The integration of advanced communication technologies with modern vehicular systems has driven the evolution of vehicular ad-hoc networks (VANETs). These networks enable a seamless exchange of road safety information between vehicles and traffic management authorities via wireless links. However, the open nature of these communication channels introduces significant risks to the privacy and security of transmitted messages. To address these challenges, Liet al. (IEEE Trans. Inf. Forensics and Security, vol. 19, pp. 9629–9642, 2024) proposed a lattice-based authentication scheme designed for fog-assisted VANETs. This protocol utilizes lattice cryptography to ensure resilience against quantum attacks and employs fog computing to tackle scalability issues. Despite these advancements, a detailed analysis uncovers several vulnerabilities and inefficiencies in their design. This study identifies an anonymity disclosure attack on Liet al.’s scheme, compromising its privacy guarantees. In addition, redundancies in the signature generation process impose excessive computational burdens on resource-constrained vehicular devices. To address these shortcomings, this work introduces a lattice-based anonymous batch-verifiable authentication (LBABVA) scheme. Rigorous security analysis proves the scheme’s security in the random oracle model, while efficiency evaluations reveal significant improvements. The proposed scheme reduces the computational cost of the signing phase to 14.57% and the signature verification phase to 83.99% of the corresponding costs of the previous design, highlighting its superior performance and suitability for practical applications.

Generalized Frequency Division Multiplexing (GFDM) is considered a strong candidate to replace Orthogonal Frequency Division Multiplexing (OFDM) in 5G MIMO networks because of its enhanced spectral utilization and design flexibility. Despite these advantages, GFDM faces the drawback of producing a relatively high Peak-to-Average Power Ratio (PAPR), which limits the efficiency of power amplifiers. To address this issue, the Partial Transmit Sequence (PTS) method is often employed for PAPR reduction. Nevertheless, the effectiveness of PTS is hindered by the intensive computational effort required for searching multiple phase factors. To overcome this challenge, we propose a method that integrates the Enhanced Squirrel Search Algorithm (ESSA) with an adaptive parameter control mechanism and a Grey Wolf Optimizer (GWO), enabling a dynamic balance between exploration and exploitation during phase factor selection. This improvement reduces the computational overhead, accelerates the convergence, and enhances the robustness of the phase sequence optimization. Simulation results show that the Hybrid PTS-ESSA-GWO-RPSM model achieves superior PAPR reduction compared to conventional ESSA-based approaches, while also providing better BER and SNR performance under varying channel conditions. The proposed method therefore offers an efficient trade-off between complexity and PAPR reduction, making it suitable for practical deployment in MIMO-GFDM-based 5G systems. The proposed scheme is evaluated against related methods by analyzing key performance indicators, including Complementary Cumulative Distribution Function (CCDF), Bit Error Rate (BER), Peak-to-Average Power Ratio (PAPR), and Signal-to-Noise Ratio (SNR).

Optimization algorithms are crucial for energy-efficient routing in Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) because they help minimize energy consumption, reduce communication overhead, and improve overall network performance. By optimizing the routing paths and scheduling data transmission, these algorithms can prolong network lifetime by efficiently managing the limited energy resources of sensor nodes, ensuring reliable data delivery while conserving energy. In this work, we present Greylag Goose-based Optimized Clustering (GGOC), which aids in selecting the Cluster Head (CH) using the proposed critical fitness parameters. These parameters include residual energy, sensor sensing range, distance of a candidate node from the sink, number of neighboring nodes, and energy consumption rate. Simulation analysis shows that the proposed approach improves various performance metrics, namely network lifetime, stability period, throughput, the network’s remaining energy, and the number of clusters formed.

The rapid expansion of smart technologies and IoT has made Wireless Sensor Networks (WSNs) essential for real-time applications such as industrial automation, environmental monitoring, and healthcare. Despite advances in sensor node technology, energy efficiency remains a key challenge due to the limited battery life of nodes, which often operate in remote environments. Effective clustering, where Cluster Heads (CHs) manage data aggregation and transmission, is crucial for optimizing energy use. Motivated from the above, in this paper, we introduce a novel metaheuristic approach called Hippopotamus Optimization-Based Cluster Head Selection (HO-CHS), designed to enhance CH selection by dynamically considering factors such as residual energy, node location, and network topology. Inspired by natural behaviors, HO-CHS effectively balances energy loads, reduces communication distances, and boosts network scalability and reliability. The proposed scheme achieves a 35% increase in network lifetime and a 40% improvement in stability period in comparison to the other existing schemes in literature. Simulation results demonstrate that HO-CHS significantly reduces energy consumption and enhances data transmission efficiency, making it ideal for IoT-enabled consumer electronics networks requiring consistent performance and energy conservation.

Energy efficiency and minimization of redundant transmissions are critical challenges in Wireless Sensor Networks (WSNs), especially in heterogeneous IoT environments where sensor nodes (SNs) are resource-constrained and deployed in remote or inaccessible areas. This paper aims to address the dual problem of uneven energy distribution and limited network lifespan by proposing a novel Artificial Protozoa Optimizer-based Cluster Head Selection (APO-CHS) algorithm. The proposed APO-CHS is inspired by the adaptive behavior of Euglena, integrating foraging, dormancy, and reproduction mechanisms to optimize cluster head and relay node selection through a multi-objective fitness function. The function incorporates residual energy, node density, neighbor distance, and energy consumption rate to guide the selection process effectively. Additionally, to tackle communication inefficiency, a lightweight data aggregation scheme is employed. This scheme reduces redundant transmissions by introducing a multi-level aggregation model that eliminates full, partial, and duplicate data in both intra-and inter-cluster communication. The simulation results demonstrate that the proposed framework improves network stability by 29.24%, extends network lifetime by 283.96%, and increases throughput by over 60% compared to baseline methods, thus making it a highly efficient and scalable solution for energy-aware IoT-enabled WSN applications.

The increasing demand for ultra-reliable and low-latency communication (URLLC) in vehicle road cooperation systems (VRCS) has propelled the development of intelligent and efficient optimization techniques. This paper presents a Quantum Deep Q-Network (QDQN) based approach for minimizing latency in a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) enabled VRCS. STAR-RIS improves signal coverage and energy efficiency by simultaneously serving users in both transmission and reflection modes. However, latency optimization remains a critical challenge due to dynamic environments and computational complexity. The proposed QDQN technique integrates quantum computing principles with deep reinforcement learning (DRL) to accelerate decision making and optimize resource allocation in real time. Using quantum parallelism and entanglement, QDQN reduces convergence time while effectively learning the dynamic state of the communication environment. The simulation results demonstrate that the proposed method achieves a significant latency reduction compared to conventional DRL and classical Q-learning techniques. This study highlights the potential of quantum-enhanced reinforcement learning for future URLLC applications in intelligent vehicular networks.

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

With the advent of smart and autonomous vehicles, a number of novel data-intensive and latency-critical vehicular communication applications have emerged. However, dynamic vehicular mobility and urban environments introduce severe propagation challenges, leading to increased latency. In order to reduce latency in Vehicle Road Cooperative Systems (VRCS), this research introduces a unique architecture that combines Asynchronous Federated Learning (AFL) with Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS). The proposed system leverages a Markov Decision Process (MDP)-based optimization framework to minimize latency by jointly optimizing STAR-RIS elements and offloading decisions. Our approach allows vehicles to asynchronously update global models, ensuring robust learning while adapting to dynamic network conditions. The simulation results show that the recommended strategy provides at least a 20 % reduction in latency in AFL when compared to FL.

Eavesdropping and jamming attacks pose significant security threats to the integrity and availability of 5G mm Wave wireless networks. Although reinforcement learning (RL)-based schemes are considered viable solutions, the most challenging aspect is obtaining accurate training data. This paper proposes a big data-driven, secrecy-aware achievable data rate maximization scheme for a two-tier fifth-generation heterogeneous network (5G-HetNet) to defend against simultaneous eavesdropping and jamming attacks. By leveraging massive data generated in real-time from 5G networks, the proposed approach efficiently handles large-scale optimization challenges. We present a joint optimization problem on power allocation and beamforming for SUs placement, given minimum requirements for secrecy and data rates. We, then transfrom the resulting non-convex problem as a multi-agent reinforcement learning (MARL) problem by utilizing the Markov decision process (MDP). In order to tackle the MDP's large state and action spaces that are inherent in two discrete event-dynamic 5G HetNets, we propose the multiagent deep reinforcement learning (MADRL) scheme in order to jointly optimize data rate and secrecy rates. The proposed approach utilizes a double Q-architecture based double Deep Q Network (DDQN) to jointly solve the beamforming and power allocation vectors for the SUs. We compare our proposed DDQN approach to both Q-learning and DQN in terms of data and secrecy rate performance. The results of our simulations show that our proposed approach significantly improves the attainable secrecy rate by 22 % and 33% compared to DQN and Q-Learning, respectively.

Abstract Machine learning approaches, such as artificial neural networks (ANN), effectively perform various tasks and provide new predictive models for complicated physiological systems. Examples of Robotics applications involving direct human engagement, such as controlling prosthetic arms, athletic training, and investigating muscle physiology. It is now time for automated systems to take over modelling and monitoring tasks. However, there is a problem with the massive amount of time series data collected to build accurate forecasting systems. There may be inconsistencies in forecasting muscle forces due to the enormous amount of data. As a result, anomaly detection techniques play a significant role in detecting anomalous data. Detecting anomalies can help reduce redundancy and free up large storage space for storing relevant time‐series data. This paper employs several anomaly detection techniques, including Isolation Forest (iforest), K‐Nearest Neighbour (KNN), Open Support Vector Machine (OSVM), Histogram, and Local Outlier Factor (LOF). These techniques have been used by Long Short‐Term Memory (LSTM), Auto‐Regressive Integrated Moving Average (ARIMA), and Prophet models. The dataset used in this study contained raw measurements of body movements (kinematics) and the forces generated during walking (kinetics) of 57 healthy people (29 Female, 28 Male) without walking abnormalities or recent leg injuries. To increase the data samples, we used TimeGAN that generates synthetic time series data with temporal dependencies, aiding in training robust predictive models for muscle force prediction. The results are then compared with different evaluation metrics for five different samples. It is found that anomaly detection techniques with LSTM, ARIMA, and Prophet models provided better performance in forecasting muscle forces. The iforest method achieved the best Pearson's Correlation Coefficient ( r ) of 0.95, which is a competitive score with existing systems that perform between 0.7 and 0.9. The methodology provides a foundation for precision medicine, enhancing prognostic capability over relying solely on population averages.

Maritime communication, critical for global oceanic trade, faces challenges and opportunities with advancements in Information and Communication Technology (ICT). Traditional methods are susceptible to interception due to open channels, limited authentication, jamming, and other security risks. Securing vessel movements and locations in Internet of Vessels (IoV) is essential to prevent unauthorized data interception and tampering during transmission. We propose a ship authentication method using location-based secure keys to ensure the confidentiality of a vessel’s whereabouts. The proposed scheme’s robustness is validated through formal and informal security analyses, and formal verification using the Scyther automated verification tool, demonstrating its effectiveness against potential attacks in maritime networks. Comparative studies indicate that utilizing location-based keys maintains anonymity and untraceability without imposing significant computational or communication burdens. Experimental findings from comprehensive big data analytics and simulations using NS3 validate the scheme’s feasibility and performance.

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.

The rapid expansion of Internet of Things (IoT) applications, ranging from environmental monitoring to industrial automation, has placed unprecedented demands on wireless sensor networks (WSNs). Ensuring sustained operation and reliable data delivery under stringent energy constraints and uneven traffic patterns remains a vital research challenge. This paper introduces a cluster head (CH) structuring framework powered by a hybrid Superb Fairy Wren Optimization Algorithm (SFOA) that orchestrates dynamic CH rotation to balance energy usage and alleviate hotspots. A composite fitness function is formulated by integrating residual node energy, distance to sink, neighbor density, transmission latency, and energy consumption rate. Additionally, hierarchical data aggregation is employed at both intra and inter cluster levels to minimize communication overhead. Evaluation results demonstrate a 33.99 % increase in network stability duration and a 30 % extension in overall network lifetime compared to state of the art protocols. To our knowledge, this is the first integration of a Fairy Wren inspired metaheuristic with CH based clustering for WSN energy management, yielding significant improvements in network longevity and reliability.

Electric vehicle (EV) charging stations (CSs) are increasingly prevalent due to the growing adoption of renewable energy. Solar CSs’ main difficulties are energy efficiency, security, traceability, and sustainability. This article presents a novel blockchain-enabled EV charging framework that addresses these challenges using the Ethereum virtual machine (EVM), the Metamask wallet, and smart contracts (SCs). This article introduces solarcoins, a digital currency for trading solar energy, which reduces human intervention while fostering trust, transparency, and privacy among EV users. The proposed solution ensures secure communication between CS operators and EV users, enhancing both security and traceability. The proposed solution ensures secure communication between CS operators and EV users, enhancing both security and traceability. To quantify the sustainability and efficiency of the proposed system, the framework performances are tested and evaluated by varying numbers and types (Read, Write, and Transfer) of transactions using Hyperledger caliper and Go Ethereum. The overhaul Performance metrics were measured under varied transaction rates and control parameters by varying the number of validator nodes (1 node to 5 nodes), such as transaction latency, throughput, resource utilization, and so on. The performance of three major functions—open, query, and transfer—was recorded and analyzed. The results show that the query transaction is faster than open and transfer and the latency increases linearly with increased transaction rate. At 1000 transaction per second, the open function has a latency of 260.22 s, whereas the query function has a latency of 104.12 s and the transfer function has a latency of 345.73 s. The average memory usage for 1node-clique is 1224.0 MB, while it is 76.8 MB for 5node-clique. Results reveal that with an increase in the number of cliques (Validator CSs), memory utilization decreased linearly. This happens because all framework transactions are distributed across each EV CS network. The SCs deployment and operational costs were measured. Complexity analysis reveals that functions, such as getStation, getUser, getStationState, etc., exhibit constant time complexity O(1), while the registerUser and addStation functions have linear space and time complexity O(n).

Software-defined networking (SDN) centralizes and abstracts network control, potentially introducing single points of failure. To enhance scalability and flexibility, a distributed SDN (DSDN) approach is essential. This research introduces MLB-DSDN, an energy-efficient multipath load-balancing protocol for flow control in DSDNs, with a specific focus on an eco-friendly aerial computing environment. MLB-DSDN protocol identifies multiple routes between source and destination nodes, dynamically distributing data packets based on an inverse proportionality mechanism relative to route traversal time. This strategy balances traffic loads across various channels, significantly reducing the total routing time for data packet delivery. Moreover, the proposed framework enhances network performance and resilience in dynamic, high-mobility environments. It achieves this by incorporating unmanned aerial vehicles (UAVs) and satellite nodes as mobile network components. Experimental results demonstrate that MLB-DSDN improves average response time by 14.40% and increases average transactions per second by 14.63%, surpassing state-of-the-art methodologies. The integration of UAVs and satellites contributes to an additional 10% improvement in network throughput and a 12% reduction in latency compared to ground-based solutions alone. These findings highlight the robustness and efficiency of MLB-DSDN in enabling seamless and reliable data dissemination across terrestrial and aerial networks. Thus, the proposed framework enhances scalability, reliability, and flexibility in aerial computing, offering a robust and adaptable solution for resilient modern networks.

Abstract Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

With the rise of Big data generated by Internet of Things (IoT) smart devices, there is an increasing need to leverage its potential while protecting privacy and maintaining confidentiality. Privacy and confidentiality in big data aims to enable data analysis and machine learning on large-scale datasets without compromising the dataset sensitive information. Usually current big data analytics models either efficiently achieves privacy or confidentiality. In this article, we aim to design a novel blockchain-enabled secured collaborative machine learning approach that provides privacy and confidentially on large scale datasets generated by IoT devices. Blockchain is used as secured platform to store and access data as well as to provide immutability and traceability. We also propose an efficient approach to obtain robust machine learning model through use of cryptographic techniques and differential privacy in which the data among involved parties is shared in a secured way while maintaining privacy and confidentiality of the data. The experimental evaluation along with security and performance analysis show that the proposed approach provides accuracy and scalability without compromising the privacy and security.

Federated Learning (FL) has made remarkable achievements in medical and e-healthcare services. Different healthcare institutions can jointly train models to facilitate intelligent diagnosis. However, the model gradients transmitted among these institutions may still leak private information about the local models and training datasets. Additionally, in the current FL schemes, institutions with different quantities or qualities of medical data usually get the same training models, which may significantly hamper their motivation. Therefore, ensuring privacy and fairness in collaborative training remains a challenge. To address this issue, we propose a privacy-enhanced and fair FL scheme (EPFFL) to support distributed large-scale data sharing of e-healthcare services. In the training process, participants upload the encrypted model gradients according to their sharing wishes to the blockchain while storing their training data locally. Hence, the FL initiator can only get the aggregated gradients from the blockchain rather than the local data of other participants. Moreover, EPFFL ensures fairness by evaluating the participants’ contributions, i.e., participants with different data qualities and sharing levels can obtain the final models with different accuracies at the end of the training. Through theoretical and simulation analysis, the scheme shows superior functionalities on privacy preservation and fairness with the ideal model accuracy.

The plethora of Internet of Things (IoT) devices and their diversified requirements have opted to design security mechanisms that cover all major security requirements. Wireless Local Area Networks (WLANs) is the most common network domains where IoT devices are launched, particularly because of its easy availability. Security, in other words authentication however, remains to be a major constriction for IoT-WLAN deployments. Though there are IoT based authentication protocols prevailing, such protocols are either prone to threats such as perfect forward secrecy violations, insider with database access attack, traceability attack, stolen device attack, ephemeral secret leakage, or they consume excessive computational and communication resources that result in an unprecedented burden for the IoT system. This paper presents an Extensible Authentication Protocol (EAP) based mechanism for IoT devices deployed in a WLAN that addresses the above security issues and achieves cost-effectiveness. Validation follows an informal and formal approaches (using GNY and BAN logic, and Scyther verification tool) for the proposed protocol, demonstrating its robustness. Our performance analysis shows that the proposed protocol is lightweight and more secure in contrast to the state-of-the-art solutions. In addition, performance of the proposed protocol subjected to unknown attacks is investigated, which deduces that the proposed protocol has less overhead under unknown attacks than its competitors. A prototype of the protocol has been developed to demonstrate its feasibility and accuracy.

In the coming years, massive amounts of data are likely to be traded in vehicle-to-grid (V2G) networks to enhance traffic efficiency and safety through vehicular communications. However, several challenges need to be addressed before realizing the full potential of V2G networks. These challenges include the privacy-preservation of users, secure caching, scalability in deep environments, unreliability in high mobility events, and low efficiency in large networks. To overcome these challenges in V2G networks, named data networking (NDN) offers a good solution. It provides a new future Internet architecture: “named content-based” rather than “host addresses.” The main focus of NDN in V2G networks is to provide data availability, network performance, data retrieval, and data distribution. However, the presence of NDN in V2G networks introduces several issues like privacy and trust among vehicular nodes. Hence, this article proposes a system model based on blockchain technology in NDN-enabled V2G networks. This model provides secure and fast named content searching and enhances the trust among vehicular nodes. The simulation results show that the block propagation latency of NDN-based blockchain is less than the IP-based blockchain systems. In addition, the performance of the proposed scheme outperforms an existing scheme. The reason is to use the proof-of-authority consensus mechanism compared to proof of work, which uses high mining computation power. Furthermore, the proposed scheme increases the trust and transparency in NDN-enabled V2G networks.

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.

Uncrewed aerial vehicles (UAVs) have made a substantial contribution to vehicle communications in recent times, and they provide viable ways to improve connection in contemporary transportation networks. However, maintaining consistent signal coverage, the limited computation capacity of UAVs, and getting past obstructions to maintain direct communication with vehicles is still tedious. To address the same, In this paper, an edge-enabled digital twin (DT) of UAV with an intelligent reflecting surface (IRS)-aided vehicular network is investigated. We specifically concentrate on the issue of minimizing the net energy consumption of the system in task-offloading while simultaneously optimizing IRS phase-shift, power allocation and task-offloading parameters through the use of DT architecture. We first describe the specified non-convex optimization issue as a Markov decision process (MDP) to address it. Eventually, we propose a hybrid federated learning (HFL) algorithm that aims to maximize energy efficiency (EE) by optimising related parameters. This method also enhances the system’s overall performance by lowering energy consumption and using the combined experiences of several agents. Compared to the benchmark schemes, HFL proves to be 20.5% and 47.6% more efficient than MAD2PG and DQN respectively. Simulation results affirm that the suggested method outperforms the benchmark techniques in terms of EE and learning accuracy.

Unmanned aerial vehicles (UAVs) have enabled numerous inventive solutions to multiple problems, considerably facilitating our daily lives; however, UAVs frequently rely on an open wireless channel for communication, making them susceptible to cyber-physical threats. Also, UAVs cannot execute complicated cryptographic algorithms due to their limited onboard computing capabilities. Balancing high-security levels and minimum computation costs is imperative when developing a security solution for UAVs. Consequently, several proxy signature schemes have been proposed in the literature to fulfill these requirements. Nevertheless, many of these solutions face the issue of high computation costs, and some exhibit security vulnerabilities that could not be more feasible options for UAV communication. Considering these constraints in mind, in this article, we introduce an improvised certificate-based proxy signature scheme (ICPS), which leverages the concept of hyperelliptic curve cryptography (HECC) to meet the security and efficiency requirements of UAV networks. The proposed ICPS scheme offers a range of notable features, including its ability to address key escrow and secret key distribution issues. The proposed ICPS scheme’s security hardness has been evaluated using the widely known security tool, the random oracle model (ROM), proving its resilience against known and unknown cybersecurity threats. Finally, this study conducts a performance comparison of the proposed scheme against existing schemes, emphasizing its outstanding cost-efficiency. Notably, the computation cost is measured at 5.3536 ms and the communication cost at 1120 bits, substantially lower than relevant existing schemes.

In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks' operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.

Unmanned aerial vehicles (UAVs) in beyond 5G (B5G) are crucial for revolutionizing various industries, including surveillance, agriculture, and logistics, by enabling high-speed data transfer, ultra-low latency communication, and ultra-reliable connectivity. However, integrating UAVs into B5G networks poses various security and privacy concerns. These risks encompass the possibility of unauthorized access, breaches of data, and cyber-physical attacks which jeopardize the integrity, confidentiality, and availability of UAV operations. Moreover, UAVs in B5G networks are also at high risk from the application of machine learning (ML)-based attacks by exploiting vulnerabilities in ML models, leading to adversarial manipulation, data poisoning and model evasion techniques, which can compromise the integrity of UAV operations, lead to navigation errors, and expose sensitive data collected by UAVs. Considering the aforementioned security and privacy concerns, this review article presents emerging security and privacy solutions for UAVs in B5G networks. Firstly, We introduce the essential background of integrating UAVs into B5G networks and discuss the advantages and security challenges which the emerging integrated network architecture have. Then, we proceed to analyze and examine the security and privacy landscape by including threats and requirements of UAVs in B5G networks. Based on these threats and requirements, solutions from physical layer security (PLS), blockchain (BC), federated learning (FL) and post-quantum cryptography (PQC) are discussed and explored in details. Moreover, potential future research directions are discussed in details as open research issues.

