry faces challenges in production planning, but current heuristic algorithms and solvers have limitations in scalability and local optima. Moreover, data security concerns are often overlooked. To address these issues, this paper introduces the FAG-Scheduler, a federated reinforcement learning approach integrating asynchronous advantage actor-critic, gated recurrent unit algorithms, and federated learning. By sharing model parameters instead of raw data, data security is ensured among participants. The FAG-Scheduler achieves optimal solutions in under 5 seconds and demonstrates high adaptability to other manufacturing contexts. It presents potential applications with significant improvements over conventional methods.

Toward green Big Data System (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

Critical infrastructure is a collection of physical and cyber systems, which are essentially required to support the day-to-day operations of our daily life. In the critical infrastructure, the computing systems (i.e., Internet of Things (loT) devices) communicate through the Internet. Therefore, most of the time, critical infrastructures are targeted by hackers by launching some cyber-attack, i.e., ransomware. Hence we require some security mechanisms to protect the data and systems of critical infrastructures. This paper proposes a scheme for the detection, analysis and mitigation of ransomware attacks to protect Internet of Things (loT)-enabled critical infrastructure (in short, RADM-ICI). We also practically demonstrated RADM-ICI and computed essential performance parameters, i.e., accuracy and F1-score under different machine learning models. The conducted security analysis of RADM-ICI proved its excellent security for the ransom ware attacks. During the performance comparison of the proposed RADM-ICI and other similar competing existing schemes, it has been observed that the proposed RADM-ICI achieved better accuracy than the other existing competing schemes.

Machine learning methods are extensively used to develop new drugs and materials, and molecular big data plays a vital role in this process. A large amount of chemical molecules in documents should be well utilized for the construction of molecular big data. Although optical character recognition technology has been widely applied to extract molecular data from scanned images, the recognition accuracy should be improved because of the complexity and sparsity of molecular structure and the fuzziness of scanned molecular images. In this paper, a novel Transformer-based model is used to automatically extract molecular features from images. Furthermore, the extracted molecular features are translated into InChI descriptors by another Transformer model. The experimental results suggest that the proposed method outperforms other deep learning based methods including ResNet and LSTM (Long Short Term Memory). Moreover, the data extraction process is visualized so that the interpretability of the model could be guaranteed. This is significant in understanding the mechanism of molecule representation. Meanwhile, the results also demonstrate that the proposed method could extract molecular features in a fine granularity, such as atoms and chemical bonds.

Smart devices are increasingly important in daily life as they can provide information and capture usage behavior. This paper proposes a machine learning-based system to assist in human health management on smart devices. The system architecture includes a data layer, function layer, and application layer with the goal of helping individuals identify healthful vegetables best suited to their dietary needs. The proposed system utilizes the Extreme Learning Machine (ELM) algorithm to accurately recognize vegetable images. Compared to deep learning techniques, ELM has more efficient training and inference processes, making it better suited for smart device applications. The experiment with the collected vegetable image dataset found that the relu activation function and Gaussian distribution weight initialization method yielded optimal performance for the proposed system. Additionally, ELM outperformed deep learning techniques with small amounts of data. A case study was implemented on the Android platform to demonstrate the feasibility of the proposed system.

Social networks have become significant communication platforms for tracking user sentiments, interests, and activities, particularly during the COVID-19 pandemic when individuals have been confined to their homes. While static social contact networks during COVID-19 have been widely studied, the dynamic characteristics of social interactions have not been thoroughly investigated. In this paper, we model social interactions using dynamic graph methods and apply the proposed approach to analyze the social interactions within a Weibo dataset. Moreover, characteristics of dynamic graphs including node degree distributions are studied. Our analysis illustrates that social interaction frequencies are increasing and social interaction communities are forming as the COVID-19 pandemic continues to evolve. Furthermore, we show that the trend of social interaction frequencies is consistent with the trend of infected person numbers in the real world as compared by KL divergence. These results suggest that interventions targeting users with higher social interaction frequencies and users belonging to the same communities might aid in the development of effective epidemic prevention policies.

Vehicular Edge Computing (VEC) is gradually evolving into one of the most prevalent paradigms for vehicular computation. This is due to its ability for effectively handling the tasks of varied complexity. VEC based task orchestration has therefore emerged as an exciting research domain. A large number of task orchestration schemes have been proposed that exploit its technical capabilities. However, identifying the most appropriate vehicles for such edges still remain a challenge. In this work, we propose an intelligence based Task Orchestration Scheme integrated with Vehicular Cloud Edge Networks that uses Federated learning (FL) for forming vehicular edges. FL is a privacy preserving technique with no data being shared centrally. This scheme uses characteristics of vehicles such as computational capacity and their starting as well as ending point for creating the edges. Obtained results depict the improved performance of this scheme as compared to conventional schemes.

Millimeter wave (mmWave) technology provides abundant high-capacity channel resources for vehicular communications. However, the mobility of vehicles and the blocking effect of mmWave propagation brings new challenges to communication security. From the perspective of cooperative secure communication, this paper proposes a deep reinforcement learning (DRL)-based joint relay and jammer selection scheme in mmWave vehicular networks. The mmWave base station selects idle vehicles as relay transmission nodes to overcome the severe blocking attenuation of the multi-user downlink legitimate transmissions. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropper while the users are not disturbed. We utilize the asynchronous advantage actor-critic (A3C) learning algorithm to optimize the cooperative vehicle selection with the objective of maximizing the total secrecy capacity. Besides, we set the secrecy rate punishment mechanism to guarantee the secrecy performance of each vehicle. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance.

With the explosive growth of real-time applications in satellite systems, Time Sensitive Networking (TSN) is explored to be introduced to provide bounded low latency network services. Nevertheless, some efforts in delay ensuring techniques are ongoing, guaranteeing deterministic low latency communication in satellite networks is still a significant problem. In the article, we first present a Digital Twin-based TSN framework in which digital twin technology is introduced for the purpose of reducing the management cost and optimizing the performance of satellite networks. The virtual model of the scheduling method working in satellite systems is explored and created for the simulation and prediction of the forwarding delay results. Deep convolution generative adversarial network (DCGAN) is adopted to train the scheduling model. The simulation experiments verified that the digital twin could mirror the scheduling behavior and predict the delay in dynamic environments.

Integrated space-air-ground network (ISAGN) has been regarded as an important infrastructure of the next-generation network, which can offer massive access and seamless connections for users in a wide coverage area. This paper utilizes non-orthogonal multiple access (NOMA) technique to improve the spectrum efficiency of the ISAGN. Besides, two-way relay technique is introduced in ISAGN to boost the spectrum efficiency. Then, we conducted the ergodic capacity of two-way unmanned aerial vehicle (UAV)-aided ISAGN with NOMA. We first briefly establish a two-way UAV-aided ISAGN, by considering the imperfect channel state information (CSI) and successive interference cancellation (SIC). To obtain deeper insights, the closed-form expression of ergidic capacity for the considered system is derived. Finally, numerical simulations are provided to evaluate the performance of the system and reveal the impacts of imperfect factors.

Integrated sensing and communication (ISAC) has been seen as a promising technology to satisfy the dual requirements of communication and sensing for the emerging applications in the next-generation wireless networks. In this paper, we research one down-link multi-cell orthogonal frequency division multiple access (OFDMA) ISAC system, in which a group of collaborative ISAC base stations send signals to their corresponding communication users, and concurrently work with multiple sensing receivers to estimate locations of multiple targets. Specifically, we investigate the joint sub-channel assignment and power allocation for users and targets to maximize the sum-rate, while ensuring the minimal signal-to-interference-plus-noise ratio (SINR) constraint for each user and the maximal Cramer-Rao lower bound (CRLB) requirement for each target. We propose a deep reinforcement learning (DRL) approach to address the above sub-channel assignment and power allocation problems. In our approach, we adopt the dueling deep Q network (DDQN) and the deep deterministic policy gradient (DDPG) network to output the sub-channel assignment policy and power allocation policy separately. Simulation results aim to prove the effectiveness of our proposed algorithm.

There has been exponential growth in the field of Internet of Things (IoT)-enabled e-health domain, where several technologies are interconnected with each in order to provide low-cost and efficient services to users. The smart healthcare devices monitor the physiological conditions of a patient and then send data to connected servers (i.e., health server). The smart healthcare devices, servers and associated software applications come under one umbrella to provide live tracking, monitoring and analysis of the the healthcare data to the concerned users. It is also considered as Internet of Medical Things (IoMT) communication environment. All medical records are considered critical and sensitive in nature and therefore any leakage of medical data could potentially turn out to be a the lethal to patients. With the innovation in technology, there is a constant security threat to all smart healthcare devices, which is a main issue in e-health system. Cyber threat actors are actively trying to break into the system or network to gain unauthorized access and privileges to manipulate the data. Therefore, there is a strong urge for cyber security in this domain to secure all computing devices from any computer attack. Proper authentication, authorization, a secure session key exchanges, etc., are required to create a secure channel among the communicating devices. In this paper, an authentication and key agreement scheme to secure the communication of e-health system (named as ASKA-EH, in short) is proposed. The provided security analysis of ASKA-EH proves its security against various attacks. The comparative performance analysis of proposed ASKA-EH and other existing schemes of ehealth system reveals that ASKA-EH is superior than the existing schemes.

The reduced-capability new radio (NR RedCap) was introduced in 3GPP Rel-17 to cater to the use cases that are not yet best served by current NR specifications, such as smart grid and industrial wireless sensors. For the grant-free access system in smart grid, the resistance to impulse noise is a key issue. By using fractional low-order covariance and constant modulus based tensor decomposition, this paper skillfully enables user identification in this scenario while suppressing the effect of impulse noise. The proposed scheme uses spread spectrum signal as the pilot signal. And the user identity is represented jointly by the spread spectrum sequence and information codes. In this condition, we start by transforming the pilot signals into a tensor. The fractional low-order covariance is then used to suppress the impulse noise, and the constant modulus is used to improve the performance of the algorithm during the iterative process of tensor decomposition. Finally the sensor identity is confirmed by the decomposition result. Simulation results show that the proposed scheme can greatly improve the performance of user identification under impulse noise channel. Specifically, the identification rate of the proposed algorithm valued 99.815% outperformed that of AMP valued 89.1471% when generalized signal-to-noise ratio GSNR = 0 dB. In addition, the proposed scheme can also correctly estimate the channel gain from the sensors to the base station in impulsive noise environment.

For the grant-free random access system in the Internet of Thing (IoT) scenario, the recovery of the pilot sequence and the identification of the IoT device is a crucial issue. Contrapose the problem that the existing grant-free access schemes cannot accurately recover the pilot sequence in the intensive industrial zone with impulse noise, this paper proposes to use spread spectrum signal as pilot signal and proposes an estimation algorithm based on joint k-means and M estimation accordingly. This algorithm dynamically suppresses the influence of noise with adaptive weighted function according to the estimated noise energy in the iterative process. First, the received signal is segmented to obtain samples. Second, the samples are clustered using the K-means algorithm. In the iterative process of the algorithm, cluster centers are used to estimate the energy of signal noise. According to the estimation result of the noise energy, the adaptive weighted function is used to dynamically update the cluster centers and the similarity between samples and cluster centers. Finally, assigning +1 or −1 to the samples according to the clustering results, and then the estimation of pseudo-code sequence (PN sequence) is realized while impulse noise is suppressed. Simulation results show that the proposed algorithm can greatly improve the performance of PN sequence estimation under impulse noise channel. The bit error ratio (BER) of the proposed algorithm valued 0.008 outperformed that of EVD valued 0.3 when the generalized signal-to-noise ratio (GSNR) is −4dB. In particular, the proposed algorithm has better performance when the noise distribution has heavier tails, which is different from traditional algorithms.

Abstract Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time‐ (GEOTime) based condition alongside Priority Attribute role‐based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME‐PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud.

The main challenge faced by video‐based real‐world anomaly detection systems is the accurate learning of unusual events that are irregular, complicated, diverse, and heterogeneous in nature. Several techniques utilizing deep learning have been created to detect anomalies, yet their effectiveness on real‐world data is often limited due to the insufficient incorporation of motion patterns. To address these problems and enhance the traditional functionality of anomaly detection systems for surveillance video data, we propose a weakly supervised graph neural‐network‐assisted video anomaly detection framework called AD‐Graph. To identify temporal information from a series of frames, we extract 3D visual and motion features and represent these in a language‐based knowledge graph format. Next, a robust clustering strategy is applied to group together meaningful neighbourhoods of the graph with similar vertices. Furthermore, spectral filters are applied to these graphs, and spectral graph theory is used to generate graph signals and detect anomalous events. Extensive experimental results over two challenging datasets, UCF‐Crime and ShanghaiTech, show improvements of 0.35% and 0.78% against a state‐of‐the‐art model.

The industrial demands of immersive videos for virtual reality/augmented reality applications are crescendo, where the video stream provides a choice to the user viewing object of interest with the illusion of “being there.” However, in industry 4.0, streaming of such huge-sized video over the network consumes a tremendous amount of bandwidth, where the users are only interested in specific regions of the immersive videos. Furthermore, for delivering full excitement videos and minimizing the bandwidth consumption, the automatic selection of the user’s Region of Interest in a 360° video is very challenging because of subjectivity and difference in contentment. To tackle these challenges, we employ two efficient convolutional neural networks for salient object detection and memorability computation in a unified framework to find the most prominent portion of a 360° video. The proposed system is four-fold: 1) preprocessing; 2) intelligent visual interest predictor; 3) final viewport selection; and 4) virtual camera steerer. First, an input 360° video frame is split into three Field of Views (FoVs), each with a viewing angle of 120°. Next, each FoV is passed to the object detection and memorability prediction model for visual interestingness computation. Furthermore, the FoV is supplied as a viewport, containing the most salient and memorable objects. Finally, a virtual camera steerer is designed using enriched salient features from YOLO and LSTM that are forwarded to the dense optical flow to follow the salient object inside the immersive video. Performance evaluation of the proposed system over our own collected data from various Websites as well as on public data sets indicates the effectiveness for diverse categories of 360° videos and helps in the minimization of the bandwidth usage, making it suitable for industry 4.0 applications.

The exponential growth of the Internet of Everything (IoE), in recent times, has revealed many underlying security vulnerabilities of the nodes forming IoE networks. The extension of conventional security protocol to these devices has been greatly complicated by the prevalence of restricted computational hardware and limited battery life. Modern learning-based algorithms have shown the potential to secure the IoE networks without undue duress on the nodes’ limited capabilities. In this article, a machine learning-based architecture has been proposed to identify malicious and benign nodes in an IoE network operating with big data. A novel approach for the cooperation of XGBoost and deep learning models along with a genetic particle swarm optimization (GPSO) algorithm to discover the optimal architectures of individual machine learning models has been proposed. Through simulations, it is shown that GPSO-based learning algorithms provide reliable, robust, and scalable solutions. The proposed model significantly outperforms other security protocols in the classification of malicious and benign nodes forming an IoE network.

Blockchain (BC) and artificial intelligence (AI) technologies have independent applications in multiple industries, including banking, finance, healthcare, construction, transportation, hospitality, manufacturing, and insurance, to name a few. Moreover, these two technologies can be integrated seamlessly, thanks to their complementary and mutually supportive features. AI algorithms can make the medical BC storage efficient by their processing algorithms, also playing the role of knowledgeable gatekeepers. BC can support AI models by providing secure, sizeable, traceable, diverse, and immutable healthcare data for the training purpose. The integration of BC and AI has multiple use cases in the healthcare industry ranging from disease prediction to pandemic management. Previously, researchers have reviewed the applications of each of these technologies in healthcare independently. Although the integration of BC and AI has been fruitful, to the best of our knowledge, there has been no work in the past reviewing the confluence of these two technologies in the healthcare sector. We have classified the works based on two different classification schemes: 1) application-based and 2) AI-training paradigm-based classification. We have also provided a compilation of tools used in the integrated systems of BC and AI for healthcare. We identified that the integration of BC and AI technologies had been applied in quite different areas of healthcare ranging from biomedical research to pandemic management. It is also noted that the supervised learning algorithms and federated learning paradigm for secure decentralized AI model training are often used in the integration. Our findings reveal that majority of the reviewed works use BC as a secure database for AI models. Furthermore, we also have pointed out the potential applications of these two technologies in healthcare.

Digital twins (DTs), defined as the virtual representation of a real-world entity or system, act as a mirror to provide a way to simulate, predict physical behaviors, and possibly control the real-world entity where applicable. Originating in the industry, advances in computing capacity and recent progress in artificial intelligence (AI)-based analytics make DTs attractive to a broader set of use cases including mobile networks.

6G communication networks are expected to become an integral part of the infrastructure needed for developing a smart society in the future. Addressing the challenges on the road towards realizing 6G network requirements in terms of quality of service, user experience, and security, is therefore of utmost importance. The digital twin (DT) technology can potentially improve the efficiency, reliability, and security of 6G networks. Digital twins for mobile networks (DTMNs) are seen as a key factor in harnessing the full benefits of 6G. Using digital twins can help address several problems, including network optimization, fault diagnosis, and fault management. Furthermore, DTMNs can characterize the physical entities in a 6G network and their relationships to each other, build their virtual models, and use simulation, learning, and reasoning capabilities to make predictions and support informed decision-making,

Abstract Parkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.

The current advances in wearable sensors show the shining future of socially implemented Internet-of-Medical-Things (IoMT) devices (e.g., smartwatches). However, the recent machine learning approaches cannot be applied well in these devices, because almost all the processing in the IoMT devices is now being performed in classic forms (mainly as centralized computing) or based on cloud services. This topical collection has tried to extend our knowledge about how to apply collaborative learning to IoMT considering social edge/fog nodes’ facilities.

Fog computing-based Internet of Things (IoT) architecture is useful for various types of delay efficient network communications and services, like digital healthcare. However, there are privacy and security issues with the fog computing-based healthcare systems, which can further increase the risk of leakage of sensitive healthcare data. Therefore, a security mechanism, such as access control for fog computing-based healthcare systems, is needed to protect its data against various potential attacks. Moreover, the blockchain technology can be used to solve the digital healthcare’s data integrity related problems. The use of Artificial Intelligence (AI) further makes the system more effective in case of prediction of health related diseases. In this paper, an AI-enabled secure communication mechanism in fog computing-based healthcare system (in short, AISCM-FH) has been proposed. The security analysis of the proposed AISCM-FH is provided using the standard random oracle model and also with the heuristic (non-mathematical) security analysis. A pragmatic study determines the impact of the proposed AISCM-FH on key performance indicators. Moreover, we include a detailed performance comparison of AISCM-FH with other relevant existing schemes to show that it has low communication and computation costs, and provides superior security and extra functionality attributes as compared to those for other competing existing approaches.

Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction.

Vehicle Edge Computing (VEC)-assisted computational offloading brings cloud computing closer to user equipment (UEs) at the edge of the access network by delivering various services to the UEs with limited processing power and battery. However, in fifth-generation and beyond 5G (B5G) networks, where UEs’ service requests and locations change dynamically, the deployment of static edge server deployments may lead to an increase in latency and total energy consumption. This paper presents a latency-energy-aware, efficient task offloading scheme for connected autonomous vehicular networks. Firstly, vehicles are assembled into clusters, in which vehicle can transmit tasks to the other vehicle, while on the other hand, the VEC server is used for processing the data. We developed a joint resource allocation and offloading decision optimization problem to minimize network latency and total energy usage. Due to the non-convex character of the optimization issue, we employed the Markov decision process (MDP) to convert it to a reinforcement learning (RL) problem. Then, we used a soft-actor critic-based scheme to achieve the optimal policy for resource allocation and task offloading to reduce the total latency and energy consumption for connected autonomous vehicles. Simulation analysis reveals that the proposed scheme attains 46.6% and 17.2% lesser delay, and 28.8% and 20.0% consumes less energy than the Hybrid DRL with Genetic Algorithm (HDRL-GA) and DRL based collaborative Data Scheduling (DRL-CDSS) state-of-art schemes.

The evolution and rapid adoption of the Internet of Things (IoT) led to a rise in the number of attacks that target IoT environments. IoT environments are vulnerable to several attacks because many devices lack memory, processing power, and battery. Most of these vulnerabilities are relatively easy to mitigate when best practices are followed. However, even when best practices are followed, an attack to obtain a device credential and use it to generate false data is difficult to detect. Such an attack is called a replication attack and its impact can be catastrophic in crucial IoT scenarios such as smart transportation. In this sense, this paper proposes a solution to detect these attacks by analyzing abnormal network traffic through machine learning.

The Information Centric Networking (ICN) is a future internet architecture to support efficient content distribution in a vehicular environment. In-network caching in ICN provides a realistic solution for vehicular communication due to storage of content replicas inside network vehicles. However, the challenge still exists while caching content replicas in resource constraint vehicles (such as limited power and cache capacity) to minimize the communication latency. To address the above mentioned challenge, this paper proposes EPC - an ICN based Energy efficient Placement of Content chunk that fits well in a vehicular environment. The proposed resource management strategy mainly aims to reduce the content fetching delay by caching content replicas towards the network edge router. The EPC strategy decides on placement of content chunks on each vehicle by jointly considering residual power of current vehicle, local popularity of content, and caching gain. The EPC supports efficient utilization of network available resources by allowing only vehicles with their residual power greater than threshold to perform chunk caching and hence, further offers reduced content duplication in the whole network. The effectiveness of the proposed scheme is evaluated in Icarus- an ICN simulator for analyzing the performance of ICN caching and routing strategies. The EPC outperforms various state of the art caching strategies approximately by 30% when gets evaluated in terms of offered cache hit ratio, content retrieval delay, and the average number of hops utilized for fetching the requested content.

In this paper, we investigate the auxiliary vehicle positioning system and localization method for intelligent transportation systems, as a supplement to the Global Navigation Satellite System which is prone to large positioning deviations and even failures in occluded scenes such as urban canyons and tunnels. The time modulated antenna arrays are first introduced into the positioning system, avoiding mutual coupling between antennas and greatly reducing the hardware cost of the vehicle terminal. The auxiliary positioning framework for the smart vehicle is advocated in conjunction with existing radio frequency signals. To take full advantage of the multiple auxiliary sources around the road net, Doppler shifts embedded into the received signals are unearthed, and a smoothed block sparse reconstruction is developed for directly locating the vehicle, providing significant enhancements of degrees of freedom and the localization accuracy. Additionally, the proposed direct localization method is robust to multichannel gain and phase mismatch in practice, and the array perturbations can be estimated and compensated without any calibration source. Extensive simulation results corroborate that the proposed system and method achieves superior localization accuracy (approximately 0.22 m error at SNR of 10 dB), outperforming its state-of-the-art counterparts.

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.

Internet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices’ connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution asCerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm.

The decentralization and transparency have provided wide acceptance of blockchain technology in various sectors through numerous applications. The claimed security services by blockchain have been proved using various cryptographic techniques, mainly public key infrastructure and digital signatures. However, the use of generic cryptographic primitives using large prime numbers or elliptic curves with logarithms is going to be an issue with quantum computers as those techniques are vulnerable in post-quantum era. Therefore, the paradigm shift from pre-quantum to the post-quantum era has necessitated new cryptographic developments which are robust against quantum attacks and applicable in blockchain for post-quantum decentralization. Therefore, we have presented a solution for post-quantum decentralization in the blockchain. It uses lattices with polynomials for identity-based encryption (IBE) and aggregate signatures for the consensus to ensure efficiency and suitability in post-quantum blockchain applications. We experiment the proposed approach based on delay, throughput, energy consumption and complexity. The comparative results prove that the presented work is efficient.

The fast-growing Internet of Things (IoT) have generated a vast number of IoT tasks, and these tasks are usually featured by strict response latency requirements. To cater for the time-sensitive IoT application scenarios, vehicular fog computing (VFC) can be adopted to serve the offloading requests from the IoT devices. However, current works in VFC seldom consider the task execution failures that are actually inevitable owing to limited computing resources in VFC compared to cloud computing. Hence, we strive to enhance the VFC system by incorporating the failures for task execution into our system model, which makes task offloading more general and practical. We formulate our energy consumption optimization as a mixed integer nonlinear programming problem and further put forward an iterative algorithm to solve it. We validate our approach by extensive simulation and the experimental results have proven its advantages in terms of the optimal values.

A lack of well-designed security solutions within the millimeter-Wave (mmWave) cellular vehicle-to-everything (V2X) communications significantly impedes the development of applications within the intelligent transportation system. Cooperative jamming is envisioned as a potential technology that can enhance physical layer security performance for plane networks by selectively choosing jammers from the perspective of the legitimate receiver. We propose a blockage-and-power-based jammer selection strategy to address potential security pitfalls in a mmWave cellular V2X network. With the help of jammers whose interference power falls within the acceptance range of legitimate receivers, transmission confidentiality is secured simultaneously without escalating the instability of connections caused by the time-varying nature of V2X networks. We derive the theoretical expression of secrecy outage probability and secrecy throughput based on our preliminary analysis of association probability from the stochastic geometry approach. Numerical results demonstrate that the proposed secure transmission scheme outperforms other cooperative jamming schemes in terms of secrecy throughput.

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving data throughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, the distributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technology represents the further integration of deep learning and wireless communication. In this paper, we propose Argute Distributed Uplink Beamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamic distributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/access point (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BS adopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditional distributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structure ensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM) compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an SNR gain of about 5dB to 5.3dB over the single vehicle transmission mode.

MOOCs can be used to provide specialized and continuing medical education in times of Covid-19. The procedure to evaluate the satisfaction of this MOOC aimed at primary care health professionals for the detection and management of suicidal risk had descriptive statistics, Cronbach's Alpha, and CHAID analysis (Chi-square Automatic Interaction Detector) to find the factor that most influenced the satisfaction of this course. This evaluation was complemented with thematic analysis. CHAID analysis of this MOOC course, the satisfaction of 53% Excellent was explained by the Course Content Assessment with a value of P <.001. The results of the thematic analysis were that 75% of the learning obtained corresponds to the general objective of the course. 53% of the most relevant topics of this MOOC were considered useful and of interest to their profession. Health professionals liked the final interview and the practical cases, they requested more real cases to better manage the risk of suicide. The achievement of the objective of this MOOC helps to contribute to the prevention of suicide. We can learn that this type of course is feasible at a technological level and that it requires a great commitment or interest from health professionals to carry it out satisfactorily in times of Covid-19.

With a subtle rise in the vehicular traffic, it has become necessary for the users to be more aware of their surroundings. Vehicular AdHoc Network (VANET) provides a solution to connect these vehicles with the data centers to leverage this information for the benefit of the users. It renders end-to-end details for traffic flow monitoring and dynamic route scheduling of vehicles to prevent unforeseen incidents. Current researches suggest that numerous IoV architectures authenticate the vehicles enabling inter-vehicular communication. To improve the quality of service, this work leverage vehicle-to-roadside communication, and priority based batch-verification system. Further, to ensure safer computations of time-critical information and faster processing of event-driven messages, vehicular edge computing based hierarchical framework is proposed. The security analysis proves that this framework can substantially improve power consumption and mitigate security issues such as traceability, identity privacy-preserving, replay attacks, denial of service attacks, and impersonate attacks.

Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.

Video surveillance has shown encouraging outcomes to monitor human activities and prevent crimes in real time. To this extent, violence detection (VD) has received substantial attention from the research community due to its vast applications, such as ensuring security over public areas and industrial settings through smart machine intelligence. However, because of changing illumination, complex background and low resolution, the analysis of violence patterns remains challenging in the industrial video surveillance domain. In this paper, we propose a computationally intelligent VD approach to precisely detect violent scenes through deep analysis of surveillance video sequential patterns. First, the video stream acquired through the vision sensor is processed by a lightweight convolutional neural network (CNN) for the segmentation of important shots. Next, temporal optical flow features are extracted from the informative shots via a residential optical flow CNN. These are concatenated with appearance-invariant features extracted from a Darknet CNN model. Finally, a multilayer long short-term memory network is plugged to generate the final feature map for learning the violence patterns in a sequence of frames. In addition, we contribute to the existing surveillance VD data set by considering its indoor and outdoor scenarios separately for the proposed method's evaluation, achieving a 2% increase in accuracy over surveillance fight data set. Experiments also show encouraging results over the state of the art on other challenging benchmark data sets.

Contrary to the public cloud storage services that impose users to accept the security restrictions delivered by the service provider, users in the private cloud benefit from self-managed, authenticated data access services. However, this may lead to security issues. A critical challenge is the provision of secure and authenticated data storage for the data owner. Moreover, the data owner should be able to access the stored data and share it with others in a controlled manner. In this article, a secure and authenticated data storage, access, and sharing model is proposed for private cloud storage, which has three components. The data storage component provides the user with secure storage of information. The data-sharing component enables sharing the stored data under the control of the data owner. The data access component enables authenticated access to the cloud storage. The security analysis demonstrates that the model is secure against various attacks. The scheme is validated to be secure via the Scyther tool, BAN Logic, and in Random Oracle Model. The performance analysis regarding the computation and communication cost via simulation in OMNeT++ show that it obtains the required security goals and efficiency of computation and communication, compared to the related methods.

Emerging next-generation Internet yields proper administration of a wide-ranging dynamic network to assist rapid ubiquitous resource accessibility, whilst providing higher channel bandwidth. Since its inception, the traditional static network infrastructure-based solutions involve manual configuration and proprietary controls of networked devices. It then leads to improper utilization of the overall resources, and hence experiences various security threats. Although transport layer security (TLS)-based solution is presently advocated in the said framework, it is vulnerable to many security threats like man-in-the-middle, replay, spoofing, privileged insider, impersonation, and denial-of-service attacks. Moreover, the current settings of the said tool do not facilitate any secure and reliable mechanisms for data forwarding, application flow routing, new configuration deployment, and network event management. Also, it suffers from the single point of controller failure issue. In this article, we propose a new private blockchain-enabled fine-grained access control mechanism for the SDN environment. In this regard, attribute-based encryption (ABE) and certificate-based access control protocol are incorporated. This proposed solution can resist several well-known security threats, and alleviate different system-level inconveniences. The formal and informal security inspections and performancewise comparative study of the proposed scheme endorse better qualifying scores as compared to the other existing competing state-of-the-art schemes. Besides, the experimental testbed implementation and blockchain simulation show the implementation feasibility of the proposed mechanism.

Internet of Things (IoT) is considered nowadays as the most important and indispensable support to ensure all types of communication, over almost all sectors of activities. The specificity of each area, as well as its own requirements in terms of Quality of Service (QoS), make this communication difficult to ensure and thus, face multiple challenges. One of these challenges is related to the needed autonomy for IoT, not only in terms of available resources (energy and bandwidth for example) but also in terms of self-configuring and self-organizing within the network. It is in this context that we propose a new cross layers approach for better self-configuring and self-organizing of devices and communications within IoT environments. The proposed approach is named 2SAEC-IoT (self-organizing and self-configuring algorithms for efficient communications within IoT) that leads to guarantee an efficient data communication for IoT applications. 2SAEC-IoT is a cross layers solution since it considers important communication parameters related to three levels which are MAC, network, and transport. The proposed approach allows the continuity of services for IoT applications, especially for those with very sensitive data (e-health for example), by tolerating possible communications failures or devices breakdown. The evaluation of the proposed approach shows a clear improvement in terms of QoS, and energy efficiency compared to those obtained by three other IoT networks using different communication algorithms.

With the increasing importance of data security, privacy protection has gradually risen to a strategic position, especially IoT data privacy protection. The concern for data security has become a national strategy. The discovery of potential risks of privacy data is of great significance, such as the risk of data privacy leakage, data security vulnerabilities, etc. In this article, starting from the privacy data protection mechanism in the Industrial Internet of Things (IIoT) scenario, we proposed a method based on generative adversarial imitation learning (GAIL) to discover the privacy data security risks in IIoT by training privacy protection agents using a large amount of expert data on privacy protection. Finally, our proposed method is validated by relevant simulation experiments, and the results show that our proposed method has wide generalizability and reliability to obtain the maximum payoff of the agents and thus, reduce the risk of data security leakage.

With the exponential growth of connected Internet of Things (IoT) devices around the world, security protection and privacy preservation have risen to the forefront of design and development of innovative systems and services. For low-value IoT devices that identify and track billion of goods in various industries—such as radio-frequency identification (RFID) tags—this involves multiple challenges in very constrained environments. IoT devices aim to design low-cost, low-complexity infrastructure while enabling robust authentication protocols with reduced latency and energy consumption. Given these challenges, in this article, we present a new lightweight authentication protocol for IoT applications, employing an authenticated-encryption (AE) cryptosystem with associated data (AEAD). Since AEAD algorithms provide data confidentiality and message integrity simultaneously, security analysis [Real-or-Random (RoR) and Scyther] results prove the robustness of the proposed protocol against IoT threats. Furthermore, to measure the computation and communication cost, FPGA and ASIC simulations using four different AEAD candidates of National Institute of Standards and Technology (NIST) lightweight cryptography competition are executed. The implementation results [e.g., 4744 gate equivalent (GE) and 0.87-mw power] clearly show that our novel design can be applied to a wide range of constrained IoT devices complying with low-cost, lightweight, and high-speed requirements.

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.

A digital-twin (DT)-enabled Internet of Medical Things (IoMT) system for telemedical simulation is developed, systematically integrated with mixed reality (MR), 5G cloud computing, and a generative adversarial network (GAN) to achieve remote lung cancer implementation. Patient-specific data from 90 lung cancer with pulmonary embolism (PE)-positive patients, with 1372 lung cancer control groups, were gathered from Qujing and Dehong, and then transmitted and preprocessed using 5G. A novel robust auxiliary classifier GAN (rAC-GAN)-based intelligent network is employed to facilitate lung cancer with the PE prediction model. To improve the accuracy and immersion during remote surgical implementation, a real-time operating room perspective from the perception layer with a surgical navigation image is projected to the surgeon’s helmet in the application layer using the DT-based MR guide clue with 5G. The accuracies of the area under the curve (AUC) of our new intelligent IoMT system were 0.92 and 0.93. Furthermore, the pathogenic features learned from our rAC-GAN model are highly consistent with the statistical epidemiological results. The proposed intelligent IoMT system generates significant performance improvement to process substantial clinical data at cloud centers and shows a novel framework for remote medical data transfer and deep learning analytics for DT-based surgical implementation.

The advent of vehicular edge computing (VEC) has generated enormous attention in recent years. It pushes the computational resources in close proximity to the data sources and thus, caters for the explosive growth of vehicular applications. Owing to the high mobility of vehicles, these applications are of latency-sensitive requirements in most cases. Accordingly, such requirements still pose a great challenge to the computing capabilities of VEC, when these applications are outsourced and executed in VEC. Against this backdrop, we propose a new mathematical model, which, respectively, generalizes the computation and communication models, and applies application-oriented caching into VEC in this article. Based on this model, a new strategy is further proposed to optimize the average response time of applications over an infinite time-slotted horizon for VEC. A long-term energy consumption constraint is imposed to guarantee the stability of the VEC system, and the Lyapunov optimization technology is adopted to tackle this constraint issue. Two greedy heuristics are put forward to help find the approximate optimal solution in the drift-plus-penalty-based algorithm. Extensive experiments have been conducted to evaluate the response time and energy consumption in the caching-assisted VEC. The simulation results have shown that the proposed strategy can dramatically optimize the average response time while satisfying the long-term energy consumption constraint.

Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this article, software-defined network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN-assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency.

The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption and decryption is usually directly stored and managed by users or third-party organizations, which has security and privacy implications. To address this potential security and privacy risk, we propose a Shamir threshold cryptography scheme for IIoT data protection using blockchain: STCChain. Specifically, in our solution, the edge gateway uses a symmetric key to encrypt the data uploaded by the IoT device and stores it in the cloud. The symmetric key is protected by a private key generated by the edge gateway. To prevent the loss of the private key and privacy leakage, we use a Shamir secret sharing algorithm to divide the private key, encrypt it, and publish it on the blockchain. We implement a prototype of STCChain using Xuperchain, and the results show that STCChain can effectively prevent attackers from stealing data as well as ensuring the security of the encryption key.

Summary Internet of things environment is adopted widely in different industries and business organizations with varying capacity. It provides a favorable environment to outsource the crowdsourced data in the cloud to minimize the cost of computation, which is called crowdsourcing. Crowdsourcing is a technique where individuals or organizations obtain goods and services. A professional or industry outsource the crowdsourced data in the cloud, where confidentiality and authenticity of data become essential. Signcryption is the cryptographic technique that serves both the authenticity and the privacy of transmitted messages. This technique ensures secure authentic data transmission and storage. Therefore, this paper proposes an identity‐based signcryption scheme. In the proposed PSSCC framework, the user does pairing free computation during signcryption, which makes efficient calculation on user‐side. Moreover, PSSCC framework is proved secure under modified bilinear Diffie‐Hellman inversion and modified bilinear strong Diffie‐Hellman problems. The performance analysis of PSSCC with related schemes indicates that the proposed system supports efficient communication along with less computation cost.

The papers in this special section focus on demand response applications in cloud computing technologie.s The use of distributed energy resources for self-generation and self-consumption along with Information and Communications Technologies and the Internet of Things is rapidly increasing the ability of the consumers and prosumers to actively engage with the electric energy system. Sustained consumer and prosumer engagement in demand response programs has been identified as a key factor in future electric energy systems, especially with a high penetration of renewable energy sources. This engagement has allowed demand-side resources to play a larger role in energy and reserve markets, whether by generating, storing or participating in demand response programs through increased flexibility, towards the consumer-driven energy transition. However, in real life, there is still a long way to go until demand response solutions take off and become entirely integrated into the daily life of the consumers, thus utilizing their full potential. Stronger engagement of consumers and prosumers is needed, as well as more flexibility services for system operation, benefiting Smart Grid developments.

This article proposes a SaTya scheme that leverages a blockchain (BC)-based deep learning (DL)-assisted classifier model that forms a trusted chronology in fake news classification. The news collected from newspapers, social handles, and e-mails are web-scrapped, prepossessed, and sent to a proposed Q-global vector for word representations (Q-GloVe) model that captures the fine-grained linguistic semantics in the data. Based on the Q-GloVe output, the data are trained through a proposed bi-directional long short-term memory (Bi-LSTM) model, and the news is classified as real-or-fake news. This reduces the vanishing gradient problem, which optimizes the weights of the model and reduces bias. Once the news is classified, it is stored as a transaction, and the news stakeholders can execute smart contracts (SCs) and trace the news origin. However, only verified trusted news sources are added to the BC network, ensuring credibility in the system. For security evaluation, we propose the associated cost of the Bi-LSTM classifier and propose vulnerability analysis through the smart check tool for potential vulnerabilities. The scheme is compared against discourse-structure analysis, linguistic natural language framework, and entity-based recognition for different performance metrics. The scheme achieves an accuracy of 99.55% compared to 93.62% against discourse structure analysis. Also, it shows an average improvement of 18.76% against other approaches, which indicates its viability against fake-classifier-based models.

Smart connected vehicles are becoming standardized with the incorporation of information and communication technology. Connected vehicles are employed for surveillance and management of road traffic, navigation assistance, etc., by inheriting different analytical and communication techniques. With the Social Internet of Things (SIoT), interogrowthperable and shared computing models are adopted by the connected vehicles to perform application-specific decisions. By considering the need for computation models in smart connected vehicle networks, this article introduces a shared adaptive computing model (SACM) for improving the reliability of vehicle control and traffic management. This computing model considers multiple features of the in-range vehicles in detecting traffic and providing guided solutions for reliable routing in a smart city environment. This computing model is aided by the conditional support vector machine (SVM) for differentiating the complexity of multiflow data processing from the neighboring vehicles. The physical and connectivity-based factors from the smart vehicle using SVM classification learning improve the decision reliability and reduce the computing time and complexity.

With the massive growth of Internet-of-Things (IoT) devices, how to provide users with recommendation services in the IoT environment has become a research hotspot. Group discovery, as a prerequisite step of group recommendation that can be used to assist groups of users to select services in IoT-enriched environments, has an important impact on recommendation performance. However, existing group recommendation solutions assume that a user belongs to a specific group and ignore the possible correlation between the user’s preferences and other groups’ preferences. In addition, existing solutions treat group members as equal individuals and assign them equal weights, which makes it hard to meet the user’s accurate recommendation requirements. Furthermore, these methods focus on group members’ explicit preference information while ignoring implicit preferences. To address these problems, we propose a group discovery method based on collaborative filtering and knowledge graph (GD-CFKG). This method first uses the attention mechanism to learn the embedding of service entities from knowledge graphs and interaction between users and services to achieve users’ own preferences embedding. Considering that the preferences of similar users will help to attain accurate target user’s preferences, we then train users’ final preferences embedding by collaborative filtering and word2vec method. We conduct experiments to evaluate our approach using the MovieLens and Douban data sets. Experimental results show that our proposed method has better group recommendation performance than those baseline methods.

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one of the potential solutions. Distributed intelligence can greatly relieve server pressures via plenty of terminal devices, and these devices collaboratively perceive and handle the mass data to improve the reliability, s-calability and security of industrial IoT systems. As future IoT system will embrace more wireless sensors and devices, the high-performance computing, high-bandwidth and low-latency communication are excessively required, many new research opportunities and challenges for distributed intelligence over Internet of things have arisen. To promote the development of distributed intelligence technology, this special section (SS) focuses on various technologies and platforms regarding industrial IoT systems. This special section received nearly 50 submitted manuscripts, out of which 10 of them have been accepted after a rigorous peer review. Each manuscript is reviewed by multiple rounds of review with at least three or four reviewers, the problems to be solved and the innovation of each manuscript are mainly concerned. Then the accepted papers are summarized as follows in details. Considering the joint optimization of the offloading decision and resource allocation under limited resource constraints in collaborative edge computing networks with multiple IIoT devices and MEC servers, an improved differential evolution algorithm [7] is proposed to minimize the weighted sum of cost of energy consumption and time delay, which can effectively reduce the system delay and energy consumption. In order to improve the performance of task scheduling in cloud computing, Attiya et al. [1] propose a novel hybrid swarm intelligence method MRFOSSA, which uses a modified Manta-Ray Foraging Optimizer (MRFO) and the Salp Swarm Algorithm (SSA). MRFOSSA is superior to other methods in terms of makespan time and cloud throughput. The research goal of the paper [5] is to design an intelligent computing offloading strategy for industrial applications in order to optimize costs and mitigate energy losses. Then the paper proposes to combine a fog controller and AI-based learning techniques so that the fog controller can intelligently assign tasks to the most appropriate fog devices and find the appropriate path to the target. Considering the resource utilization efficiency under dynamic overload requests and network states in IIoT, Chen et al. [2] propose DRL-based intelligent SFC orchestration scheme and jointly optimize the VNF deployment and SFC embedment by the improved DDQN algorithm, which can improve the performance of resource utilization rate, execution cost and delay compared with other representative schemes. To solve the problem of resource allocation and energy cost in Internet of Vehicles, Kong et al. [8] design a joint computing and caching framework and formulate the problem as a reinforcement learning problem to minimize the energy cost. On this basis, the optimization algorithm based on DDPG is proposed, which can effectively decrease energy costs. To reduce the query numbers of the object model when constructing adversarial examples, Zhang et al. [10] propose generating adversarial examples with shadow model (GASM), i.e., transfering the query operations to the designed shadow model, which can achieve high attack success rates. Chen et al. [3] revise a Decentralized-Wireless-Federated-Learning algorithm (DWFL) which utilizes the superposition property of the analog scheme. It can solve the problem of single failure, limited bandwidth resource and privacy protection in wireless federated learning algorithm, which can be applied widely in wireless IoT networks. To reduce the resource consumption in CNN-based applications, Jia et al. [6] propose the CNN-based Resource Optimization APProach which utilizes model compression and computation sharing to optimize inner-model and inter-model respectively, and the comparison results show the superior performance in scalability and the decrease of resource cost. In mobile crowdsensing activities, Gao et al. [4] propose a differential Location Privacy-preserving Mechanism based on Trajectory obfuscation (LPMT) to protect the location privacy of mobile users, which includes three operations: stay points extraction, stay points obfuscation and stay points sampling. In order to mimic the task-free bottom-up visual attention process by predicting salient regions on natural images, Umer et al. [9] propose a Pseudo Knowledge Distillation (PKD) model based on knowledge distillation and pseudo labelling technique, which is computationally efficient and suitable for real-time on-device saliency prediction.

The papers in this special section focus on artificial intelligence-enabled software defined industrial networks. With the development of intelligent manufacturing, new manufacturing modes such as personalized customization and networked collaboration have been widely developed. These new manufacturing modes require frequent data exchanges between manufacturing machines and industrial information systems through the networks, and dynamically change according to the variations of orders, business, and environments, which cannot be supported in traditional manufacturing modes that focus on local and fixed processes. The current industrial network architecture cannot meet the needs of the aforementioned upcoming manufacturing mode. For example, there are many industrial network protocols, forming a complex industrial heterogeneous network, which seriously affects the interconnections between the underlying devices and the upper layer application systems. In addition, the layering information technology (IT) networks and the operation technology (OT) networks in the factory have hindered the developments of the industrial networks and intelligent manufacturing. There is an urgent need to build a flat, efficient, and flexible industrial network to support the new manufacturing modes.

The papers in this special section focus on artificial intelligence Internet of Things for medical things (AIoMT), with particular emphasis on medical sensors for remote patient monitoring and body area interfacing. Examines issues involving design and implementation, practice use, measurements, and patient monitoring.

Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people's emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications. Eye blinking and movements introduce lots of artifacts into raw EEG data, which seriously affect the quality of EEG signal and the subsequent emotional EEG feature engineering and recognition. In this paper, we propose a fully automatic and unsupervised ocular artifact identification and removal algorithm named automated canonical correlation analysis (CCA)-multi-channel wiener filter (MWF) (ACCAMWF). Firstly, spatial distribution entropy (SDE) and spectral entropy (SE) are computed to automatically annotate artifact segments. Then, CCA algorithm is used to extract neural signal from artifact contaminated data to further supplement the clean EEG data. Finally, MWF is trained to remove ocular artifacts from multiple channel EEG data adaptively. Extensive experiments have been carried out on semi-simulated EEG/EOG dataset and real eye blinking-contaminated EEG dataset to verify the effectiveness of our method when compared to two state-of-the-art algorithms. The results clearly demonstrate that ACCAMWF is a promising solution for removing EOG artifacts from emotional EEG data.