With an increase in the manufacturing of Electric Vehicles (EV s), it is difficult to allocate a different IP address to each EV for content transfer in a vehicle-to-grid (V2G) network. Moreover, with increasing EV s, the network traffic load on the local area aggregator increases. In such a scenario, caching at neighboring nodes can improve network efficiency along with reducing the burden on cellular links. However, the existing Internet protocol-based communication architecture lacks in-network caching. In this paper, we propose content content-centric network based content transfer strategy for the V2G network. In particular, we use the mobility pattern of EV s to capture the inter-contact times for a pair of nodes and present a caching placement scheme to achieve maximum content offloading ratio. The formulated problem also considers the limited cache capacities of each node and is justified to be an NP-hard caching placement problem. The results section depicts that the performance evaluation of the proposal outperforms random caching and popularity-based caching strategies with respect to content offloading ratio and average access delay.

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low, and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther tool demonstrates that location-based keys as proposed in our protocol are secure against location inference and spoofing attacks among others.

The battery issue with Internet of Things (IoT) devices has been identified as a feasible solution in the shape of forthcoming backscatter communication technology. Wireless sensor networks, for example, that use backscatter communication technology can effectively monitor remote situations without requiring regular battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To overcome this issue, we proposed a solution that employs several unmanned aerial vehicles (UAVs) to aid in data collection. These UAVs may approach the backscatter sensor node (BSN), activate it, and then collect data. Our goal is to lower the overall flight duration required for rechargeable UAVs after the data collection mission is completed. The simulation results show that the proposed algorithms PDQN may outperform multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) approaches.

Image style transfer is a challenging task in computational vision. Existing algorithms transfer the color and texture of style images by controlling the neural network's feature layers. However, they fail to control the strength of textures in different regions of the content image. To address this issue, we propose a training method that uses a loss function to constrain the style intensity in different regions. This method guides the transfer strength of style features in different regions based on the gradient relationship between style and content images. Additionally, we introduce a novel feature fusion method that linearly transforms content features to resemble style features while preserving their semantic relationships. Extensive experiments have demonstrated the effectiveness of our proposed approach.

Internet of Things (IoT) applications are switching from general to precise in different industries, e.g., healthcare, automation, military, maritime, smart cities, transportation, logistics, and many more. In the healthcare domain, these applications had demonstrated an incredible improvement in patient assessment, monitoring, and prescription, etc., with ease of access through the Internet. Despite its benefits, this technology also offers several security challenges for the research community and healthcare stakeholders, because of its wireless communication and open-area deployment. To explore, patient wearable devices and other networking entities follows unstructured communication format to share their accumulated data in the network, which makes them susceptible to manifold security threats. Considering the significance of these applications, data acquisition, processing, storage, and assessment on client and remote sides need a high standard of secure communication infrastructure. Therefore, security of these applications is one of the major obstacles that prevent their widespread use in different healthcare domains. To discuss different security constraints, in this paper, we present a comprehensive survey of the theoretical literature from 2015-to-2023 to highlight the unresolved security problems of this emerging technology. Based on the evaluated literature pros and cons, we determine the security requirements and challenges of Healthcare-IoT (HC-IoT) applications. Following this, we demonstrate future research directions that could be useful for the researchers and industry stakeholders working in this domain. To demonstrate the uniqueness of this work and claim its contribution, we compare our work section-wise with previously published papers to answer the question of reviewers, editors, students, and readers, why this review article is required in the presence of already published review articles.

The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and actuators with industrial software tools and applications. IIoT aims to enhance manufacturing and industrial processes by capturing and analyzing real-time industrial data. However, the heterogeneous and homogeneous nature of IIoT networks makes them vulnerable to several security threats. As data is transmitted over an insecure communication medium, intruders may intercept communication among different entities and perform malicious activities. Consequently, ensuring the security and privacy of data transmitted in IIoT networks is essential. Motivated by the aforementioned challenges, this article presents a deep-learning-integrated blockchain framework for securing IIoT networks. Specifically, first, we design a private blockchain-based secure communication among the IIoT entities using session-based mutual authentication and key agreement mechanism. In this approach, the Proof-of-Authority (PoA) consensus mechanism is used for verification of the transactions and block creation based on the voting of miners over the cloud server. Second, we design a novel deep-learning-based intrusion detection system that combines contractive sparse autoencoder (CSAE), attention-based bidirectional long short-term memory (ABiLSTM) networks, and softmax classifier for cyberattack detection. The practical implementation of blockchain and deep-learning techniques proves the effectiveness of the proposed framework.

This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a random forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast data set of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive data set containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high-pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on recurrent neural network and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.

The vehicle-road cooperation system (VRCS) facilitates vehicle-to-vehicle (V2V) communication for future vehicle usage in sixth generation (6G) networks. The implementation of the 6G network has made it possible for V2V communication to enhance network density, optimize transmission mode selection, and offer connectivity between vehicles while guaranteeing Quality of Service (QoS). However, there are inherent challenges, such as limited bandwidth, diverse QoS requirements, interference, and power constraints, associated with resource allocation and mode selection in V2V and vehicle-to-everything (V2X) communication. In this article, we jointly optimized the mode selection and resource allocation problems in VRCS by using simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS). The proposed model utilizes quantum federated reinforcement-learning (QFRL)-based augmented intelligence algorithms within the STAR-RIS VRCS framework. The proposed QFRL algorithm is a promising solution for advanced decision making, automation to improve traffic flow, reduces traffic congestion, and improve safety in the STAR-RIS assisted VRCS. Additionally, by leveraging the unique processing advantage of quantum computing will make the VRCS more capable of handling the enormous amount of real-time data that IoT devices send, which is necessary for the intelligent services it offers. The proposed model QFRL-based STAR-RIS assisted VRCS approach maximizes vehicle-to-infrastructure (V2I) user capacity while meeting the reliability requirement of V2V pairs. Finally, the simulation results prove the superiority of the QFRL algorithm against baseline schemes like quantum federated learning (QFL), federated reinforcement learning (FRL), and federated learning (FL) algorithms for V2V pairs. Furthermore, the performance evaluation findings indicate that the proposed STAR-RIS assisted QFRL algorithm performs 20.5%, 32.2%, and 46.7% better than QFL, FRL, and FL.

The healthcare personals often use stored healthcare data to make crucial decisions, assess risk, and care for patients. The extraction of the required information from the saved healthcare data needs a healthcare ecosystem that can guarantee reliable data delivery. The reliability of cyber–physical data needs to be cross-examined using several sources of data of overlapping nature. The cross-examined data can be saved on blockchain and Solid PoD (SP) to preserve its reliability and privacy. Once the reliable healthcare data is stored on the blockchain and SP, the patients’ medical history can be delivered to data-operated systems to monitor, diagnose, and detect augmented healthcare anomalies. Cyber twins (CTs) combine the specific cyber–physical objects with digital tools portraying their actual settings. The creation of a live model for the delivery of healthcare services presents a novel opportunity in patient care comprising better evaluation of risk and assessment without hampering the activities of daily living. The introduction of blockchain technology can improve the notion of CTs by certifying transparency, decentralized data storage, data irreversibility, and person-to-person industrial communication. The storage and exchange of CT data in the healthcare ecosystem depend on disseminated ledgers and decentralized databases for storing and processing data to avoid single-point reliance. The present study develops an owner-centric decentralized sharing technique to fulfill the decentralized distribution of CT data.

As an important part of Internet of Things (IoT), the Internet of Vehicles (IoV) has been widely used in traffic intersection control, automatic driving, intelligent navigation, etc. However, due to the dynamic topology and high mobility, IoV faces the challenge of frequent disconnections, which will lead to deterioration in the performance of data dissemination. Motivated by the above, air-to-ground (A2G) integrated IoV is used to bridge the communication gaps between terrestrial vehicles to achieve efficient information transmissions. This paper investigates the application of low altitude platform stations (LAPSs) and reconfigurable intelligent surface (RIS) in A2G integrated IoV, where multiple relaying LAPSs equipped with RISs are adopted to improve the spatial multiplexing gain and create the smart radio environment. To make full use of the advantages of LAPS-and-RIS enhanced transmissions, we formulate a weighted sum rate (WSR) maximization problem by jointly considering the location, power, and phase shift. To tackle this challenging non-convex problem, we design an iterative optimization scheme, where three optimization variables are processed in turn. Simulation results demonstrate that the proposed WSR maximization scheme can significantly improve the communication performance in comparison with other state-of-the-art schemes and the baseline scheme.

The rapid increase in the adoption of Electric Vehicles (EVs) and the installation of Charging Stations (CSs) are key components for bidirectional energy transfer between EVs and CSs. However, the traditional techniques of energy trading have issues of trust, scalability, traceability, provenance, and authenticity among energy prosumers. To address these challenges, particularly information imbalances between energy buyers and sellers, we propose TokenGreen, a novel framework that leverages blockchain and Non Fungible Tokens (NFTs) to enable participants to have ownership of energy assets through investments in distributed energy generation, distribution, and clean energy infrastructure, leading to trust and transparency management among the participants. The proposed framework uses Ethereum Virtual Machine (EVM), ERC-721 NFT, Inter Planatery File System (IPFS), and Solidity smart contracts to develop an NFT based energy marketplace. Various smart contracts, contract events, functions, algorithms, have been designed and integrated into the energy marketplace to facilitate the minting, creation, purchase, and resale of NFT tokens, including energy trading. To assess the performance of the proposal, experiments are performed using tools such as Geth, Hyperledger Caliper, and the Ethereum SDK. The obtained results indicate that the average maximum latency for CreateToken reached 12.39s, while BuyToken and ResellToken reached 11.02s. Additionally, the average minimum latency for CreateToken, BuyToken, and ResellToken reached 10.46s, 10.03s, and 9.14s, respectively. On average, memory consumption ranged from 640 to 775 MB, while CPU usage averaged between 30% and 55% for each function. The performance analysis indicate CreateToken has low throughput, while BuyToken shows higher, and ResellToken exhibits the highest throughput due to fewer write operations. TokenGreen demonstrates superior performance compared to the existing state-of-the-art, considering the mentioned parameters.

By the limitations of storage capacity and computing power, Internet of Things (IoT) devices may prefer to outsource their valuable and sensitive data to cloud storage providers (CSPs) for further analysis, so it is critical to design protocols that can verify the integrity of these data remotely, while preserving the privacy of their owners. This article proposes a novel remote data integrity checking method for IoT, namely, FDUP-remote data integrity checking (RDIC), which achieves fully decentralized, efficient, and unconditionally privacy-preserving checking simultaneously, that is, the proof-checking is performed efficiently on the blockchain by the smart contracts integrated with native C/C++ codes, while the blockchain or any other entity cannot learn any information about the data content, even they have unbounded computing power. Furthermore, it is optimized for low-power IoT devices by greatly reducing the exponentiations of generating homomorphic verifiable tags to be nearly independent of the block size of the outsourced data. To defend against untrusted IoT and CSP, we present strict proofs and analyses in the aspect of correctness, soundness, and unconditionally privacy-preserving. The evaluation of theoretical performance and the prototype system deployed on a blockchain platform indicate that FDUP-RDIC is suitable for real-world IoT applications.

In industrial cyber–physical systems (ICPS), real-time condition monitoring of wear-prone components of steel rolling production equipment is a key scenario for predictive maintenance. Machine vision-based crack detection can quickly identify critical damage and prevent unplanned downtime. However, the harsh working environment poses difficulties for data collection, a large amount of noise tends to contaminate surface crack images, and complex surface crack morphology affects the recognition accuracy. The real-time and accuracy performance of traditional crack detection algorithms are hard to meet the requirement of industrial applications. To tackle this challenge, a high-precision surface crack detection architecture for rolling steel production equipment based on image semantic segmentation is proposed. First, a coordinate attention-deep convolution generative adversarial networks (CA-DCGANs)-based data augmentation method is proposed to augment the original data set with high quality. Second, a crack detection model based on multiscale learning efficient spatial pyramid network (MLESPNetV2) is proposed. It effectively improves detection accuracy to obtain semantic information strongly correlated with crack using multiscale modeling and attention mechanism. Third, A semi-supervised learning method based on multiscale learning efficient spatial pyramid-generative adversarial network (MLESP-GAN) is proposed to solve the problem of insufficient labeled data and unstable training process. Finally, extensive experimental results on KolektorSDD and CAS-Crack data sets demonstrate that the proposed MLESPNetV2 significantly improves accuracy and real-time performance compared with the benchmark model. It is therefore suitable for deployment in industrial sites for real-time health monitoring of industrial equipment.

The rapid growth of the Internet of Things (IoT) in the early 21st century has introduced complexities in delivering various services, including Quality-of-Service (QoS) management for smart agriculture sensors. Selecting optimal IoT nodes considering QoS parameters, such as energy consumption, latency, and network coverage area has become challenging. In response, this research proposes an extended form of differential evolution (DE) that incorporates a rapid adaptation approach using optimization-based design. By leveraging dynamic information from IoT devices, the proposed approach enhances exploration and exploitation capabilities, allowing for adaptive adjustment of algorithm parameters and strategies. Additionally, a novel fitness function for energy harvesting in IoT-based applications is introduced. The effectiveness of the proposed algorithm is evaluated in IoT-based applications and an IoT-service framework, with comparative analysis against state-of-the-art algorithms. The results demonstrate that the proposed approach achieves superior performance in energy harvesting QoS, delay, service cost, and maximum coverage area in the IoT-service network. This research contributes to the IoT field by offering an advanced DE algorithm that addresses limitations, providing valuable insights for QoS management in IoT-based services, particularly in the context of smart agriculture sensors.

Recent advancements in the field of the Internet of Medical Things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges, such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA)-based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this article. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

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.

This article introduces a new metric for evaluating digital innovation in enterprise transformation using textual analysis of annual reports from U.S.-listed companies. Through network analysis and topic modeling, we identified 12 topics categorized into three main areas: digital technology innovation, customer-oriented digital strategy, and digital transformation in traditional business operations. Our research indicates that digital innovation strategies are critical for maintaining competitiveness and have shifted to a more innovation management-oriented approach. We also found differences in digital innovation strategies between companies and industries. Our study contributes to the theoretical significance of enterprise management and sustainable development.

The transformer has become a prominent technique for hyperspectral image (HSI) classification, attributed to its capability to model global dependencies between features. Nevertheless, the predominant transformer-based methods rely on a direct information flow with a fixed number of tokens, causing the sequential transformer encoders to lack crucial interaction. This deficiency results in an inappropriate granularity of discriminative features and the loss of subtle patterns. In response to this limitation, we introduce a novel approach named Multi-level Class Token Transformer with Cross TokenMixer (MCTT) for HSI classification. Specifically, we explore a CNN stem network that incorporates 3D, 2D, and pointwise convolutions to encode local spatial-spectral information. The spectral-spatial features undergo transformation into semantic tokens using a semantic tokenizer. These tokens are then input into the transformer encoder to capture global interactions between different pixels. To create a hierarchical semantic representation, we propose a cross tokenmixer that integrates different levels of class tokens and patch tokens, enabling a multi-grained representation. The cross tokenmixers, with their varied number of tokens, facilitate the learning of distinct discriminative spectral-spatial representations and enable a comprehensive understanding of the HSI through a voting mechanism. Extensive experiments and ablation studies are conducted on three public HSI datasets to evaluate the performance of our proposed method. The results demonstrate the effectiveness and superior performance of our approach in HSI classification.

Industry 4.0 integrates industrial Internet of Things (IIoT), artificial intelligence, and cloud computing. The advent of the 5G era has undoubtedly provided a new impetus for the development of many Industry 4.0 applications, but it also presents some key security hurdles. The network scale is becoming larger and larger, the network environment is becoming increasingly complex, and security risks are prominent. Frequent issues, such as malicious attacks, privacy information disclosure, and data transmission security. In order to improve the reliability and security of cyberspace, this article proposes a blockchain-based hierarchical IIoT security solution mechanism. In addition, we propose a random reputation voting mechanism and blockchain (RRV-BC) scheme based on verifiable random function and reputation voting to reduce the communication cost during blockchain consensus communication. Meanwhile, the node credit scoring mechanism is introduced to dynamically evaluate the node credit. The simulation results show that the scheme improves the reliability of data communication and the fault tolerance of consensus mechanism by an average of 5% compared with the traditional practical byzantine fault tolerance (PBFT) protocol method.

In recent times, content-centric network has been evolved as one of the most popular technologies of the modern era, facilitating cache content-based data sharing, especially in vehicle-to-grid (V2G) networks. However, to get benefits from content-based caching services, it is essential to match the service providers with service requesters optimally during high mobility vehicles scenarios. Most of the existing literature proposals for this problem are based upon the centralized architecture, which may be vulnerable to congestion matching operations, with a lack of trustworthiness among nodes. Also, a content holder may spread fake information about content availability to requesters, which may affect the performance of any implemented solution in this environment. In this paper, we model the two-sided preferences of both parties (service providers and requesters) to maximize the cache content sharing using content centric network (CCN) communications. Then, we formulated a decentralized matching problem with joint transmit power of both the content providers and requesters. The framed matching game involves aspiration level and agreement functions for both the parties. Finally, a distributed blind matching algorithm (BLMA) is also proposed, which is executed by deploying a smart contract on the Ethereum network without involving an intermediate authority. Moreover, we provide the theoretical analysis on the number of successive iterations for convergence of the proposed BLMA algorithm. To verify and validate the effectiveness of the proposal, we compare it with three benchmark schemes and evaluated its performance with respect to average individual utility, latency, and energy consumption on the benchmark data sets. The results obtained show that the proposed scheme is superior in comparison to the existing state-of-the-art solutions with respect to various performance evaluation metrics.

Recent advancements in Vehicle-to-Grid (V2G) lead to efficient service provisions, such as eco-friendly environment, demand response management, charging, and discharging to the end-users. However, security and privacy preservation for the aforementioned services are key challenges keeping in view of the dependency on the existing centralized security architectures which are not resilient to fault tolerance due to a single point of failure. Hence, there is a need to design new efficient security solutions for the current V2G network, so as to provide seamless services to the end-users. Motivated by these, in this work, we proposed a bloom filter-enabled smart contract-based scheme for access control in V2G environment. In comparison to complex signature-based cryptographic techniques, we propose bloom filter-based authentication for the registered nodes for efficient storage and searching of stored data on the blockchain network. We also designed the Proof-of-Authority (PoA) consensus mechanism, which selects authority nodes dynamically to verify various transactions on the blockchain network. To validate the proposal, we implemented it on the Ethereum network on benchmark datasets using various evaluation parameters such as- latency, throughput, false positive probability, and gas cost.

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.

In this paper, we propose a platoon access authentication system for initial access process in autonomous vehicle platoons (AVPs) in which post-quantum encryption and signal processing techniques are employed to protect against both active and passive cyber-physical attacks. To avoid passive quantum cyber attacks, a quasi-cyclic moderate-density parity-check code is used to encode and decode AVP messages. Moreover, an independent component analysis-based signal separation technique is employed to eliminate the effect of high-power active cyber attacks on AVP messages. To measure the reliability of the system, we derive an analytical expression for the system failure probability, taking into account the influence of both the cyber and physical planes. The simulations show that the proposed system is effective against attacks and can help reduce system failures caused by intentional and unintentional adverse cyber-physical effects. The proposed system offers a potential solution to the challenge of protecting initial access while maintaining ultra-reliable low-latency communications between AVPs and the infrastructure.

With the maturity of 5G and Intelligent Transportation Systems (ITS) technologies and the prospect of Beyond 5G (B5G) and 6G technologies, the limited lifetime and computing of mobile devices pose significant challenges to Quality of Service (QoS). In addition, the problem of inefficient use of computing, storage, communication, and other resources still exists in communication systems. In response to the above issues, Multi-tier Computing Networks (MTCNs) migrate computationally intensive tasks to the cloud, fog, or edge with sufficient resources, thereby realizing energy-efficient collaborative computing and multi-dimensional resource sharing. However, in the MTCN environment with complex heterogeneity, and high-intensity dynamics, how to provide sustainable solutions for resource scheduling strategies is a meaningful issue. Inspired by Virtual Network Embedding (VNE) to decouple physical network configuration, we propose a multi-target-aware dynamic resource scheduling algorithm for MTCN to improve resource flexibility, which is the first attempt in this direction. Specifically, we consider differentiated QoS requirements like computing, storage, bandwidth, delay, etc., and establish multi-target-aware embedded constraints. Additionally, we present a Deep Reinforcement Learning (DRL)-based scheduling network that can interact scientifically and efficiently with the MTCN environment. It extracts environmental information as state input to better focus on dynamic characteristics as well as calculates candidate nodes and links using a three-layer network architecture and related constraints. Furthermore, the learning process is optimized through the combination of the reward mechanism and the gradient descent mechanism. Finally, comparison experiments on three widely used evaluation indicators (long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate) verify that the proposed algorithm has made an average improvement of$19.042\%$,$2.563\%$, and$3.932\%$respectively compared with all baselines.

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

In this paper, we propose a secrecy-aware energy-efficient scheme for a two-tier heterogeneous network (HetNet), consisting of a sub-6 GHz macrocell and multiple millimeter wave (mmWave) picocells. Each picocell is assumed to have several users and an eavesdropper (Eve) which intercepts the signal of the picocell users. In the proposed scheme, firstly, to maximize the secrecy energy-efficiency (SEE) of picocell users, a joint optimization problem of power control, channel allocation, and beamforming is formulated by considering the minimum secrecy rate and signal-to-interference-plus-noise ratio (SINR) constraints. Due to the non-convex nature of the aforementioned optimization problem in a highly dynamic HetNet environment, we transform it into a reinforcement learning (RL) problem using the Markov decision process (MDP). Then, a multi-agent reinforcement learning (MARL) technique is used to obtain the maximum long-term reward. Moreover, we propose a multi-agent cooperative deep reinforcement learning (DRL) scheme known asSecBoostto solve the MDP with large number of action and state spaces. It uses the dueling and double-Q architecture of dueling double deep Q-network (D3QN) to optimize power control, channel allocation, and beamforming vectors to maximize the SEE of picocells. Also, prioritized experience replay is used to increase the sampling efficiency ofSecBoost. The SEE performance ofSecBoostis compared with MARL, multi-agent deep Q-network (MA-DQN), state-of-the-art joint beamforming based secrecy energy efficiency maximization (JBF-SEEM) scheme, and one-time pad based encrypted data transmission (O-EDT). Simulation results demonstrated that the proposedSecBoostscheme achieves 14.7%, 8.33%, 30%, and 69% better average SEE in comparison to MARL, MA-DQN, JBF-SEEM, and O-EDT schemes, respectively, which reveals its effectiveness in improving SEE of picocells.