For the effective green communications amongst the vehicles, the energy-efficient routing protocol for intelligent transportation system (ITS) is essential. Due to the high speed and recurring topological variations of Vehicular sensor Networks, identifying a connected route with a sufficient latency is a difficult task with many constraints and obstacles. Therefore, to overcome this, we developed the statistical approach to theoretically determine the load congestion and consumption of energy during the lifetime of the sensor network for ITS. Hence, dynamic clustering green communication routing (DCGCR) protocol is proposed for vehicular communication. To manage energy consumption and enhance the lifetime of the network deployed on the roadside units (RSU), we analyze the evolution of energy holes and apply our analytical conclusions for ITS with WSN routing. The proposed routing protocol considers various metrics: i) energy consumption of vehicular sensor nodes,ii) network stability iii) reliability and iv) amount of data exchange among vehicles. The efficiency of the proposed computational model in calculating the lifetime of the vehicular network and energy hole evolution process is demonstrated through extensive computation results. DCGCR approach is compared with the various energy-aware routing algorithms namely, Dynamic Energy Balanced Routing (DEBR), Geographic Greedy Routing (GGR), double cost function-based routing (DCFR) and found that proposed approach achieves more accuracy with 7% less failure rate.

The continuous growth to the 6G wireless communication technology overcomes storage, stringent computation, privacy and power constraints to make an efficient and intelligent next generation transportation system to alleviate traffic jams and enhance driving experience in vehicular ad-hoc networks (VANETs). In combination with 6G technology, high availability, high reliability and occasionally high throughput are enabled in VANETs. However, the information shared in the VANET system should be secured. In this paper, an efficient batch authentication and key exchange schemes are proposed to provide a high level security by evading communication with the malicious vehicle users. In addition, an anonymous batch authentication scheme is proposed to alleviate the authentication burden on the road side units (RSUs) while performing authentication in the congested areas. Moreover, the integrity of the communicating messages is preserved in this proposed scheme to evade message modification during transmission. Even though many cryptographic schemes were proposed for batch authentication in VANETS, they suffered from lack of privacy-preservation and computational overhead. The discussion of the possible attacks section illustrates that the proposed protocol can survive against potential security attacks. In the performance analysis section, the proposed batch authentication scheme is compared with well-known existing schemes and then it is clearly revealed that the proposed scheme is computationally more efficient than the existing schemes.

With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services, and provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a huge volume of data from the various sensors, devices, and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze, and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

The Internet-of-Things vision of ubiquitous and pervasive computing gives rise to future smart irrigation systems comprising the physical and digital worlds. A smart irrigation ecosystem combined with Machine Learning can provide solutions that successfully solve the soil humidity sensing task in order to ensure optimal water usage. Existing solutions are based on data received from the power hungry/expensive sensors that are transmitting the sensed data over the wireless channel. Over time, the systems become difficult to maintain, especially in remote areas due to the battery replacement issues with a large number of devices. Therefore, a novel solution must provide an alternative, cost- and energy-effective device that has unique advantage over the existing solutions. This work explores the concept of a novel, low-power, LoRa-based, cost-effective system that achieves humidity sensing using Deep Learning techniques that can be employed to sense soil humidity with high accuracy simply by measuring the signal strength of the given underground beacon device.

Anthropometry is a method for measuring physical characteristics of the human body, particularly dealing with measures of size and shape of the body. These measurements can be performed using a tape measure, but new devices and software solutions already been employed in digital anthropometry. However, such tools do not enable the anthropometric evaluation to be performed automatically. In this paper, we present the NLMeasurer application for anthropometry, a mobile tool based on computer vision for identifying anatomical reference points (ARPs) and assessing the size of body segments. To evaluate the performance of the NLMeasurer, four participants were photographed and their images processed. The anthropometric measures calculated by the application, using different settings, were compared with those obtained using a tape measure. Results indicate no statistically significant difference (p > 0.05) between the methods, except in one configuration. This initial experiment was promising to reveal the feasibility of using NLMeasurer for anthropometry.

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. It is important to understand if a person is healthy (in terms of his everyday posture) or is suffering from a joint/bone disease as reflected by his incorrect posture. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. The aim of the proposed work is to classify the human posture during three different activities (standing, sitting and sleeping/lying) as a healthy or an unhealthy one. This is done by applying machine learning techniques on a large posture dataset which is collected with the help of MPU-6050 sensors mounted on multiple positions on the body. The performance evaluation of the proposed work reveals that the proposed work is efficient enough to classify the postures accurately.

Technology integration in the field of mental health helps prevent cognitive decline in patients. If older users do not adopt service-based Information and Communication Technologies, they will face problems in managing their daily lives. The main objective of our work is to analyze the usability of the Long Lasting Memories program among mental health professionals. This study sample consisted of 23 participants: psychologists (52.2%) and qualified assistants (47.8%). Once the intervention with the program had concluded, the participants answered a usability questionnaire with different variables. Questions relating to aspects such as the ease of use of the program, its satisfaction and sustainability were included in the questionnaire. From the point of view of the professional in charge of the intervention, the degree of usability is high.

People need to breathe, and so do other living beings, including plants and animals. It is impossible to overlook the impact of air pollution on nature, human well-being, and concerned countries' economies. Monitoring of air pollution and future predictions of air quality have lately displayed a vital concern. There is a need to predict the air quality index with high accuracy; on a real-time basis to prevent people from health issues caused by air pollution. With the help of Unmanned Aerial Vehicle's onboard sensors, we can collect air quality data easily. The paper proposes a distributed and decentralized Federated Learning approach within a UAV swarm. The accumulated data by the sensors are used as an input to the Long Short Term Memory (LSTM) model. Each UAV used its locally gathered data to train a model before transmitting the local model to the central base station. The central base station creates a master model by combining all the UAV's local model weights of the participating UAVs in the FL process and transmits it to all UAV s in the subsequent cycles. The effectiveness of the proposed model is evaluated with other machine learning models using various evaluation metrics using test data from the capital city of India, i.e., Delhi.

Vehicular ad hoc Networks have emerged as a viable alternative for enabling user applications on moving vehicles. However, maintaining acceptable levels of Quality of Service and message latency still remains a challenging task. Several solutions have been proposed for improving performance of these networks. Clustering has been considered as one of the important mechanism that structures vehicles into organize groups. However, high deployment overheads and lack of security are the major bottlenecks hindering its deployment. Software defined networking has been emerged as a promising solution on account of its characterstics such as dynamic access control and scalabilty. In view of this, TruClu: a trust based clustering mechanism that creates vehicular clusters for a Software Defined Vehicular Network is proposed. Cluster formation and cluster head selection in TruClu is based on vehicular mobility and trust value that alleviates the drawbacks of traditional clustering and also enabling trust based communication in the network. The performance of TruClu is evaluated through extensive simulations and obtained results indicate the comparable performance of the proposed scheme in terms of standard performance parameters.

The fast-growing Internet of Thing (IoT) has generated a vast number of tasks which need to be performed efficiently. Owing to the drawback of the sensor-to-cloud computing paradigm in IoT, mobile edge computing (MEC) has become a hot topic recently. Against this backdrop, we focus on the offloading of tasks characterized by intrinsic correlations in this paper, which have not been considered in most of existing works. For the sequential arrival of such correlated tasks, the future workload can be efficiently reduced by caching the current computational result. Specifically, we resort to the Lyapunov optimization to handle the long-term constraint on energy consumption. Simulation results reveal that our approach is superior to other approaches in the optimization of response latency and energy consumption.

Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons and infrastructures, and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the centralized architecture adopted in existing IoV systems is fragile to single point of failure and malicious attacks. With the emergence of blockchain technology, it has the chance to solve these problems due to its features of tamper-proof, traceability and decentralization. In this paper, we propose a privacy-preserving vehicular data sharing framework based on blockchain. In particular, we design an anonymous and auditable data sharing scheme using Zero-Knowledge Proof (ZKP) technol-ogy so as to protect the identity privacy of vehicles while preserving the vehicular data auditability for Trusted Authorities (TAs). In response to high mobility of vehicles, we design an efficient multi-sharding protocol to decrease blockchain communication costs without compromising the blockchain security. We implement a prototype of our framework and conduct extensive experiments and simulations on it. Evaluation and analysis results indicate that our framework can not only strengthen system security and data privacy, but also increase the data authenticity verification efficiency by 5x comparing to existing privacy-preserving schemes.

Increased vehicular flow on roads along with proposed deployment of autonomous vehicles has necessitated the need for accurate traffic forecasting so as to achieve effective route guidance, traffic management, public safety and congestion avoidance. Although a number of traffic forecasting algorithms have been proposed but most of these algorithms perform short term traffic predictions. However future vehicular systems also defined as intelligent VANETs will require a hybrid traffic forecasting model that predicts the vehicular traffic for varying values of time. This paper proposes a time varying forecasting model that predicts vehicular flow by utilizing Long Short-Term Memory (LSTM) and Convolutional Neural Network(CNN). The model is based on large-scale, network-wide traffic with spatio-temporal features. The temporal features learned by LSTM and spatial features learned by CNNs from the matrices are further fused with external factors to derive the final forecast. Model has been implemented on the traffic data set of Chandigarh city in India, mapped onto three two-dimensional matrices of time and space. The predicted information is then forwarded by the vehicle to all the other vehicles in their vicinity using vehicular adhoc networks. Experimental results indicate that the proposed model performs significantly better than other state-of-the-art models in terms of accuracy and efficiency.

Device-to-device (D2D) communication is one of the promising technology of the fifth-generation (5G) network. In D2D, the devices are in close proximity to each other communicate directly with or without depending upon the base station (BS), resulting in large gain, low latency, and high energy efficiency. Also, it improves the spectral efficiency by sharing the spectrum resources with cellular mobile users (CMUs). Despite these advantages, co-channel interference and eavesdropping attack on the D2D links are two major challenges. To overcome these issues, we used the power domain non orthogonal multiple access (PDNOMA) techniques with the D2D mobile groups (DMGs) under the social-domain scenario. The successive interference cancellation technique of PD-NOMA in the DMGs mitigate the intra-user and co-channel interference among the D2D receivers (DDRs), resulting in an increase in signal to interference noise ratio (SINR) and better quality of services. Furthermore, to improve the spectral efficiency, and reduce the security risk of the eavesdropper on the DMGs over each resource block (RB) in the presence of dynamic channel environment of imperfect channel state information, we used the coalition game approach. The simulated results show the proposed scheme achieves 5.5% and 27.77% higher sum rate and ensure 8.3% and 41.6% higher information secrecy as compared to first-order algorithm (FOA) and orthogonal frequency division multiple access (OFDMA) schemes.

An Artificial Intelligence (AI)-enabled and blockchain-driven Electronic Health Record (EHR) maintenance system has a tremendous potential to facilitate reliable, secure, and robust storage systems for EHRs. Such an EHR system would also facilitate researchers, doctors, and government authorities to access data for research, perform analytics, and help in making well-informed decisions. The Artificial Neural Network (ANN) is employed to classify the patients as potentially COVID-19 positive and potentially COVID-19 negative based on the clinical reports and reports of CT-scan. The data of potentially COVID-19 positive patients is stored on blockchain employing InterPlanetary File System (IPFS) protocol. The accessibility of EHR can be done by authorized entities post verification and validation of entities. We analyze the performance of various AI-based algorithms employing metrics such as loss curve, accuracy, etc. for the task of predicting the patient’s potential COVID-19 infection. The 6G network significantly mitigates the network latency and reliability issues and also facilitates the real-time transmission of information. The amount of data generated is pretty high amidst this pandemic and so we employed IPFS protocol which suffices to be a cost-effective solution, moreover satisfying all are stringent requirements. At last, we evaluate the network, security, and storage performance of our architecture MedBlock, which outperformed other state-of-the-art systems.

With the advent of the Internet, the gaming industry has grown tremendously in business, which also raises concerns for cheating and unfair gameplay. In this paper, we propose a novel approach (GiNA) using Blockchain technology to address a few problems with online Peer-to-Peer (P2P) games. GiNA uses two different data packet transfer schemes to ensure the security and authenticity of the data packet sent and received by game clients. More sensitive data uses a Smart contract-based ON-CHAIN data packet transfer solution and less sensitive data uses an OFF-CHAIN data packet transfer solution with end-to-end encryption for data security. A marketplace where peers can buy and sell purchasable assets with the help of Gicoins. Gicoins is a stable token with compliance with the ERC 20 token of Etheruem Blockchain. Later, a low cost and low bandwidth utilization data storage solution is proposed for storing data in a decentralized and distributed manner. Results show that the performance of the proposed approach GiNA is better in comparison to the traditional approaches with parameters such as latency, scalability, packet loss percentage, Blockchain (BC) performance, and data storage comparison.

The evolution of deep learning has promoted the popularization of smart devices. However, due to the insufficient development of computing hardware, the ability to conduct local training on smart devices is greatly restricted, and it is usually necessary to deploy ready-made models. This opacity makes smart devices vulnerable to deep learning backdoor attacks. Some existing countermeasures against backdoor attacks are based on the attacker’s ignorance of defense. Once the attacker knows the defense mechanism, he can easily overturn it. In this paper, we propose a Trojaning attack defense framework based on moving target defense(MTD) strategy. According to the analysis of attack-defense game types and confrontation process, the moving target defense model based on signaling game was constructed. The simulation results show that in most cases, our technology can greatly increase the attack cost of the attacker, thereby ensuring the availability of Deep Neural Networks(DNN) and protecting it from Trojaning attacks.

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration scheme which measures received signal strength interference (RSSI) for position estimation of the target. Furthermore, a comparison is carried out between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link.

Technology has become crucial in applying measures to refrain diseases' spread. Thus, computer vision can be an essential tool in this combat. This paper proposes and demonstrates an application that combines facial detection and artificial intelligence techniques to verify the use of masks by individuals within the range of the camera's vision field. In a pandemic period of COVID-19, it is necessary obtaining better control of mask usage in places where it is mandatory and providing quick identification of someone who will not comply with this rule. With this objective, a Machine Learning system was created to operate in real-time a classification of face mask usage to prevent the transmission of the virus and directly impact suppressing the pandemic.

The segmentation of instances is a key topic in image processing and computer vision. There are numerous applications such as medical image analysis, video surveillance, image compression among others, in which its algorithms show significant results. Due to the COVID-19 pandemic, most countries have been affected mainly by their economy. In the food production sector, including fishing and aquaculture, it was no different. In this context, this research has as main objective to contribute to the 2030 Agenda for Sustainable Development suggested by the United Nations (UN), through a fish detection and segmentation model based on the framework Detectron2, optimizing the time of professionals in identifying specific characteristics of a particular species. To achieve this objective, this research seeks to facilitate the recognition of patterns of parts of the fish from the segmentation of instances and to stimulate scientific research in the area through the morphological information collection of certain species. The results present an accuracy, based on the Intersection over Union (IoU) indicator, of 88.4%, providing an effective solution for the collection of these characteristics.

Mobile applications in the present have created tremendous pressure on the computational capabilities of user equipments. Against this background, mobile edge computing (MEC) has been proposed to tackle this issue, e.g., by shifting the computational workload to the edge server. We in this paper consider a caching enabled task offloading in MEC, for the sake of joint optimization of task offloading and caching. We consider both energy consumption and response latency in the optimization problem and solve the problem by an alternate optimization algorithm. Extensive experiments have been conducted to evaluate the algorithm and the simulation results have shown its advantages such as rapid response latency and powerful convergence capability.

The environmental issues, the continuous growth in electricity demand, and the increased penetration of renewable energy resources motivated the transformation of conventional power grids into modernized Smart Grids. With this Demand Response applications are implemented on Home Energy Management Systems, aiming to shape the load consumption profile of consumers, in order to reduce utility operational costs and the consumer energy bill price without affecting their convenience. For Demand Response algorithms to be fully exploited in real microgrid environments, households must be equipped with an infrastructure capable of monitoring and controlling residential loads and distributed energy resources, such as renewable energy resources and energy storage systems. Such infrastructure should be able to monitor and detect events that occur on a daily basis in real situations and that may hinder the benefits of the Demand Response algorithm, and send this information to the Home Energy Management Systems in order to keep the DR algorithm updated. In this context, this paper proposes an Internet of Things infrastructure based on an edge-fog-cloud computing architecture in order to monitor and control residential loads. The proposed infrastructure was implemented in a real testbed scenario and the results show that the proposed solution is able to assist the Demand Response algorithm within a microgrid.

Summary The exchange of information among health professionals is a common practice among clinics, laboratories, and hospitals. Cloud‐based clinical data exchange platforms enable valuable information to be available in real time and in a secure and private manner. The increasing availability of data in health information systems allows specialists to extract knowledge using pattern recognition techniques for the identification and prediction of risk situations that could lead to severe complications for a patient. Hence, this paper proposes the use of a neuro‐fuzzy machine learning technique for predicting the most complex hypertensive disorder in pregnancy called HELLP syndrome. This classifier serves as an inference mechanism for cloud‐based mobile applications, for effective monitoring through the analysis of symptoms presented by pregnant women. Results show that the proposed model achieves excellent results regarding several indicators, such as precision (0.685), recall (0.756), the F‐measure (0.705), and the area under the receiver operating characteristic curve (0.829). This technique can accurately predict situations that could lead to the death of both a mother and fetus, at any location and time.

Abstract The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose.

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike the coronavirus (Covid-19) outbreak, a remote Internet of Things (IoT) enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework that enables wireless communication of physiological signals to data processing hub where long short-term memory (LSTM)-based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions that enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In the proposed IoT protocols (TS-MAC and R-MAC), ultralow latency of 1 ms is achieved. R-MAC also offers improved reliability in comparison to state of the art. In addition, the proposed deep learning scheme offers high performance ([Formula: see text]-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support, and general wellbeing.

The evolution of Internet of Things (IoT) led to the construction of many IoT middleware, a software that plays a key role since it supports the communication among devices, users, and applications. Although various solutions and studies were proposed, they rarely address crucial privacy and security considerations, especially regarding the message queuing telemetry transport (MQTT) protocol. Moreover, in the majority of the solutions, integrating new devices is a time-consuming task performed manually that cannot be accomplished in a scenario with thousands, maybe millions of devices. In this sense, this article proposes a new IoT middleware, called In.IoT, a scalable, secure, and innovative middleware solution that addresses the middleware concerns identified in this article. In.IoT architectural recommendations and requirements are detailed and can be replicated by new and available solutions. It supports MQTT, CoAP, and HTTP as application-layer protocols. Its performance is evaluated in comparison with the most promising solutions available in the literature and the results obtained by the proposed solution are extremely promising. In.IoT is evaluated, demonstrated, validated, and it is ready and available for use.

The Internet of Medical Things (IoMT) is an extension and specialization of that original Internet of Things (IoT) concept, and applies to the interconnectedness of devices, software applications, and data which are specific to the medical industry. IoMT can add smart technologies to medical devices to monitor the progression of a disease away from the doctor’s office and learn things that could impact future care guidelines and patients. It can also provide a better way to care for our elderly by tracking vitals and heart performance, glucose and other body systems, and activity and sleeping levels. During the outbreak of pandemic (e.g., COVID-19), IoMT can even be used to detect main symptoms ubiquitously using intelligent sensors and trace the origin of the outbreak based on aggregated IoT data (e.g., geographic mobile data and purchase history). Although most of the contemporary IoMT systems can measure risks, make decisions, and take actions automatically, the lack of emotion-aware abilities will be an obstacle to more harmonious human–machine interaction and more efficient medical process. Besides, mental disorders, such as depression, schizophrenia, and anxiety, have become a more noticeable cause of suffering. The integration of emotion-aware abilities into IoMT can also contribute to monitor emotional dysregulation continuously in subjects with mental disorders or undergoing serious pandemic such as COVID-19, and give these patients personalized therapy recommendations. Research on affective computing has defined a framework to recognize, interpret, and process human affects, but more research is needed to investigate its application to biomedical applications, especially “in the wild” and over extended periods of time, and how to integrate emotion-aware abilities into IoMT organically is still an open question. This special issue aims to create a platform for researchers, developers, and practitioners from both academia and industry to disseminate the state-of-the-art results and to advance the Emotion-Aware ubiquitous computing in IoMT.

Rapid developments in industry 4.0, machine learning, and digital twins have introduced new latency, reliability, and processing restrictions in Industrial Internet of Things (IIoT) and mobile devices. However, using current information and communications technology (ICT), it is difficult to optimally provide services that require high computing power and low latency. To meet these requirements, mobile-edge computing is emerging as a ubiquitous computing paradigm that enables the use of network infrastructure components such as cluster heads/sink nodes in IIoT and cellular network base stations to provide local data storage and computation servers at the edge of the network. However, optimal location selection for edge servers within a network out of a very large number of possibilities, such as to balance workload and minimize access delay, is a challenging problem. In this article, the edge server placement problem is addressed within an existing network infrastructure obtained from Shanghai Telecom's base station data set that includes a significant amount of call data records and locations of actual base stations. The problem of edge server placement is formulated as a multiobjective constraint optimization problem that places edge servers strategically to balance between the workloads of edge servers and reduce access delay between the industrial control center/cellular base stations and edge servers. To search randomly through a large number of possible solutions and selecting those that are most descriptive of optimal solution can be a very time-consuming process, therefore, we apply the genetic algorithm and local search algorithms (hill climbing and simulated annealing) to find the best solution in the least number of solution space explorations. Experimental results are obtained to compare the performance of the genetic algorithm against the above-mentioned local search algorithms. The results show that the genetic algorithm can quickly search through the large solution space as compared to local search optimization algorithms to find an edge placement strategy that minimizes the cost function.

The Internet of Things (IoT) is a framework of various services and smart technologies that mutually communicate information between mobile devices and users or just between devices with the help of Internet connectivity. The dramatic progression of IoT helps numerous network applications and communication technologies to introduce state-of-the-art communication models for enabling interaction among mobile server, clients, and various other smart entities. Now-a-days, online mobile services have gained huge attention by providing ample convenience to the distant users. However, it is necessary to secure the information, being exchanged among mobile clients and server. Therefore, a large number of authentication protocols have been presented but majority of them are unsuitable to fulfill novel security requirements and standards. Moreover, they are incompatible for the IoT environment due to higher computation and communication complexity. Consequently, there is a dire need of developing an adequate, reliable, and cost-effective authentication protocol. In this article, we introduce a novel identity-based key agreement protocol using the puncturable pseudorandom functions for mobile clients in the IoT environment. The proposed PSK-MC protocol enables two mobile clients to accomplish mutual authentication via server. The proposed protocol is evaluated formally and informally to determine its security strength. The formal security analysis is presented using the widely used random oracle model. Moreover, all the cryptographic operations used at mobile client side are executed on a mobile device, while the operations used at the server side are implemented on a desktop machine to get the experimental results to determine computation cost. The performance analysis reveals the fact that our protocol is comparatively better than related protocols by exhibiting least communication and computation overhead.

Typical cloud systems are often prone to inherent wide area network (WAN) latency. To address this issue fog computing is proposed that enables resource-constrained Internet-of-Things (IoT) devices, to execute deadline-sensitive tasks at the edge of the network. These devices can extend their battery lifespan by intelligently offloading computations as tasks to fog nodes (FNs) in their vicinity. However, finding an optimal offloading plan in a densely connected IoT-fog network is proven to beNP-Hard. Hence, in this article, we propose a matching theory-based efficient task offloading strategy called METO that aims to reduce the total system energy and number of outages (number of tasks exceeding the deadline) in an IoT-fog interconnection network. As resource allocation involves multiple criteria, their weights are derived using criteria importance though inter criteria correlation (CRITIC). Furthermore, to rank the alternatives we use the technique for order of preference by similarity to ideal solution (TOPSIS). Based on this ranking, we formulate the overall offloading problem as a one-to-many matching game and utilize the deferred acceptance algorithm (DAA) to produce a stable assignment. Simulation is performed in two different settings comprising offloading of homogeneous and heterogeneous tasks. Extensive simulations across both environments confirm that the proposed algorithm outperforms the existing schemes with respect to improved energy consumption, completion time, and execution time. Moreover, METO also shows the reduced number of outages across baselines used for comparison.

Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations to increase support for personalized, integrated care to prevent and manage chronic conditions. Many applications related to In-Home Health Monitoring have been introduced over the last few decades, thanks to the advances in mobile and Internet of Things technologies and services. Such advances include improvements in optimized network architecture, indoor networks coverage, increased device reliability and performance, ultra-low device cost, low device power consumption, and improved device and network security and privacy. Current studies of in-home health monitoring systems presented many benefits including improved safety, quality of life and reduction in hospitalization and cost. However, many challenges of such a paradigm shift still exist, that need to be addressed to support scale-up and wide uptake of such systems, including technology acceptance and adoption by patients, healthcare providers and policymakers. The aim of this paper is three folds: First, review of key factors that drove the adoption and growth of the IoT-based in-home remote monitoring; Second, present the latest advances of IoT based in-home remote monitoring system architecture and key building blocks; Third, discuss future outlook and our recommendations of the in-home remote monitoring applications going forward.

Under the pressure of the growing millennial population and senior citizens are aging, which is one of the top societal priorities in many countries, the provision of healthcare needs to evolve and improve. A roadmap paved by World Health Organization (WHO) in March 2019 called Global Strategy on Digital Health 2020-2024, specified a grand vision of promoting healthy lives and well-beings for everyone, everywhere, at all ages[1]. WHO urges all nations to work hand in hand in developing and delivering Digital Health initiatives supported by robust government strategies that amalgamate financial, organizational, human and technological resources[2]. In particular, there are some niches areas in the strategies such as the adoption of distributed sensors and assisted living emerging in recent years. To this end, a lot of efforts both from the research community and industrial providers are anticipated to put forth in the coming decade, in implementing the concept of assisted living using hardware devices into meaningful solutions for fulfilling the growing needs of assisted living.

Summary Corporate insolvency has significant adverse effects on an economy. With the number of multinationals increasing rapidly, corporate bankruptcy can severely disrupt the global financial environment. However, multinationals do not fail instantaneously; objective strategies combined with a rigorous analysis of both qualitative and quantifiable data can go a long way in identifying an organization's financial risks. Recent advancements in information and communication technologies have made data collection and storage an easy task. The challenge becomes mining the appropriate data about a company's financial risks and implementing it in forecasting a company's insolvency probabilities. In recent years, machine learning has been incorporated into big data analytics owing to its massive success in learning complex models. Machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks, Gaussian Processes, and Adaptive Learning have been used in the analysis of Big Data to predict the financial risks of companies. In this paper, credit scoring is explored with regards to data processed using the collateral as an independent variable. The obtained results indicate that RF algorithm is promising for use in credit risk management. This research shows the advantages of the RF approach over the SVM algorithm are its speed and operational simplicity, and SVM has the benefit of higher classification accuracy than RF. The paper compares the SVM and RF algorithms to forecast the recovered value in a credit task. The execution of the projected intelligent systems uses tests and algorithms for authentication of the projected model.

Deep convoloutional networks have been widely deployed in modern cyber-physical systems performing different visual classification tasks. As the fog and edge devices have different computing capacity and perform different subtasks, models trained for one device may not be deployable on another. Knowledge distillation technique can effectively compress well trained convolutional neural networks into light-weight models suitable to different devices. However, due to privacy issue and transmission cost, manually annotated data for training the deep learning models are usually gradually collected and archived in different sites. Simply training a model on powerful cloud servers and compressing them for particular edge devices failed to use the distributed data stored at different sites. This offline training approach is also inefficient to deal with new data collected from the edge devices. To overcome these obstacles, in this article, we propose the heterogeneous brain storming (HBS) method for object recognition tasks in real-world Internet of Things (IoT) scenarios. Our method enables flexible bidirectional federated learning of heterogeneous models trained on distributed datasets with a new “brain storming” mechanism and optimizable temperature parameters. In our comparison experiments, this HBS method outperformed multiple state-of-the-art single-model compression methods, as well as the newest multinetwork knowledge distillation methods with both homogeneous and heterogeneous classifiers. The ablation experiment results proved that the trainable temperature parameter into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of authors' knowledge, this is the first IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks.

Numerous surveillance data processing is crucial in the Internet-of-Things systems with pervasive edge computing. In this process, salient object detection from surveillance videos plays an important role because it provides the human-concerned semantic cue for various industrial tasks. However, it is still challenging for the existing studies with two aspects. The first one is the redundant saliency information from moving background to disturb the detection of salient objects. The second one is the difficulty to model the spatiotemporal saliency uncertainty. To overcome these challenges. In this article, an intelligent approach is proposed for surveillance saliency detection. It enables a region-proposal-based optical flow strategy to suppress the saliency enhancement of non-salient regions due to the moving background. Besides, it develops the bidirectional Bayesian state transition strategy to model the motion uncertainty for refining the spatiotemporal saliency feature. Extensive experiments have been performed on two datasets (the increase of Fβis larger than 0.01 for DAVIS, and larger than 0.015 for UVSD), and the comparison with seven methods to evaluate the effectiveness of the proposed approach.

Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable devices. Therefore, in order for convenient real-time MI detection, it is essential to design lightweight models suitable for resource-limited portable devices. This paper proposes a novel multi-channel lightweight model (ML-Net), that provides a new solution for portable detection devices with limited resources. In ML-Net, each electrocardiogram (ECG) lead is assigned an independent channel, ensuring data independence and preserve the ECG characteristics of different angles represented by different leads. Moreover, convolution kernels of heterogeneous sizes are utilized to achieve accurate classification with only a small amount of lead data. Extensive experiments over actual ECG data from the PTB diagnostic database are conducted to evaluate ML-Net. The results show that ML-Net outperforms comparable schemes in diagnosing MI, and it requires lower computational cost and less memory, so that portable devices can be more widely used in the field of Internet of Medical Things(IoMT).

The security and privacy of healthcare enterprises (HEs) are crucial because they maintain sensitive information. Because of the unique functional requirement of omni-inclusiveness, HEs are expected to monitor patients, allowing for connectivity with vehicular ad hoc networks (VANETs). In the absence of literature on security provisioning frameworks that connect VANETs and HEs, this paper presents a smart zero-knowledge proof and statistical fingerprinting-based trusted secure communication framework for a fog computing environment. A zero-knowledge proof is used for vehicle authentication, and statistical fingerprinting is employed to secure communication between VANETs and HEs. Authenticity verification of the operations is performed at the on-board unit (OBU) fitted in the vehicle based on the service executions at the resident hardware platform. The processor clock cycles are acquired from the service executions in a complete sandboxed environment. The calculated cycles assist in developing the blueprint signature for the particular OBU of the vehicle. Hence, the fingerprint signature helps build trust and plays a key role in authenticating the vehicle's horizontal movement to everything or to different sections of the HEs. In an environment enabled for fog computing, our novel model can provide efficient remote monitoring.

Public transport systems in a metropolitan area experiences several complex issues, like resource scarcity, resource allocation, congestion, resource reliability and so on, due to the dynamic arrivals of heterogeneous commuter and exceptional occurrence of unforeseen events. The progress of these issues may lead to economic losses, under-utilization of transport resources, and commuters’ queuing delay. In this paper, we propose a novel dynamic public transport vehicle allocation scheme based on Emergent Intelligence (EI) technique in a metropolitan area. In addition, we demonstrate the EI technique’s capability for solving public transport system problems. To do so, the EI technique maintains historical information, commuters’ arrival rates, resource avaialability, deficit resources and surplus resources of neighbor depots’s agent. In the proposed scheme, the EI technique is utilized to collect, analyze, share and optimally allocate transport resources effectively. The proposed EI technique provides reliable services (allocation and scheduling) by coordinating with a reliable neighborhood depot’s agent. We have build mathematical models for estimation of resources, utilization and reliability parameters. The proposed scheme is exhaustively tested by simulation and analyzed with varying commuters’ arrival rates, number of vehicles, number of requests, and different values of reliability parameters. The proposed scheme’s results (analytical, simulation and comparison) show the reliabiltiy, accuracy and real time deployability.

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

Due to the high mobility, dynamic nature, and legacy vehicular networks, the seamless connectivity and reliability become a new challenge in software-defined internet of vehicles based intelligent transportation systems (ITS). Thus, effieicnt optimization of the link with proper monitoring of the high speed of vehicles in ITS is very vital to promote the error-free and trustable platform. Key issues related to reliability, connectivity and stability optimization for vehicular networks are addressed. Thus, this study proposes a novel reliable connectivity framework by developing a stable, and scalable link optimization (SSLO) algorithm, state-of-the-art system model. In addition, a Use-case of smart city with stable and reliable connectivity is proposed by examining the importance of vehicular networks. The numerical experimental results are extracted from software defined-Internet of Vehicle (SD-IoV) platform which shows high stability and reliability of the proposed SSLO under different test scenarios, such as vehicle to vehicle (V2V), vehicle to infrastructure (V2I) and vehicle to anything (V2X). The proposed SSLO and Baseline algorithms are compared in terms of performance metrics e.g. packet loss ratio, transmission power (i.e., stability), average throughput, and average delay transfer. Finally, the validated results reveal that SSLO algorithm optimizes connectivity (95%), energy efficiency (67%), throughput (4Kbps) and delay (3 sec).

In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems. The location parameters of autonomous vehicles can be estimated based on the deep unfolding technique, which is a recent advance of deep learning. Traditional vehicle location methods such as the global position system (GPS) can only locate the target vehicles with relatively low accuracy. The super resolution cannot be achieved when two vehicles are too close, which means that the safety incidents exist when autonomous vehicles are deployed in future intelligent transportation systems (ITS). Different from the existing massive MIMO or RIS equipped with a regular array such as uniform rectangular array (URA) and uniform circular array (UCA), we exploit a massive MIMO or a RIS equipped with a conformal array extended from traditional regular array. First, the rotation from the global coordinate system to the local coordinate system is achieved based on geometric algebra. Second, 2D-DOA estimation of autonomous vehicles is modeled as a novel block sparse recovery problem. Third, the deep network architecture SBLNet is implemented to learn the nonlinear characteristic from the DOAs of autonomous vehicles and the data received by massive MIMOs or RISs. The 2D-DOA and polarization parameters can be estimated based on SBLNet with relatively low computational complexity. Simulation results demonstrate that SBLNet performs better than the state-of-the-art methods in terms of estimation accuracy and successful probability. The SBLNet is also suitable for the practical scenario considering fast moving autonomous vehicles, while, the traditional block sparse recovery methods fail in this complex scenario.

The modern day traffic continues to evolve in terms of scale, autonomy and access to information. Every vehicle gathers information and contributes its decisions to the global vehicular network every second. With the advent of 5G equipped digital communication and sophisticated algorithms are in place for the vehicles to communicate with each other in a closely monitored, yet decentralized network, there is a need to ensure that no form of incorrect data be propagated without prior validation. There is also a requirement of maintaining cache since there is no centralized network where all vehicles report their activities and gather information from, at a required speed. The distribution of cache is a major hurdle both in terms of validation and propagation. Cache poisoning can occur if a malicious vehicle or a compromised vehicle intentionally or unintentionally puts incorrect data and other vehicles use that data to skew their own future decisions. In this paper, we explore different methodologies to address and combat the cache poisoning scenarios and suggest an efficient and secure scheme for validation and distribution of cache using the Directed Acyclic Graph (DAG) based ledger, which is based on Tangle™.

This paper investigates power allocation problem for secure multiple-input single-output transmission with artificial noise (AN). With an unknown eavesdropper, we propose an optimal adaptive power allocation scheme, which adaptively adjust the power allocation factor (PAF) according to the instantaneous channel state information of the legitimate channel. On this basis, we derive a closed-form expression for the optimal PAF aiming to minimize the secrecy outage probability (SOP). A suboptimal fixed power allocation scheme is also proposed to reduce system complexity. Moreover, exact closed-form expressions of SOP for both schemes are also obtained.

The next-generation energy system, i.e., Smart Grid (SG), empowers the real-time transfer of information using advanced metering infrastructure (AMI) and smart meter (SM) between end-consumers and grid. It accelerates various services such as automatic meter reading, time-of-use (TOU) pricing, demand-response management, and many more. Though it has growing security and privacy concerns and the detection of malicious activity is a critical security task that sacrifices the overall Quality-of-Service (QoS) of SG and Quality-of-Experience (QoE) for customers. To address the aforementioned issues, we propose a data analytics Scheme ArMor for malicious activity detection on the blockchain (BC)-based SG system. The ArMor detects data integrity issues in real-time like false data injection attack and SM failure. Here, we proposed a unique ARIMA-based malicious activity detection model and classified the customer. Then, we proposed a Smart Contract (SC)-based incentive mechanism for utility providers handling the malicious activity at their end. It prevents the entry of malicious data into the SG system as transactional data once stored in BC, it is secured using SC. The obtained results are compared against parameters like prediction accuracy, latency, and data storage cost compared to the state-of-the-art approaches to designate the efficacy of the proposed scheme.

In this paper, we propose a framework to generate cricket highlights from broadcasted cricket matches. Generating cricket highlights is a difficult problem, due to the duration and rules of the game. We formulate the highlight generation problem as a key-event initialization and key-event-closure identification problem. We propose an Inverse Hierarchical Framework, which is generic and capable of automatically generating highlights of a broadcasted cricket match. We introduce a novel context-aware approach for event-initialization and a Structural Similarity Index-based approach for event-closure detection. Despite the quality of highlights being a subjective measure we provide an evaluation of our framework by comparing it with official highlights on various metrics. We also perform a user-survey on the generated highlights. The approval of the users and overlap between the generated highlights and official highlights indicate the robustness of our framework.

Reductions in installation and storage costs have increased the demand for security systems, including video surveillance and digital authentication. The video surveillance systems, when monitored by humans, are subject to errors and are challenging to scale. Authentication systems can validate someone using a password or a card from another user. Facial recognition algorithms can solve this fault by the traffic monitoring of known individuals or intruders as well as for individual biometric authentication. Hence, this paper evaluates the FaceNet approach using the Labeled Faces in the Wild benchmark, as well as evaluates a machine learning technique known as support vector machine (SVM) for the classification of embedding generated using FaceNet. The suggested approach also models a real-time facial recognition system combining FaceNet and SVM, reaching 90% of accuracy using a medium webcam.

This work proposes an Activity-Aware Data Rate Tuning (A2D) scheme for Wireless Body Area Network (WBAN), while considering the criticality of the physiological sensed data. We consider different physical activities of the patients and thereafter, compute their health criticality. Further, on the basis of the health criticality value, the data rate of these physiological sensors are tuned. Depending on the physical activity of a patient, the value sensed by the physiological sensors may change. Consequently, when a healthy person runs, a particular sensor value may be significantly high, even if it is normal, however, the same data reading may be critical for a person who is sitting or standing. Thus, a WBAN is required to be activity-aware in order to measure the correct criticality values. We implemented in a real hardware platform system to show the effectiveness of the proposed scheme. Experimental results show that the proposed scheme is capable of tuning the data rate of different physiological sensors, based on human activity and critical conditions, while ensuring more than 90% of packet delivery ratio in intra-BAN communication and 93% in inter-BAN communication.

Blockchain technology has emerged to provide immense security solutions and create trust between the stakeholders. In a multi-application scenario, fair resource allocation is complex and challenging. Various Resource Allocation Schemes (RAS) have been proposed by the researchers across the globe, but these solutions are not sufficient to handle the security, trust, latency, and bandwidth issues in the network, which introduces vulnerabilities in the system. Motivated from the aforementioned issues, this paper proposes Block-RAS, a blockchain-based RAS to manage the demand-supply of resources between the users and resource providing companies (RPC) in a secured and trusted environment. Block-RAS provides a highly reliable, low-latency, and bandwidth optimum communication between users and RPC with embedded 6G network infrastructure. In Block-RAS, the security, trust, and transparency are achieved using ethereum blockchain, whereas the cost-effective and optimum bandwidth utilization is achieved using the Interplanetary File System (IPFS). Finally, the performance evaluation of Block-RAS is done by a comparative analysis of the proposed approach with traditional approaches that are dependent on centralized 5G based schemes where the Block-RAS outperforms in terms of delay, packet-loss, blockchain block-size, scalability, and network bandwidth utilization.

In the Internet of Health Things based e-Health paradigm, a large number of computational-intensive tasks have to be offloaded from resource-limited IoHT devices to proximal powerful edge servers to reduce latency and improve energy efficiency. However, the lack of global state information (GSI), the ultra-reliable and low-latency communication (URLLC) constraints, and the adversarial competition among IoHT devices have imposed new challenges for task offloading optimization. In this paper, we formulate the task offloading problem as an adversarial multi-armed bandit (MAB) problem. In addition to the average-based performance metrics, bound violation probability of queuing delays and statistical properties of excess values are employed to characterize URLLC constraints. Then, we propose an energy-aware and URLLC-aware Task Offloading scheme based on the exponential-weight algorithm for exploration and exploitation (EXP3) named UTO-EXP3. Guaranteed performance with a bounded deviation can be achieved by UTO-EXP3 based on only local information. The effectiveness and reliability of UTO-EXP3 are validated through simulation results.

Segmentation is a crucial step to obtain success for classifying medical images. However, it is a highly complex task due to the abnormal shapes and the presence of other artifacts. In this study, a melanoma segmentation approach based on deep learning is proposed. In conjunction with post-processing techniques, the proposed modified U-net network has proven to be highly effective in lesions segmentation. The experiments were performed in two public datasets (PH2 and DermIS) and reached an average Dice coefficient of 0.933 in the PH2 dataset and Dice = 0.872 in the DermIS dataset. Considering the high-performance methodologies available in the literature, the proposed solution is very promising, surpassing other methods with very promising results.

Emotions influence all aspects of human behavior. All of these aspects shape people's lives, directly impacting their ways of life. Some diseases are directly linked to emotions. Among them, depression is one of the diseases with the greatest impact on society. Hence, faced with this problem, the objective of this study is to present a context-aware solution based on text mining for gestational depression prevention. This system uses text mining to analyze documents filled from pregnant women in order to identify their feelings through natural language processing techniques and probabilistic algorithms. As a case study, the analyzed texts were obtained from forms answered by pregnant women. The model performance is evaluated using metrics associated with the confusion matrix. The results show that the proposed model has achieved a reliable performance in all metrics, mainly when classifying new cases. Thus, the results obtained by the model can be used as support to health professionals in monitoring high-risk pregnancies.

The COVID-19 pandemic has wreaked havoc all over the world. The rising number of cases have overburdened healthcare systems even in the most developed countries. To ease the burden on healthcare systems a quick and efficient testing technique is needed. Currently, the RT-PCR testing is done with time consuming and laborious an alternative is a detection from Chest X-Ray images. It has been discovered in published studies that Chest X-Rays of COVID-19 patients have specific malformations that can be used to identify a positive case. Inspired by the work done on “COVID-Net” by Linda Wang, Zhong Qiu Lin and Alexander Wong, a Deep Learning approach to detect coronavirus from Chest X-Ray images is used in this study. To surpass previous results the EfficientNet Convolutional Neural Network (CNN) model is proposed. This model not only achieves +2% accuracy, but it also attains higher sensitivity and Positive Predictive Values. The study uses the open source COVIDx dataset. It has approximately 14,000 X-Ray images. To the best of authors' knowledge, this dataset contains the largest number of COVID-19 positive cases. The study offers a Deep Learning approach contributing to create an efficient COVID-19 detector that can be used in the real world.

Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules on chest CT scans provides a chance to design an effective treatment. The focus of this study is the classification problem of benign and malignant pulmonary nodules in CT images. Thus, it is proposed to apply texture maps directly to the 3D nodules as a previous of the feature extraction process. For this, the local binary patterns (LBP), with branches, such as using neighbors with borders (LBP-6), average dimensions (LBP-M), and a 3×3×3 neighborhood (LBP-3×), to highlight the nodule texture. Convolutional Neural Networks, such as DenseNet, ResNet, and LeNet, were used as attribute extractors using the 3D texture maps computed. Then, those deep features are used as input to train a Random Forest classifier. In the experiments, it is used LIDC-IDRI image database. The LIDC-IDRI database was used with two segmentation process, one made by radiologists, present in the base itself (B1), and one performed automatically by a third party (B2). In B1, the best result was the original nodules' attributes extracted with the DenseNet architecture reaching an accuracy of 0.8371, a specificity of 0.9130, sensitivity of 0.7328, and Kappa of 0.6591. In B2, the best result was a combination of attributes of the original nodule combined with the extracted LBP-6 with LeNet architecture that reached an accuracy of 0.9037, a specificity of 0.8453, sensitivity 0.9266, and Kappa of 0.7641. In conclusion, it is possible to improve the classification accuracy by including a texture map computation as part of the process.

Metastatic tumor is one that spreads from its place of origin to other parts of the body. A tumor formed by metastatic cancer cells is called a metastatic tumor or metastasis. The early identification of these tumors is essential to increase the chances of success in treating the disease. However, for this identification it is necessary to analyze extensive tissues of the affected organs, which is a tiring and error-prone task. In this paper, it is present three deep learning strategies for automatic identification of metastasis in histopathological images. For the development and evaluation of these strategies it was used the PCam database, which is composed of 327,680 color images extracted from histopathological exams of sections of lymph nodes. The obtained results using the fine tuning technique are promising, showing that deep learning models can be used for metastasis identification.

COVID-19 is an infectious disease caused by a type of coronavirus recently discovered, called SARS-CoV-2. It has infected more than 20 million people worldwide and it is responsible for more than 737,000 deaths. This work presents a study that explores linear regression mechanisms combined with a sliding and cumulative time window approach to provide inputs to assist in decision making for public policies, within the scope of the COVID-19 pandemic evolution, whether they are hardening or easing the isolation. Data from five states of Brazil were collected and applied a Ridge regression to predict the curve behavior of cases and deaths of COVID-19. As a result, an Explained Variance Status (EVS) up to 0.998 and 0.999 is presented, considering cases and deaths, respectively. It was concluded that sliding time window bring more information about the infection than cumulative, since public policy changes in a few time-lapse.

Lung cancer is one of the leading death causes by cancer worldwide. Early diagnosis increases the patient's cure chances. This diagnosis is made by computed tomography, an imaging exam that provides accurate information about the nodule. However, it depends on many external factors, from equipment quality to the fatigue of expert who analyzes. Image processing techniques might be great allies in early nodule detection, once it has no human limitations. This study presents an evaluation of two deep learning approaches, 3D U-Net and 3D V-Net, with different configurations of architectures, parameters, and data augmentation distribution applied to pulmonary nodules segmentation. The best results obtained mean an IoU of 0.74 and 0.99 for 3D U-Net and 3D V-Net, respectively. The second network obtained the best results because it is a much more robust network than the 3D U-Net, since it is a network developed for volumetric data processing.