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

The rapid development of Artificial Intelligence (AI) has had a significant impact on various industries, including healthcare. The Internet of Medical Things (IoMT) has played a vital role in this evolution. However, while AI has contributed to many benefits in healthcare, concerns about data privacy and security persist. To address these concerns, we propose a framework that combines Federated Learning (FL) and Differential Privacy (DP) to enhance data protection within IoMT. By integrating FL’s decentralized approach with DP’s mechanism to prevent data reconstruction from model outputs, we can improve data confidentiality. This integrated approach is used to develop and analyze high-performing Convolutional Neural Networks (CNNs) for detecting Tuberculosis using chest X-ray datasets. The framework undergo thorough performance evaluation, utilizing various metrics to establish its superiority over baseline models. The results demonstrate the effectiveness of our framework as a robust solution for secure and private AI applications in healthcare.

Unmanned aerial vehicles (UAVs) are widely recognized as crucial supplementary component of 6G networks. Owing to the key attributes of UAVs (mobility, flexibility, and adjustable altitude), UAVs can serve as flying base stations (BSs), flying relays and mobile terminals in order to expand the service coverage and derive more applications. Softwarization is regarded as dominant attribute of network architecture of 6G, mainly realized by network function virtualization (NFV) and software defined networking (SDN). In this paper, we concentrate on researching the resource orchestration and allocation of end-to-end (E2E) slice services in softwarized UAVs-assisted 6G terrestrial networks. Problem models of UAVs-assisted 6G terrestrial networks and E2E slice are firstly introduced. Then, the problem formulation of resource orchestration and allocation of E2E slice is presented. Afterwards, one novel framework design, abbreviated as ReOrcAll-UAVs-6G, is detailed. When receiving one E2E slice, our ReOrcAll-UAVs-6G checks the available softwarized resources. If having available softwarized resources, our ReOrcAll-UAVs-6G turns to serving the slice and fulfilling slice’s tailored resource demands. During the orchestration and allocation phase, wireless and wired resource requests of this slice are considered and executed. Evaluation work and gained results of ReOrcAll-UAVs-6G and selected approaches are illustrated and analyzed. Gained results reveal that our ReOrcAll-UAVs-6G achieves apparent performance advantage, comparing with all selected approaches.

In existing research on cluster head selection schemes in the Internet of Vehicles (IoV), designing a stable cluster structure poses a significant challenge. Choosing a centrally-located cluster head that can respond rapidly is crucial for meeting various requirements. To address the aforementioned challenges, this paper introduces a machine learning-based IoV cluster head selection scheme (HSCS). We introduce a new metric termed N-cycle Average Virtual Cluster Delay (XTn) for appropriate cluster head selection. To accommodate the high dynamism of vehicles, a machine learning model is integrated to predict cluster head selection metrics across different periods, and a set of cluster head selection guidelines is formulated. Experimental results demonstrate that our proposed HSCS ensures a relatively low average intra-cluster delay while maintaining a longer cluster head retention time, and it exhibits commendable robustness.

Over the last decades, the healthcare industry has been revolutionized heavily, especially after the Covid-19 surge. Various artificial intelligence (AI) approaches have also been explored during this era for their applicability in healthcare. However, traditional AI techniques and algorithms are prone to overfitting with minimal robustness to unseen or untrained data. So, there is a need for new techniques which can overcome the issues mentioned earlier. Federated learning (FL) can help design specific AI services for the network of hospitals with less overfitting and more robust modules. However, with the inclusion of FL, the problem related to user privacy is the biggest challenge, making the use of FL in the real world a grand challenge. Most solutions presented in the literature used blockchain technology to mitigate the issues mentioned earlier. However, it prevents third-party systems from penetrating the decision process, but the network devices can access shared data. Moreover, blockchain implementation requires new paradigms and infrastructure with an additional overhead cost. Motivated by these facts, the paper presents a limited access encryption algorithm incorporating FL (LEAF) framework, i.e., an encryption technique that solves privacy issues with the help of edge-enabled AI models. The proposed LEAF framework preserves user privacy and minimizes overhead costs. The authors have evaluated the performance of the LEAF framework using extensive simulations and achieved superior results. The achieved accuracy of the proposed LEAF framework is 3% higher than that of the traditional centralized and FL-based systems without compromising user privacy. In the best scenario, the proposed framework’s encryption process also compresses the data size by 4–5 times.

The propagation environment was uncontrollable in first-generation to fifth-generation (5G) wireless technologies. This behavior of the wireless propagation environment is one of the prime constraints in harnessing the performance of wireless networks. This problem can be addressed in sixth-generation (6G) wireless networks by deploying intelligent reflecting surfaces (IRSs). IRS’s amplitude and phase reflecting coefficient of reflecting units (RUs) can be adjusted via a programmable controller to meet the network requirements. On the other hand, in 5G and 6G wireless communication networks, non-orthogonal multiple access (NOMA) is a robust and well-admired multiple access scheme among the other multiple access counterparts in terms of spectrum efficiency and link capacity. NOMA allows many user equipment (UE) by utilizing non-orthogonal distribution of resources. Therefore, the combination of IRS and NOMA is one of the dominant technologies for 6G wireless networks. Based upon the importance of NOMA and IRS in the initial development of 6G wireless networks, this paper presents a comprehensive survey on IRS-assisted NOMA-based networks, considering their designs and challenges. In this work, the concept and structure of IRS-assisted NOMA have been explained with an in-depth analysis of the frameworks. It also includes some challenges of IRS-assisted NOMA in wireless communication networks. Further, applications and future research directions of IRSassisted NOMA networks are discussed.

Machine Learning (ML) and Artificial Intelligence (AI) can harness the immense amount of operational data from clouds to services, to social and communication networks. In the era of data science and connected devices of all varieties, Intelligence have found ways to improve operations and management of next generation networks, systems, and services. Further research is therefore needed to understand and improve the potential and suitability of ML/AI in the context of network, system, and service operations and management. This will provide deeper understanding and better decision making based on largely collected and available operational and management data. It will also present opportunities for improving ML/AI algorithms on aspects such as reliability, dependability, and scalability, as well as demonstrate the benefits of these methods in control and management systems. Moreover, there is an opportunity to define novel platforms that can harness the vast operational data and advance ML/AI algorithms to drive management decisions in open and highly programmable networks, clouds, and data centers.

During the winter season in India, the AQI experiences a decrease due to the limited dispersion of APs caused by MFs. Therefore, we developed a sophisticated green predictive model GAP, which utilizes our designed green technique and a customized big dataset. This dataset is derived from weather research and tailored to forecast future AQI levels in the Indian subcontinent during winter. This dataset has been meticulously curated by amalgamating samples of APs and MFs concentrations, further adjusted to reflect the yearly activity data across various Indian states. The dataset reveals an amplified national emissions rate for$\boldsymbol {PM_{2.5}}$,$\boldsymbol {NO_{2}}$, and$\boldsymbol {CO}$pollutants, exhibiting an increase of 3.6%, 1.3%, and 2.5% in gigagrams per day. ML/DL regressors are then applied to this dataset, with the most effective ML/DL regressors being selected based on their performance. Our paper encompasses an exhaustive examination of existing literature within the realm of air pollution epidemiology. The evaluation results demonstrate that the prediction accuracy of GAP when utilizing LSTM, CNN, MLP, and RNN achieve accuracies of 98.53%, 95.9222%, 96.1555%, and 97.344% in predicting the$\boldsymbol {PM_{2.5}}$,$\boldsymbol {NO_{2}}$, and$\boldsymbol {CO}$concentrations. In contrast, RF, KNN, and SVR yield lower accuracies of 92.511%, 90.333%, and 93.566% for the same AQIs.

As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide an energy allocation strategy for V2G.

Cloud computing is a computing facility which allows its clients to outsource their information on distant cloud based servers without having the pressure of maintaining it. Of several remote data storage issues, data integrity preservation problem has gained much attention of researchers all over the world. Numerous research work has been done by researchers all over the world in the field of data integrity audit in cloud computing. Proposed work is an efficient and stable data integrity verification scheme based on Schnorr signatures. In Schnorr signatures, the signature verification equation is linear and also batch verification of several blocks is possible. Most existing schemes are based on Boneh Lynn and Shacham (BLS) and RSA signature schemes. However, in contrast with existing schemes, the proposed scheme is highly efficient and safe and pays lower verification computation costs proven experimentally in this paper.

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.

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.

The use of a large-scale antenna array has achieved significant performance gains in anti-jamming communications. However, due to the hardware cost and power consumption constraints, it is impractical to deploy such large-scale antenna array at the user side. Inspired by the remarkable advantages of reconfigurable intelligent surface (RIS), we propose an active-passive cascaded RIS-aided receiver architecture, which facilitate the deployment of a large-scale antenna array at the user side in a cost- and energy-efficient way and provides additional degree-of-freedom for beamforming design. Building upon this architecture and considering the practical angular channel state information (CSI) imperfection, a worst-case achievable rate maximization problem is formulated for anti-jamming communications. To handle the non-convex problem, a low-complexity optimization framework is proposed, where the new anti-jamming criterion, Pareto-dual scheme, unified unit-modulus zero-forcing scheme, and conventional-cyclic coordinate descent algorithm are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify that the proposed architecture and optimization framework are capable of achieving excellent performance with low complexity.

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.

Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

Distributed Industrial Internet of Things (IIoT) has entirely revolutionized the industrial sector that varies from autonomous industrial processes to automation of processes without human intervention. However, threat hunting and intelligence is the most complex task in distributed IIoT. Besides, there exist no standard architectures for hunting micro services orchestration in distributed IIoT systems. The authors propose an efficient and self-learning autonomous multivector threat intelligence and detection mechanism to proactively defend IIoT systems/networks. Our proposed novel compute unified device architecture-empowered Convolutional LSTM2D (ConvLSTM2D) mechanism is highly scalable with self-optimizing capabilities to proficiently tackle diverse dynamic variants of emerging IIoT sophisticated threats and attacks. For a comprehensive evaluation, the authors employed a current state-of-the-art data set with 21 million instances comprised of varying attack patterns and prevalent threat vectors. Moreover, the proposed technique is compared with our constructed contemporary deep learning (DL)-driven architectures and benchmark algorithms. The proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff in speed efficiency.

With the rapid development of edge intelligence in wireless communication networks, mobile-edge networks (MENs) have been broadly discussed in academia. Supported by considerable geographical data acquisition ability of mobile Internet of Things (IoT), the MENs can also provide spatial locations-based social service to users. Therefore, suggesting reasonable points-of-interest (POIs) to users is essential to improve user experience of MENs. As the simple user-location data is usually sparse and not informative, existing literature attempted to extend feature space from two perspectives: 1) contextual patterns and 2) semantic patterns. However, previous approaches mainly focused on internal features of users, yet ignoring latent external features among them. To address this challenge, in this article, a deep distributed-learning-based POI recommendation (Deep-PR) method is proposed for situations of MENs. In particular, hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes. Besides, propagation operations are embedded to iteratively reoptimize expressions of the feature space. The successive effect of the above two aspects contributes a lot to more fine-grained feature spaces, so that a recommendation accuracy can be ensured. Two types of experiments are also carried out on three real-world data sets to assess both efficiency and stability of the proposed Deep-PR. Compared with seven typical baselines with respect to four evaluation metrics, obtained results of the overall performance of the Deep-PR are excellent.

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual’s mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject’s cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

Mobile-edge computing (MEC) is a new architecture that provides services to the edge of networks. The software and hardware platforms are positioned at the network edge close to end-users. Emerging developments in MEC can be used for healthcare applications, such as remote patient monitoring, diagnosis, and treatment purposes. The remote access to the data can arise security and privacy issues. Unauthorized access or data leakage can hamper the complete security of the system. This makes the system inconvenient, untrusted, less suitable, and vulnerable. This article is aimed to propose a security framework for the privacy preservation of patient data in a MEC environment where the services are accessed at the network edge. A lightweight cryptographic technique is proposed by including a chaotic map and a DNA sequence of organisms for encryption of electronic health records (EHRs). The identity privacy will be maintained by using anonymous authentication. The framework’s performance is evaluated with memory usage and encryption time and found satisfactory results.

Smart devices in an Internet of Things (IoT) generate a massive amount of big data through sensors. The data is used to build intelligent applications through machine learning (ML). To build these applications, the data is collected from devices into data centers for training ML models. Usually, the training of models is performed on central server, but this approach requires the transfer of data from devices to central server. This centralized training approach is not efficient because the users are much less likely to share data to the centralized data centers due to privacy issues and bandwidth limitations. To mitigate these issues, we propose an efficient hybrid secure federated learning approach with the blockchain to securely train the model locally on devices and then to store the model and its parameters into the blockchain for traceability and immutability. A detailed security and performance analysis is presented to show the efficacy of the proposed approach in terms of security, resilience against many security attacks, and cost effectiveness in computation and communication as compared to other existing competing schemes.

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space–air–ground-integrated network (SAGIN) could provide a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multidimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This article proposes a service provision method based on service function chaining (SFC) to solve this problem. Network function virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federated learning (FL)-based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then, an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue–cost ratio, respectively.

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart network's performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Moran'sIindex tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.

Low-rate denial of service (LDoS) attacks exploit the security vulnerabilities of network protocols adaptive mechanisms to launch periodic bursts. These attacks result in the severe destruction of the quality of service of TCP applications. Therefore, detection of LDoS attacks is a concern among scientific communities. However, the existing coarse-scale detection methods yield poor detection performance and adaptability. To achieve the accurate detection of LDoS attacks, an adaptive Kohonen Network based fine-grained detection (AKN-FGD) model for LDoS attacks is proposed. Based on the burst and periodicity characteristics of attack traffic, the Smith-Waterman (SW) algorithm is used to estimate the pulse period, which is the length of the detection unit. Subsequently, cluster analysis is performed for each detection unit using the adaptive Kohonen network (AKN) algorithm because the discreteness of traffic suffering from LDoS attacks is more pronounced than that of legitimate traffic. Finally, the existence of LDoS attacks can be verified in view of a novel decision metric, denoted as the anomaly degree, based on the clustering results. We conducted experiments not solely in traditional networks using NS3 and in a test-bed environment but also in a software-defined network (SDN), with accuracies of 99.7%, 99.8%, and 95.6% for detecting LDoS bursts, respectively. The experimental results show that the AKN-FGD scheme not only enables accurate fine-grained detection, that is, it can detect every attack burst, but also estimates the start and end times of the attacks. Moreover, we have compared the AKN-FGD scheme with some other detection methods, and a comparison of the results show that our proposed approach displays better detection performance.

Recent development of smart cities includes advanced and necessary use of modern smart grid (SG), than the traditional power grid. The paradigm of SG has also transformed houses into a home area networks (HAN). In HAN, several smart devices and appliances are connected to the electricity control centers (ECC). Appliances share their load and consumption related information to ECC through smart meters. The consumption data may be used for supply-demand management, for example, by ramping production up or down as needed. However, security and privacy of the consumers data are greatly important, since fine-grained smart meter data may reveal an users presence/absence in his/her house. To address this issue, several public-key-based or identity-based data aggregation schemes have been proposed in the literature. However, most of such schemes either suffer from the complexity of certificate management or key escrow problem. To eliminate these issues, in this paper we propose an efficient certificate-based data aggregation (CB-DA) scheme. In the proposed CB-DA scheme, the owner selects a secret key and then use the secret key along with certificates as decryption/signing keys.

The need of fuzzy clustering arises in many real-world applications such as clumping the users based on their web browsing behavior where the behavior of a user can be similar to two different sets of users at the same instance. The aptness of fuzzy clustering for data streams is further intensified given their concept evolving nature. Data streams can be clustered either by following clustering-by-variable approach or clustering-by-example approach. Most of the existing fuzzy clustering-by-variable methods are applicable to numeric data streams only. In this article, a fuzzy hierarchical clustering method is proposed for clustering multiple nominal data streams using clustering-by-variable approach. The fuzzy affinity of data streams to different clusters is calculated using normalized cosine similarity to the cluster centroids. It handles the concept evolution by updating the hierarchical clustering structure by either merging and/or splitting the nodes depending on the extent to which the node entropy changes. The performance of the proposed method is analyzed and compared to hierarchical clustering for multiple nominal data streams (HCND), semifuzzy online divisive-agglomerative clustering, and nTreeClus on synthetic as well as real-world web-browsing dataset where it has outperformed all three in terms of cluster quality as quantified by Dunn index, modified Hubert$\Gamma$statistic, and adjusted rand index. Furthermore, the experimental results show that the proposed method is highly promising with regard to capturing fuzzy clusters as indicated by Xie-Beni index, partition coefficient, and partition entropy.

With the development of deep neural networks, hyperpsectral image (HSI) classification systems have achieved a significant improvement. These systems require numerous and accurate labeled hyperspectral data to be adequately trained. However, noisy labels are inherent in real-world hyperspectral systems, resulting in unreliable decisions. To handle noisy labels in hyperpsectral classification, an end-to-end attentive-adaptive network (AAN) is proposed for robust HSI classification training. The goal is to build a classifier with strong generalization capabilities that can be applied to both clean and noisy training sets without explicit noise label pre-treatment. Specifically, a spectral stem network with non-adjacent shortcut is exploited initially to re-distribute the sensitive layers for noisy labels to achieve robust spectral representation. Then, a group-shuffle attention module is proposed to capture the discriminative and robust spatial-spectral features in the presence of noisy labels. Finally, an adaptive noise-robust loss function is developed to fight against noisy labels by learning a parameter to balance the normalized cross entropy (NCE) and reverse cross entropy (RCE). Experimental results on three HSI benchmark datasets with simulated noisy labels demonstrate the effectiveness of AAN on HSI classification.

The intelligent detection process for industrial anomalies employs artificial intelligence methods to classify images that deviate from a normal appearance. Traditional convolutional neural network (CNN)-based anomaly detection algorithms mainly use the network to restructure abnormal areas and detect anomalies by calculating the errors between the original image and reconstructed image. However, the traditional CNNs struggle to extract global context information, resulting in poor anomaly detection performance. Thus, a masked Swin Transformer Unet (MSTUnet) for anomaly detection is proposed. To solve the problem of insufficient abnormal samples in the training phase, an anomaly simulation and mask strategy is first applied on anomaly-free samples to generate a simulated anomaly and, then, the Swin Transformer's powerful global learning ability is used to inpaint the masked area. Finally, a convolution-based Unet network is used for end-to-end anomaly detection. Experimental results on industrial dataset MVTec AD show that MSTUnet achieves superior anomaly detection and localization performance.

Industry 5.0 is aimed at merging the cognitive computing capabilities of deep neural networks (DNNs) with human resourcefulness in collaborative operations. DNNs have been widely used in Industrial Artificial Intelligence of Things (Industrial AIoT) systems. However, DNNs are vulnerable to adversarial attacks, which bring a considerable risk to Industrial AIoT systems. The adversary uses adversarial examples crafted on the local ensemble model to attack black-box target of Industrial AIoT systems, resulting in catastrophic consequences. It is essential to study ensemble adversarial attack and defense strategies in black-box scenarios. Nevertheless, current ensemble attacks' performance is limited by the diversity of local models and ensemble strategies, and defensive strategies are inefficient. To solve these problems, we propose two novel deep fusion methods from both an attacker's and a defender's perspective. For initiating attacks, we propose deep fusion attack. The erosion models are applied to compensate for local models' insufficiency in diversity. We fuse erosion models in the output space, and the feature space simultaneously and continuously accumulate historical gradients to retain adversarial information, thereby improving transferability. Extensive experimental results show that our approach achieves superior performance in black-box attacks, and the average success rate of our attack reaches a compelling 87.4%. For constructing defenses, we propose deep fusion defense, using a fusion of multiple predictions with erosion models as a novel approach. We successfully increase the model's robustness by more than 90% on the ImageNet dataset.

Vehicle-to-grid (V2G) technology is used in the modern eco-friendly environment for demand response management. It helps in reducing the carbon footprints in the environment. However, security and privacy of the information exchange between different entities are significant concerns keeping in view of the information exchange via an open channel, i.e., Internet among different entities such as plug-in hybrid electric vehicles (PHEVs), charging stations (CSs), and controllers in V2G environment. With an exponential rise in Electric vehicles (EVs) usage across the globe, there is a requirement of developing a seamless charging infrastructure for charging and billing. Moreover, secure information flow needs to be maintained at different levels in such an environment. Hence, this paper proposes a blockchain-based demand response management for efficient energy trading between EVs and CSs. In this proposal, miner nodes and block verifiers are selected using their power consumption and processing power. These nodes are responsible for the authentication of various transactions in the proposal. We also proposed a game theory-based solution to support energy management and peak load control off-peak and peak conditions. The proposed scheme has been evaluated using various performance evaluation metrics where its performance is found superior in comparison to the existing solutions in the literature.

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.

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs.

Unmanned Aerial Vehicles (UAVs), in conjunction with 6G, are a potential tool for monitoring agricultural lands and various agricultural applications. There have been several trajectory generation algorithms proposed for surveying agricultural land. However, in most situations, the accuracy of the trajectory is hampered by several factors, namely the complex geographical topography, performance, connectivity with the Ground Control Station (GCS) and positioning error of the UAV during flight, amongst others. Therefore, in this paper, we propose Drones Trajectory Generation employing an improved Genetic Algorithm (GA) and Non-Uniform Rational P-Splines (NURPS) based optimizer (DTG-GN). The improved GA utilizes a novel dual fitness function parameter to select an optimal path to map the weed-infested regions. The path chosen is often impeded by the high number of sudden turns, affecting the UAV’s speed profile and the path’s continuity. Therefore in the NURPS optimization, the computation of the intermediate knots vector between the control points improves the smoothness of the path. Furthermore, the accuracy of detecting the weed-infested area and the path length are employed to optimize the path. Besides, a 6G network is utilized for communicating the path between the GCS and the UAV to ensure seamless connectivity. Thus the time taken by DTG-GN to generate the optimal trajectory reduces by 38.815% for 50 control points. DTG-GN also reduces the average trajectory length by 45.67% for 50 control points, establishing its supremacy over conventional trajectory generation algorithms.