Diabetes is one of the fastest-growing chronic diseases in the world. Diabetic retinopathy, a complication of Diabetes that affects vision, and if not treated promptly, can lead to total blindness of the patient. This abnormality has no cure, but if discovered in its early stages, there is a high chance that the patient will not reach total blindness. Detection of retinal background exudates is essential for the early diagnosis of diabetic retinopathy. In this paper, we present a deep learning model with a Convolutional Neural Network to diagnose exudates' presence or absence. The best results are about 99.52% sensitivity, 100% specificity, and about 99.76% accuracy for 1,608 images. Thus, the authors believe the proposed method can integrate a clinical system.

Epileptic seizure prediction is a critical patient-specific challenging task, relies on big data streams, and is essential for patient care. With the aid of recent advances, together with forthcoming technologies and powerful computing capabilities, smart healthcare attracts great attention in harnessing Intelligent Computing to yield seizure prediction and detection. However, it is unclear how even simple classification methods can be employed to carry out such challenging and mission-critical tasks. This paper investigates the performance impact of mainstream supervised Machine Learning techniques with different configurations in predicting epileptic seizures. A lab-premised testbed, along with neurophysiological data in dogs, enables the set of tests. Through analysis in the Area Under the ROC Curve (AUC) Key Performance Information (KPI), it was found that classification committees show improved performance capabilities than single classifiers. Overall, we believe this work represents a step towards making seizure prediction more accurate and widely available in different computational platforms.

This paper proposes a novel routing protocol for opportunistic networks called Fuzzy logic-based Q-Learning Routing Protocol (FQLRP), which uses fuzzy based Qlearning for efficient routing. The proposed protocol predicts the next optimal forwarder of a message based on a reward mechanism that considers the node's energy, movement, and buffer space as parameters. Throughout the routing process, the residual energy of each node and the energy distribution of a group of nodes, are both considered in determining a reward function, which in turn helps in deciding the most suitable forwarders of the message towards its destination. Simulation results show that the proposed FQLRP scheme outperforms the Q-Learning based routing and the Epidemic routing protocols, chosen as benchmarks, in terms of delivery rate, average delay and overhead ratio.

The development of nanomedical systems through the Internet of Bio-Nano Things (IoBNT) paradigm promotes designing of therapeutic models to facilitate drug transport and delivery. Such systems utilize microbial communities such as bacteria, which act as biosensors for molecular communication. We model the drug transport and delivery system by considering more realistic properties and characteristics of the biosensor community. We devise a Markov Decision Process (MDP) to model the biosensor lifecycle while considering division and death as parameters to regulate the model. This aids in estimating the required number of drug encapsulated biosensors. The proposed model indicates an increase in the number of instances of biosensor-target interactions that would be required for a better understanding of system dynamics. The proposed approach suggests a populace-aware coordination scheme with 3.5% increase in population, along with 20 -50% increase in information delivery. The solution proposed here can be harnessed in designing the number of optimum drug dosages. We show the effectiveness of our model with 90% increase in average biosensor lifetime, while highlighting the increase in the energy utilized in the network.

Access control is one of the important security services that is essential for an Internet of Things (IoT)-enabled authorized user using his/her smart mobile device to authenticate with the trusted Hospital Authority (HA) in a hospital. After mutual authentication, a secret key is established among the user and HA for secure data transmission. The secure data (transactions) gathered by the HA from the users in the hospital is encrypted using a shared key among various trusted hospital authorities involved in the private blockchain network of hospitals. The HA of each hospital is responsible for constructing the blocks in the blockchain using the encrypted transactions because the data in healthcare application is treated as confidential and private. To deal with this important problem, we design a novel access control scheme using private blockchain technology. The proposed scheme is shown to be secure against various well-known attacks. Moreover, the proposed scheme provides better security and functionality features, and also requires low communication and computational costs as compared to relevant approaches.

During the last few years, several works have dealt with Decision-Making Information System (DIS) design in a range of areas to support in adequate decision-making. However, several of these works and solutions provided with the DIS, reflect limits at the level of their different phases. To facilitate the implementation of any DIS, the main objective of this paper is to study the issues in the DIS. This paper is a literature investigation on the difficulties, and failures at the different phases of a DIS, in a set of domains. This deep review was carried out on basis of around twenty references and papers. It allowed to detect, identify, and determine the existing issues and their relevance because untreated yet, those treated but not resolved, those resolved but still with major drawbacks, and those completely solved. This is systematic contribution for decision-making project, thanks to the expression of a large investigation field, (of open questions to resolve), and opportunities for future researches.

With the progress of new technologies, certain areas such as health have seen their functioning modes to be changed to accommodate the trend of the actual world. Then, new concepts have emerged such as telemedicine, e-health, or the Internet of Medical Things (IoMT). These innovations quickly spread around the world to turn the tide. But nevertheless, some West African countries, such as Senegal, are still trying to get to grips in the perfect use of these tools by trying to provide solutions that address real societal problems. This paper proposes an IoMT application for diagnosis and help of diabetics, DiabLoop. The application measures blood glucose levels at reconfigurable time intervals to detect potential diabetes or monitor the progress of existing diabetes. An algorithm-based intelligent part is built-in that allows the system to use a knowledge base to make decisions automatically to help or send an urgent notification to a registered physician, guardian and/or to a parent. The implementation and case studies for validation show that the application facilitates the daily life of diabetics, which is an innovation in the field of health in West Africa.

We in this paper consider a scenario where multiple vehicles jointly provision computing resources to obtain their benefits in the contexts of vehicular fog computing. A community that vehicles can freely join and leave is sponsored by a road side unit (RSU) and thus a resource pool is established such that tasks can be performed by sufficient computing resources. RSU as a coordinator takes in charge of decision making for task scheduling. A permutation of community members is established in advance and updated periodically so as to make the most suitable decision. A task scheduling strategy is proposed from the perspective of service oriented architecture. We have carried out the experiments to investigate our approach and the experimental results have revealed our approach has a great advantage over other approaches in terms of pursuing the values of the community.

Internet of Medical Things (IoMT), intended for real-time health monitoring, are generating quantity of health data such as electrocardiogram, oxygen saturation, and blood pressure every second. The captured data should be processed and analyzed in a delay sensitive way which is vital to the survival rate for cardiovascular and cerebrovascular diseases. In this regard, Unmanned Aerial Vehicles (UAVs) have already demonstrated the enormous potentials. To begin with, due to better line-of-sight, wider communication and more flexible on-demand deployment, UAVs can realize seamless wireless connection to IoMT. Furthermore, UAVs can act as fog nodes to provision services for IoMTs such as task performing and data analysis. We in this paper focus on a sub-problem, i.e., the placement of UAVs over the serving area when they function as fog nodes. In the airborne fog computing, the placement of UAVs has an important influence on energy consumption and exploration area, let alone the communication coverage of the personal health devices on the ground. Therefore, we in this paper propose a particle swarm optimization (PSO) based algorithm to optimize the UAV placement over the serving area for the IoMT devices. We have conducted extensive simulations to evaluate it. The results show that our approach can significantly reduce the number of UAVs needed to deploy while considering the communication coverage and other factors.

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.

Summary The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. A fully automatic model‐based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI. However, identification the tumor kind utilizing the texture features. A total of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model‐based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%, with 89 sensitivity and 94 specificity.

Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have to sense the channels periodically. Due to this, a lot of time is wasted which could have been utilised for transmission of data. Employing road side units (RSUs) in sensing can prove to be useful for this purpose. The RSUs may select the channel and allocate it to the vehicles on demand. However, this sensing should be proactive. RSUs should know in advance the channel to be allocated when requested. For this purpose, a deep reinforcement learning algorithm namely deep reinforcement learning based optimal channel selection is proposed in this study for training the network according to the previously sensed data. Proposed protocol is simulated and results are compared with the existing methods. The packet delivery ratio is increased by 2%, throughput is increased by 1.8%, average delay is decreased by 2% and primary user collision ratio is reduced by 3.2% when compared with similar recent work by varying number of vehicles. On the other hand, when compared with similar recent work by varying channel availability, the packet delivery ratio is increased by 4.5 %, throughput by 4.3%, average delay is decreased by 3% and PU collision ratio by 5.5%.

The public transportation system (PTS) in a metropolitan area is a nonlinear, dynamic, and complex system. Managing and providing suitable public transportation services are difficult. In this article, we propose an Internet of Things-based intelligent PTS (IoT-IPTS) in a metropolitan area. An IoT is used to interconnect transportation entities, such as vehicles, commuters (mobile phones), routes (sensors), roadside units (RSUs), etc., in a metropolitan area. The IoT provides the seamless connectivity between different networking technologies whenever the commuters or vehicles move from one location to another location. Hence, IoT provides the suitable seamless public transportation services in the metropolitan area. In addition, we have used context information of transportation entities, such as routes condition, traffic density, number of routes available, traffic congestion, vehicles' movement, and their mobility, which are stored in the cloud. The stored context information in cloud along with the IoTs are used to find the relevant routes, alternative modes, departure times, and many more for providing public transportation services in a metropolitan area. The proposed IoT-IPTS makes use of static and mobile agents with the emergent intelligence technique (EIT) for collecting, analyzing, and sharing context information. The analyzed context information is used to form the policies to provide the best available public transportation services to the commuters in a metropolitan area. The software-defined network is used to enable the cloud computing and EI network to manage the public transportation services to the commuters.

Interconnection and intercommunication between people, processes, and things have been enhanced with the development of the Internet of Things (IoT) and extended with fog computing for better efficiency. Fog computing improves the network services and circumvents the problem of escalated data management as an interface between cloud and terminal with the requirement of secure data transmission. In this article, a novel security framework is designed for fog computing intended for improving the security of IoT. This framework uses single point of aggregation from fog interface and lattice cryptographic approach to establish the security for the services. Azure cloud platform and Sage lattice cryptographic base have been employed to evaluate the performance of the framework. It is noticed that the Level 1-Level 2 (L1-L2) cache implementation in the system significantly improves the efficiency of the framework. The results show that the proposed framework is robust in preventing attacks on fog nodes and additionally provides an advantage for low communication overhead.

Cloud systems are inefficient in processing delay-sensitive applications due to the WAN latency associated. To augment the processing of cloud services and provide delay-free computation, fog computing is used. The delay sensitivity of the tasks and heterogeneity of the fog-cloud hybrid architecture calls for efficient resource allocation policies. The decision making must be precise and also multiple criteria must be considered while deciding which resources to allocate. In this article, we propose two variants of analytic hierarchy process (AHP)-based resource allocation policies for fog-cloud hybrid systems. The proposed resource allocation policies consider network load, in addition, to the compute load during decision making. The overall aim of the resource allocation policies is to reduce the delay incurred by each task. The allocation policies differ in the way they assign weights to each criterion of optimization. One of the resource allocation policies uses predetermined weights for compute and network while the second method finds the weights dynamically from the overall data. The experimental results show that the proposed approach outperforms existing resource allocation approaches thereby showing the usefulness of AHP-based optimization in fog-cloud hybrid systems.

Social networks are becoming popular, with people sharing information with their friends on social networking sites. On many of these sites, shared information can be read by all of the friends; however, not all information is suitable for mass distribution and access. Although people can form communities on some sites, this feature is not yet available on all sites. Additionally, it is inconvenient to set receivers for a message when the target community is large. One characteristic of social networks is that people who know each other tend to form densely connected clusters, and connections between clusters are relatively rare. Based on this feature, community-finding algorithms have been proposed to detect communities on social networks. However, it is difficult to apply community-finding algorithms to distributed social networks. In this paper, we propose a distributed privacy control protocol for distributed social networks. By selecting only a small portion of people from a community, our protocol can transmit information to the target community.

Rail transit systems are important parts of modern cities such as the urban subways and intercity trains. During the travelling time, many passengers access the networks via cellular communications for working or entertainment. However, intermittent connection of LTE signals usually decreases the user experiences, especially due to the poor LTE link quality of underground subways. On the other hand, we observe that there are usually multiple cellular operators covering a city. For example, AT&T and T-Mobile in American cities, China Mobile and China Unicom in Chinese cities. Based on the different eNodeB distributions of different operators, we propose to study a new problem cross-operator switch. Unlike the handover between eNodeBs from one operator, cross-operator switch can switch to an eNodeB belonging to other operators. Although current mobile phones support dual SIM cards and Android supports the quick swap between two cards, it is still challenging to determine which eNodeB to switch because of no information exchange between different operators. To address this challenge, we propose a robust Crowdsensing based Switch (CrowdSwitch) solution dedicated for rail transit systems. CrowdSwitch utilizes mass mobile devices of passengers to sense and collect LTE signals along subway tracks, builds the signal heat map (S-Map) in the cloud servers, and finally recommends the optimal switch taking advantages of the known tracks. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and always recommend the optimal operator for users to connect.

Security and privacy are the major concerns in cloud computing as users have limited access on the stored data at the remote locations managed by different service providers. These become more challenging especially for the data generated from the wearable devices as it is highly sensitive and heterogeneous in nature. Most of the existing techniques reported in the literature are having high computation and communication costs and are vulnerable to various known attacks, which reduce their importance for applicability in real-world environment. Hence, in this paper, we propose a new cloud based user authentication scheme for secure authentication of medical data. After successful mutual authentication between a user and wearable sensor node, both establish a secret session key that is used for future secure communications. The extensively-used Real-Or-Random (ROR) model based formal security analysis and the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool based formal security verification show that the proposed scheme provides the session-key security and protects active attacks. The proposed scheme is also informally analyzed to show its resilience against other known attacks. Moreover, we have done a detailed comparative analysis for the communication and computation costs along with security and functionality features which proves its efficiency in comparison to the other existing schemes of its category.

As one of the next-generation network technologies for data centers, wireless data center networks have important research significance. Smart architecture optimization and management are vital for wireless data center networks. With the ever-increasing demand for data center resources, the deployment of the data servers are on the rise. However, traditional wired links among servers are expensive and inflexible. Benefitting from the development of intelligent optimization and other techniques, this article studies a high-speed wireless topology for wireless data center networks. A radio propagation model based on a heat map is constructed. The line-of-sight issue and the interference problem are also discussed. By simultaneously considering the objectives of coverage, propagation intensity, and interference intensity, as well as the constraint of connectivity, the topology optimization problem is formulated as a multiobjective optimization problem. To seek the solutions, several state-of-the-art serial multiobjective evolutionary algorithms (MOEAs), as well as parallel MOEAs, are employed. Prior knowledge is preferred for the grouping, and parameter adaptation is conducted in the distributed parallel algorithms. Experimental results demonstrate that the parallel MOEAs perform effectively in the optimization results and efficiently in time consumption.

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 papers in this special section focus on intelligent informatics for the edge of things in smart industrial ecosystems. In the recent years, Internet of Thing (IoT) has been widely deployed in numerous areas ranging from the development of smart cities and smart homes, smart grid, smart vehicles, smart health to the smart manufacturing and industrial management. By investigating and collecting huge amounts of data in an intelligent manner, these smart systems can improvise the decision making, business flows, automate industrial control processes, production, and economic results. With this motivation, IoT has made way into every corner of modern smart industrial ecosystem and economy.

Opportunistic Networks are composed of wireless nodes opportunistically communicating with each other following the store, carry and forward mechanism. These networks are designed to operate in an environment characterized by high delay, intermittent connectivity and non-guarantee of the end-to-end path between the sender and the destination. The messages are transmitted on the basis of best-effort procedure. If the nodes are not able to forward the message for reasons like missing connectivity, insufficient buffer space or low-confidence among nodes, the messages are temporarily buffered according to the waiting-list policy and it is resumed when the connection is established again. The nodes drop the message on the basis of delete policy in a congested network environment. While there are multiple policies for effective buffer utilization in Opportunistic Networks such as FIFO, LIFO, and Random, none allow message transmission on the basis of message- type. In this paper, a Priority based Buffer Management Technique (PBMT) has been introduced that considers the priority of a message to address the aforementioned problems. This policy allows solving the underlying problem of transmitting messages in a random fashion, by transmitting them in a systematic and orderly method. The proposed PBMT shows considerable difference in routing processes. Simulation results that are provided, confirm that the proposed PBMT is more secure and efficient than traditional buffer management policies for opportunistic networks by using the Haggle INFOCOM 2006 real mobility data trace.

The Internet of Drones (IoD) is a multi-layered, control architecture to regulate and coordinate the navigation of Unmanned Aerial Vehicles (UAV) in a shared public airspace. UAVs have the potential to be employed in public space for pur- poses like surveillance, monitoring, package delivery, emergency services, etc. For proper operation, an efficient path planning among IoD is required so that they can adaptively decide their path for data dissemination. Most of the existing solutions for this problem have made unreasonable assumptions and do not offer scalability. The scheme proposed in the paper provides a network architecture for the scalable solution of UAVs in an urban environment addressing issues of path planning, safety, privacy, and network connectivity. The scheme has been tested using exhaustive simulation and results prove that the proposed scheme is efficient in terms of reducing the overall cost and delivery time with the increasing weight of payload in the drones.

The multi-set intersection cardinality operation is used for calculation of similarity between two sets which has various applications such as cluster analysis, image segmentation, social network analysis, etc. The need of Privacy Preserving Computation of Multi-set Intersection Cardinality (PPCMIC) operation is raised when two parties want to compute similarities between their datasets without disclosing their data to each other. Existing methods for PPCMIC are either insecure or inefficient. In our work, to address this gap, PPCMIC protocol based on lightweight randomization protocol is proposed which is secure and efficient in terms of computation cost. The experimental work has been done on simulated and real datasets to show that proposed protocols are more efficient then the existing techniques.

In this work the problem of energy conservation in wireless Internet of Things (IoT) devices is being addressed for machine-to-machine (M2M) communication. IoT connected devices (i.e. glasses, set-top-boxes, home appliances etc.) can affect the energy levels of the IoT ecosystem and can play an active role in the level of QoS/QoE provided to the end-users, for any service demands, on-the-move. To this end, this work proposes a novel offloading methodology that hosts a “resource-aware” recommendation scheme, which allows the efficient monitoring of energy draining applications that run in an IoT ecosystem. The proposed framework allows users to have a continuous on-demand service provision where devices can actively provide the available resources to be exploited in the IoT ecosystem. Considering the latter, this work utilises an Edge-based Computing offload mechanism in M2M communication for resource-aware recommendation. The work assesses the proposed framework in the context of (i) the offered reliability for IoT services by assistive recommendation scheme and (ii) the energy conservation for a number of devices, forming the IoT ecosystem during the offloading process.

Ambient Assisted Living (AAL) is becoming a necessity in today's world. It provides care to the elderly patients who are under observation. With the advancements in the technology, the ability of health systems to indulge in the patient's life and remote monitoring has proven useful to prevent catastrophes. Automatic sensing based on sensors and computer vision enabled devices has taken up the field of AAL a notch ahead. Motivated from the aforementioned discussion, in this paper, we propose, a fretwork named as HRIDaaY (an architecture for remote monitoring of the heart rate of a patient) by using a ballistocardiogram sensor and fog computing (FC). We further demonstrate a data compression technique at the fog layer to reduce the bandwidth utilization. Then, a comparison is drawn using alone-Cloud and as fog- cloud combination implementation. Finally, the simulation results demonstrate that HRIDaaY has better accuracy of heart rate monitoring in comparison to the state-of-art schemes.

Datacenter is an irreplaceable and crucial infrastructure to power the ever-growing Internet services and applications. In response to today's application constraints (e.g., throughput, end-to-end delay, bandwidth), most datacenter networks are designed to maintain multiple parallel paths between any given pair of hosts. Consequently, multi-path routing has emerged as a technology of choice which can fully utilize the dormant path diversity. However, due to the growing heterogeneity of datacenter topologies and resource requirements, it demands intensive efforts to configure the right multi-path routing algorithms in real deployment according to the specific service targets. Such user burdens are caused by the lack of empirical knowledge about characteristics of various routing algorithms. To fill this gap, we develop a customized simulator DRE based on OMNET++ simulation environment and INET framework, and measure 5 state-of-the-art multi-path routing algorithms in datacenter covering various topologies and metrics. Apart from evaluating the standard macro metrics, we propose three new micro metrics to explore path-level characteristics, which have not been studied before. Our simulator provides a user-friendly interface for users who are interested in datacenter measurements, and the measurement results can promote future multi- path routing algorithm designs.

Surfing the Internet with mobile phones is a popular fashion to kill time in subways. However, users usually meet the intermittent connectivity caused by the high- speed movement, leading to poor user experience. We observe that almost every city is covered by multiple cellular operators. In addition, more and more mobile phones support multiple SIM cards. These motivate us to leverage multiple cellular operators together for a reliable connectivity, which is also a trend for next generation cellular network. It is challenging to build collaborations between operators, because there is no interaction between different operators and the traditional handover methods would consume much time on cross-operator cellular detection. To address these challenges, we propose a Crowdsensing based Switch (CrowdSwitch) system between multiple cellular operators in subways. CrowdSwitch uses a large number of mobile phones to measure and collect wireless signals from different locations along subway tracks, uploads them to cloud servers for analysis, and finally recommends the optimal switch strategy to users. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and recommend the optimal operator for users to connect.

With the development of smart cities, the demand for artificial intelligence (AI) based services grows exponentially. The existing works just focus on cloud- edge or edge-device cooperative AI which suffers low learning efficiency of AI, while edge-to-edge cooperative AI is still an unresolved issue. Moreover, the existing researches concentrate on the computation offloading of the AI-based task, ignoring that it is a brain-like task performing sophisticated processing to raw data, which leads to the high latency and low quality of the learning services. To address these challenges, this paper proposes an on-demand learning offloading mechanism for edge-to-edge cooperative AI. Firstly, the principle of the learning capability and its offloading are proposed for the formal description of the learning resources migration. Secondly, the proposed mechanism realizes the bilateral learning offloading utilizing edge-to-edge and cloud-edge collaborations to handle AI-based tasks with high learning efficiency and resource utilization rate. Moreover, we model the edge-to-edge learning offloading allocation based on the concatenation of deep neural network (DNN) subtasks and their heterogeneous requirement of learning resources. Simulation results indicate the rationality and efficiency of the proposed mechanism.

In this opportunity, we present the Development of a model to train mental health staff in the prevention of suicide. This paper describes a general model for carrying out the design, development, and conduct of training courses for health professionals using e-learning and a learning management (LMS) platform. The model presented is novel in the sense of including aspects related to the multidisciplinary nature of professionals who collaborate interactively with their knowledge and experience in the direction of adequately training medical personnel in techniques of identification and prevention of suicide. To this set of human resources described in the model, is added the ability to use effective and low-cost technologies such as the LMS platform, Moodle. The presentation of the model is enriched by the brief inclusion of its real deployment with the medical staff of the Zamora Hospital, Spain.