In-advance availability of parking information plays an important role in parker/traveller decision-making for parking, curbing congestion, and managing parking lots efficiently. Specifically on-street parking poses many challenges compared to the off-street ones. Many users such as, store owners, municipal authorities, and police demand slots for on-street parking Free of Charges (FoC) for a short duration. In last few years, parking authorities collected data to attract the attention of researchers to present data-centric solutions for various problems such as, minimization of parking prices, maximization of revenue, and balacing the congestion at parking lots associated with smart parking systems. Motivated from the aforementioned problem, this paper proposes a scheme based on machine learning and game theory for dynamic pricing and allocation of parking slots in on-street parking scenarios. The dynamic pricing and allocation problem is modeled as Stackelberg game and is solved by finding its Nash equilibrium. Two types of Parking Users (PUs), i.e., Paid Parking Users (PPUs) and Restricted Parking Users (RPUs) are considered in this work. RPUs avail parking slots FoC once a day. PPUs compete to minimize the prices, and RPUs compete to maximize the FoC granted duration. The Parking Controllers (PCs) compete to maximize revenue generated from PPUs and to minimize total FoC parking duration granted to the RPUs. The random forest model is used to predict occupancy, which in turn is used to generate parking prices. Seattle city parking and its prices data sets are used to predict occupancy and to generate prices, respectively. In order to test the performance of communication system, the proposed DyPARK Pricing and Allocation Scheme (PAS) is compared with its four variants and is found worth. The proposed scheme is also compared with other state-of-the-art schemes using various performance evaluation metrics. Simulated results prove the superiority of the proposed scheme in comparison to the other state-of-the-art schemes.

In recent years, growing research interest from both industry and academia has been aroused to the vehicular networks, which is regarded as the fundamental component of the modern intelligent transportation system (ITS). Lots of remarkable research outputs with respect to secure vehicular data interactions and user privacy preservation has been witnessed. However, the existing schemes all focus on the common vehicular communication scenarios where facilities are deployed, whereas the secure data exchange in the abnormal infrastructure-less vehicular environment has not been properly investigated. To deal with unpredictable abnormal situations caused by artificial or natural disasters such as earthquakes and floods, a distinctive vehicle-assisted aggregate authentication mechanism for infrastructure-less vehicular networks is presented in this paper. With assistance from the neighboring vehicles, the homomorphic signature involving all requesting vehicles is generated and forwarded to the remaining functional RSUs. Meanwhile, vehicular group communication among the validated entities is enabled. Additionally, the fault-tolerant verification method is adopted such that the ineffective entities can be easily distinguished and removed without interfering with other requesting vehicles. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.

Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known asSecGreen.SecGreenhas an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as– physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of theSecGreenis compared with the existing schemes,Stable & Scalable Link Optimization (SSLO), and Secure & Energy-Efficient Blockchain-enabled (SEEB)respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, theSecGreenscheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposedSecGreenscheme in comparison to these existing competitive schemes in the literature.

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

The integration of Internet of Things (IoT) with 5G simply creates additional threat landscape and any network infrastructure is more vulnerable. Severe attacks on networks potentially damage organization reputation, customers or tenants lose confidence, and impacts operational and maintenance cost. Intrusion detection systems (IDSs) are an effective approach to mitigate threats. We present a novel IDS mechanism in which the unique Radio Frequency (RF) features of IoT devices are used to create a learning model which is later used to identify the illegitimate devices in the network. Leveraging the Deep Autoencoder (DAE), the existing steady-state feature extraction is generalized. The performance evaluation is conducted using a real data set from different aspects including the mobility of the nodes. The proposed IDS is broken down into pluggable virtual network function (VNF) components and its evaluation is presented for its integration into the 5G network slicing ecosystem from the perspective of the European Telecommunications Standards Institute (ETSI) standards. A Proof of Concept (PoC) is presented using ETSI Open Source NFV Management and Orchestration (OSM-MANO) test bed, deployed on AWS cloud systems, to show how the proposed approach would fit in with a real-life MANO.

To realize the Interconnection of Everything (IoE) in the 6G vision, the space-based, air-based, and ground-based networks have shown a trend of integration. Compared with the traditional communications system, Space-Air-Ground Integrated Networks (SAGINs) can provide a seamless global network connection, while making full use of different network characteristics for synergy and complementarity. However, the increasing global coverage of the Internet, the growing number and variety of smart terminals, and the emergence of various high-bandwidth services have led to an explosion in communication data transmission. Despite the continuous development of communication technologies such as airborne processing and forwarding and high-throughput satellites, the quality of service (QoS) and quality of experience (QoE) for different users still cannot be guaranteed due to the power limitations of satellites and the scarcity of spectrum resources. In this work, drawing on wireless edge caching, considering that the relay of SAGIN has edge caching capability, the hot task is cached in the network nodes in advance. More, this process is optimized using distributed Deep Reinforcement Learning (DRL), thereby reducing transmission delay and relieving the pressure of task offloading on space-based networks. Compared with advanced related works, the long-term node utilization, link utilization, long-term average revenue-to-cost ratio and acceptance ratio of the proposed algorithm are increased by about 4.22%, 31.36%, 11.75% and 7.14%, respectively.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes.

Energy consumption is one crucial aspect in IoT and healthcare applications. Reconfigurable intelligent surface (RIS) is composed of man-made passive reflective elements which can configure the channel environment with lower energy consumption. In this paper, we focus on the RIS-assisted D2D networks. We obtain analytical closed-form expressions for the outage performance. Based on this, we then discuss the performance under high SNR case, as well as weak interference case. The corresponding closed-form simpler approximations are also presented. Due to the existence of interference, 0 order outage diversity is obtained. Numerical results show the agreement between Monte Carlo simulations and analytical results in various network configurations.

The scarcity of freshwater resources throughout the world has raised the demand for the optimal utilization of these resources. Internet-of-things (IoT) based wireless sensor networking is an interesting and vital technology that has seen substantial growth in recent years. Agricultural applications are one of the fields where it is extensively used and effectively implemented to manage irrigation requirements. In smart agriculture, reference evapotranspiration ($ET_{0}$) has a great significance in determining the precise crop water requirement. Hence, accurate estimation of$ET_{0}$is critical to avoid under or over-irrigation without compromising the agricultural productivity. This paper presents Genetic algorithm (GA) based optimized Long short term memory (LSTM) model (LSTM-GA) to estimate reference evapotranspiration ($ET_{0}$) using climate data acquired by IoT based wireless sensor networks (WSN). Specifically, we examine the temporal property of climate data by proposing a systematic way of determining the time window size for the LSTM model using Genetic algorithm. The climate variables include daily maximum temperature ($T_{max}$), minimum temperature ($T_{min}$), solar radiation ($R_{s}$), sunshine hours (SSH), wind speed ($U_{2}$), relative humidity (Rh), and vapor pressure (Vp) of Ludhiana station. The proposed hybrid model was validated against the benchmark technique FAO-PM for estimating$ET_{0}$. The performance comparison revealed that LSTM-GA provided reliable results and outperformed the stand-alone LSTM model

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surface (RIS) are the emerging technologies for 5G and beyond networks. These two techniques reduce inter-user interference and enhance the network's coverage performance. Despite this advantage, these two techniques are not able to satisfy the diversified quality of service (QoS) requirements of cellular mobile users under the presence of existing multiple access schemes. To tackle this issue, we integrate non-orthogonal multiple access (NOMA) with both these techniques. In this paper, our goal is to maximise the energy efficiency (EE) of the overall network by optimising the powers of UAVs and the phase shift matrix of RIS. The formulated problem is in a mixed-integer non-convex programming form. So, to solve this problem, a deep deterministic policy gradient (DDPG) approach is used in a centralised manner under a time-varying channel. The proposed NOMA-RIS scheme for multi-UAV networks achieves higher EE than the orthogonal multiple access (OMA)-RIS and random selection schemes, according to numerical results.

Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.

Smart grids are an improvement of the traditional electric grids. They allow a much higher degree of automation and more efficient power distribution. Nonetheless, due to automation, these grids become more vulnerable to cyber attacks. Hence, cyber security becomes a major milestone to overcome before we can permanently shift to smart grids. Electric theft is one of the most dangerous cyber attacks in a smart grid. It allows users to lie about their load profiles and decrease their electricity bills. Several research studies have been conducted regarding the detection of such cyber attacks in a smart grid, but none of them consider weather information as a feature. This paper proposes a novel machine learning-based approach to smart grid electricity theft detection using both the load profile of a household and the weather features. The results show that our current approach using both load and weather information perform much better than previous approaches that only use load information.

Abstract There is a drastic increase in urbanization over the past few years, which requires energy‐efficient and optimized solutions for transportation, governance, quality of life in a smart city among all the citizens. The Internet‐of‐Energy (IoE) ecosystem offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoE applications is increased while IoE devices continue to grow. Therefore, smart city solutions must have the ability to utilize energy and handle the associated challenges efficiently. Moreover, energy Optimization (EO) techniques can be used to reduce energy consumption to meet the sustainability goals in IoE. Different techniques have been proposed for EO in various fields by researchers worldwide. Computing systems also need energy optimization. The energy consumption in the data center, clouds, and blockchain (BC)‐based architectures are a point of concern at the current time. Due to the enormous energy demands of these systems, we cannot take advantage of the latest technologies to their fullest. Due to the emergence of new technologies and some limitations of proposed techniques, we can still not optimize energy usage more than some extent. There is minimal exploration done in energy optimization in BC‐based systems. In this paper, we have proposed a survey on the energy optimization techniques in various systems, including the optimization techniques in BC‐based systems. We have proposed a taxonomy that classifies energy optimization techniques. We have also proposed an energy‐efficient consensus mechanism, Proof‐of‐High Performance optimization (named as PoHPo), for High‐Performance Computing (HPC) based ecosystems. The open issues and challenges are then discussed in EO. The survey intends to propose future directions for industry professionals, green‐energy stakeholders, and researchers worldwide to explore this topic further.

The recent advances in Internet of Things (IoT)-based healthcare systems pave the path for the development of body-to-body network (BBN), wherein a group of wireless body area network (WBAN) users collaborates and shares their individual resources. Since these WBAN users have individual decision-making capabilities and are self-centric in nature, they always aim to maximize their own performance while expecting benefits through resource sharing. In this article, we analyze the interaction among participating WBANs in BBN and develop joint data uploading and relaying strategy. In BBN, each WBAN not only utilizes its resources (uplink capacity and battery energy) to upload physiological data but also trades resources with other participating WBANs. Specifically, WBAN users with unused resources trade with other users deprived of Internet connection and low battery for mutual gain. Therefore, we model this interaction as an$N $-person bargaining game and design an efficient incentive mechanism to facilitate user cooperation. The proposed mechanism ensures efficient resource sharing and fair division of mutual benefit among the participating WBAN users. Also, we propose a distributed algorithm for the practical implementation of the proposed mechanism in decentralized BBN. The simulation results demonstrate that the proposed mechanism always improves WBAN user’s individual performance together with the overall BBN performance. Furthermore, the overall performance increases with an increase in participating WBAN users’ resource heterogeneity.

Compared with 5G networks, 6G networks are guaranteed to provide various tailored end-to-end network services and emerging cloud-edge applications. Network slicing (NS) is regarded as the key enabler of 6G networks. Softwarization and virtualization technologies, such as software-defined networking and network function virtualization, are accelerating the way toward NS of 6G networks. The resource allocation issue in 6G NS is very crucial, worthy more research attention. In this article, we propose one efficient resource allocation algorithm, labeled asTailoredSlice-6G, so as to realize the tailored slices in 6G. When receiving one slice request, ourTailoredSlice-6Gwill identify the slice resource type in the first place. Then, ourTailoredSlice-6Gwill select its most suitable subalgorithm to do the resource allocation and slicing deployment. Each type of slice corresponds to its specific resource allocation subalgorithm, inserted in theTailoredSlice-6Galgorithm. In addition, each subalgorithm inTailoredSlice-6Gis guaranteed to run within polynomial time. Thus,TailoredSlice-6Ghaving the potential to be promoted to real networking application. To highlight the merits ofTailoredSlice-6G, we do the comprehensive simulation. Simulation results vividly reveal that ourTailoredSlice-6Goutperforms the selected heuristics that are representative in the literature.

This article proposes a new blockchain-envisioned key management protocol for artificial intelligence (AI)-enabled industrial cyber–physical systems (ICPSs). The designed key management protocol enables key establishment among the Internet of Things (IoT)-enabled smart devices and their respective gateway nodes. The blocks partially constructed with secure data from smart devices by fog servers are provided to cloud servers that are responsible for completing blocks, and then mining those blocks for verification and addition in the blockchain. The most important application of the private blockchain construction is to apply AI algorithms for accurate predictions in Big data analytics. A detailed security analysis along with formal security verification show that the proposed scheme resists various potential attacks in an ICPS environment. Moreover, practical testbed experiments have been conducted using the multiprecision integer and rational arithmetic cryptographic library (MIRACL). Furthermore, a detailed comparative analysis shows superiority of the proposed scheme over recent relevant schemes. In addition, the practical implementation using the blockchain for the proposed scheme demonstrates the total computational costs when the number of transactions per block and also the number of blocks mined in the blockchain are varied.

Up to now, the coronavirus disease 2019 (COVID-19) has been sweeping across all over the world, which has affected individual’s lives in an overwhelming way. To fight efficiently against the COVID-19, radiography and radiology images are used by clinicians in hospitals. This article presents an integrated framework, named COVIDNet, for classifying COVID-19 patients and healthy controls. Specifically, ResNet (i.e., ResNet-18 and ResNet-50) is adopted as a backbone network to extract the discriminative features first. Second, the spatial pyramid pooling (SPP) layer is adopted to capture the middle-level features from the features of ResNet. To learn the high-level features, the NetVLAD layer is used to aggregate the features representation from middle-level features. The context gating (CG) mechanism is adopted to further learn the high-level features for predicting the COVID-19 patients or not. Finally, extensive experiments are conducted on the collected database, showing the excellent performance of the proposed integrated architecture, with the sensitivity up to 97% and specificity of 99.5% of the ResNet-18, and with the sensitivity up to 99% and specificity of 99.4% of the ResNet-50.

This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems.

Internet of Things (IoT) refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments with each other. The regular monitoring of IoT network traffic generated from IoT devices is important for their proper functioning and detection of malicious activities. One such crucial activity is the classification of IoT devices in the network traffic. It enables the administrator to monitor the activities of IoT devices which can be useful for proper implementation of Quality of Service, detect malicious IoT devices, etc. In the literature, various methods are proposed for IoT traffic classification using various machine learning algorithms. However, the accuracy of these machine learning algorithms depends on the data generated from various IoT devices, features extracted from network traffic, site at which IoT is deployed, etc. Moreover, the selection of features and machine learning algorithms are manual operations that are prone to error. Therefore, it is important to study the network traffic characteristics as well as suitable machine learning algorithms for accurate and optimized IoT traffic classification. In this article, we perform an in-depth comparative analysis of various popular machine learning algorithms using different effective features extracted from IoT network traffic. We utilize a public data set having 20 days of network traces generated from 20 popular IoT devices. Network traces are first processed to extract the significant features. We then selected state-of-the-art machine learning algorithms based on the recent survey papers for the IoT traffic classification. We then comparatively evaluated the performance of those machine learning algorithms on the basis of classification accuracy, speed, training time, etc. Finally, we provided a few suggestions for selecting the machine learning algorithm for different use cases based on the obtained results.

As an extension of cloud computing, edge computing has attracted the attention of academia and industry because of its characteristics of low latency, high bandwidth, and low energy consumption. However, due to limited terminal resources and insufficient security design, the edge computing environment still faces many challenges in terms of data security and privacy protection. Among them, how to effectively control access to outsourced data is one of the main issues. In this article, we propose a lightweight and verifiable ciphertext-policy attribute-based encryption (CP-ABE)-based multiauthority access control scheme for edge computing-assisted Internet of Things (IoT), which adopts the method of outsourcing decryption to mitigate the computational cost of data users with limited resources. In addition, our scheme realizes the feature of attribute revocation, and the design of the multiauthority mechanism enables our scheme to avoid the problem of key escrow. Therefore, our proposed scheme not only ensures data confidentiality but also can resist the collusion attack. Besides, our scheme is secure against the chosen plaintext attack in the random oracle model under the decision$q$-BDHE assumption. Finally, we compared our scheme with some related work in performance, and the results demonstrate that our scheme is efficient in computation and communication. Because our scheme greatly mitigates the overhead of data users, it is very suitable for edge computing supported IoT applications with restricted computation resources.

Internet of Things (IoT)-enabled wireless sensor network (WSN) permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. However, the common IoT devices usually have very limited battery power, which is not frequently rechargeable. Thus, an energy-efficient mechanism is required to operate IoT-enabled WSN. To address the limited power shortcoming of IoT-enabled WSN, we propose an optimized genetic algorithm (GA) for cluster head (CH) election (OptGACHE). The CH election using GA incorporates four different criteria, namely: 1) node density; 2) distance; 3) energy; and 4) heterogeneous node’s capability for the development of fitness function. These criteria help in optimizing intracluster distance, systematic utilization of node’s energy in the cluster, reducing hop count, and promoting selection of highly capable nodes for CHs. The proposed movable sink strategy shortens the length of communication distance between sink and CH and also diminishes the hotspot problem. Furthermore, the incorporated dynamic sensing range adjustment minimizes the overlapping of sensing range of CH along with cutting down transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, namely, network’s remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.

With rising temperatures and events contributing to climate change, the world is facing extreme weather patterns. Recently, Australia was hit hard by bushfires, the most devastating fires ever faced by the country. The economic damage reported was nearly one billion Australian dollars and an estimated three billion native animals were killed or adversely affected. Given the extent and intensity of this damage, researchers are seeking effective solutions to enable the prediction of fire before it starts to increase the time available for firefighters to protect lives and assets and prepare to mitigate the fires. This motivated us to investigate an approach to address this critical problem. In this article, we propose a machine learning (ML)-based approach that detects anomalies in spatiotemporal measurements of environmental parameters (e.g., temperature, relative humidity, etc.). In the proposed approach, an ML-based model learns the normal spatiotemporal behavior of the environmental data (collected over a period of one year). This is carried out during a one-time training phase. Then, during the detection phase, any spatiotemporal pattern in the real-time data (received from the field sensors) that is different than the normal pattern will be identified by the model as anomaly which indicates a possible bushfire situation. Following this, we propose a supplementary classification model based on Moran’s I index to ensure that the detected anomalies are not due to either a sensor failure or a security attack (which are common in Internet of Things). We developed three different ML models for performance evaluation and comparison and used the Forest Fire data set to train them. The results of our experiments confirm the effectiveness of the proposed approach in the early detection of fire symptoms.

The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like normal flu; asymptomatic version of COVID-19 has become a critical issue. Therefore, as a precautionary measurement, the oxygen level needs to be monitored by every individual if no other critical condition is found. It is not the only parameter for COVID-19 detection but, as per the suggestions by different medical organizations such as the World Health Organization, it is better to use oximeter to monitor the oxygen level in probable patients as a precaution. People are using the oximeters personally; however, not having any clue or guidance regarding the measurements obtained. Therefore, in this article, we have shown a framework of oxygen level monitoring and severity calculation and probabilistic decision of being a COVID-19 patient. This framework is also able to maintain the privacy of patient information and uses probabilistic classification to measure the severity. Results are measured based on latency of blockchain creation and overall response, throughput, detection, and severity accuracy. The analysis finds the solution efficient and significant in the Internet of Things framework for the present health hazard in our world.

In the era of the Internet of Things (IoT), fog computing has become an enticing concept for supporting delay-sensitive tasks by offering versatile and convenient computing and communication services to the end users, in conjunction with cloud services. Most of the existing research mainly draws attention to the communication delay minimization and completion time reduction in the hierarchical fog networks without giving the priority to select the suitable computing device during failure or resource unavailability of the current computing devices. By motivating the above-mentioned challenges, in this article, we propose a deadline-aware dynamic task placement (DDTP) strategy to offload and place the tasks to a suitable computing device in fog networks. In this context, we design a new federated fog framework consisting of several fog clusters in which the cluster head, termed as master fog node, acts as a fog controller that controls and manages the data distribution among the other fog nodes, termed as slave fog nodes. The proposed DDTP strategy selects the suitable computing device for each incoming task as per the deadline and ensures to meet the deadline constraints of the tasks using a dynamic task allocation policy. Finally, a dispatch-constrained offloading policy is developed to reassign the failed tasks to the available fog nodes in the network. Comprehensive simulation results depict the efficiency of the proposed strategy over the existing baseline algorithms in terms of various performance matrices.

Network data traffic has expanded exponentially over the past decade, resulting in massive congestion in heterogeneous networks. Nevertheless, it is nearly impossible to run latency-intensive applications to the end-users local processing unit due to limited system resources. Recently, fog computing has come up with a solution to reduce such data congestion by offloading some or the whole part of the task to the nearby fog nodes (FNs) or the clouds. But this offloading policy becomes more complex when the FN is unable to process the task and further offload it to another neighboring FN or the cloud. In this context, in this study, we have analyzed the offloading strategy in a hierarchical fog-cloud network consisting of several heterogeneous fog devices along with a helping fog and a centralized cloud server. We have considered the most possible practical situation where the FNs are equipped with different CPU frequencies and hence, the power consumption is also different. The total system cost is formulated as a mixed-integer nonlinear problem that aims to reduce the overall delay in the proposed network. To solve the NP-hard problem, we transform it into quadratically constrained quadratic programming (QCQP) formation and further solve it by the separable semidefinite relaxation (SDR) method. Finally, by adopting several benchmark data, we conduct comprehensive simulations to test the efficiency of the proposed offloading profile. The simulation results depict that the proposed strategy outperforms in many aspects when compared to various baseline algorithms.

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.

This article proposes an optimized genetic algorithm (GA)-based sustainable and secure green data collection/transmission method for IoT-enabled WSN in healthcare by optimizing intracluster distance, systematic utilization of node’s energy, and reducing hop count. For secure transmission of data, the communication data is encrypted using stream cipher and a pseudo-randomly generated security key. Additionally, the proposed movable sink and data collection/transmission strategies shorten communication distance between sink and cluster head (CH) which diminishes the hotspot problem. The direct data collection helps in transmitting data directly to the sink, when the sinks are nearer to the sensor nodes with respect to CH. Further, the incorporated dynamic sensing range minimizes overlapping of sensing range with a significant decrement in the transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, such as remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.

With the widespread utilization of Internet-enabled smart devices (SDs), the Industrial Internet of Things (IIoT) has become prevalent in recent years. SDs exchange information through the open Internet, which creates security and privacy concerns for the exchanged information. To address these concerns, various solutions exist in the literature which, because of high computational and communication overheads, are not appropriate for the resource-constricted IIoT environment. This article proposes a resource-efficient authentication protocol for the IIoT, called REAP-IIoT, which employs a lightweight cryptography (LWC)-based authenticated encryption with associative data (AEAD) primitive AEGIS along with hash function. LWC-based AEAD primitives are suitable for resource constraint SDs because they require fewer computational resources. Moreover, REAP-IIoT renders the privacy-preserving user authentication functionality and establishes a session key (SK) between SDs deployed in the IIoT environment and users. Both user and SD utilize the established SK for encrypted communication. The security of SK, established during the authentication and key exchange (AKE) process of REAP-IIoT, is validated through the broadly accepted random or real model. Besides, Scyther-based security verification is conducted to illustrate that REAP-IIoT is secure and can protect the man-in-the-middle and replay attacks. Additionally, the informal security analysis is carried out to show that REAP-IIoT is protected against various covert security risks. A thorough comparison reveals that REAP-IIoT renders enhanced security characteristics apart from its low communication, storage, and computational overheads than the relevant AKE protocols.

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

Lightweight cryptography (LWC)-based authenticated encryption with associative data (AEAD) cryptographic primitives require fewer computational and energy resources than conventional cryptographic primitives as a single operation of an AEAD scheme provides confidentiality, integrity, and authenticity of data. This feature of AEAD schemes helps design an access control (AC) protocol to be leveraged for enhancing the security of the resource-constrained Internet of Things (IoT)-enabled smart grid (SG) system with low computational overhead and fewer cryptographic operations. This article presents a novel and robust AC protocol, called RACP-SG, which aims to enhance the security of resource-constrained IoT-enabled SG systems. RACP-SG employs an LWC-based AEAD scheme, ASCON and the hash function, ASCON-hash, along with elliptic curve cryptography to accomplish the AC phase. Besides, RACP-SG enables a smart meter (SM) and a service provider (SEP) to mutually authenticate each other and establish a session key (SK) while communicating across the public communication channel. By using the SK, the SM can securely transfer the gathered data to the SEP. We verify the security of the SK using the widely accepted random oracle model. Moreover, we conduct Scyther-based and informal security analyses to demonstrate that RACP-SG is protected against various covert security risks, such as replay, impersonation, and desynchronization attacks. Besides, we present a comparative study to illustrate that RACP-SG renders superior security features while reducing energy, storage, communication, and computational overheads compared to the state of the art.

The explosive growth of Internet of Vehicle (IoV) applications has made information security a significant issue. Mobile IoV users are dynamic and the communication environment is very complex, which makes it very difficult to guarantee real-time secrecy communication performance. Thus, a reliable and effective evaluation and prediction of secrecy performance is critical. In this article, we have derived novel expressions for secrecy performance. A grey wolf optimization generalized regression (GWO-GR) algorithm is proposed to predict the secrecy performance and carry out the secrecy performance assessment. A generalized regression (GR) neural network is designed. Out of the input and output layers, the proposed GR network has a pattern layer and a summation layer, which can obtain a global convergence of network results. To further optimize the GR network, the grey wolf optimization algorithm is used to obtain the best spread factor for it, which can accelerate its rapid convergence. Through the simulated numerical results, we can obtain: 1) the proposed GWO-GR prediction algorithm is shown to provide better performance prediction results than other machine-learning-based methods; 2) in particular, the prediction accuracy is improved by 17.7%; and 3) the execution time has an 88.9% reduction.

With the advent of the Internet-of-Things (IoT) era, more and more devices are connected to the IoT. Under the traditional cloud-thing centralized management mode, the transmission of massive data is facing many difficulties, and the reliability of data is difficult to be guaranteed. As emerging technologies, blockchain technology and edge computing (EC) technology have attracted the attention of academia in improving the reliability, privacy, and invariability of IoT technology. In this article, we combine the characteristics of the EC and blockchain to ensure the reliability of data transmission in the IoT. First, we propose a data transmission mechanism based on blockchain, which uses the distributed architecture of blockchain to ensure that the data is not tampered with; second, we introduce the three-tier structure in the architecture in turn; and finally, we introduce the four working steps of the mechanism, which are similar to the working mechanism of blockchain. In the end, the simulation results show that the proposed scheme can ensure the reliability of data transmission in the IoT to a great extent.

The development of intelligent cyber–physical systems (ICPSs) in the virtual network environment is facing severe challenges. On the one hand, the Internet of Things (IoT) based on ICPSs construction needs a large amount of reasonable network resources support. On the other hand, ICPSs are facing severe network security problems. The integration of ICPSs and network virtualization (NV) can provide more efficient network resource support and security guarantees for IoT users. Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources, and security constraints to ensure the rationality and security of resource allocation in ICPSs. In particular, we use the reinforcement learning (RL) method as a means to improve algorithm performance. We extract the important attribute characteristics of the underlying network as the training environment of the RL agent. The agent can derive the optimal node embedding strategy through training, so as to meet the requirements of ICPSs for resource management and security. The embedding of virtual links is based on the breadth first search (BFS) strategy. Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the constraints of computing, storage, and security 3-D resources. Finally, we design a large number of simulation experiments from the perspective of typical indicators of VNE algorithms. The experimental results effectively illustrate the effectiveness of the algorithm in the application of ICPSs.

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This article proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.

Summary From the last few years, we have witnessed the fourth generation industrial revolution (Industry 4.0), impact of which will be seen in the years to come in various disciplines such as healthcare, transportation, IoT, smart grid, autonomous vehicles, and image processing. These applications in Industry 4.0 may have data in the form of images, speech signals, videos having high dimensions containing multiple dimensions to represent data along different axis. So, the complexity of data processing increases with an increase in the dimensions of the dataset. Complexity can be viewed in terms of detecting and exploiting the relationships among different features of the dataset. These complexities among different attributes can be reduced with the help of dimensionality reduction techniques. These techniques reduce the dimensions from the original input dataset to a lower dimensional dataset. Dimensionality reduction methods are broadly categorized into two types asfeature extraction and feature selection. In feature selection method, out of the original set, a subset of features are identified to get a smaller subset which can be used to build the model whereas, the feature extraction method reduces the dataset of high dimensions to a lower dimension space, that is, a space with a less number of features having different values in comparison to the original dataset. Keeping focus on these points, in this article, we have compared and analyzed different data dimensionality reduction techniques which reduce the dimensions of a large and complex dataset during data processing. In addition, we have discussed various data dimensionality reduction techniques and compared these techniques with respect to various parameters. The comparison among various techniques provides insights to the readers about the applicability of a specific technique to the stand‐alone or a group of applications.

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

The demand for remote data storage and computation services is increasing exponentially in our data-driven society; thus, the need for secure access to such data and services. In this article, we design a new biometric-based authentication protocol to provide secure access to a remote (cloud) server. In the proposed approach, we consider biometric data of a user as a secret credential. We then derive a unique identity from the user's biometric data, which is further used to generate the user's private key. In addition, we propose an efficient approach to generate a session key between two communicating parties using two biometric templates for a secure message transmission. In other words, there is no need to store the user's private key anywhere and the session key is generated without sharing any prior information. A detailed Real-Or-Random (ROR) model based formal security analysis, informal (non-mathematical) security analysis and also formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool reveal that the proposed approach can resist several known attacks against (passive/active) adversary. Finally, extensive experiments and a comparative study demonstrate the efficiency and utility of the proposed approach.

The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people’s lives in all directions. The rapid development of the IoT technology accelerates the process of the era of “Internet of everything” but also changes the role of terminal equipment at the edge of the network. It has changed from a single data user to a dual role of both producing and using data. And collaborative edge computing (CEC) has been born in time. CEC itself can not only solve the problem of computing and storage but also combines with the deep learning (DL) model to make full use of edge computing ability. However, as the core of DL, the robustness of neural network is often not high. In addition, edge devices of CEC are facing a highly dynamic environment, which can easily cause the edge network to be attacked by malicious devices. Therefore, user privacy protection and security issues for CEC deserve more attention. To avoid privacy leakage and security crisis of CEC in social IoT systems, a data protection method based on data disturbance method and adversarial training viewpoint is introduced in this article. Besides, a new adversarial sample generation method based on the firefly algorithm (FA) is proposed. This method reduces the time complexity of traditional by an order for magnitude compared with traditional generative adversarial network (GAN) generation. Since sentences, information on CEC in the IoT system is characterized by a large amount of data, strict confidentiality, and high-security requirements, and they are usually high-risk information on privacy leakage. The proposed method is conducted to the sentence similarity analysis model based on a convolutional neural network (CNN) in the CEC scene to test the feasibility of the method. Compared with the original CNN, the accuracy of the model using the confrontation training method is improved by 4.8%. At the same time, the security value of our model is 2.1% higher than that of the simple CNN model, and it has the best security performance among the four comparison models. Further experiments have demonstrated that the model performs better in its capacity of resisting disturbance and can effectively help multiple organizations to implement data usage and sentence information on the requirements of user privacy protection, data security, and government regulations.

With the development of cloud computing, the great amount of storage data requires safe and efficient data sharing. In multiparty storage data sharing, first, the confidentiality of shared data is ensured to achieve data privacy preservation. Second, the security of stored data is ensured. That is, when stored shared data are subject to frequent access operations, the address sequence or access pattern of data is hidden. Therefore, determining how to ensure the untraceability of stored data or efficient hide the data access pattern in sharing stored data is a challenge. By employing the proxy re-encryption algorithm and oblivious random access memory (ORAM), a privacy-preserving and untraceable scheme is proposed to support multiple users in sharing data in cloud computing. On the one hand, group members and a proxy use the key exchange phase to obtain keys and resist multiparty collusion if necessary. The ciphertext obtained according to the proxy re-encryption phase enables group members to implement access control and store data, thereby completing secure data sharing. On the other hand, this article realizes data untraceability and a hidden data access pattern through a one-way circular linked table in a binary tree (OCLT) and obfuscation operation. Additionally, based on the designed structure and pointer tuple, malicious users are identified and data tampering is prevented. The security analysis shows that the protocol designed in this article can meet the security requirements of proxy re-encryption and ORAM. Both theoretical and experimental analyses demonstrate that the proposed scheme is secure and efficient for group data sharing in cloud computing.

Cybertwin leverages the capabilities of networks and serves in multiple functionalities, by identifying digital records of activities of humans and things, from the Internet of Everything (IoE) applications. Cybertwin emerges as a promising solution along with next-generation communication networks, i.e., 6G technology; however, it increases additional challenges at the edge networks. Motivated by the aforementioned perspectives, in this article, we introduce a new cybertwin-driven edge framework using 6G-enabled technology with an intelligent service provisioning strategy for supporting a massive scale of IoE applications. The proposed strategy distributes the incoming tasks from IoE applications using the deep reinforcement learning technique based on their dynamic service requirements. Besides that, an artificial-intelligence-driven technique, i.e., the support vector machine (SVM) classifier model, is applied at the edge network to analyze the data and achieve high accuracy. The simulation results over the real-time financial datasets demonstrate the effectiveness of the proposed service provisioning strategy and the SVM model over the baseline algorithms in terms of various performance metrics. The proposed strategy reduces the energy consumption by 15% over the baseline algorithms, while increasing the prediction accuracy by 12% over the classification models.

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

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.

Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only models objects but also infers their relationships. Existing works usually learn coarse global context when predicting relationships leading to excessive redundant information being considered. In this article, we first classify objects into different subgroups according to the degree of correlations with several hub objects. Then, we propose a multihub driven attention network (MHDANet) based on deep learning that drives the information to pass within the subgroups and forces objects to attend more to related objects. Consequently, MHDANet learns compact relation-aware features of visual scenes and predicts accurate and diverse relationships. Experimental results show that MHDANet achieves superb performance on scene graph generation on real-world datasets and especially alleviates the imbalance of predicted relationship categories.

Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98$\%$. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.

This paper proposes a generic scheme VaCoChain, that fuses blockchain (BC) and unmanned aerial vehicles (UAVs) underlying fifth-generation (5G) communication services for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics. The scheme offers 5G-tactile internet (5G-TI) based services for UAV communication networks (UAVCN) monitored through ground controller stations (GCs). 5G-TI enabled UAVCN supports real-time dense connectivity at ultra-low round-trip time (RTT) latency of [Formula: see text] and high availability of 99.99999%. Thus, it can support resilient vaccine distributions in a phased manner at government-designated nodal centers (NCs) with reduced round trip delays from vaccine production warehouses (VPW). Further, UAVCNs ensure minimizes human intervention and controls vaccine health conditions due to shorter trip times. Once vaccines are supplied at NCs warehouses, then the BC ensures timestamped documentation of vaccinated persons with chronology, auditability, and transparency of supply-chain checkpoints from VPW to NCs. Through smart contracts (SCs), priority groups can be formed for vaccination based on age, healthcare workers, and general commodities. In the simulation, for UAV efficacy, we have compared the scheme against fourth-generation (4G)-assisted long term evolution-advanced (LTE-A), orthogonal frequency division multiplexing (OFDM) channels, and traditional logistics for round-trip time (RTT) latency, logistics, and communication costs. In the BC setup, we have compared the scheme against the existing 5G-TI delivery scheme (Gupta et al.) for processing latency, packet losses, and transaction time. For example, in communication costs, the proposed scheme achieves an average improvement of 9.13 for block meta-information. For 4000 transactions, the proposed scheme has a communication latency of 16 s compared to 36 s. The packet loss is significantly reduced to 2.5% using 5G-TI compared to 16% in 4G-LTE-A. The proposed scheme has a computation cost of 1.6 ms and a communication cost of 157 bytes, which indicates the scheme efficacy against conventional approaches.

Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling(PETS)using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs),i.e.,smart grids and service consumers,i.e.,EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader’s side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers’ side,i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs’ energy load in comparison to the existing state-of-the-art scheme.

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

Towards achieving the goal of green transportation, the usage of battery powered electric vehicles (BEVs) has been continuously growing across the globe. However, considering the limited number of Charging Stations (CSs) in the cities, electric vehicle charging problem has become a challenging task, especially, due to the constraints of longer waiting time and dynamic pricing at the CHs. This issue has led to the degradation in Quality of Experience (QoE) for BEV drivers. Moreover, Charging Point (CP) service providers in the cities also suffer from lack of space which causes higher congestion at the CSs. In this context, we propose ChaseMe, a heuristic scheme for optimizing CS management by scheduling BEVs based on availability and type (fast/ultra-fast) of CPs by considering delay and charging time for CPs reservation. The proposed heuristic scheme consists of two soft computing techniques i) Harris Hawk Optimization (HHO) and ii) Fuzzy Inference System (FIS). Former technique is used to map the CP reservation requests to the best-suited CS by considering Quality of Service (QoS) parameters and acting as a global optimizer. FIS locally manages CPs at a particular CS in coordination with proposed meta-heuristic technique. The experimental results prove the benefits of the proposed ChaseMe framework as compared to the state-of-the-art techniques considering various charging metrics for BEVs.

With the development of Internet of Vehicles (IoV), the integration of Internet of Things (IoT) and manual vehicles becomes inevitable in Intelligent Transportation Systems (ITS). In ITS, the IoVs communicate wirelessly with other IoVs, Road Side Unit (RSU) and Cloud Server using an open channel Internet. The openness of above participating entities and their communication technologies brings challenges such as security vulnerabilities, data privacy, transparency, verifiability, scalability, and data integrity among participating entities. To address these challenges, we present a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV). P2SF-IoV integrates blockchain and deep learning technique to overcome aforementioned challenges, and works on two modules. First, a blockchain module is developed to securely transmit the data between IoV-RSU-Cloud. Second, a deep learning module is designed that uses the data from blockchain module to detect intrusion and its performance is assessed using two network datasets IoT-Botnet and ToN-IoT. In contrast with other peer privacy-preserving intrusion detection strategies, the P2SF-IoV approach is compared, and the experimental results reveal that in both blockchain and non-blockchain based solutions, the proposed P2SF-IoV framework outperforms.

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

This work proposes a traffic-light scheduling framework using the deep reinforcement learning technique to balance the traffic flow and to prevent congestion in the dense regions of the city via a software-defined control interface. A software-defined control enabled architecture is proposed to monitor the traffic conditions and it generates the traffic light control signal (Red/Yellow/Green) accordingly. For an intelligent traffic light control signal, a Deep Reinforcement Learning (DRL) model is proposed which takes vehicular dynamics as inputs from the real-time traffic environment such as heterogeneous vehicles count, speed, traffic density etc. To determine the congestion, a threshold policy is proposed and deployed on control server which generates the congestion prevention signal. A DRL agent operates in the coordination of congestion prevention signal and generates an effective traffic light control signal. The proposed model is evaluated through a realistic simulation on Indian city OpenStreetMap by using a well-known open-source simulator (SUMO). The comparative results show that the proposed solution improves several performance metrics such as average waiting time, throughput, average queue length, and average speed in the interval of 28.34% – 66.62%, 24.76% – 66.60%, 30.89% – 69.80%, and 16.62% – 43.67% respectively over other states of the art approaches.

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others. Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e., achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain.

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

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

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN’s heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

In this paper,Interference Reduction Scheme for Device-to-Device (D2D) Mobile Groups Using Uplink NOMA (ISHU)is proposed to maximize the throughput of the network. To achieve this goal, we integrated uplink non-orthogonal multiple access (NOMA) in the D2D mobile groups (DMGs). DMGs improve the spectral efficiency by sharing the resources with cellular mobile users (CMUs) whereas, uplink NOMA in DMGs associate the large number of D2D mobile users (DMUs) with the D2D transmitter (DDT). ISHU jointly optimizes the user group association and resource allocation in the uplink NOMA-enabled DMGs. The problem of joint user association and resource allocation is formulated as a mixed integer non-linear programming. To address this problem, we divided the problem of interference mitigation in two sub-problems and solved it independently. First, for joint user group association and sub-carrier assignment, a 3-D matching game is designed between the DMUs, DDT, and sub-carriers, respectively. Second, to optimize the power of DMUs across each sub-carriers, the branch and bound (BB) technique is used. Also, to reduce the complexity of ISHU scheme, the successive convex approximation low complexity (SCALE) technique is used across each sub-carrier. Simulated results demonstrated that ISHU provides 3.846 and 26.92 percent superior throughout as compared to the existing uplink conventional NOMA and OFDMA schemes.

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Sensor communication in the smart home environment is still in its infancy as the information exchange between sensors is vulnerable to security threats. Many traditional solutions use single-layer or multi-layer (i.e., onion routing protocol) encryption/decryption algorithms. But, in the traditional onion routing protocol, if the directory server is compromised, it may not track the malicious onion nodes within the onion network. It questioned the path anonymity of the onion routing protocol. Motivated by this, we proposed a blockchain and onion routing (OR)-based secure and trusted framework in the paper. The anonymity of the proposed OR network is maintained by storing and tracking the onion nodes threshold values through the blockchain network. A long short-term memory (LSTM) model is also utilized to classify the sensors data requests as malicious and non-malicious. The performance of the proposed system is evaluated with different performance metrics such as F1 score and accuracy. The LSTM model significantly improves the initial detection rate of malicious data requests from smart home sensors. Over these benefits, we considered the entire communication via 6G channel, reducing the overall communication latency. Additionally, the OR network is simulated over the shadow simulator to analyze the OR network’s performance considering parameters such as packet delivery ratio and malicious onion node detection rate.

Massive machine type communications (mMTC) is one of main application cases in 5G, which is supposed to support communications of massive number of machine-type devices (MTDs). Distributed queue (DQ) is a variant of tree splitting protocol which combines an m-ary tree splitting algorithm with a set of simple smart rules, organizing every terminal in one out of two virtual queues. Theoretically, DQ allows access to infinite terminals and is stable under any traffic condition, which alleviates the unstable problem of slotted ALOHA, and is especially suitable for mMTC. However, its theoretical comprehensive performance analysis as well as related statistical characteristics is still missing, which severely restricts the full manifestation of its performance advantages. In view of this, the paper proposes a general performance analysis framework for DQ, with which full probability space of DQ evolution process is presented for the first time. To be more specific, probability distribution function (PDF), mean and variance of throughput, latency and energy consumption of DQ is analytically derived to comprehensively evaluate performance. Taking the IEEE 802.15.4 standard for mMTC as example, numerical results validate the accuracy of the proposed analysis framework and the stability of DQ, present effects of number of MTDs, number of contention slots (${m}$), and maximum number of transmissions (${L}$) on DQ in terms of aforementioned performance metrics. These results together provide good reference to find appropriate value of${m}$and${L}$to balance the performance metrics and enable more practical network optimization.

Machine learning and artificial intelligence can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, machine learning and artificial intelligence have found ways to improve operations and management of information technology and communications.

Authentication receives enormous consideration from the research community and is proven to be an interesting field in today’s era. User authentication is the major concern because people have their private data on devices. To strengthen user authentication, passwords have been introduced. In the past, the text-based password was the traditional way of authentication, but this method has particular shortcomings. The graphical password has been introduced as an alternative, which uses a picture or a set of pictures to generate a password. In the future, it is a requirement of such approaches to maintain robustness and consume fewer energy resources to become suitable for the Green Internet of Things (IoT). Similarly, diverse graphical password authentication mechanisms have been used to provide users with better security and usability. In this article, we conduct an extensive survey on the existing approaches of graphical password authentication to highlight the challenges required to be addressed for Green IoT. In comparison to other existing surveys, the objective is to consolidate the graphical password technique and to identify the problem associated with it. Besides, this survey will also identify the vulnerabilities of the graphical password against several potential attacks. We have also examined the strengths and weaknesses of each technique along with the future research directions. This study also evaluates the usability of each approach by considering learnability, memorability, and so forth and also presents a comparative analysis with security.

Multicast communication plays a pivotal role in Edge based Mobile Ad hoc Networks (MANETs). MANETs can provide low-cost self-configuring devices for multimedia data communication that can be used in military battlefield, disaster management, connected living, and public safety networks. A Multicast communication should increase the network performance by decreasing the bandwidth consumption, battery power, and routing overhead. In recent years, a number of multicast routing protocols (MRPs) have been proposed to resolve above listed challenges. Some of them are used for dynamic establishment of reliable route for multimedia data communication. This article provides a detailed survey of the merits and demerits of the recently developed techniques. An ample study of various Quality of Service (QoS) techniques and enhancement is also presented. Later, mesh topology-based MRPs are classified according to enhancement in routing mechanism and QoS modification. This article covers the most recent, robust, and reliable QoS-aware mesh based MRPs, classified on the basis of their operational features, and pros and cons. Finally, a comparative study has been presented on the basis of their performance parameters on the proposed protocols.

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the long-range dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset.

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

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.

Growing population and migration to cities have given birth to multiple urban issues. Traffic congestion is one of the most prominent ones with severe side effects like fuel wastage, loss of lives, and slow productivity. The traditional traffic control system deploys programming logic control (PLC) which uses round-robin scheduling algorithm. However, few recent works have proposed IoT-based framework which requires the deployment of a series of sensors. In this paper, we propose an IoT-based framework that uses the existing network of CCTV cameras at the junction. An edge device is used to estimate the traffic density and detect emergency vehicles using YOLO v3 -Efficient Net. These two parameters are used as an input to a novel traffic control algorithm. The performance of the proposed framework has been evaluated by analyzing its properties using the UA-DETRAC dataset. The proposed framework achieves 68.10% vehicle detection accuracy.