Nowadays, access to healthcare services in rural or remote regions remains a major issue in both developing and developed countries. The advent of mobile health (m-Health) services is becoming a major improvement for patients. The main objective of this paper is the development and Quality of Experience (QoE) evaluation of a mHealth solution for healthcare professionals in remote areas. The system architecture is based on a Service Oriented Architecture (SOA). Android OS was chosen for developing the application, mainly, due to its open source APIs and the vast diversity of covered mobile devices. The system was evaluated and demonstrated in a real pilot in cooperation with a healthcare institution involving 42 patients and 4 healthcare professionals. A total of 294 patients evaluated the solution. Hardware issues, such as network disconnection and energy issues were reported in 4% of all the cases. This system reduced significantly care costs lessen the need for physical contact between patient and physician.

Recently, the automatic text summarization has been widely used in text compression tasks. The Attention mechanism is one of the most popular methods used in the seq2seq (Sequence to Sequence) text summarization models. The current attention mechanisms usually use the hidden states of the encoder and the decoder to generate attention distributions. However, they ignore the information of the word waiting to be input into the decoder, leading to possible failures to obtain accurate attention distributions. In this work, we propose a novel attention mechanism further adding the decoder inputs into the operation of generating attention distributions. To our best knowledge, this is the first time that the decoder input has been added to the process of calculating the attention vector. The attention mechanism we proposed to generate the attention distributions considers context similarities as well as semantic similarities, which is closer to the behavior of the human summarizer. We also applied our attention mechanism to the seq2seq based summarization model and trained it on a large corpus containing hundreds of thousands of article-summary pairs. The experimental results on two summarization datasets demonstrate that our attention mechanism outperforms the existing well-known ones. For the popular evaluation metric of the text summarization, our method obtains a 2.93 ROUGE-2 score relative gain compared with the popular attention mechanism Bahdanau Attention, and a 2.21 ROUGE-2 score improvement compared with the best baseline method Luong Attention.

With the incoming era of 5G communication, the number of mobile devices is anticipated to increase dramatically. Flexible network resource allocation is required urgently to meet the mobility needs of a large number of users, accelerating the rise of network virtualization. However, the existing researches on virtual network embedding (VNE) consider less the virtual node migration caused by user mobility. In this paper, we attempt to address the problem of VNE supporting user mobility. The concepts of interruption penalty and blocking penalty are proposed to quantify the impact of virtual node mobility on infrastructure providers (InPs) and refine the revenue model of InPs. Then we propose a location-constrained 5G VNE algorithm, where a virtual node and virtual link pair embedding method is designed to increase the probability of successful VNE. Based on the proposed VNE algorithm, we further propose a virtual network re-embedding algorithm that can dynamically migrate the embedding of virtual nodes following user mobility. The virtual node migration is triggered by predicting the locations of virtual nodes and selecting the target physical nodes with the minimum number of re-embedding. Simulation results show that the proposed algorithm outperforms the existing VNE algorithms with higher InP revenue.

Few decades ago, wellness management systems were not in the position to give salutary to their users. One of the possible reasons is inefficient resources and minimum technological infrastructure which do not allow a comprehensive structure pertaining to specific user. Sleep apnea is one such problem which is related to the permanent condition that involve stagnation of breathing. Although incurable, it can be minimized by maintaining a healthy lifestyle. Motivated from this, in this paper, we propose a health manager directive system to investigate the precise medical condition of sleep apnea. A recommender system is used which suggests the healthy lifestyle schedule to reduce the apnea severity in a patient. A Probabilistic Markov model (PMM) is used to adhere the activities based on time consumption in different activities performed by the patient. We evaluated the recommendation cycle on three patients to demonstrate the reductions in apnea cycles by indicating sound sleep patterns. Numerical results show that the proposed recommendation System suggest a relative improvement in sleep quality for all patients as compared to pre-existing expensive detection and relief schemes for sleep apnea patients.

To reduce the delay for accessing real-time data access from various applications (healthcare, transportations, virtual reality etc.), there is an exponential increase in the usage of Tactile Internet (TI) technology in recent era. Motivated from this, in this paper, we propose a TI-based random forest (RF) learning algorithm for heart disease predictions. The aim of this paper is to monitor and analyse the human activities for real-time data collection. The proposed approach is an analysis of heart ailments and can be used regularly for the health measure. For this purpose, the RF model is trained to map the collected sensor data features to output normal and abnormal states of the patient suffering from heart disorientation. Moreover, it removes excessive dependence on input values and cover possible alternate paths. Simulated results demonstrate that the proposed approach reduces the average delay and provides less training time in comparison to the pre existing conventional techniques.

The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.

Internet of Things (IoT) has been deeply challenging current Internet and emerging architectures, including 5G and future Internet. Many architectural limitations, such as weak security, data distribution efficiency, provenance and traceability of sources, excessive human intervention, lack of interoperability, and service-awareness in devices configuration, have been exposed and called the society attention. This paper addresses these limitations by properly integrating five strategies: 1) efficient IoT data exchanging, storage and processing via information-centric networking (ICN); 2) contract-based IoT services composition; 3) software-control/management of IoT devices accordingly to the services requirements; 4) naming and name resolution of the physical and virtual entities, proving identifier/locator splitting and contextualized self-organization; and 5) name-based routing and network caching. Considering the current state-of-the art, the main contributions of this paper can be summarized as follows: proposal of a novel service-defined architecture (SDA), in which device configurations are a reflex of the real service needs (given by established contracts); combination of ICN benefits with named-services; and perennial identification of IoT devices, services, and data using self-verifiable naming. All integration work has been supported by a convergent architecture called NovaGenesis (NG), demonstrating its viability as an alternative for the current IoT architectures. A proof-of-concept prototype has been implemented in laboratory under real conditions. The experimental results indicate competitive performance in terms of data transfer, memory and CPU consumption. Embedded NG has smaller RAM and ROM requirements when compared to a similar RPL + 6LowPAN stack. Data exchanging has been performed in few milliseconds in a local area network.

As the rapid development of 5G and Internet of Things (IoT) techniques, more and more mobile devices with specific sensing capabilities access to the network and large amounts of data. The traditional architecture of the cloud computing cannot satisfy the requirements, such as low latency, fast data access for IoT applications. Mobile edge computing (MEC) can solve these problems, and improve the execution efficiency of the system. In this paper, we propose a privacy preserving data aggregation scheme for MEC assisted IoT applications. In our model, there are three participants, i.e., terminal device (TD), edge server (ES), and public cloud center (PCC). The data generated by the TDs is encrypted and transmitted to the ES, then the ES aggregates the data of the TDs and submits the aggregated data to the PCC. At last, the aggregated plaintext data can be recovered by PCC through its private key. Our scheme not only guarantees data privacy of the TDs but also provides source authentication and integrity. Compared with traditional model, our scheme can save half of communication cost, and is very suitable for MEC assisted IoT applications.

IPv6 over low-power wireless personal area networks (6LoWPANs) are comprised of resource-constrained nodes equipped with sensing capabilities that communicate with each other and are linked to the Internet contributing to the Internet of Things (IoT). Because of their constraints, these objects are exposed to attacks from inside the 6LoWPANs. Typically, intrusion detection systems (IDSs), are employed to identify such attacks. However, the alarms generated by IDSs provide no information about the severity of the attacks or to which extent the attacks affect the sensor data routed by the nodes. To address these issues, this paper proposes the node security quantification (NSQ), a probabilistic model which makes it possible to quantitatively assess, in an online manner, the security status of the constrained nodes and the sensor data users consume. The security awareness capability provided by this model can support the security-related decision-making process of both network administrators and users. The obtained simulation results demonstrate that NSQ quantifies node and data security with high accuracy, while keeping performance and energy overhead very low.

The growing demand for energy and the increasing carbon footprint in the globe has made electricity utilities to move from nonrenewable energy to renewable energy. The integration of renewables into the electric grid is increasing day-by-day. The consumers' energy consumption needs to be managed wisely and effectively. The Internet of Things has helped in connecting all homes and appliances to the Internet. With smart homes, it is possible to study consumer's usage patterns and their demand for energy. During peak hours of the day, the demand for energy increases and have to be met by the utilities by starting up additional coal-fired generation. This makes peak hour usage of electricity costly. This paper studies the usage behavior of consumers from their historical data and predicts the demand for energy every hour for the individual consumer for the next 72 h using time series analysis. Also, the work statistically studies the usage pattern of appliances in every home thereby finding which appliances play a significant role during the peak hour usage. This paper will help utilities understand how their consumers use electricity and can encourage consumers to shift usage of peak hour appliances to nonpeak hours. Also, consumers can grant control of individual appliances to utilities, to curtail the load during peak hours to reduce the demand.

Internet of Vehicles (IoV) is an intelligent application of Internet of Things (IoT) in smart transportation that takes intelligent commitments to the passengers to improve traffic safety and efficiency, and generate a more enjoyable driving and riding environment. Fog cloud-based IoV is another variant of mobile cloud computing where vehicular cloud and Internet can co-operate in more effective way in IoV. However, more increasing dependence on wireless communication, control, and computing technology makes IoV more dangerous to prospective attacks. For secure communication among vehicles, road-side units, fog and cloud servers, we design a secure authenticated key management protocol in fog computing-based IoV deployment, called AKM-IoV. In the designed AKM-IoV, after mutual authentication between communicating entities in IoV they establish session keys for secure communications. AKM-IoV is tested for its security analysis using the formal security analysis under the widely accepted real-or-random (ROR) model, informal, and formal security verification using the broadly accepted automated validation of Internet security protocols and applications (AVISPAs) tool. The practical demonstration of AKM-IoV is shown using the NS2 simulation. In addition, a detailed comparative study is conducted to show the efficiency and functionality and security features supported by AKM-IoV as compared to other existing recent protocols.

The Internet of Drones (IoD) provides a coordinated access to unmanned aerial vehicles that are referred as drones. The on-going miniaturization of sensors, actuators, and processors with ubiquitous wireless connectivity makes drones to be used in a wide range of applications ranging from military to civilian. Since most of the applications involved in the IoD are real-time based, the users are generally interested in accessing real-time information from drones belonging to a particular fly zone. This happens if we allow users to directly access real-time data from flying drones inside IoD environment and not from the server. This is a serious security breach which may deteriorate performance of any implemented solution in this IoD environment. To address this important issue in IoD, we propose a novel lightweight user authentication scheme in which a user in the IoD environment needs to access data directly from a drone provided that the user is authorized to access the data from that drone. The formal security verification using the broadly accepted automated validation of Internet security protocols and applications tool along with informal security analysis show that our scheme is secure against several known attacks. The performance comparison demonstrates that our scheme is efficient with respect to various parameters, and it provides better security as compared to those for the related existing schemes. Finally, the practical demonstration of our scheme is done using the widely accepted NS2 simulation.

The integration knowledge of wearable sensors, wireless communications, and artificial intelligence have brought forth the smart health systems, which empower the consumer’s to make a difference to their well-being by connecting data to personalized analysis to timely insights. Therefore, the real-time data obtained directly reflects the personal status of interest and can be used in a variety of healthcare applications in the Internet of Things (IoT), from preventive treatment to diagnostics and rehabilitation, as well as in virtual and augmented reality environments.

The high proliferation of on-line gaming along with the high demands of availability of network resources, created the need for the development of Cloudified services that will augment computation capabilities of mobile devices. To this end, this work elaborates on the design, the development and the comparative evaluation with other similar models, as well as with real-time comparisons through emulators, of a process-offloading scheme that is based on a mobile opportunistic cloud computing approach. According to the proposed approach, each mobile device with access to interactive -delay sensitive- multimedia content (i.e. online gaming with processing power requirements) exploits several network-centric parameters, by using Nano-Mobile Data Centers for an interactive, collaborative and real-time manipulation of the available resources. The communication and the social context is used by the mobile nodes with other communication related parameters, towards achieving the efficient execution of the offloading process in order to support adequate quality of service. The proposed scheme allows interactive mobile users to efficiently exploit their resources, while the processes that cannot be locally handled (by each device), are effectively offloaded. The scheme aims at prolonging the lifetime of each mobile device and maximizing the efficiency in running context interactive applications. The efficiency of the proposed scheme is validated through comparative performance evaluations with other similar schemes, indicating the level of the mobile nodes lifetime extensibility that is offered, in contrast to existing approaches.

Both limited main memory size and memory interference are considered as the major bottlenecks in virtualization environments. Memory deduplication, detecting pages with same content and being shared into one single copy, reduces memory requirements; memory partition, allocating unique colors for each virtual machine according to page color, reduces memory interference among virtual machines to improve performance. In this paper, we propose a coordinate memory deduplication and partition approach named CMDP to reduce memory requirement and interference simultaneously for improving performance in virtualization. Moreover, CMDP adopts a lightweight page behavior-based memory deduplication approach named BMD to reduce futile page comparison overhead meanwhile to detect page sharing opportunities efficiently. And a virtual machine based memory partition called VMMP is added into CMDP to reduce interference among virtual machines. According to page color, VMMP allocates unique page colors to applications, virtual machines and hypervisor. The experimental results show that CMDP can efficiently improve performance (by about 15.8 percent) meanwhile accommodate more virtual machines concurrently.

In the last few years, there has been an exponential increase in the deployment of 5G-based test beds across the globe with an aim to reduce the latency for accessing various applications. The integration of generic services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), critical machine-type communication (cMTC), and ultra-reliable low-latency communications (URLLC) can improve the performance of 5G-based applications. This service heterogeneity can be achieved by network slicing for an optimized resource allocation and an emerging technology, Tactile Internet, to achieve low latency, high bandwidth, service availability, and end-to-end security. In this paper, we discuss the application-specific nonorthogonal multiple access (NOMA)-based communication architecture for Tactile Internet which allows nonorthogonal resource sharing from a pool of eMBB, mMTC, cMTC, and URLLC devices to a shared base station. We summarize various variants of NOMA and their suitability for future low latency Tactile-Internet-based applications.

Device-to-device (D2D) two-hop cooperative communication improves the network coverage and throughput to provide the quality of service and quality of experience to the end users. Nonorthogonal multiple access (NOMA) can be used at the D2D transmitter to improve the spectral efficiency of the network. But, two-hop transmission with NOMA suffers from delay and interference from the neighboring nodes. To resolve the aforementioned issues, in this paper, we propose Tactile Internet (TI) driven delay assessment for D2D communication (DIYA) scheme, which works in two phases. In the first phase, a full duplex communication at relays (intermediate nodes) is used to have the first- and second-hop transmission simultaneously in the same time slot. Then, TI-based communication is used at D2D transmitter to increase the speed of transmission. In the second phase, pricing-based three-dimensional (3-D) matching is proposed to improve the throughput of the cell edge users along with the mitigation of cochannel interference. Also, the power of the D2D transmitter is optimized using successive convex approximation with low complexity, which converts the nonconvex optimization problem of subchannel allocation and power control into convex problem. Numerical results demonstrate that DIYA achieves higher throughput with reduced delay in comparison to other existing orthogonal multiple access (OMA) and NOMA-based schemes.

Due to the ever-increasing requirements of delay-sensitive and mission-critical applications in 5G, mobile edge computing is promising to react and support real-time interactive systems. However, it is still challenging to construct a 5G-enabled traffic management system, owing to the qualification of ultra-low latency and ubiquitous connectivity. Furthermore, the computing resources and storage capacities of edge nodes are limited, thus computation offloading is a fundamental issue for real-time traffic management. This paper puts forward a hybrid computation offloading framework for real-time traffic management in 5G networks. Specially, we consider both nonorthogonal-multiple-access-enabled and vehicle-to-vehicle-based traffic offloading. The investigated problem is formulated as a joint task distribution, subchannel assignment, and power allocation problem, with the objective of maximizing the sum offloading rate. After that, we prove its NP-hardness and decompose it into three subproblems, which can be solved iteratively. Performance evaluations illustrate the effectiveness of our framework.

Mobile cloud computing (MCC) allows mobile users to have on-demand access to cloud services. A mobile cloud model helps in analyzing the information regarding the patients' records and also in extracting recommendations in healthcare applications. In MCC, a fine-grained level access control of multiserver cloud data is a prerequisite for successful execution of end-users applications. In this paper, we propose a new scheme that provides a combined approach of fine-grained access control over cloud-based multiserver data along with a provably secure mobile user authentication mechanism for the Healthcare Industry 4.0. To the best of our knowledge, the proposed scheme is the first to pursue fine-grained data access control over multiple cloud servers in a MCC environment. The proposed scheme has been validated extensively in different heterogeneous environment where its performance was found good in comparison to other existing schemes.

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

The papers in this special section examines the use of interactive virtual environments in the field of neuroscience. Virtual environments is a technology able to establish a relationship between the user and the environment created, enabling real-time integration with controlled virtual objects. A virtual environment can be explored through visual and haptic devices, without real restrictions. The iteration derives from the communication between human actions and the outcome of these actions, processed by the computer generating a response inside the virtual environment. The interaction can be passive, such as watching television, or active, for instance in the case of users manipulating their body movements or a particular object inside a virtual scenario.

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.

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.

This paper proposes a new online signature verification system based on fuzzy modelling which involves the modification of the fuzzy membership function with the help of structural parameters. By using these structural parameters, the signature of a given user can be easily verified. The proposed approach relies on the extraction of features from the sample data collected from users under different situations and time intervals. Two distinct methodologies have been suggested to obtain the structural parameters: the first method is based on the Shannon entropy functions coupled with an objective function defined in terms of error while the second is based on the interaction among the input fuzzy sets which is computed using s-norms. Both genuine and forged signatures were tested using the proposed techniques with encouraging results.

The Internet of Things (IoT) concept of connecting everything to the Internet is ambitious and disruptive. Its market is promising and data centric, which is stored and processed in a software known as IoT middleware. However, a plethora of middleware solutions is available and most of the comparisons presented in the literature are qualitative, which is insufficient when choosing a solution for a real scenario. Moreover, the few quantitative comparisons that are available in the literature can only determine the best solution in separated given categories. This paper complements the conclusions of a quantitative comparison study that is available in the literature through a comparison of five middleware solutions across five different scenarios. Such conclusions were possible through PROMETHEE, a multicriteria decision-making method (MCDM). This study also confirms that best solution depends on which criteria are prioritized in a given scenario. The outcome was analyzed in detail and it was concluded that MCDMs are useful when choosing the best middleware platform to deploy in a given IoT solution. Orion (a Fiware project), InatelPlat, and Sitewhere are the platforms that performed better in the study.

The prolonged usage of non-renewable resources like petroleum and coal have adverse affect on the environment and has led to energy crisis in the world. In order to mitigate the situation, efficient strategies have been proposed for generation, distribution and consumption of energy obtained from renewable sources such as tidal, wind and solar power. With the advent of Smart Grids being developed world wide, the most widely accepted strategy is Demand-Response management. In this strategy, the customers or end-users are incentivized to change their energy-utility behavior with time in response to fluid price changes or to induce lower energy consumption during peak demand time. The system is controlled by a cloud of servers that monitor the demand- supply chain over a network all the time. This brings up the issue of security within the operations of the system. Current security mechanisms such as Rivest-Shamir-Alderman (RSA) public key encryption, Advanced Encryption Standard (AES) symmetric encryption, Elliptic Curve Cryptography (ECC) public-key cryptosytem and the recently proposed works are not future- proof in the world of post-quantum cryptography. This paper proposes a lattice based cryptographic scheme to ensure proper security in the system. The proposed scheme has been proven secure against major known attacks.

Security has become one of major concern especially in critical applications like e-healthcare. To cater to the security needs in e-healthcare, this paper proposes a novel scheme which prevents data from unauthorized fabrication and preserves the integrity of data. The proposed scheme also removes overhead of integrity validation from user's end as this work is assigned to a trusted third party, i.e., a proxy server. For this purpose, the patient's data given by user is sent to proxy server along with user's signature where it is broken down in the form of blocks. A `tag' is then generated for each block using lightweight elliptic curve cryptography (ECC). This block-tag pair is then uploaded on the data server which is used for integrity checking. Whenever a patient's data access request is raised, the block of data is retrieved using tag value and integrity is then verified. In addition to it, a lightweight lattice-based authentication scheme is proposed in the paper to authenticate the users. The request is served only when the user is deemed authentic and there is no modification in the original data sent by the user. The effectiveness of the proposed authentication scheme has been proven by performing its analysis in terms of computation time and communication cost. Moreover, the superiority of the proposed data integrity scheme has been validated by comparing it with the traditional discrete logarithmic scheme.

Vehicular ad-hoc networks (VANET) have been used in many application and services ranging from intelligent transportation to e-healthcare. However, in VANET, one of the major challenges is the cluster head (CH) selection as it influences vehicle mobility, transmission range, and inter-vehicle distance. However, for stable cluster formation in VANET, it is essential that these constraints must be considered while selecting the CH. However, with an increase in the number of nodes in a cluster, the existing CH selection schemes become inefficient which leads to a substantial increase in the execution time for aforementioned applications. Hence, to address this issue, a rough set-based scheme is presented in this paper for CH selection with an aim to reduce the CH selection time. To achieve this aim, the concept of cluster member fields (which represents similar nodes) has been used which reduces the number of nodes participating in the CH selection. The proposed scheme has been evaluated with respect to various performance metrics such as CH selection time and CH reliability (on the basis of vehicle density and average velocity of vehicles in the clusters). The results obtained confirm that the CH selection time in the proposed scheme is less and CH reliability in more as compared with an existing scheme.

Among various tools invented to help improve people's oral health, water flossers can achieve better performance than traditional and electronic toothbrushes, and are less harmful than dental floss, especially for those with orthodontic teeth or tooth implant surgeries. However, the water flossers available in the market serve no monitoring or recording functions that can help consumers clean their teeth in a more efficient way. To capture users' motions, this study develops a novel smart water flosser, installing an Inertial Measurement Unit (IMU) sensor on the handle of the flosser. We determine the motion cycle using signal processing techniques and extract a set of statistical characteristics from the data set. We then train and compare different machine learning models as classifiers to recognize the motions of the handle. We find that the Random Forest model achieves the best detection accuracy at 97% and 85% of the whole feature set and optimized set, respectively. Finally we implement an Android App that connects the smart water flosser with a Bluetooth module to show the washing area in real-time and record relevant information for further guidance.

When a large area coverage is the key application, localization and tracking techniques are facing challenges on current Internet of Things (IoT) scenario, mainly regarding areas with critical propagation environments or tracking mobile objects, such as in agriculture and cattle farming. In order to achieve a good communication solution, Low Power Wide Area Network (LPWAN) protocols offer different solutions profiles. As such, this paper presents the design and deployment of a solution based on Long Range (LoRa) modulation protocol to attend localization and tracking applications using only one base station and so, a GPS device to provide the coordinates to be transmitted. The proposal is evaluated, demonstrated, and validated in a real scenario and it is ready for use.

A Smart Meter (SM) is an electronic device that records and monitors power consumption at time intervals and can send this information to the monitoring and billing center of power companies. Thus, SMs are responsible for providing bi-directional communication between consumers and a Smart Grid central system. However, the development of a reliable SM solution is still a challenge as the majority of current works are limited to theoretical proposals. The deployment of SMs infrastructure in a real testbed is crucial not only for application of theoretical models in real environments but also to address many premises that may emerge in practical scenarios. Then, this paper proposes a low-cost Smart Meter able to provide bidirectional communication between homes and Electric Power Companies (EPC) using IEEE 802.15.4. To present and experimenting the performance of the produced SM, an Advanced Metering Infrastructure (AMI) that allows EPCs and users monitoring the energy consumption and billing in real-time through a mobile application was created. In addition to the SM applicability demonstration, a brief network performance assessment to verify the limitations of adopted communication interfaces was considered. Thus, based on real experimentation and demonstration, it was concluded the high applicability and potentiality of the proposed work in real large scale scenarios.