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.

Financial institutions have made lives easier for a lot of individuals and organizations that would earlier use to face capital shortage now and then. Therefore, it becomes necessary to make the financial systems more reliable, secure, time-conserving, and cost-effective. Although several approaches have already been proposed, all of these tend to fail on at least one of the key features, i.e., trust. Motivated by this, in this paper, we propose DeLend, an Ethereum blockchain-based peer-to-peer (P2P) lending system. In DeLend, the problems of security, trust, and reliability have been solved with the help of Ethereum-based smart contracts (SCs). To make the system middlemen-free and much more cost-effective, we use the interplanetary file system (IPFS) protocol as a data storage. Through extensive simulation, we show that DeLend requires less bandwidth, which makes it a suitable enabling technology for the next generation of cellular networks, i.e. 6G. Finally, DeLend’s performance evaluation demonstrates its efficacy compared to traditional lending schemes.

The rapid growth and advancement of technology in industries provide a better quality of services to the end-user in the industrial Internet of Things (IIoT). The digital twin (DT) is an innovative technology recently developed in Industry 4.0 to provide a virtual representation of physical components, products, or equipment such as computer numerical control (CNC) machines. It can be used to run simulations before manufacturing. However, traditional DT platforms lack data privacy, traceability, immutability, authentication of stakeholders. Moreover, manual prediction of the wearing of the tool condition of the CNC machine is challenging. Motivated from these gaps, in this paper, we propose a six-layered architecture for DT of CNC, which predicts CNC tool wear detection using a novel ensemble technique based soft voted prediction model consisting of XGBoost, random forest, and AdaBoost models. The proposed architecture also incorporates the public Ethereum blockchain (BC) to maintain the aforementioned issues of authentication, traceability, and transparency through constraints and automation programmed into the smart contracts (SC) developed. We evaluate the proposed scheme’s performance through simulation and compare it with other traditional approaches concerning several performance parameters (accuracy, F1-score, precision, and recall). The result shows that the proposed approach outperforms the traditional approaches on these same performance parameters such as accuracy, F1-score, precision, and recall.

The COVID-19 pandemic has adversely affected the lives of millions of people worldwide. With an alarming increase in COVID-19 cases, it is important to detect and diagnose COVID-19 in its early stages to prevent its spread. To diagnose remote patients, the Internet can be useful for accessing data of that patient. But, the Internet has also had issues related to data security, reliability, and privacy. Motivated by these challenges, in this paper, we propose a Blockchain (BC) based COVID-19 detection scheme (BCovX) for fast and reliable diagnosis of COVID-19 using chest X-Ray (CXR) images. For fast and accurate detection of COVID-19 using CXR, BCovX consists of a Convolutional Neural Network (CNN) model, using which a patient can be diagnosed for COVID-19 remotely. CNNs have performed successfully in medical imaging classification. BCovX provides reliable and secure data access and exchange using BC and smart contracts (SC). To solve issues related to data storage and its associated cost, the InterPlanetary File System (IPFS) protocol is used to store medical data. We also present a real-time SC developed in Solidity to govern the transaction between the patient and the doctor. The SC has been compiled and deployed on Remix Integrated Development Environment (IDE). Finally, we have evaluated the performance of BCovX with traditional schemes in terms of storage cost, bandwidth requirements, and accuracy of the CNN model.

The 6G-based spectrum bands allocation to telecom providers would guarantee ultra peak rates, high availability, and extremely low-latency for various user applications. However, the spectrum allocation still suffers from the limitations of fair allocation process, delays in auction process, and collusive bidding due to inherent centralization. Thus, this paper proposes a scheme, Block6Tel, that integrates blockchain (BC) in 6G-envisioned spectrum allocation to ensure secure and trusted band allocation among telecom providers, and ensure transparency among telecom stakeholders. The scheme operates in two phases. First, a 6G-based protocol stack model is proposed that leverages a cell-free communication infrastructure. Then, in the second phase, a BC-based auction algorithm is proposed for inter-operator spectrum allocation, and resource allocations among service providers are finalized. Finally, smart contracts (SC) are executed among telecom providers as bidders, and government authorities (GA) as auctioneers. Through extensive simulations, we prove the superiority of Block6Tel compared with traditional static allocation approaches, in terms of parameters like- resource utilization, requests overhead, and allocation fairness. The results demonstrate that the proposed scheme outperforms the traditional schemes using various parameters.

The paper proposes a sixth-generation (6G)-enabled cellular vehicle-to-anything (C-V2X)-based scheme, Res6Edge, that supports high-data ingestion rate through artificial intelligence (AI) models at edge nodes, or Edge-AI. Through Edge-AI in 6G supported C-V2X, we address the research gaps of earlier schemes based on fifth-generation (5G) resource orchestration. 6G improves decision analytics and real-time resource sharing among C-V2X ecosystems. The scheme operates in three phases. In the first phase, a layered network model is proposed for V2X communication based on 6G-aggregator and core units. Then, based on the proposed stack, in the second phase, 6G resource allocation is proposed through macro base station (MBS) units. MBS ensures channel gain and reduces energy loss dissipation. Finally, in the third phase, an intelligent edge-AI scheme is formulated based on deep-reinforcement learning (DRL) to support responsive edge-cache and improved learning. The proposed scheme is compared to 5G baseline services in terms of parameters like- throughput, latency, and DRL scheme is compared to random allocation approaches. Through simulations, Res6Edge obtains a V2X user throughput of 43.24 Mbps, compared to 0.7 Mbps for 4 x 108connected ACV sensors. The reduced latency is ≈ 13.84 times of 5G. DRL learning algorithm achieves a satisfaction probability of 0.5 for 500 vehicles, compared to 0.35 using conventional schemes. The obtained results indicate the viability of the proposed scheme.

Abstract From the past few years, Unmanned Aerial Vehicles (UAVs) has proved an immense potential in providing the cost and time‐efficient solutions to the various societal applications such as healthcare, supply chain, and video & surveillance. It has many data security and privacy issues, and researchers across the globe have given many solutions to protect data from cyber‐attacks. Many of them have suggested cryptographic‐based solutions, which is very compute extensive. Very few researchers have suggested Blockchain (BC)‐based solutions, but their solutions may suffer from high data storage cost as well as network latency, reliability, and bandwidth issues. To overcome the above‐mentioned issues, this paper proposed an InterPlanetary File System and BC‐based secure UAV communication scheme over the 6G network. This proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Then, the research challenges and future directions for further improvement of the proposed system have been presented.

The delay of vehicle emergency has led to many serious consequences. A series of studies has been carried out in the field of information security in vehicularad hocnetworks (VANETs). However, open issues such as the authentication of emergency vehicle (EV) avoidance are remaining unsolved. In this article, we propose a novel lightweight authentication protocol to avoid EVs in VANETs. In our protocol, after completing the first mutual authentication with the nearest roadside unit (RSU), EV can complete the mutual identity authentication with the subsequent RSUs without repeating cumbersome calculations. Additionally, EV is required to verify the legitimacy of the driver’s identity when starting to avoid some illegal driving behavior. The RSUs will broadcast avoidance information to ordinary vehicles in their jurisdiction to remind them to clear a temporary emergency lane for EVs in advance. With temporary emergency lanes, emergent mission delays due to traffic congestion could be reduced. The security analysis and efficient analysis prove that our protocol is practical and efficient against attacks, such as impersonation attacks, device theft attacks, reputation attacks, etc.

Unmanned aerial vehicles (UAV) can provide efficient and effective solutions for the development of smart cities. They have been widely used in civilian and military applications, such as data acquisition, data dissemination, audio and video surveillance, aerial photography, crop surveys, and real-time medical care. The network communication and security challenges in UAV networks are explored by research organizations across the globe, but still, many challenges remain unsolved like sensitive and end-user-related applications. Moreover, traditional UAV communication is not adequate to deal with the high mobility and dynamic features of UAV. So, there is a need for an efficient and secure network of UAV as they have been widely used in hostile environments. Motivated from the aforementioned facts, in this article, we present a broad survey on the architecture, requirements, and use cases of 6G technology. It also presents a solution taxonomy based on the applications of UAV communication. Based on the findings from the survey, we present a blockchain-envisioned security solution and 6G-enabled network connectivity in UAV communication. A summary of future research directions for the integration of blockchain and 6G technology in UAV communications is also presented. Then, we present a case study of a blockchain-envisioned UAV communication using 6G networks to secure Industry 4.0 applications.

In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users' fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep Q-network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.

As stated by Spock, “change is the essential process of all existence,” which is reflected in everyday applications in our daily lives. We, as humans, just need to find a way to make the best use of the current technological advances. The pandemic has managed to exploit our deepest vulnerabilities and insecurities. We need to cope with a lot of things, just to be comfortable in the new normal. Hence, we can rely on technology, the greatest asset developed by humans. In this article, we discuss how we can enhance the work environment in offices post-pandemic. We combine federated learning with emotion analysis to create a state-of-the-art, simple, secure, and efficient emotion monitoring system. We combine facial expression and speech signals to find out macroexpressions and create an emotion index that is monitored to find the mental health of the user. Federated learning enables users to locally train the model without compromising his/her privacy. In place of sending data to the centralized server, the proposed scheme sends only model weights that are combined at the server to make a better global model, which is further pushed back to the users. This model is then trained interorganizational as it does not violate the privacy or data sharing to achieve optimal results. The data collected from users are monitored to analyze the mental health and presented with counseling solutions during low times. Technology is a panacea that has enabled us to survive in this pandemic, and by using our solution to improve work culture and the environment in post-pandemic times.

The applications of unmanned aerial vehicles (UAVs) in military, intelligent transportation, agriculture, rescue operations, natural environment mapping, and many other allied domains has increased exponentially during the past few years. Some of the use cases of their applications range from aerial surveillance, data retrieval to their use in real-time communicative networks. Though UAVs were traditionally used only outdoors, many of its indoor applications like for rescue operations, inventory tracking in warehouses, etc., have recently emerged and these use cases are being actively explored. One of the major challenges for indoor drone applications is navigation and obstacle avoidance. Due to indoor operations, the global positioning system fails in accurate localization and navigation. To address this issue, we introduce a scheme that facilitates the autonomous navigation of UAVs (which have an onboard camera) in the indoor corridors of a building using deep-neural-networks-based processing of images. For a deep neural network, the selection of a good combination of hyperparameters for a better prediction is a complicated task. In this article, the hyperparameters tuning of a convolutional neural network is achieved by using genetic algorithms. The proposed architecture (DCNN-GA) is compared with state-of-the-art ImageNet models. The experimental results show the minimum loss and high performance of the proposed algorithm.

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

Virtualization is an essential feature in the IoT-resource-constrained environment due to which the service providers are facing challenges to minimize the energy consumption by IoT devices. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption of IoT devices, which are an integral part of the modern data centers. A lot of research work has focused on efficient management of energy consumption by virtue of virtual machine consolidation. The existing virtualization techniques may not be suitable for this problem due to high computational overhead. As containers have been recently getting much popularity to encapsulate fog services, so they are the best candidate to handle this problem, especially for intelligent IoT devices. Keeping the focus on all these issues, in this article, we propose an energy-efficient container migration scheme by migrating the container from the source host server to the destination host server to meet the container's resource requirement. We used a novel approach to find the best destination host for container placement to solve host overload or underload problems using the best-fit container placement technique. The results obtained on the benchmark data set with respect to various performance evaluation metrics prove the efficacy of the designed scheme in comparison to the other existing state-of-the-art schemes.

Spamming is emerging as a key threat to the Internet of Things (IoT)-based social media applications. It will pose serious security threats to the IoT cyberspace. To this end, artificial intelligence-based detection and identification techniques have been widely investigated. The literature works on IoT cyberspace can be categorized into two categories: 1) behavior pattern-based approaches and 2) semantic pattern-based approaches. However, they are unable to effectively handle concealed, complicated, and changing spamming activities, especially in the highly uncertain environment of the IoT. To address this challenge, in this article, we exploit the collaborative awareness of both patterns, and propose a Collaborative neural network-based spammer detection mechanism (Co-Spam) in social media applications. In particular, it introduces multisource information fusion by collaboratively encoding long-term behavioral and semantic patterns. Hence, a more comprehensive representation of the feature space can be captured for further spammer detection. Empirically, we implement a series of experiments on two real-world data sets under different scenarios and parameter settings. The efficiency of the proposed Co-Spam is compared with five baselines with respect to several evaluation metrics. The experimental results indicate that the Co-Spam has an average performance improvement of approximately 5% compared to the baselines.

Governments and public sector entities around the world are actively exploring new ways to keep up with technological advancements to achieve smart governance, work efficiency, and cost optimization. Blockchain technology is an example of such technology that has been attracting the attention of Governments across the globe in recent years. Enhanced security, improved traceability, and lowest cost infrastructure empower the blockchain to penetrate various domains. Generally, governments release tenders to some third-party organizations for different projects. During this process, different competitors try to eavesdrop the tender values of others to win the tender. The corrupt government officials also charge high bribe to pass the tender in favor of some particular third party. In this article, we presented a secure and transparent framework for government tenders using blockchain. Blockchain is used as a secure and immutable data structure to store the government records that are highly susceptible to tampering. This work aims to create a transparent and secure edge computing infrastructure for the workflow in government tenders to implement government schemes and policies by limiting human supervision to the minimal.

In this work, we propose and develop healthcare device interoperability (HeDI)—a system to enable device interoperability in IoT-enabled in-home healthcare monitoring platforms. The system consists of multiple sensors, each connected wirelessly to an edge device, acting as a wireless communication gateway to a remote server. The system initiates information handshaking between the sensor adapters and edge device at the beginning of the operation, which is later used to detect the sensor settings to process the data received from the sensor. The system is scalable and dynamically accommodates multiple sensors without any predefined ontologies at the edge device. The implementation of our system avoids dependencies on a system’s physical ports. The low form factor and wireless connectivity of the adapter make the system portable and convenient for in-home health monitoring. Additionally, the system allows multiple homogeneous sensors to operate at the same time in the same system. We implement and evaluate our system with a 3-lead ECG, pulse, and temperature sensors against two different network configurations—star and mesh. We use the data set generated from our implemented system for performance analysis. The network-level analysis of our system shows an average packet delivery ratio of 0.92 for star network configuration and 0.98 for mesh network configuration, ensuring the reliability of performance and its suitability for healthcare monitoring systems.

We consider a fifth-generation (5G)-empowered future Industrial IoT (IIoT) networking problem where IIoT machines are capable of communicating and sharing their data networking knowledge gained (and experiences) with other neighboring devices/tools. For such an IIoT setting, deep-learning (DL)-based communication protocols are known to be highly efficient but having a computationally complex training procedure in terms of both time/space and volume of data sets. One solution for such training is to be completed offline for each equipment and machines of IIoT before deployment. A better approach would be to replicate the model from the expert existing machine and implant it into new machines. Such training for the transfer of knowledge can be done by manufacturers using high computational power, even for large-scale DL models. After sufficient training and the desired level of accuracy, the trained machines can be deployed in the smart factory equipment to perform life-long collaborative learning. We design a novel distributed transfer learning (TL) framework to maximize multipath communication networking performance for Industry 4.0 environment. To conduct seamless sharing of knowledge gain by the multipath TCP (MPTCP) agents and tackle retraining issues of DL-based approaches, we investigate TL for MPTCP from the IIoT networking perspective. With relevant insights from transfer and collaborative learning, we develop a distributed TL-MPTCP framework to accelerate the learning efficiency and enhance the performance of newly deployed machines. Our approach is validated with numerical and emulated NS-3 experiments in comparison with the state-of-the-art schemes.

In the last few years, we have witnessed the cache localization as one of the most challenging problems for device-to-device (D2D) communication in the IoT environment. It has been a major performance bottleneck due to cache localization problem in D2D communication as there are advancements in cellular technology, especially in 5G base stations (BSs) deployment around the globe. It is due to the fact that with an increase in the enormous amount of the number of users and devices, there has been an increase in the demands of service availability within a fraction of seconds by the end users. It results in an increase in burden on the existing network infrastructure with respect to Quality of Service (QoS) and Quality of Experience (QoE) provisions to the end users and service providers. However, caching the most popular content on the user equipments (UE's) can resolve the aforementioned problems. Motivated from these facts, in this article, we propose a model to address the problem of the cache localization decision making. In the proposed scheme, first, we collected the data set traces to predict the cache locations. Then, we predicted the locations where the user can cache the most accessed content using machine learning classification models. The classification models used in the proposed solution are decision tree and random forest. The metrics used for evaluation of the results obtained are access delay and energy consumption of the UEs. On comparing the proposal with the other existing state-of-the-art models, we observed that the random forest model yields higher accuracy as compared to other existing models. Also, we have observed that the access delay is maximum at the user's end when contents are shared with the gateway.

The pervasiveness of newly introduced Internet-of-Things (IoT) devices has opened up new opportunities in healthcare systems, for example in facilitating remote patient monitoring. There are, however, security and privacy considerations in the transmission of data from these devices to the backend server, and across heterogeneous IoT networks. In this study, we propose a novel privacy-preserving scheme which is based on blockchain and swarm exchange techniques to facilitate seamless and secure transmission of user data (e.g., electronic health record (EHR)-related information) through secured swarm nodes of peer-to-peer communications. BIoTHR refers to the proposed scheme on the private blockchain-assisted EHR management using IoT. Specifically, new blockchain and swarm exchange infrastructures are suggested as a backbone of the proposed scheme to ensure secure and reliable data transmission and timely monitoring of data sent across IoT networks. An autonomous encryption-decryption mechanism is also utilized, along with a dynamic and modular server assistance technology to deploy EHR transmission in a secure manner. Moreover, several swarm-listen, announcement, peer open and peer closing algorithms are incorporated to employ the actual power of pervasive EHR transmission for better e-healthcare service provisioning. The proposed scheme is developed using the open-source tools of GnuPG, IPFS, and Golang. Proposed study simulates a number of heterogeneous IoT-based health sensor nodes, namely, body temperature, pulse rate, and oxygen saturation, i.e., SPO2, galvanic skin response, and blood glucose in blockchain-assisted swarm exchange framework. The results reveal that the proposed scheme, in terms of blockchain-IoT, swarm exchange and EHR transmission, outperforms several peer techniques.

In modern decentralized Internet-of-Things (IoT)-based sensor communications, pseudonoise-diffusion oracles are heavily investigated as random oracles for data exchange among peer nodes. As these oracles are generated through algorithmic processes, they pass the standard random tests for finite and bounded intervals only. This ensures a false sense of privacy and confidentiality in exchange through open protocol IoT-stacks in public channels, i.e., Internet. Recently, blockchain (BC)-envisioned random sequences as input oracles are proposed about financial applications, and windfall games like roulette, poker, and lottery. These random inputs exhibit fairness, and nondeterminism in SC executions termed as probabilistic smart contracts (PSCs). However, the IoT-enabled PSC process might be controlled and forged through humans, machines, and bot-nodes through physical and computational methods. Moreover, dishonest entities like contract owners, players, and miners can co-ordinate together to form collusion attacks during consensus to propagate false updates, which ensures forged block additions by miners in BC. Motivated by these facts, in this article, we propose a BC-envisioned IoT-enabled PSC scheme,SaNkhyA, which is executed in three phases. In the first phase, the scheme eliminates colluding dishonest miners through the proposed miner selection algorithm. Then, in the second phase, the elected miners agree through the proposed consensus protocol to generate a stream of random bits. In the third phase, the generated random bit-stream is split through random splitters and fed as input oracles to the proposed PSC among participating entities. In simulation, the scheme ensures a trust probability of 0.38 even at 85% collusion among miners and has an average block processing delay of 1.3 s compared to serial approaches, where the block processing delay is 5.6 s, thereby exhibiting improved scalability. The overall computation and communication cost is 28.48 ms, and 101 bytes, respectively, that indicates the efficacy of the proposed scheme compared to the traditional schemes.

In the recent years, the blockchain (BC) technology has been used in various applications ranging from financial sector to healthcare sector. Moreover, developing BC-based solutions for these applications has been an area of interest among the academia and industry professionals. Diamond has huge potential to become an investment asset, but there are some issues, which hinder the progress of the diamond industry, such as provenance, supply chain traceability, involvement of third party in the verification process, and reliability of transactions. BC seems to be a promising technology, which bridges the gap between the diamond industry and the burgeoning financial markets. Individuals are always confident that the crystals they bought are legitimate and that stolen property can be handed back to the legitimate owner easily. Motivated from these facts, in this article, we surveyed the adoption of BC in the diamond industry, and also present pros and cons of this integration. Then, we discuss issues of the diamond industry operations and based on the literature review, we suggest their probable countermeasures. Then, we analyze various open research issues and challenges associated with integrating the BC in the diamond industry. Finally, we present a case study on frameworks, such as Everledger and Tracr, which highlights the real-time challenges of integrating BC in the diamond Industry.

The coronavirus (COVID-19) outbreak has a significant impact on people’s lives, occupations, businesses, and economies globally. The world economic market is experiencing a big shift and the share market has observed crashes day-by-day. Even, the Indian economy has witnessed a slowdown in the current pandemic, and recovery of it is quite difficult. The restrictions and restrain strategies (e.g., lockdown and social distancing) introduced by the government leave many professions and facilities in a dormant state, catalyzing economy downfall. It necessitates to improve economy along with control strategies of COVID-19, which is a challenging task. To handle the above-mentioned issues, this article proposes a novel economy-boosting scheme, i.e.,$\xi $boost, which is a fusion of artificial intelligence (AI) and big data analytics (BDA) integrated with the Internet-of-Things (IoT)-based data communication. Here, a bidirectional long short-term memory (LSTM) model is anticipated for early prediction of total positive cases as well as the economy. Then, it calculates an optimal subsegment of days, in which trade and commerce related restrictions could be reduced to control a sharp decline in the economy. Next, a spark-based pre and post unlock (PPU) analytics is carried out on the rise of COVID-19 cases to validate the intensity of testing in the country and deciding economy-boosting activities. Then, the$\xi $boostscheme is evaluated based on various factors such as prediction accuracy and others while comparing to existing approaches. It facilitates healthy and profitable smart cities by the means to control pandemic with subsequent economy rise.

The advent of the Internet of Things (IoT) has escalated the sharing of information among various smart devices many fold, irrespective of their geographical location. Recently, applications, such as e-healthcare, farm monitoring, border security, smart transportation, etc. have attracted wide attention from the research community. However, as devices in the Industrial-IoT (IIoT) environment share their information using the Internet, security issues, such as authentication, integrity, and confidentiality of data pose various challenges to the research community for the successful implementation of any solution. To handle these issues, several digital signature-based schemes have been designed in the past. However, because of the usage of the identity-based public-key cryptography (IDPKC) or certificate-less-based public-key cryptography (CLPKC), these schemes suffer from key escrow or secret key distribution problems. To eliminate these flaws, this article presents a short digital signature scheme without pairing in certificate-based setting with aggregation in IIoT environment. Besides, in IIoT environment, communication and computational costs are also considered as imperative challenges. In this regard, the pairing free construction, short length signature and aggregation make it a communication and computational efficient signature scheme. The performance comparison of the proposed scheme shows that our scheme causes less computational overhead and takes significantly less execution time as compared to the existing schemes, which is imperative for the resource limited IIoT devices. We also demonstrate that signature aggregation and verification cost is 6.67(n+2) ms which is much less in comparison to the verification cost ( 20.01n ms) of n short signatures.

The application of Internet of Things (IoT) has generally penetrated into people's life and become popular in recent years. The IoT devices with different functions are integrated and applied to various domains, such as E-health, smart home, Industrial IoT (IIoT), and smart farming. IIoT obtains the general attention among these domains, which allows the authorized user remotely access and control the sensing devices. The user suffices to attain the real-time data collected by sensing devices during the process of production. However, these data is usually transmitted via an insecure channel, which brings the problem of the security and privacy arising from the hostile attacks in IIoT. To resist the hostile attacks by the adversary and protect the security of the transmitted data, we propose a secure multifactor authenticated key agreement scheme for IIoT to support the authorized user remotely accessing the sensing device. The scheme adopts password, biometrics, and smart card to identify the user in the IIoT environment. We employ the secret-sharing technology and Chinese remainder theorem to construct a group key among legitimate sensing devices, and then this group key is utilized to assist in negotiating a secure session key between the user and multiple sensing devices. The proposed scheme is suitable for the resource-constrained IIoT as it only uses hash function, bitwise XOR operation, and symmetric cryptography. The performance analysis indicates that our scheme has less communication and computational costs in contrast to other correlative schemes. Besides, the security analysis indicates that our scheme can withstand many known attacks.

Internet of Things provides complicated value-added services to mobile intelligent terminal users. Different sensors collect various data from the users and transmit the data to the mobile intelligent terminal for storage. Consequently, a great amount of personal and sensitive information related to these rich and colorful applications is stored in the mobile intelligent terminal. Mobile intelligent terminals have become the prominent target of network attackers. Security breach and privacy leakage severely thread the application development of the Internet of Things. We present a privacy-preserving implicit authentication framework using users' behavior features sensed by the mobile intelligent terminal based on the artificial intelligence methodology. More precisely, we first summarize the security and privacy requirements for the security authentication of the mobile intelligent terminal. Then, we present a privacy-preserving implicit authentication framework using the cosine similarity and partial homomorphic public-key encryption scheme. Finally, a performance evaluation of the proposed protocol is conducted. The result shows that the communication and computation efficiency of our protocol is more efficient than other related protocols.

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

An electroencephalogram (EEG)-based remote pathology detection system is proposed in this study. The system uses a deep convolutional network consisting of 1D and 2D convolutions. Features from different convolutional layers are fused using a fusion network. Various types of networks are investigated; the types include a multilayer perceptron (MLP) with a varying number of hidden layers, and an autoencoder. Experiments are done using a publicly available EEG signal database that contains two classes: normal and abnormal. The experimental results demonstrate that the proposed system achieves greater than 89% accuracy using the convolutional network followed by the MLP with two hidden layers. The proposed system is also evaluated in a cloud-based framework, and its performance is found to be comparable with the performance obtained using only a local server.

With the growth in the Internet of things (IoT) paradigm, there has been a tremendous makeshift in how the distributed devices work to achieve a common goal. However, it remains essential that all these devices work in a coherent manner to perform a collective action. This makes the task of resource provisioning extremely important in such a paradigm. The end-user level in IoT mostly comprises of low computation and communication powered devices. Improper utilization of the available resources in such a scenario burdens the complete system and degrades the quality of service. In such a scenario, the use of cloud computing techniques can help to manage the resources effectively. More so, with the emergence of relatively newer cloud-based technologies such as edge and fog computing, resource management in the IoT has become far more effective. These technologies bring the computation and communication capabilities closer to the IoT devices where some of the services can be offloaded to the edge devices. These devices are called IoT edge devices and they provide a unique opportunity to tackle some of the existing and pertinent issues for resource management in IoT paradigms; yet at the same time, they face their own set of challenges. However, the use of IoT edge devices in a traditional IoT paradigm results in better utilization of the available resources as well as improving the overall quality of service. Keeping this in mind, this special issue addressed some of the aspects related to resource management in IoT edge devices with the focus on various challenges faced, and potential techniques and solutions to address such challenges by leveraging IoT edge devices. We received numerous submissions in the issue, and we accepted 13 high-quality submissions for publication as a result after following a rigorous review process. Each of the accepted papers is summarized as follows. In the first paper, Khan et al.1 presented "A cache-based approach toward improved scheduling in fog computing" for efficient resource allocation in the fog computing environment, while maintaining the quality of service. The authors use first-in first-out scheme to place the jobs in queue and cache the job type, fog server, arrival time, time to leave, and internal processing time. The jobs are then moved from the queue by the fog broker which selects fog server having sufficient required power and resources to execute the job. The authors' proposed cache-based scheme showed promising results in terms of reducing the execution time, latency, processing delays and power consumption as compared to the conventional first-come-first-serve and shortest job first policies. The second paper on "Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges" by Dewangan et al.2 sheds light on various types of resource provisioning schemes and classified those into different categories (to help understand them better) on the basis of the underlying technique and their overall objective. This survey helps to understand the optimal schemes for catering to different performance metrics such as time, cost, energy, service level of agreement rate, power consumption, resource utilization, etc. Moreover, the authors also highlighted some of the current research challenges in the domain of resource management. The next paper, "An energy efficient and low overhead fault mitigation technique for internet of thing edge devices reliable on-chip communication" by Ibrahim et al.3 presents a coding scheme to make the network-on-chip fault-tolerant. The network-on-chip provides communication backbone in the underlying network for which the proposed scheme handled both single and multibit adjacent bit errors. The next paper in this issue is on "Design and data analytics of electronic human resource management activities through Internet of Things in an organization" by Nasar et al.4 The authors focus on designing a data analytical human resource management system for IoT devices in an organization for ensuring the policies, strategies, and practices within the organization. The activities covered under this improved system include e-recruitment, e-Selection, e-performance management, e-learning, and e-compensation and the performance of the system was validated on four Kaggle databases. In the fifth paper on "A Mobile Data Offloading Framework based on a Combination of Blockchain and Virtual Voting", Hassija et al.5 enable mobile users to offload computation tasks to resource-rich mobile-devices in order to reduce energy consumption and enhance performance. The authors used directed acyclic graphs (DAGs) for mobile offloading algorithm where the users can securely submit a transaction (powered by blockchain) request for task offloading a DAG, while a game-theoretic scheme was employed in order to model the interactions between various mobile devices for bargaining cost and time. The sixth paper by Lu is on "Security of Internet of Things edge devices".6 The paper focuses on securing the edge nodes and edge gateways in IoT to meet its future security needs to eliminate the data leakage risk. The edge nodes were optimized by using a cache replacement algorithm, namely Max-PSN and the results illustrate that the proposed mechanism performed superiorly to the lead frequently used and least recently used algorithms with respect to the hit rate and average response speed of centralized and distributed systems. In the seventh paper, Balasubramanian and Jolfaei present "A scalable framework for healthcare monitoring application using the Internet of Medical Things".7 The authors made use of IoT for providing real-time alarm and assistance in order to ease the activities of pregnant women by merging the advantages of event-driven and assistive care loop framework architecture. In the next paper, Bodkhe and Tanwar shed some light on "Secure data dissemination techniques for IoT applications: Research challenges and opportunities".8 As the name suggests, the authors presented a comprehensive summary of secure data dissemination schemes present in the existing literature for IoT applications along with their potential research issues and possible countermeasures. The majority of the researched literature in this survey covers the Internet of Vehicles, Internet of Drones, and Internet of Battlefield things with respective open issues and challenges of each of these. As countermeasures, the authors researched opportunities in the directions of the requirement of secure dissemination protocols, efficient data aggregation methods, and cluster-based data dissemination. The ninth paper on "Comparative study of support vector machines and random forests machine learning algorithms on credit operation" by Teles et al.9 compares the support vector machine (SVM) and random forest (RF) scheme for their application to predict financial risks on credit operation. The outcomes of this paper suggest that while both can be effectively used for the specified task, RF has an advantage of the speed and operational simplicity over SVM; while SVM has the benefit of higher classification accuracy. The tenth paper presented by Zhao et al. titled "Message-Sensing Classified Transmission Scheme Based on Mobile Edge Computing in the Internet of Vehicles".10 The authors make use of mobile edge computing for secure message transmission by prioritizing secure messages using the analytic hierarchy process to guarantee a higher transmission level for urgent messages. Moreover, using the Lagrangian relaxation method, an optimal task offloading model was devised for delay and energy loss by assigning different weight factors to these parameters. The next paper is "FPFTS: A Joint Fuzzy PSO Mobility-aware Approach to Fog Task Scheduling Algorithm for IoT Devices" by Javanmardi et al.11 The authors build a fog task scheduler leveraging the particle swarm optimization along with fuzzy theory to assign tasks of the users to fog devices. The proposed task schedular was tested on iFogSim simulator and results show that it outperformed first-come-first-serve and delay-priority algorithms with respect to delay and network utilization. Zhang et al.,12 in their paper "Service offloading oriented edge server placement in smart farming" made use of the edge resources to support the real-time intelligent controls in smart farming. The authors presented a service offloading oriented architecture for reducing delay in data transmission from sensors to the edge servers while balancing the load on the servers and optimizing the energy consumption. The final accepted paper in this special issue is on "A metaheuristic optimization approach for energy efficiency in the IoT networks" by Iwendi et al.13 The authors proposed a hybrid metaheuristic algorithm, namely, WOA-SA, for optimizing the energy consumption of the sensors in IoT-based wireless sensor networks. The two metaheuristic approaches, namely, whale optimization algorithm and simulated annealing for choosing the cluster heads in order to optimize the energy consumption in the network. The proposed approach was found to be more effective than its counterparts in terms of load, temperature, residual energy, and cost function. We sincerely hope that after reading the accepted contributions in this special issue would help the readers of the journal and a wider research community to gain knowledge on the presented research challenges, techniques and solutions, and encourage them to further work on different aspects of resource management in IoT devices. We thank the editor-in-chief and editorial board members for providing us with the opportunity to conduct a special issue in Software: Practice and Experience. We also like to thank the administrative staff, reviewers and most importantly, the authors, for their help and contributions in successful organization of this issue.

Pixel-value-ordering (PVO) is one of the most widely used reversible data hiding (RDH) framework which efficiently utilizes smooth pixels of the cover image to provide high-fidelity stego-image but with limited embedding capacity. This letter proposes an RDH scheme based on local moment driven pixel value ordering (LM-PVO) which further enhances the effectiveness of smooth pixel's utilization by dividing the fixed-size blocks into two groups. Pixels of each group are sub-divided into two sub-groups based on the local moment of the block so that correlation among the pixels of each sub-group is enhanced. Thus doing, the pixels of each sub-group are grouped based on their intensity values instead of their position as in the conventional PVO-based schemes; this enables information hider to embed a higher amount of secret data while also enhancing the stego-image quality. Experimental results also validate the superiority of the proposed scheme over the existing PVO-based RDH schemes.

With the evolution of the Internet of Vehicles (IoV), there has been an overwhelming increase in the number of connected vehicles in recent times. Due to this reason, massive amounts of data generated by connected vehicles makes traditional host-centric approach inevitable in IoV ecosystem. Moreover, the existing TCP/IP based congestion control mechanisms cannot be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput. So, in this article,11.This article is an extended version of paper entitled “Deep Learning-based Content Centric Data Dissemination Scheme for Internet of Vehicles“ published in IEEE ICC, 20-24 May 2018, Kansas City, USADiLSe: A Lattice-based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles is designed, which works in three modules. The first module, i.e., deep learning based content centric data dissemination scheme, works in three phases. 1) In the first phase, the connection probability of vehicles is computed to identify stable and reliable connections using Weiner process model. 2) In the second phase, a convolutional neural network based scheme is presented for estimating the social relationship score among vehicle-to-vehicle pair. 3) In the third phase, a content centric data dissemination scheme is presented. However, the mobility of vehicles in IoV ecosystem gives them the liberty to move in/out of the network without IP assignment. This makes it necessary to replicate the content at each node for providing fault tolerance. So, in the second module, a data replication scheme for fault tolerance in IoV network is designed, which is followed by an access control mechanism for read/right access for network content in third module. Finally, in the last module, a crucial lattice-based exchange and authentication scheme using blockchain is also designed for handling secure communication in IoV ecosystem. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained prove the efficacy of the proposed scheme concerning various performance metrics.

Nowadays, rumor spreading has gradually evolved into a kind of organized behaviors, accompanied with strong uncertainty and fuzziness. However, existing fuzzy detection techniques for rumors focused their attention on supervised scenarios that require expert samples with labels for training. Thus, they are not able to well handle the unsupervised scenarios where labels are unavailable. To bridge such gap, this article proposed a fuzzy detection system for rumors through explainable adaptive learning. Specifically, its core is a graph embedding-based generative adversarial network (Graph-GAN) model. First of all, it constructs fine-grained feature spaces via graph-level encoding. Furthermore, it introduces continuous adversarial training between a generator and a discriminator for unsupervised decoding. The two-stage scheme not only solves the fuzzy rumor detection under unsupervised scenarios, but also improves robustness of the unsupervised training. Empirically, a set of experiments are carried out based on three real-world datasets. Compared with seven benchmark methods in terms of four metrics, the results of the Graph-GAN reveal a proper performance, which averagely exceeds baselines by 5–10%.

Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy support vector machine with within-class density information (FSVM-WD). It utilizes a novel feature extraction technique by optimizing the block size to extract image micropatterns and computing center of mass (CoM) for each pixel to extract feature vectors. The performance of the proposed framework is evaluated using a CMTHis dataset comprising histopathological images of canine mammary tumor (CMT), a prevalent neoplastic disease in female dogs, and an established model for human breast cancer. Data analysis reveals that stain normalization and magnification influence the performance of the CoMHisP framework, with the best results achieved at lower magnifications after stain normalization. The proposed framework achieves a classification accuracy of 97.25% ($\pm$1.80%) using a FSVM-WD classifier, outperforming both traditional ML and deep FE-VGGNET16-based feature descriptors. To the best of our knowledge, this is the first time a CoM-based feature descriptor has been proposed for histopathological image analysis of CMTs and its performance was evaluated using a fuzzy SVM-based classifier. The proposed method performs well with datasets of limited size and low-magnification images and, therefore, has the potential to provide rapid and accurate diagnosis in low-cost clinical settings.

Recently, Zhanget al.proposed a novel public data integrity verification scheme for the cloud storage using indistinguishability obfuscation ($iO$), and extend it to support batch verification and data dynamic operations (IEEE Transactions on Information Forensics and Security, vol. 12, no. 3, pp. 676–688, Mar. 2017). However, we find that the scheme has two flaws: (a) the self-checking of the uploaded blocks and tags inStorephase is not reliable, i.e., it is easy to generate invalid block-tag pairs without being detected; (b) the extended scheme for data dynamic operations suffers from a chosen message attack, i.e., if some uploaded blocks match a certain pattern, the cloud storage is able to replace any existing block by a forged one without being detected, which violates the scheme’s security model. Then, we provide solutions to these problems while preserving all the desirable features of the original scheme.

To improve the quality of service (QoS) needed by several applications areas, the Internet of Things (IoT) tasks are offloaded into the fog computing instead of the cloud. However, the availability of ongoing energy heads for fog computing servers is one of the constraints for IoT applications because transmitting the huge quantity of the data generated using IoT devices will produce network bandwidth overhead and slow down the responsive time of the statements analyzed. In this article, an energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the QoSs required by users. In addition to the standard MPA, we proposed the other two versions. The first version is called modified MPA (MMPA), which will modify MPA to improve their exploitation capability by using the last updated positions instead of the last best one. The second one will improve MMPA by the ranking strategy based reinitialization and mutation toward the best, in addition to reinitializing, the half population randomly after a predefined number of iterations to get rid of local optima and mutated the last half toward the best-so-far solution. Accordingly, MPA is proposed to solve the continuous one, whereas the TSFC is considered a discrete one, so the normalization and scaling phase will be used to convert the standard MPA into a discrete one. The three versions are proposed with some other metaheuristic algorithms and genetic algorithms based on various performance metrics such as energy consumption, makespan, flow time, and carbon dioxide emission rate. The improved MMPA could outperform all the other algorithms and the other two versions.

Vehicle-to-grid (V2G) networks have been emerged as a new technology in the smart grid (SG). These networks allow a two-way flow of energy-trading between electric vehicles (EVs) and charging stations (CSs) in the SG. EVs are regarded as one of the most effective tools to reduce energy demands. It will bring a great impact on our society and human life. Thus, during energy trading between EVs and CSs, various security, and privacy challenges occur in V2G networks. Although several proposals have been proposed, still there are many issues like lack of integrity, mutual authentication, and identity privacy-preservation make the system more vulnerable. Researchers have used the centralized system in V2G networks which may act as a single point of failure. So, for deploying secure V2G networks in the SG, we propose an energy-trading scheme having blockchain between three communicating parties, i.e., EVs, CSs, utility center. The proposed system is divided into three phases, first, the registration process provides identity privacy-preservation to the EVs and CSs, second, the searching process makes the registration and key-generation steps faster, and third, the authentication process provides mutual authentication between them and a blockchain network is used to execute transactions using Merkle Root Hash. The security analysis result shows that the proposed scheme is secure for energy-trading in V2G networks. The performance evaluation results illustrate that our scheme has less communication cost and computation time as compared to the existing proposals.

Internet of Things has revolutionized the ways and means of use and management of electric grid systems. Now, the old mechanical grids are equipped with smart devices that not only automate the traditional grid but enable two way communications between the user and power suppliers called smart grid. Although, a lot of protocols have been developed to enable a secure communication between suppliers and consumers, cyber-physical systems (CPSs) are prone to privacy issues where adversaries may have access to particular users' information. In this article, an anonymous orthogonal code-based privacy preserving scheme, named ALPHA, is proposed for CPSs. The CPS is considered as a basic unit of the modern smart grid, which aggregates the power consumption from smart devices by keeping the user information confidential, anonymous, and untraceable. The proposed scheme, ALPHA, uses orthogonal bit codes and a systematized method to authenticate and manage the anonymity and untraceablity of user data along with low communication and computation overheads.

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

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

Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we proposeEDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.

6G-enabled network in a box (NIB) is a multigenerational, rapidly deployable hardware, and software technology for the communication. 6G-enabled NIB provides high level of flexibility which makes it capable to provide connectivity services for different types of applications as it is effective for the communications of after disaster scenario, battlefields scenario, and industrial scenario. In 6G-enabled NIB deployed industrial applications, various passive and active attacks are possible because the involved entities communicate over insecure channel. In this article, a new remote user authentication and key management scheme is proposed for securing 6G-enabled NIB deployed for industrial applications, which we call in short as UAKMS-NIB. The security analysis shows the resilience of UAKMS-NIB against various types of possible attacks. The practical demonstration of UAKMS-NIB is also provided to measure its impact on the network performance parameters. Finally, a comparative analysis with other closely related existing schemes shows that UAKMS-NIB performs better than the existing schemes.

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.

Internet of Medical Things (IoMT) platform serves as an interoperable medium for healthcare applications by connecting wearable sensors, end-users, and clinical diagnosis centers. This interoperable medium provides solutions for disease diagnosis; predicting and monitoring end-user health using physiological vital signs sensed wearable sensor data. The communicating and data exchanging internet of things (IoT) platform imposes latency and overloading uncertainties in the heterogeneous environment. This article introduces cognitive data processing for uncertainty analysis (CDP-UA) to improve WS data management's efficiency. CDP-UA addresses uncertainties in two levels namely aggregation and dissemination of WS data. The uncertainties in synchronizing aggregation and dissemination slot mapping are addressed using classification learning. In the dissemination process overloaded intervals are identified and segregated using regression learning and conditional sigmoid function analysis. The joint learning process helps to classify overloaded and latency-centric dissemination and aggregation instances to improve WS data delivery in the clinical/medical analysis center. The experimental analysis shows that the proposed method is reliable in achieving less uncertainty factor, latency, and overloaded intervals for varying disseminations and sensing intervals.

The rapid and convenient travel of people and the timely transportation of goods depend on the correct decision of the Intelligent Transportation Systems (ITS). Due to the decision-making of ITS requires a large amount of data to support, UAV-enabled periodic data collection is an effective method. However, due to the limited resources of UAV, UAV cannot directly collect data from all storage devices, resulting in unfair data collection. Therefore, we propose a UAV Speed Control based Fairness Data Collection (USCFDC) scheme. First, since the fairness of data collection will affect the decision-making of ITS, a framework for controlling the flight speed of the UAV is proposed to improve the fairness of data collection. The flight speed of UAV will slow down in areas with a large number of nodes, thereby improving the fairness of data collection. Second, a novel method is proposed to maximize the amount of data collected by UAV from each node. With this method, the value of the amount of data will be used as the dichotomous value in the dichotomy algorithm, and the UAV must collect a certain amount of data from each node. The upper and lower limits of the dichotomy algorithm are adjusted according to the time duration for UAV to collect data. Compared with previous schemes, the fairness of data collection can be improved by a maximum of 15.89% under the same flight time of UAV. Besides, the energy consumption is reduced by 49.31%-52.55% and the flight time of the UAV is reduced by 48%-62.38% when the amount of collected data is the same.