In recent times, smart communities such as-smart grid, smart healthcare, and smart manufacturing units consists of large number of connected devices equipped with advanced processing and communication capabilities. The focus of these smart communities have shifted towards the use of intelligent processing and control for providing better quality of service (QoS) to the end user domain. To support this aspect, software defined networking (SDN) is being widely deployed in different domains such as-data center networks, fog/edge computing, smart grid, and vehicular networks. The variable requirements of different applications in smart communities make it necessary to deploy flexible and scalable SDN. The dynamic flow management capability of SDN has lots of potential that needs to be effectively explored in order to provide QoS guarantee for traffic generated from different smart applications. In this direction, in this paper, an ensembled scheme for QoS-aware traffic flow management in SDN is designed. The proposed scheme works in three phases: 1) a linear ordering scheme for dependency removal of the incoming packets is designed, 2) an application-specific traffic classification scheme is designed, and 3) a queue management scheme is designed for efficient scheduling of traffic flow. The proposed scheme is evaluated over an experimental setup. The results obtained shows that the proposed scheme behaves effectively with respect to different QoS parameters.

The next generation of mobile telephony aims to attain the increasing demands of users in terms of flexibility, bandwidth, spectral efficiency, energy efficiency, low latency, and quality of service. In this context, adaptations to the transmission system are needed in order to be able to cope with the exponential high-speed data increase with reliability, but also to cope with the expected applications, such as the Internet of Things (IoT) and tactile Internet. Researches have studied the adaptations that should be performed and one of the modifications is the physical layer used to transmit the signal. This paper aims to analyze and optimize the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing (OFDM) and General Frequency Division Multiplexing (GFDM) which is a technique among the different types of modulation studied for 5G, operating in an Additive White Gaussian (AWGN) channel. This work also presents the performance of the possible received power levels according to the receiver and Ultra High Frequency (UHF) channels in the case of digital television. Fourier Transform is used as the mathematical model for performance evaluation and analyzes. The obtained results show that GFDM performs better in terms of interferences when Zero Forcing Receiver (ZFR) equalization is considered, which plays a key role in digital transmission.

Opportunistic networks (OppNets) is a subclass of delay-tolerant networks characterized by unstable topology, intermittent connectivity, and no guarantee of the existence of an end-to-end path between the source and destination nodes. In such networks, data forwarding from a source node to a destination node is a challenge. In this paper, an energy-aware routing protocol for OppNets (so-called Energy-efficient Location Prediction-based Forwarding for Routing using Markov Chain (ELPFR-MC)) is proposed, in which the next best hop selection of a message relies on the use of the node's residual energy and its location based on delivery probability. Simulation results show that ELPFR-MC is superior to E-Prophet, E-PRoWait, E-EDR and the Distance and Encounter based Energy-efficient Protocol for Opportunistic Networks (DEEP), where E-Prophet, E-PRoWait, E-EDR are respectively the energy-aware implemented versions of the Prophet, PRoWait, and Encounter and Distance-based Routing (EDR) protocols. The proposed ELPFR-MC outperforms DEEP in terms of node's residual energy by 6.34%, number of dead nodes by 6.58%, message delivery probability by 16.83% and average latency by 8% respectively when number of nodes are varied.

The rise of 5th Generation (5G) based network systems provide the prospect for an unprecedented technological revolution in different aspects of current network infrastructures to fully satisfy the high demands of smart space. This work addresses the challenges that raise in exploiting the potential of WiFi WLAN-sharing technology in 5G Ultra-Dense Networking (UDN) use cases. We investigate new complementary aspects of emerging 5G technologies such as Network Function Virtualization (NFV) and Fog computing to design a unique WiFi WLAN-sharing ecosystem to allow complying with 5G UDN critical requirements. In the resulting approach, we empower WiFi WLAN-sharing infrastructures with Fog computing capabilities and follow a slice-defined approach, aiming to provide differentiated services at unprecedented levels, on top of the same infrastructure through customized, isolated and independent building blocks. The solution also enables slices to accommodate applications besides networking functions, seeking to provide ultra-low latency rates by leveraging direct linkage to data producer entities. A proof of concept was conducted by carrying out experiments in a real laboratory testbed, allowing insights into the feasibility and suitability of slicing WiFi WLAN-sharing systems.

Smart grid (SG) is a modernized power grid that uses information and communication technologies for bidirectional flow of information between the power utilities and the consumers. Nowadays, the focus of SG has shifted towards intelligent processing and control of various operations in order to provide high quality of experience to the end users domain (consumers, smart devices, utility, etc). Therefore, in near future, for smooth execution of various operations in SG, high volume of data is expected to move across different inter-connected smart devices. So, to handle this challenge, a self-configurable network technology known as software-defined networking (SDN)that provides faster and dynamic forwarding of data through adaptable flow-table management is a viable solution. However, in SDN- enabled SG systems, security and privacy are major challenges that need to be handled effectively. So, in this paper, a lattice-based cryptosystem for secure communication in SG environment, called LaCSys, is presented which works in three phases. In first phase, a secure authentication between all the network communication entities based on lattice based key exchange scheme is designed using a third party auditor (TPA). In second phase, a lightweight lattice-based public-key encryption scheme is designed to provide data confidentiality and integrity. In last phase, a temporary key-based scheme for detection of suspicious activity is designed. The proposed crytosystem is evaluated and compared with existing scheme in order to prove its effectiveness.

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 process of knowledge discovery in health databases has steadily been developing. Data mining (DM) techniques integrate several knowledge fields for extraction of reliable, understandable, and useful patterns, such as statistics and artificial intelligence (AI). In this sense, this paper proposes the application of a machine learning (ML) technique, named support vector machine (SVM), for the recognition of patterns in a pregnancy database. This approach has outperformed other ML methods, representing a valuable tool for smart decision support systems (DSSs) and mobile health (m-health) applications. For the performance assessment of the proposed model, this work uses the 10-fold cross-validation method. This ML based technique obtained encouraging results with an accuracy of 0.821, a true positive (TP) rate of 0.839, a false positive (FP) rate of 0.268, and receiver operating characteristic (ROC) area of 0.785. These indicators show that this approach is an excellent pattern recognizer for pregnancy care. This research provides a comprehensive inference mechanism for mobile DSSs capable of enhancing the care provided to women who are at a risk of developing pregnancy-related problems. Thus, this work can contribute to improve the maternal and fetal health conditions, predicting preterm birth risk early.

Healthcare-oriented wireless sensor network (HWSN) is one of the applications of wireless sensor networks in e-health. It not only can better achieve the physiological information of people, but also more efficiently reduce the Iatency regarding information collection and transmission. However, similar to other distributed networks, it also faces enormous security challenges, especially from internal attacks. It is difficult to distinguish many attack behaviors from interference in the complex healthcare scenarios, such as On-Off attack. In this paper, we propose a Binomial Distribution-based Trust Management Scheme (BDTMS) for HWSN. The proposed method can rapidly detect and effectively defend against On-Off attacks. In addition, the proposed method is also applicable to defending against bad mouthing attacks. Simulation results show that, compared with the Time-window-based Resilient Trust Management Scheme (TRTMS), our proposed BDTMS achieves better performance in defending against On-Off attack under obstacle movement, especially with higher detection accuracy.

MAC layer adaptation is very crucial for supporting dense and diverse data requirements of sensor networks in smart cities, powered by energy harvesting. In this paper, we perform MAC layer optimization for maximizing throughput subject to application-specific needs and energy availability in Solar Energy Harvesting Wireless Sensor Networks (EH-WSNs). In contrast to previous schemes that limit energy consumption based on current availability only, we propose Energy Prediction based Energy Management algorithm (EPEM). This algorithm exploits energy prediction and sets threshold rate of energy consumption to ensure accumulation of sufficient energy for non- energy harvesting period. Our analysis shows that MAC optimization (MO) along with EPEM algorithm not only improves performance by 72% but also avoids energy scarcity during non-energy harvesting period.

Solid waste management is a significant worldwide problem, mainly for municipalities located within large urban areas. Efficient waste management is an essential requisite for a clean and safe environment, and keep cities away from the harm caused by the mismanagement of solid waste produced in urban centers. There are many technologies for managing waste collection as well as recycling, but among the studied related works, none addresses the citizen's perspective, focusing only on the collection performed by the landowners. This paper proposes an integrated system combining identification through ultrasonic sensors and load cell sensors, location by Global Positioning System (GPS), and communication through Global System of Mobile Communications (GSM) / General Packet Radio Service (GPRS). In other words, everything to provide citizens with a better disposal methodology for waste generated in their homes, besides being easily integrated with the municipal collection service to assist in efficient collection scheduling by promoting optimized routes. The solution is demonstrated, validated, and is ready for use.

Localization and tracking applications are bringing new challenges to the Internet of Things (IoT) scenarios, especially when it comes to applying this concept in areas of high coverage. The emerging Low Power Wide Area Network (LPWAN) protocols offer, in their own way, solutions for this type of applications. This paper presents the design and implementation of a localization and tracking solution based on the Sigfox protocol that uses only one base station and so, a GPS device to supply the coordinates to be transmitted. An Information Centric Network (ICN) concept applied IoT network application model as a propose of a new information-driven service architecture. High level Application Program Interfaces are the main connection between services and information. The proposal is evaluated, demonstrated and validated in a real scenario and it is ready for use.

One of the most fundamental tasks in the socially aware network (SAN) paradigm is to explore the attributes and behavior of users, which helps to design more suitable and efficient protocols. Particularly, detection of shilling attackers by mining users’ behavior is a frequently discussed topic in many social scenes like recommender systems based on collaborative filtering. As the performances of collaborative filtering are entirely based on ratings provided by users, they are vulnerable to shilling attacks which perform injection of biased profiles into rating databases to alter the systems. Current shilling attack detection methods detect spam users through artificially designed features, which are neither robust nor efficient enough. This paper illustrates a novel convolutional neural network-based method named CNN-SAD, which applies transformed network structure to exploit deep-level features from users rating profiles. Since the achieved deep-level features elaborate users rating more precisely than artificially designed features, CNN-SAD can detect shilling attacks more efficiently. According to the experimental results, the proposed method is capable of detecting the vast majority of obfuscated attacks precisely and outperforms other state-of-the-art algorithms, which contributes to applications and security in SAN.

Internet of Things (IoT) is a term used to describe an environment where billions of objects, constrained in terms of resources (“things”), are connected to the Internet, and interacting autonomously. With so many objects connected in IoT solutions, the environment in which they are placed becomes smarter. A software, called middleware, plays a key role since it is responsible for most of the intelligence in IoT, integrating data from devices, allowing them to communicate, and make decisions based on collected data. Then, considering requirements of IoT platforms, a reference architecture model for IoT middleware is analyzed, detailing the best operation approaches of each proposed module, as well as proposes basic security features for this type of software. This paper elaborates on a systematic review of the related literature, exploring the differences between the current Internet and IoT-based systems, presenting a deep discussion of the challenges and future perspectives on IoT middleware. Finally, it highlights the difficulties for achieving and enforcing a universal standard. Thus, it is concluded that middleware plays a crucial role in IoT solutions and the proposed architectural approach can be used as a reference model for IoT middleware.

Due to the widespread popularity of Internet-enabled devices, Industrial Internet of Things (IIoT) becomes popular in recent years. However, as the smart devices share the information with each other using an open channel, i.e., Internet, so security and privacy of the shared information remains a paramount concern. There exist some solutions in the literature for preserving security and privacy in IIoT environment. However, due to their heavy computation and communication overheads, these solutions may not be applicable to wide category of applications in IIoT environment. Hence, in this paper, we propose a new biometric-based privacy preserving user authentication (BP2UA) scheme for cloud-based IIoT deployment. BP2UA consists of strong authentication between users and smart devices using preestablished key agreement between smart devices and the gateway node. The formal security analysis of BP2UA using the well-known real-or-random model is provided to prove its session key security. Moreover, an informal security analysis of BP2UA is also given to show its robustness against various types of known attacks. The computation and communication costs of BP2UA in comparison to the other existing schemes of its category demonstrate its effectiveness in the IIoT environment. Finally, the practical demonstration of BP2UA is also done using the NS2 simulation.

Social development and technological advances have enabled the Internet of Vehicles (IoV) to combine the social factors to form a new intelligent transportation system: Social IoV (SIoV). The emergence of SIoV helps to find new traffic management solutions of the serious problems caused by the ever-increasing traffic flow. In this paper, we propose an algorithm called social vehicle route selection (SVRS) to reduce traffic congestion and achieve the purpose of traffic flow control. First, a social clustering method for SIoV is designed by utilizing both historical and current driving information. Then we use game evolution to calculate the optimal route for vehicles, and prove the vehicle route selection game is a potential game and its strategy selection converges to Nash equilibrium. Extensive simulations are carried out to evaluate the SVRS with several performance criteria. Our analysis and simulation results demonstrate that SVRS algorithm can achieve high performance in clustering the vehicles and reducing traffic congestion.

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

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.

Energy management in commercial buildings is a challenging task due to their specific set of requirements. One such building that has not been fully investigated in the literature to provide energy efficiency is a university building. There are many challenges associated while managing the energy of a university building, such as-scheduling of classes, availability of faculty, and capacity of classrooms. To address these challenges for providing better energy efficiency, an efficient heating, ventilation, and air-conditioning (HVAC) management scheme for a university building is presented in this paper. The HVAC loads are chosen as these are more flexible in the classrooms than other loads, such as-lighting and projectors. In this paper, the HVAC energy management problem is formulated as a mixed-integer linear programming (MILP) problem. To solve this problem, a heuristic-based algorithm is proposed, which optimally minimizes the use of HVAC without affecting user comfort. Moreover, it also minimizes the cost of rescheduling the classes on a given day. The results obtained on the dataset traces taken from a university building clearly indicate that the proposed scheme reduces the energy demand of HVAC systems by 19.75% for an entire week without affecting the user comfort. Moreover, this scheme shows superior performance when compared with existing commercial demand response management schemes with respect to load reduction and cost savings.

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.

With advancements in information and communication technology, there is a steep increase in the remote healthcare applications in which patients can get treatment from the remote places also. The data collected about the patients by remote healthcare applications constitute big data because it varies with volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges which requires a specialized approach. To address this challenge, a new fuzzy rule based classifier is presented in this paper with an aim to provide Healthcare-as-a-Service. The proposed scheme is based upon the initial cluster formation, retrieval, and processing of the big data in cloud environment. Then, a fuzzy rule based classifier is designed for efficient decision making for data classification in the proposed scheme. To perform inferencing from the collected data, membership functions are designed for fuzzification and defuzzification processes. The proposed scheme is evaluated on various evaluation metrics, such as average response time, accuracy, computation cost, classification time, and false positive ratio. The results obtained confirm the effectiveness of the proposed scheme with respect to various performance evaluation metrics in cloud computing environment.

Periodic beacons in vehicular ad hoc networks are transmitted with high message frequency to achieve the higher level of awareness required for the vehicular safety applications. Currently, the existing 10-MHz control channel in the dedicated short range communication (DSRC) standard is not compliant with the communication requirements in vehicular safety applications, i.e., the stipulated amendments in the DSRC offer little relief to congestion caused by the periodic beacon transmissions on the control channel, which adversely affects the message reception. In this paper, we seek to address the problem of congestion with a transmit power adaptation approach, based on the principles of cooperative game theory. The proposed approach, Adaptive transmit power Cooperative Congestion Control (AC3), is designed to allow vehicles to select their transmit power autonomously with respect to their local channel congestion. Since the number of neighbours of each vehicle and their corresponding transmit power levels vary, AC3 requires that each vehicle reduces its transmit power fairly during congestion. Explicitly, the proposed approach introduces the notion of marginal contributions of vehicles towards congestion and determines a fair power decrease for vehicles using a shapely value system model. This model requires the vehicles with the highest marginal contributions to reduce the most transmit power and vice versa. The simulation results demonstrate the utility of the proposed approach (i.e., capability to determine fair power decrease for an effective congestion control).

With the pace of modern life quickening and increasing work stress, people don't have enough time to focus on their health and communicate with family members. The loneliness and chronic diseases (e.g., obesity, depression, diabetes, and dementia) have become more prevalent. In the paper, we propose SmartBuddy, a novel integrated mobile sensing and detecting system for monitoring people's family activities, which can motivate the user to do proper physical exercise and establish good relationships with family members for maintaining their physical and mental health. Specifically, SmartBuddy firstly uses smartphones and Apple Watches built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, etc. Secondly, SmartBuddy can accurately detect family activities including occurrence/duration of meal, cooking, TV viewing, conversations, in an unobtrusive manner based on sensed data. Thirdly, SmartBuddy will propose a personal plan to suggest the user doing some exercise and making continuous progress in the process of communicating with family members. We have fully implemented SmartBuddy on the Android platform and perform testbed experiments. The experimental results demonstrate that SmartBuddy is easy to use, accurate, and appropriate for family activities with the accuracy of 80% and the user satisfaction degree of 84.5%.

Handoff in cognitive radio networks (CRNs) is a situation that arises whenever a secondary user (SU) has to switch from its current channel to a new target channel in case the primary user (PU) reclaims the current channel. Often when a SU switches to a target channel, it finds that it has to share the target channel with the coexistent users. These coexistent users can either be the interrupted or non-interrupted SUs who also wish to share the same channel. Long waiting in a queue or simultaneous access to the channel may decrease the SU's and network's throughput considerably. The SUs may even behave selfishly to maximize their own throughput. This paper analyzes the interactions and behavior of the SUs during the target channel sharing through non-cooperative, mixed strategic, and cooperative games. The benefits of the SUs and the overall network is analyzed by finding the Nash equilibrium and the Nash bargaining solution (NBS) for the non-cooperative, mixed strategy, and cooperative game respectively.

The e-Health core concept includes Web usage in an integrated way with tools and services for healthcare. This definition improves access, efficiency, and clinical care quality process that are necessary for a service delivery improvement. Decision support systems (DSSs) belong to a plethora of e-Health concept dimensions. For these systems construction, it is important to find a reliable intelligent mechanism capable to identify diseases that can worsen the patient's clinical condition. Thus, this paper proposes the use of tree-based data mining (DM) techniques for the hypertensive disorders prediction in the risk gestation. It presents the modeling, performance evaluation, and comparison between the tree based classifiers ID3 and NBTree. The 5-fold cross-validation method realizes the performance comparison. Results show that the NBTree classifier obtained better performance, presenting F-measure 0.609, ROC area 0.753, and Kappa statistic 0.4658. This classifier can be a key to a smart system development capable to predict risk events in pregnancy. Therefore, DSSs are a leading solution for the reduction of both mother and fetal mortality.

The main purpose of wireless sensor networks (WSNs) is to gather physical data from a target region and make them available for interested users. However, the unique characteristics of sensor nodes (e.g., limited resources, deployment in open areas) make them vulnerable to physical attacks. Once a node is physically captured by an adversary, it can be modified to perform malicious activities to corrupt the gathered sensor data of the WSN. Although intrusion detection system (IDS) can be deployed to raise alerts when malicious nodes are detected, the alerts usually do not provide any insights on the criticality of the attack or on how sensor data from other nodes may be affected. This paper introduces the dynamic security measure (DSM), a security metric model that enables an online quantitative security analysis of sensor nodes and data provided to users. In particular, DSM combines information about the communication pattern of nodes with IDS alerts received, in real-time, to estimate the probability that data messages from each node have not been compromised by ongoing attacks. Experimental results show that DSM can accurately estimate security level with low performance overhead and power consumption.

In recent years, medicine has seen how technology was going day by day more present to become necessary. At the same time, security became a critical aspect, since private patient medical data are handled. In this field in which gather mobile technologies with medicine, security has great importance. Therefore, it is essential to conduct security audits to mobile applications which deal with private information and confidential patient data. The main objective of this paper is to carry out an audit of security of an mHealth Android application. Taking HeartKeeper application to self-manage cardiac patients, a series of tests and modifications are conducted to check its strengths and weaknesses. The methodology consists in attempting to decompile the application HeartKeeper. Applying to the source code techniques of reverse engineering, we will try to perform an analysis that allows us to carry out the security check of the Android application HeartKeeper. It can be applied to audit security on any other Android application. In this way, it provides developers a tool that allows them to check the security of any Android app. Among these vulnerabilities found, the most relevant is that which allows us to inject code to steal some private information. This information should only be accessible from the application itself and only once the user is authenticated. As solutions, we propose different protections. These are: protection against decompilation, against code analysis, and against modified applications. It is very important to carry out a comprehensive review of the mobile applications' strength, since they are increasingly present in our lives and they manage sensitive and protected data. It is highly recommended to install applications from trusty sources, as they are the official app stores like Google Play Store in Android and iOS App Store.

Despite the fact that infant mortality rates have been decreased in recent years, this issue stills being considered alarming to Brazilian health system indicators. In this context, the GISSA framework, an intelligent governance framework for Brazilian health system, emerges as a smart system for the Federal Government program, called Stork Network. Its main objective is to improve the healthcare for pregnant women as well as their newborns. This application aims to generate alerts focusing on the health status verification of newborns and pregnant woman to support decision-makers in preventive actions that may mitigate severe problems. Therefore, this paper presents the LAIS, an Intelligent health analysis system that uses data mining (DM) to generate newborns death risk alerts through probability-based methods. Results show that the Naïve Bayes classifier presents better performance than the other DM approaches to the used pregnancy data set analysis of this work. This approach performed an accuracy of 0.982 and a Receiver Operating Characteristic (ROC) Area of 0.921. Both indicators suggest the proposed model may contribute to the reduction of maternal and fetal deaths.

From the last few years, Wireless body area networks (WBANs) have attracted a lot of attention from both academia and industry due to an increase in real-time data capturing and processing for patient monitoring. This has become possible due to the technological advancements in which high computing and communication facilities are available for most of the modern handheld devices. In this environment, computing resources are available close to the proximity of the end users using the most popular technology called as Fog computing (devices used in the fog computing are called as fog devices). Most of the solutions reported in the literature for this purpose have used the traditional cloud-based infrastructure in which there may occur a long delay for getting the response even for data which is not of very huge amount which may cause a performance degradation for most of the implemented solutions (such as for treatment of neurological diseases where a real-time monitoring is required) in this environment. Hence, to cope up these issues, in this paper, we proposed a fog computing based patient monitoring system for ambient assisted living (FAAL). Data traces of the movement of the patients (for neurological diseases) are collected using sensor nodes using body area networks (BANs) and are passed using the fog gateways. To reduce the load on the communication infrastructure, an efficient clustering algorithm for data transmission is also presented in the paper. Performance of the proposed solution has been evaluated using the parameters such as-latency, and data overloading. Results obtained clearly show the superior performance of the proposed scheme as compared to the non-fog computing based environment.

Physical therapy has a lot of importance for the well being and a better quality of living for an elderly patient. One integral constituent of any patient regime is the home-based exercise that a patient works on in a much comfortable environment. Although the benefits are well known, there is a big lag between the exercises prescribed by the therapists and the ones actually done by the patient. There is no cost effective and non-complex methods available to quantify the exercises performed by the patient. In this paper, a study was performed to check the validity and efficiency of a system consisting of a Smart IoT enabled speaker, which contains an orchestrator. Which is speech learning unit, an exercise database at the edge, and connected to the cloud, where the generated reports are stored and transferred for further analysis, if required. We report the efficiency of the system compared to the ratings of a physical therapist, a standard currently being used.

Software-Defined Networking (SDN) emerges as a recent paradigm that grants a holistic network visibility and flexible network programmability, facilitating rapid innovation of protocol and services. Although SDN provides greater control over traffic flow than ever before, it also introduced new challenges and issues to be addressed with its management. In that light, the security and reliability of SDN have been neglected subjects. This paper presents a system designed to proactively monitor network traffic and autonomously detect anomalies which may impair the proper network functioning. In addition to the identification of anomalous events, the proposed approach also provides routines that allow the mitigation of the anomalies effects.

As the multitude and complexity of the cloud market increases, the evaluation and selection of cloud services becomes a burdensome task for the users. With the extraordinary rise of available services from various Cloud Service Providers (CSPs), the role of cloud brokers has become more and more important. This paper proposes an optimal cloud broker model to address the challenge of optimally allocating multiple cloud system resources to multiple mobile user's requests with different requirements. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria. The overall system cost takes into consideration the cost of occupying computing resources, the communication costs, the request traffic, as well as various security risk degrees and resource requirements from the various mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is calculated by means of the Value Iteration Algorithm. Some analysis are conducted and numerical results are presented, demonstrating the feasibility of the proposed cloud broker design.

Botnets have been a serious threat to the Internet security. With the constant sophistication and the resilience of them, a new trend has emerged, shifting botnets from the traditional desktop to the mobile environment. As in the desktop domain, detecting mobile botnets is essential to minimize the threat that they impose. Along the diverse set of strategies applied to detect these botnets, the ones that show the best and most generalized results involve discovering patterns in their anomalous behavior. In the mobile botnet field, one way to detect these patterns is by analyzing the operation parameters of this kind of applications. In this paper, we present an anomaly-based and host-based approach to detect mobile botnets. The proposed approach uses machine learning algorithms to identify anomalous behaviors in statistical features extracted from system calls. Using a self-generated dataset containing 13 families of mobile botnets and legitimate applications, we were able to test the performance of our approach in a close-to-reality scenario. The proposed approach achieved great results, including low false positive rates and high true detection rates.

Recent studies about the use of cloud platforms indicate that more than three-fourths of healthcare institutions apply these technologies in their daily lives. However, in most cases the cloud is based on “Infrastructure as a Service”, instead of developing new applications using the benefits that can bring us this type of technology. For these reasons, this paper shows a private cloud-based system to provide a new application based on thermographic video streaming. In this case, the cloud architecture is divided into three zones. The first zone is where the patient is under medical supervision through body temperature analysis using a global thermal camera. The second zone is the cloud itself, where the video is encoded, but taking into account that the received video must ensure a correct measurement of the temperature. The third zone is the access network, where people or processes receive and analyze these videos. Finally, a performance evaluation using several video codecs and bitrates is done in order to see which one is more accurate and fits better medical requirements. Finally, we conclude that the system works properly, indicating which video codecs are more suitable for each scenario.

With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.

Internet of Things (IoT) distributed secure data management system is characterized by authentication, privacy policies to preserve data integrity. Multi-phase security and privacy policies ensure confidentiality and trust between the users and service providers. In this regard, we present a novel Two-phase Incentive-based Secure Key (TISK) system for distributed data management in IoT. The proposed system classifies the IoT user nodes and assigns low-level, high-level security keys for data transactions. Low-level secure keys are generic light-weight keys used by the data collector nodes and data aggregator nodes for trusted transactions. TISK phase-I Generic Service Manager (GSM-C) module verifies the IoT devices based on self-trust incentive and server-trust incentive levels. High-level secure keys are dedicated special purpose keys utilized by data manager nodes and data expert nodes for authorized transactions. TISK phase-II Dedicated Service Manager (DSM-C) module verifies the certificates issued by GSM-C module. DSM-C module further issues high-level secure keys to data manager nodes and data expert nodes for specific purpose transactions. Simulation results indicate that the proposed TISK system reduces the key complexity and key cost to ensure distributed secure data management in IoT network.

The incidence of hypertension associated with pregnancy contributes significantly to increase maternal and fetal deaths during pregnancy and childbirth. Due to its high incidence rate and several complications, the study of this disorder has been subject of numerous investigations in an attempt to determine its prevention and improve the treatment conduction. In this context, this paper uses a data mining (DM) technique, named random forest (RF), applied to health care to early identification of these disorders. It also presents the modeling, performance assessment, and comparison with other DM methods to evaluate the performance of the proposed model. Results showed that the RF classifier had a regular performance, presenting the best values for true positive Rate (TP Rate) and recall in the prediction of preeclampsia superimposed on chronic hypertension compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other tree-based methods need to be evaluated, as well as other DM techniques. Discovering reliable information of pregnant women suffering from the hypertensive disease is an important path to reduce the high rate of deaths, mainly, in developing countries where 99% of these deaths occur.

Objectively quantifying the classification accuracy of Intrusion Detection Systems (IDSs) is of fundamental importance. Evaluation metrics have been proposed to measure the effectiveness of traditional IDSs, but none of those metrics seems suitable to evaluate the distributed collaborative IDSs that are generally employed in Wireless Sensor Networks (WSNs). This is because in WSNs each IDS output (i.e., alarm or absence of alarm) results from a consensus decision among several nodes, as opposed to an individual decision of a single node. In this paper, we present the trust probability (Pt) metric, which is defined as the probability that an IDS draws the right conclusion in its collaborative decision-making process. This metric is computed based on the properties of the individual nodes that contribute to the IDS global conclusions. We provide numerical examples as well as a detailed analysis of Pt. Moreover, we show how Ptcan be used to find the best operating point of a given IDS and to compare different IDSs. Finally, since Ptis a measure of how much each IDS global output should be trusted, we discuss how this metric can be used in real-time to rank IDS alerts.

The current century has brought an unimaginable growth in information and communications technology (ICT) and needs of enormous computing. The advancements in computer hardware and software particularly helped fuel the requirements of human beings, and revolutionized the smart products as an outcome. The advent of wearable devices from their development till successful materialisation has only taken less than a quarter of a century. The huge benefits of these smart wearable technologies cannot be fully enjoyed until and unless the reliability of a complete system is ensured. The reliability can be increased by the consistent advancements in hardware and software in parallel. User expectations actually are the challenges that keep the advancements alive while improving at an unmatchable pace. The future of wearable and other smart devices depends on whether they can provide a timely solution that is reliable, richer in resources, smaller in size, and cheaper in price. This paper addresses the threats and opportunities in the development and the acceptance of immersive and wearable technologies. The hardware and software challenges for the purpose of development are discussed to demonstrate the bottlenecks of the current technologies and the limitations that impose those bottlenecks. For the purpose of adoption, social and commercial challenges related to innovation and acceptability are discussed. The paper proposes guidelines that are expected to be applicable in several considerable applications of wearable technologies, for example, social networks, healthcare, and banking.

Summary By applying the cloud manufacturing paradigm in the regional enterprise cluster, the enterprises or facilities may collaborate extensively for efficiently utilizing the manufacturing resources. It is valuable to explore and design the strategies of facilities selection for the autonomic control of the collaboration behaviors in production. In this paper, we model the collaboration relations in the regional enterprise cluster as a generalized social collaboration network and explore the dynamic growth process of the Facilities Collaboration Network for different strategies of facilities selection, including the random selection with and without preference, and the balanced selection with and without preference. With performance indexes such as network size, the distribution of node degree and act degree, clustering coefficient, the average shortest distance, and the number of n‐cliques, we present and analyze the characteristics of these strategies for cloud manufacturing. Next, based on these characteristics, we propose 2 mechanisms for self‐optimization in facilities collaboration, including the dynamic weighing of facilities and the concentrated processing of successive subtasks in the process. We also analyze the mechanisms' effects on the characteristics of Facilities Collaboration Network and the performance in manufacturing. Copyright © 2016 John Wiley & Sons, Ltd.

Smart grid (SG) technology has recently received significant attention due to its usage in maintaining demand response management in power transmission systems. In SG, charging of electric vehicles becomes one of the emerging applications. However, authentication between a vehicle user and a smart meter is required so that both of them can securely communicate for managing demand response during peak hours. To address the above mentioned issues, in this paper, we propose a new efficient three-factor user authentication scheme for a renewable energy-based smart grid environment (TUAS-RESG), which uses the lightweight cryptographic computations such as one-way hash functions, bitwise XOR operations, and elliptic curve cryptography. The detailed security analysis shows the robustness of TUAS-RESG against various well-known attacks. Moreover, TUAS-RESG provides superior security with additional features, such as dynamic smart meter addition, flexibility for password and biometric update, user and smart meter anonymity, and untraceability as compared to other related existing schemes. The practical demonstration of TUAS-RESG is also proved using the widely accepted NS2 simulation.

Computer security is something that brings to mind the greatest developers and companies who wish to protect their data. Major steps forward are being taken via advances made in the security of technology. The main purpose of this paper is to provide a view of different mechanisms and algorithms used to ensure big data security and to theoretically put forward an improvement in the health-based environment using a proposed model as reference. A search was conducted for information from scientific databases as Google Scholar, IEEE Xplore, Science Direct, Web of Science, and Scopus to find information related to security in big data. The search criteria used were “big data”, “health”, “cloud”, and “security”, with dates being confined to the period from 2008 to the present time. After analyzing the different solutions, two security alternatives are proposed combining different techniques analyzed in the state of the art, with a view to providing existing information on the big data over cloud with maximum security in different hospitals located in the province of Valladolid, Spain. New mechanisms and algorithms help to create a more secure environment, although it is necessary to continue developing new and better ones to make things increasingly difficult for cybercriminals.

Hypertensive disorders are the leading cause of deaths during pregnancy. Risk pregnancy accompaniment is essential to reduce these complications. Decision support systems (DSS) are important tools to patients' accompaniment. These systems provide relevant information to health experts about clinical condition of the patient anywhere and anytime. In this paper, a model that uses the Naive Bayesian classifier is introduced and its performance is evaluated in comparison with the Data Mining (DM) classifier named J48 Decision Tree. This study includes the modeling, performance evaluation, and comparison between models that could be used to assess pregnancy complications. Evaluation analysis of the results is performed through the use of Confusion Matrix indicators. The founded results show that J48 decision tree classifier performs better for almost all the used indicators, confirming its promising accuracy for identifying hypertensive disorders on pregnancy.

Cognitive radio is an optimistic technology to implement the concept of dynamic spectrum access and provide a flexible way to share the spectrum among the primary and secondary users. In this paper, a game theoretic-based model is presented using the concept of Nash Equilibrium for spectrum sharing. In this model, interference and number of radios on each link are considered as parameters for designing the game. An algorithm for channel allocation among the users is also presented. From the simulation analysis, it is observed that the system performs satisfactorily in terms of network utilization. Also, the Taguichi method is applied and an analysis of variance (ANOVA) is performed, proving that the design parameters taken into consideration in our proposed method are impactful.

This paper aims to evaluate OphthalDSS, a new mobile Decision Support System (DSS) for red eye diseases diagnosis, and presents the results after evaluating the Quality of Experience (QoE) by medical students of the University of Valladolid, Spain. The main utilities that OphthalDSS offers, will be a study guide for physicians and medical students, and a clinical decision support system for primary care professionals. The decision algorithm will be implemented by an Android mobile App and the QoE will be evaluated by a short inquiry. The algorithm OphthalDSS is capable of diagnosing more than 30 eye's anterior segment diseases. A total of 67 medical students have evaluated the QoE. The different blocks in which the survey is organized have achieved a score of at least the mean value. Most of the surveyed students agree with that OphthalDSS does the function that they expected, the information presented in it is totally reliable, it is intuitive and has got an appropriate appearance, and they highlight the decision algorithm's effectiveness.

Significant advances on smart decision support systems (DSSs) development have influenced important results on pregnancy care. Nevertheless, even considering the efforts to reduce the number of women deaths due to problems related to pregnancy, this decrease presented less impact than other areas of human development. Hypertensive disorders in pregnancy, particularly pre-eclampsia and eclampsia, account for significant proportion of perinatal morbidity and maternal mortality. In this context, this paper proposes an inference model that uses data mining (DM) techniques capable for operating in a data set to extract patterns and assist in knowledge discovery. Identifying hypertensive crises that complicate pregnancy, it can impact in a meaningful reduction the incidence of sequelae and death of pregnant women. Comparison between two Bayesian classifiers is performed in this work to better classify the hypertensive disorders severity. Results showed that Naïve Bayes classifier had an excellent performance, presenting better precision and F-measure, compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other Bayesian methods need to be evaluated, as well as other DM techniques such as those based on artificial intelligence (AI) and tree-based methods.

Hypertension is the main cause of maternal death. Preeclampsia can affect pregnant women before or during pregnancy. Identification of patients with higher risk for preeclampsia allows some precautions that are taken to prevent its severe disease and subsequent complications. In medicine, there are different situations that deal with a large range of information, which needs a thorough assessment to be able to help experts in the decision-making process. Smart decision support systems allow grouping all existing information and finding pertinent information from it. Bayesian networks offer models that allow the information capture and handle situations of uncertainty. This paper proposes the construction of a system to support intelligent decision applied to the diagnosis of preeclampsia using Bayesian networks to help experts in the pregnant's care. The processes of qualitative and quantitative modeling to the construction of a network are also presented. The main contribution of this work includes the presentation of a Bayesian network built to help decision makers in moments of uncertainty in care of pregnant women.

Aggressive driving is the main cause of traffic accidents all over the world. Aggressive steering is the second leading cause of traffic accidents, just behind speeding. To detect aggressive steering and encourage drivers to cultivate better driving behaviors, automatic recognition of different vehicle steering modes is required so a decision can be taken on whether the behavior is aggressive or not. This paper investigates various patterns of turning, changing lanes and U-turns, and then design and implement a system termed MultiWave that utilizes the gyroscope of a smartphone to automatically detect vehicle steering patterns. MultiWave divides driving behaviors into different categories by analyzing the data collected by gyroscope sensors. Since it analyzes the gyroscopic sensor data of turning, changing lanes and U-turns, MultiWave is flexible and robust for most urban situations. The classification accuracy of MultiWave can reach averages of 92%, 76.5%, and 87% for vehicle turning, changing lanes and U-turn, respectively.

It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, we can use the network resources more intelligently to deploy new services faster. This paper considers an inter-DC elastic optical network (IDC-EON) and studies how to orchestrate the multicast-oriented NFV trees (M-NFV-Ts) in it efficiently. We first consider an offline scenario in which all the M-NFV-Ts are known and need to be served in the network. A mixed integer linear programming (MILP) model is formulated to solve the problem exactly, and we also propose a heuristic based on path-intersection (PI) to reduce the time complexity. With extensive simulations, we show that the proposed heuristic can approximate the MILP's performance on low-cost M-NFV-T provisioning but only requires much shorter running time. Next, the online scenario where the M-NFV-Ts can come and leave on-the-fly is addressed, and we leverage PI to design two online algorithms for orchestrating dynamic M-NFV-Ts in IDC-EONs, i e., with either the batch (B-PI) or sequential (S-PI) scheme. Simulation results indicate that compared with S-PI, B-PI can reduce blocking probability effectively.

With the successful deployment of the fourth-generation cellular networks around the world, long-term evolution (LTE) and LTE-advanced (LTE-A) have become key technologies to enable Internet of Things (IoT) applications. To accommodate various streaming data of IoT applications, LTE/LTE-A standards have defined several quality-of-service (QoS) classes for different traffic characteristics, in terms of traffic bit-rate, tolerable delay, and packet loss rate. Moreover, to meet the stringent power-saving requirements for IoT devices, LTE/LTE-A standards also have defined the discontinuous reception/transmission (DRX/DTX) mechanism to allow devices to turn off their radio interfaces and go to sleep when no data need to be received or transmitted from/to the evolved Node B (eNodeB).

Abstract With the rapid expansion of technology, the wireless sensor network has been spawned in recent years. Recently, Chi et al. proposed an improved energy‐efficient access control scheme for wireless sensor networks based on elliptic curve cryptography. In this article, we analyze the scheme of Chi et al. and point out that their scheme cannot withstand the replay attack. To surmount the weaknesses of the Chi et al. scheme, we propose a secure energy‐efficient access‐control scheme for wireless sensor networks based on elliptic curve cryptography. In addition, we prove that our scheme is secure and efficient with respect to various types of attacks. Copyright © 2015 John Wiley & Sons, Ltd.

In recent times, Plug-in Electric Vehicles (PEVs) have emerged as a new alternative to increase the efficiency of smart grids (SGs) in a vehicles-to-grid (V2G) environment. The V2G environment provides a bidirectional power and information flow, so that users can have an optimized usage as per their requirements. However, uncontrolled and unmanaged power distribution may lead to an overall performance degradation in V2G environment. One reason for this uncontrolled and unmanaged flow may be due to the usage of power by unauthorized users. To address this issue, we propose a Bayesian Coalition Negotiation Game (BCNG) as a utility for secure energy management for PEVs in the V2G environment. We have used a BCNG along with Learning Automata (LA), wherein LA are stationed on PEVs and are assumed as the players in the game. To provide an approach based on resilience for any misuse of electricity consumption, a new Secure Payoff Function (SPF) is proposed. The players take actions and update their action probability vector using the SPF. A Nash Equilibrium (NE) is also achieved in the game using convergence theory. Our proposal is evaluated with various metrics. The proposed scheme also provides mutual authentication and resilience against various attacks during power distribution.

Wireless sensor networks (WSNs) are a promising technology for several industrial and quotidian applications. IPv6 is the most consensual solution to connect such networks to the Internet, and 6LoWPAN is the adaptation layer to run IPv6 over WSNs. Self-organization and self-configuration are key characteristics of WSN because they minimize the network configuration efforts and simultaneously increase the network robustness but they can also be exploited to perform security attacks. This paper proposes a network admission control solution for 6LoWPAN WSN that prevents unauthorized nodes from using the network to communicate either with the legitimate nodes and with the Internet, reducing in this way the security attacks that can be performed. The proposed solution includes node presence detection and authentication, administrative node authorization, and data filtering to discard frames from/to unauthorized nodes. It uses the standard 6LoWPAN neighbor discovery and RPL protocols, minimizing the number of additional required control messages. It includes cryptographic mechanisms, based on the AES symmetric key algorithm, to guarantee node authenticity and integrity, source authenticity, and data freshness of data frames. This paper also presents the design and deployment of a laboratory testbed validating the proposed network admission control solution.

The papers in this special section were presented at three well-known conferences organized in 2014: EAI Mobihealth, IEEE HealthCom, and IEEE Biomedical and Health Informatics. EAI Mobihealth is an annually organized conference, which started in 2010, to address the demands of the rapidly evolving disciplines of wireless communications, mobile computing, and sensing technologies in healthcare. The IEEE-Healthcom is held every year since 1999 in different countries in Asia, Europe, and in America. It aims at bringing together interested parties working in the field of healthcare to exchange ideas, discuss innovative and emerging solutions, and develop collaborations. The IEEE Biomedical Health Informatics Conference started in 2013 and is organized every year providing the forum to showcase enabling technologies of computing, devices, imaging, sensors, and systems that optimize the acquisition, transmission, processing, storage, retrieval, visualization, and analysis of medical data. The aim of this special section is to present an overview of recent advances in sensing technologies, monitoring of patients, security and privacy of data transfer, provision of collaborative environments, data gathering and analysis from various sources, and predictive models, which all finally target the best strategy for patient monitoring and treatment.

Network architectures based on mobile devices and wireless communications present several constraints (e.g., processor, energy storage, bandwidth, etc.) that affect the overall network performance. Cooperation strategies have been considered as a solution to address these network limitations. In the presence of unstable network infrastructures, mobile nodes cooperate with each other, forwarding data and performing other specific network functionalities. This article proposes a generalized incentive-based cooperation solution for mobile services and applications called MobiCoop. This reputation-based scheme includes an application framework for mobile applications that uses a Web service to handle all the nodes reputation and network permissions. The main goal of MobiCoop is to provide Internet services to mobile devices without network connectivity through cooperation with neighbor devices. The article includes a performance evaluation study of MobiCoop considering both a real scenario (using a prototype) and a simulation-based study. Results show that the proposed approach provides network connectivity independency to users with mobile apps when Internet connectivity is unavailable. Then, it is concluded that MobiCoop improved significantly the overall system performance and the service provided for a given mobile application.

The future of ambient assisted living (AAL) especially eHealthcare almost depends on the smart objects that are part of the Internet of things (IoT). In our AAL scenario, these objects collect and transfer real-time information about the patients to the hospital server with the help of Wireless Mesh Network (WMN). Due to the multi-hop nature of mesh networks, it is possible for an adversary to reroute the network traffic via many denial of service (DoS) attacks, and hence affect the correct functionality of the mesh routing protocol. In this paper, based on a comparative study, we choose the most suitable secure mesh routing protocol for IoT-based AAL applications. Then, we analyze the resilience of this protocol against DoS attacks. Focusing on the hello flooding attack, the protocol is simulated and analyzed in terms of data packet delivery ratio, delay, and throughput. Simulation results show that the chosen protocol is totally resilient against DoS attack and can be one of the best candidates for secure routing in IoT-based AAL applications.

The wide dissemination of Internet around the world changed the way people communicate among them. The evolution of the information and communication technologies (ICT) or artificial intelligence (AI) allowed the creation of new types of services involving electronic devices with huge potential. The emergence of intelligent personal assistants (IPAs) is one of such potential example. Combining the IPA concept with the recent paradigm of Internet of Things (IoT), offers new possibilities and new services to end users, since they could learn more about other entities present in the surrounding environment. This paper proposes a novel mobile gateway solution for a ubiquitous mobile health scenario, in which information gathered from a body sensor network (BSN) is used by an IPA belonging to another person, typically mentioned to as caretaker. The mobile gateway receives real time information related to location, heart rate, and possible falls of the monitored person and acts as a communication channel between the BSN sensors and the IPA platform. Furthermore, the paper presents a performance evaluation study of the proposed mobile IoT-based gateway demonstrating and validating its feasibility.

In the last decade vehicular communications have been the focus of research, not only by the related scientific community but also by the automotive industry. Several architectures were proposed in order to overcome some issues found in such networks. Vehicular Delay-Tolerant Networks (VDTNs) try to improve vehicular communications by deploying the Delay-Tolerant Networks (DTNs) store-carry-and-forward model and assuming the bundle layer placement under the network layer. Although all the improvements already achieved by VDTNs, there are still several challenges that must be overcome. One of these challenges is how to stimulate nodes to cooperate and minimize the impact of misbehavior nodes on the network performance. A node may be unwilling to cooperate due to a selfish behavior or to save its own resources from being compromised. To detect, isolate, and exclude this type of nodes a reputation system with different mechanisms (allowing to punish selfish nodes in different ways) was implemented in VDTNs. This paper adapts the already proposed reputation system to perform together with a hybrid system, which main goal is to incentive selfish nodes to share their resources with others, instead of immediately excluding them from the network. With the proposal of this hybrid system, two incentive mechanisms were also created. Across all the experiments, it was shown that by incentive selfish nodes to cooperate contributes to an increase of the overall network performance, when compared to an approach that excludes them immediately from the network.