In recent years, the huge expansion of Datacenters (DC) to execute billions of end-user applications in real-time leads to a large amount of energy consumption across the globe. So, the traditional TCP/IP-based networks which are being used for DC inter-connections are facing challenges of managing stringent Quality-of-Service (QoS) requirements of different applications of the end-users and service providers. Moreover, the existing solutions rely on distributed architecture and do not scale for large scale data centers. The issue of high power consumption at DC arises with the increase in the number of nodes and links in the network. Also, it becomes problematic on the DC whenever the underlying network resources (switches and routers) are not efficiently utilized at the time of peak data traffic resulting in high operational cost of energy utilization. However, Software-Defined Networking (SDN) emerges as one of the leading technologies to address the aforementioned issues using the programmable switches and controllers. Inspired from these facts, in this paper, we have formulated the Energy-Aware Routing (EAR) problem of DCs as a Mixed Integer Non-Linear Programming (MINLP) for which an Energy-Efficient Fast Flow Forwarding (EnFlow) scheme is designed. The EnFlow scheme uses the power-saving mode of the network to solve the EAR problem. It has three modules namely- priority scheduling, routing, and re-routing. The first module works according to the First-in-First-Out Push Out Priority (FIFO-POP) scheduling using the multiple OpenFlow switches. The FIFO-POP is designed to save the energy usage of multiple switches by reducing the average waiting time of incoming packets in the queue buffers. The second module is based upon an efficient flow re-routing for a new node and link adaptation to provide the maximum bandwidth to the wired links. The third module is based upon the meta-heuristic Ant Colony Routing (ACR) to execute the stochastic decision policy on the network controller for computation of the shortest path of the forwarding nodes. The proposed EnFlow scheme is simulated using the data traces of 34 cities of NorthAmerica zone with Omnet++ 5.1 using various performance evaluation metrics. The results obtained demonstrated that the proposed EnFlow scheme is 24.55% and 71.15% more energy-efficient in comparison to the RE-FPR and ILP-EAR schemes. Also, it consumes 9.72% and 40.83% lower energy in comparison to the FFHA and EXR schemes respectively.

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

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.

The autonomous vehicular technology is approaching a level of maturity that gives confidence to end-users in many cities around the world for their usage so as to share the roads with manual vehicles. Autonomous and manual vehicles have different capabilities which may result in surprising safety, security, and resilience impacts when mixed together as a part of the intelligent transportation system (ITS). For example, autonomous vehicles can communicate electronically with one another, make fast decisions and associated actuation, and generally act deterministically. In contrast, manual vehicles cannot communicate electronically, are limited by the capabilities and slow reaction of human drivers, and may show some uncertainty and even irrationality in behavior due to the involvement of humans. At the same time, humans can react properly to more complex situations than autonomous vehicles. Unlike manual vehicles, the security of computing and communications of autonomous vehicles can be compromised thereby precluding them from achieving individual or group goals.

Social Internet of Vehicles (SIoV) is an evolving vehicular networking framework integrating the next generation smart devices with vehicular communications. Green computing and communication under disruptive vehicular environment is one of the challenging tasks for enabling SIoV. In this context, green traffic data dissemination in SIoV environments is modelled as an NP-hard problem focusing on heterogeneous traffic data, transmission distance from next generation smart devices and probabilistic delay in transmissions due to disruptive vehicular environment. An adopted meta-heuristic solution namely Two-Way Particle Swarm Optimization (TWPSO) is developed for the green traffic data dissemination problem in SIoV considering software defined vehicular network architecture. Extensive simulation experiments were performed to assess the performance of TWPSO as compared to the state-of-the-art techniques. The critical analysis of the comparative results attest the green computing oriented benefits of TWPSO under real SIoV environments.

While Vehicular Ad-hoc Networks (VANETs) can potentially improve driver safety and traffic mangement efficiency (e.g. through timely sharing of traffic status among vehicles), security and privacy are two ongoing issues that need to be addressed. Hence, security solutions such as conditional privacy-preserving authentication (CPPA) protocols have been proposed. However, CPPA protocols are generally far from being ready for deployment in VANETs, for example due key/certificate management limitations in PKI-based protocols or intractable private key updating in ID-based protocols. Although serveral blockchain-based CPPA (BCPPA) protocols have been proposed to mitigiate these challenges, there still exist some intractabilities such as revoking private key, or frequent interactions, or requiring an idea hardware. Thus, in this paper, we are motivated to propose a novel BCPPA protocol without these existing issues. Specifically, we present a PKI-based solution (using a typical digital signature protocol, such as ECDSA) based on Ethereum (a public blockchain), which is designed to facilitate secure communication in VANETs. In other words, we combine the blockchain technology and a key derivation algorithm to realize an effective certificate management. This reduces the need for participating vehicles to store a large number of private keys. To reduce the verification time cost, our BCPPA suppotrs replacing ECDSA with modified ECDSA for batch verification or directly adopting other PKI-based signatures with batch verification. In addition to introducing the concrete design, we also present the security requirements that our BCPPA protocol can satisfy. We then implement BCPPA in the Ethereum test network (i.e.Rinkeby) and provide simulations using VanetMobiSim and NS-2 to show its feasibility (i.e. milliseconds).

The Internet of connected Vehicles (IOV) can collect, process, compute and release the information of intelligent transportation systems. IOV is an integrated service system that can support the applications for automatic driving, intelligent transport and information services. As the number of incidents on IOV has been on the rise in the past few years, IOV security is becoming increasingly important in the IOV architecture. One of the most notable risks of IOV faces is intelligent terminal security. The vehicle's intelligent terminal can be used to launch for further attacks on the on-board operating system to penetrate into the internal network of connected vehicle, and consequently threaten the safety of the vehicle. Thus, it is of paramount importance that we protect the security of the intelligent terminal. We propose two intelligent terminal based privacy-preserving multi-modal implicit authentication protocols to protect the security of the intelligent terminal in IOV. The proposed protocols use the password and the vehicle owner's behavior features as the authentication factors to protect the security of the intelligent terminal. Since the vehicle owner's behavior features are sensitive and the privacy information of the user must be protected, we also consider the privacy protection of the behavior features. Our protocols do not reveal any information about the vehicle owner's behavior features to the authentication server and the adversary except the ciphertext size of the feature vector. We analyze the security of our proposed protocol and compare them with other related protocols in terms of computation and communications costs. Our results demonstrate that our proposed protocols yield better security and efficiency.

Despite the myriad of attributes found in a single style image, existing neural style transfer methods produce outputs with limited variety–typically only a single realization of the style image. They also do not provide an easy way to control the stylization process, limiting the creative freedom of users. In this paper, we propose Neural Style Palette (NSP), a method for interactively generating a variety of stylized images from only a single style input. Our approach allows human influence in the stylization process, a design inspired by Hybrid Human-Artificial Intelligence. Like a color palette,NSPenables a meaningful interaction by presenting a collection of sub-textures, which we also refer to as anchor styles, that act as a visual guide for the users. These anchor styles capture different attributes in the single style image that the users can creatively blend to create their desired realizations. To offer a diversified selection in theNSP, we constrain the anchor styles to be distant from one another while maintaining faithfulness to the original style image. This is possible through our two proposed novel losses: a style-separation loss that encourages the sub-textures to be distinct and a unification loss to ensure that the sub-textures center around the original style while encouraging additional diversity. We perform several experiments to prove the effectiveness of our method and generalize to improve existing methods.

Artificial Intelligence (AI) has the potential to leverage the immense amount of operational data of clouds, services, and social and communication networks. As a concrete example, AI techniques have been adopted by telcom operators to develop virtual assistants based on advances in natural language processing (NLP) for interaction with customers and machine learning (ML) to enhance the customer experience by improving customer flow. Machine learning has also been applied to finding fraud patterns which enables operators to focus on dealing with the activity as opposed to the previous focus on detecting fraud.

The advancements in the internet of things (IoT) require specialized security protocols to provide unbreakable security along with computation and communication efficiencies. Moreover, user privacy and anonymity has emerged as an integral part, along with other security requirements. Unfortunately, many recent authentication schemes to secure IoT-based systems were either proved as vulnerable to different attacks or prey of inefficiencies. Some of these schemes suffer from a faulty design that happened mainly owing to undue emphasis on privacy and anonymity alongside performance efficiency. This article aims to show the design faults by analyzing a very recent hash functions-based authentication scheme for cloud-based IoT systems with misunderstood privacy cum efficiency tradeoff owing to an unadorned design flaw, which is also present in many other such schemes. Precisely, it is proved in this article that the scheme of Wazid et al. cannot provide mutual authentication and key agreement between a user and a sensor node when there exists more than one registered user. We then proposed an improved scheme and proved its security through formal and informal methods. The proposed scheme completes the authentication cycle with a minor increase in computation cost but provides all security goals along with privacy.

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

With the evolvement of the Internet of things (IoT), privacy and security have become the primary indicators for users to deploy IoT applications. In the gateway-based IoT architecture, gateways aggregate data collected by perception-layer devices and upload message packets to platforms, while platforms automatically push different categories of data to different applications. However, security in processes of data transmission via gateways, storage in platforms, access by applications is the major challenge for user privacy protection. To tackle this challenge, this article presents a secure IoT scheme based on a fine-grained multi-receive signcryption scheme to realize end-to-end secure transmission and data access control. To enhance the security of online application decryption keys, we design a distributed threshold decryption scheme based on secret-sharing. Moreover, from the provable security perspective, we demonstrate that the scheme can achieve the expected IND-CCA security and EUF-CMA security. After the performance analysis, evaluation results show that the computational performance is efficient and linearly subject to the number of messages and the number of receivers.

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle this challenge, this article proposes an efficient service recommendation and evaluation scheme, called EPRT , which is based on a similarity calculation and trust discovery method. This scheme uses homomorphic encryption technology to encrypt the sensitive data and combines the threshold mechanism and double-trap mechanism to realize the secure computing on the encrypted data, so as to ensure that the plaintexts of the final calculation results (e.g., recommendation value and evaluation truth) are only obtained by the authorized subject. In addition, a detailed security analysis shows that the proposed EPRT scheme can achieve the expected security. In addition, performance comparison results are carried out, demonstrating its effectiveness and accuracy.

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices, wearables, sensors, and micro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions.

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make full use of these massive data to provide better services for smart tourism. However, due to the skewed and imbalanced visiting for point of interest located at different places, it is of great significance to predict the tourist flow of each place, which can help the service providers for designing a better schedule visiting strategy in advance. Against this background, this article proposes a multi-graph convolutional network framework, named AMOUNT, for tourist flow prediction. To capture the diverse relationships among POIs, AMOUNT first constructs three subgraphs, including the geographical graph, interaction graph, and the co-relation graph. Then, a multi-graph convolution network is utilized to predict the future tourist flow. Experimental results on two real-world datasets indicate that the proposed AMOUNT model outperforms all other baseline tourist flow prediction approaches.

Medical systems incorporate modern computational intelligence in healthcare. Machine learning techniques are applied to predict the onset and reoccurrence of the disease, identify biomarkers for survivability analysis depending upon certain health conditions of the patient. Early prediction of diseases like diabetes is essential as the number of diabetic patients of all age groups is increasing rapidly. To identify underlying reasons for the onset of diabetes in its early stage has become a challenging task for medical practitioners. Continuously increasing diabetic patient data has necessitated for the applications of efficient machine learning algorithms, which learns from the trends of the underlying data and recognizes the critical conditions in patients. In this article, an ensemble-based framework named e DiaPredict is proposed. It uses ensemble modeling, which includes an ensemble of different machine learning algorithms comprising XGBoost, Random Forest, Support Vector Machine, Neural Network, and Decision tree to predict diabetes status among patients. The performance of eDiaPredict has been evaluated using various performance parameters like accuracy, sensitivity, specificity, Gini Index, precision, area under curve, area under convex hull, minimum error rate, and minimum weighted coefficient. The effectiveness of the proposed approach is shown by its application on the PIMA Indian diabetes dataset wherein an accuracy of 95% is achieved.

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™.

Load balancing is an important part of a distributed computing environment which ensures that all devices or processors perform the same amount of work in an equal amount of time. Most load balancing algorithms assume similar processing power and workload for all the processors. However, now systems have become more complex and can have processors of different capabilities, workload, and configurations. In this paper, we propose an alternative algorithm for scheduling tasks. We configure the clusters dedicated to a particular type of task(real-time, dataintensive, etc.). Labels have been defined for each job to classify them into these categories. Then we modify the algorithm to introduce load balancing techniques using task migration.

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

Due to the low data-rate, high propagation delay, floating node mobility, and high error probability, underwater multimedia communication is still challenging. In this paper, we propose an acoustic-based reliable streaming network for resource-constrained underwater communication. The proposed protocol uses a Null Data Packet (NDP)-based contention and acknowledgment mechanism to reduce control overhead and improve reliability and energy efficiency. With the use of a lightweight Traffic Indication Map (TIM) and video compression technique, our system's efficiency for multimedia transmission is further improved. The proposed schemes provide multimedia communication without compromising on the quality of the data transmitted. We experimentally demonstrate the proposed scheme in a hydrodynamic water-tank facility on our campus. The testbed we have built in this facility is capable of running real-time video streaming. The system's performance, which is evaluated using parameters such as coverage, range, latency, and energy consumption, was found to prove the proposed solution's validity.

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.

With the rapid development of the Industrial Internet of Things (IIoT), various IoT devices and sensors generate massive industrial sensing data. Sensing big data can be analyzed for insights that lead to better decisions and strategic industrial production by using advanced machine learning technologies. However, vulnerable IoT devices are easy to be compromised thus causing IoT devices failures (i.e., anomalies). The anomalies seriously affect the production of industrial products, thereby, it is increasingly important to accurately and timely detect anomalies. To this end, we first introduce a Federated Learning (FL) framework to enable decentralized edge devices to collaboratively train a Deep Anomaly Detection (DAD) model, which can improve its generalization ability. Second, we propose a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model to accurately detect anomalies. The CNN-LSTM model uses CNN units to capture fine-grained features and retains the advantages of LSTM unit in predicting time series data. Third, to achieve real-time and lightweight anomaly detection in the proposed framework, a gradient compression mechanism is applied to reduce communication costs and improve communication efficiency. Extensive experiment results based on realworld datasets demonstrate that the proposed framework and mechanism can accurately and timely detect anomalies, and also reduce about 50% communication overhead when compared with traditional schemes.

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.

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.

Content Centric Networks (CCN) has been evolved as a promising internet architecture that focuses on content centric approach for content requests rather than host centric approach. CCN provide in-network caching and content distribution capability improves Quality-of-Service by reducing intermediatory hop count and server load, which condequently reduces bandwidth requirements. Existing work in CCN emphasis on minimizing content caching operations and maximizing network hit ratio. In this paper, we have investigated the effect of in-network caching based on content provider distance and node centrality parameters over network hit ratio. A novel content placement approach named CPNDD (Content Placement based on Normalized Node Degree and Distance), has been proposed that collectively implement both parameters to intelligently select caching location in the network to maximize gain in hit ratio. The weightage of both parameters has been computed using extensive simulation on abilene network topology. We have compared our scheme with several peer caching algorithms in CCN. Simulation results are obtained for different cache size, exponent value of zipf distribution and number of requests. The results demonstrate that CPNDD increases in-network hit ratio gain upto 40% as comparison to existing algorithms.

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.

In the 21st century, the power quality and availability with customer demands to the society is the main challenging factor right now. Therefore, the gird monitoring system become a vital issue to monitor power grid system. The current smart grid system mainly focuses on smart metering system and improving the customer utility communication system. On customer management side, although those advancement provides an extra benefit, in spite of, the management of a grid system is one of the major dominating field in the era of Internet of Thing (IoT). From the field of industry and academia researches, Wireless sensor networks (WSNs) is getting to much popularity for monitoring power grid. Moreover, saving node energy enhance the lifespan of whole monitoring network. Due to lack of energy making policy, unnecessary activating all participate nodes consume node energy drastically, which is the main reason for shortening the lifetime of monitoring system. To solve this issue, maintenance technology provides the best opportunity to preserve node energy. This study investigates the issues that are associated with energy consumption using maintenance protocols in prospect of Rampal, Bangladesh power plant data. The modelling data has been collected through literature survey. Extensive simulation work has done for monitoring Rampal power using WSN. Finally, a comparative study of maintenance protocols were performed to maintain optimal network correction and thereafter extending the lifetime of monitoring network.

The penetration of electric vehicles (EVs) embedded with information and communication technology (ICT) devices and tools form a huge connected network that can be viewed as Internet-of-EVs(IoEV). The huge data gathered in IoEV network needs to be processed at cloud-based infrastructure which has abundant resources. However, due to the high mobility of the EVs, resource management from the remote cloud service providers has become one of the most difficult tasks to be performed in this environment. In this regard, data analytics fused with fog or edge computing can be leveraged to increase the resource availability in V2G environment where resources are provided to the EVs on the edge of the network. Keeping these points in mind, this paper presents a new framework for integration of cloud computing and IoEV on the edge of the network which provides flexibility to the end users for smooth execution of various applications. In addition, a resource allocation and job scheduling strategy for EVs at the edge of the network is presented in the paper. The results obtained with respect to various performance metrics confirm the applicability of the proposed scheme for future applications in V2G scenario.

Industry 4.0 revolution has emerged as an escalator for increased productivity and cost savings in smart factories. However, they pose a big challenge for network architecture in providing a flexible way for continuous data flow. The necessity of reliable connectivity in the industrial ecosystem has paved the path for the evolution of software-defined industrial networks. However, the nature of data traffic in the industrial environment further pushes the need for adaptive flow forwarding and control. Therefore, in this article, AdaptFlow, an adaptive flow forwarding scheme, is designed for software-defined industrial networks. AdaptFlow includes: 1) application-specific traffic classification using self-organized maps; 2) B+ tree-based flow table management; 3) queuing model for analyzing the waiting time; and 4) energy-aware flow forwarding algorithm. The evaluation of AdaptFlow scheme shows improvements in minimizing delay and energy consumption while increasing network capacity in contrast to the OpenFlow architecture.

Increasingly, governments around the world, particularly in technologically advanced countries, are exploring or implementing smart homes, or the related smart facilities for the benefits of the society. The capability to remotely access and control Internet of Things (IoT) devices (e.g., capturing of images, audios, and other information) is convenient but risky, as vulnerable devices can be exploited to conduct surveillance or perform other nefarious activities on the users and organizations. This highlights the necessity of designing a secure and efficient remote user authentication solution. Most of the existing solutions for this problem are generally based on a single-server architecture, which has limitations in terms of privacy and anonymity (leading to users' daily activities being predicted), and integrity and confidentiality (resulting in an unreliable behavior auditing). While blockchain-based solutions may mitigate these issues, they still face some critical challenges (e.g., providing regulation of behaviors and privacy protection of access policy). Motivated by these facts, in this article, we construct a novel secure mutual authentication system, which can be applied in smart homes and other applications. Specifically, the proposed approach integrates blockchain, group signature, and message authentication code to provide reliable auditing of users' access history, anonymously authenticate group members, and efficiently authenticate home gateway, respectively. We also prove the security and privacy requirements, including anonymity, traceability, and confidentiality, that the proposed system satisfies, with an implementation and evaluation to demonstrate its practicality.

The notion of aggregation of data in Industrial Internet of Things (IIoT) environment is a common practice. It shortens the data and associated signatures to reduce the bandwidth requirement. The compact aggregate signature (CAS) scheme creates a constant length aggregate signature (AS). Thus, the length of the CAS is independent of the number of messages or signatures to be aggregated. This article presents the first pairing-free CAS scheme in certificate-based settings. Due to the certificate-based approach, the proposed scheme is free from key escrow and key distribution problems inherited in identity-based cryptography (IDC) and certificate-less cryptography (CLC), respectively. Being compact and pairing free, it is the least bandwidth-consuming and the most efficient provably secure aggregation method. The length and computational cost analysis show that the scheme is the most appealing to use in the IIoT environment.

Certificateless public key cryptosystem solves both the complex certificate management problem in the public key cryptosystem based on the PKI and the key escrow issue in the public key cryptosystem based on identity. The aggregator can compress n different signatures with respect to n messages from n signers into an aggregate signature, which can help communication equipments to save a lot of bandwidth and computing resources. Therefore, the certificateless aggregate signature (CLAS) scheme is particularly well suited to address secure routing authentication issues in resource-constrained vehicular ad hoc networks. Unfortunately, most of the existing CLAS schemes have problems with security vulnerabilities or high computation and communication overheads. To avoid the above issues and better solve the secure routing authentication problem in vehicular ad hoc networks, we present a new CLAS scheme and give the formal security proof of our scheme under the CDH assumption in the random oracle model. We then evaluate the performance of our proposed CLAS scheme, and the results demonstrate that our proposal is more practical in resource-constrained vehicular ad hoc networks.

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.

With an exponential increase in the popularity of Internet, the real-time data collected by various smart sensing devices can be analyzed remotely by a remote user (e.g., a manager) in the Industrial Internet of Things (IIoT). However, in the IIoT environment, the gathered real-time data is transmitted over the public channel, which raises the issues of security and privacy in this environment. Therefore, to protect illegal access by an adversary, user authentication mechanism is one of the promising security solutions in the IIoT environment. To achieve this goal, we propose a new user authenticated key agreement scheme in which only authorized users can access the services from the designated IoT sensing devices installed in the IIoT environment. In the proposed scheme, fuzzy extractor technique is used for biometric verification. Moreover, three factors, namely smart card, password and personal biometrics of a legal registered user are applied in the proposed scheme to increase the level of security in the system. The proposed scheme supports new devices addition after initial deployment of the devices, password/biometric change phase and also smart card revocation phase in case the smart card is lost or stolen by an adversary. In addition, the proposed scheme is lightweight in nature. We carry out the formal security analysis using the broadly accepted Real-Or-Random (ROR) model and also the non-mathematical (informal) security analysis on the proposed scheme. Furthermore, the formal security verification using the popularly-used AVISPA (Automated Validation of Internet Security Protocols and Applications) tool is carried out on the proposed scheme. The detailed security analysis assures that the proposed scheme can withstand several well-known attacks in the IIoT environment. A practical demonstration using the NS2 simulation study is also performed for the proposed scheme and other related existing schemes. Also, a detailed comparative study shows that the proposed scheme is efficient, and provides superior security in comparison to the other schemes.

The Information and Communication Technology (ICT) has been used in wide range of applications, such as smart living, smart health and smart transportation. Among all these applications, smart home is most popular, in which the users/residents can control the operations of the various smart sensor devices from remote sites also. However, the smart devices and users communicate over an insecure communication channel, i.e., the Internet. There may be the possibility of various types of attacks, such as smart device capture attack, user, gateway node and smart device impersonation attacks and privileged-insider attack on a smart home network. An illegal user, in this case, can gain access over data sent by the smart devices. Most of the existing schemes reported in the literature for the remote user authentication in smart home environment are not secure with respect to the above specified attacks. Thus, there is need to design a secure remote user authentication scheme for a smart home network so that only authorized users can gain access to the smart devices. To mitigate the aforementioned isses, in this paper, we propose a new secure remote user authentication scheme for a smart home environment. The proposed scheme is efficient for resource-constrained smart devices with limited resources as it uses only one-way hash functions, bitwise XOR operations and symmetric encryptions/decryptions. The security of the scheme is proved using the rigorous formal security analysis under the widely-accepted Real-Or-Random (ROR) model. Moreover, the rigorous informal security analysis and formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is also done. Finally, the practical demonstration of the proposed scheme is also performed using the widely-accepted NS-2 simulation.

The ever-growing demand for electricity in the residential sector results in creating a severe burden on electric grids. However, with the emergence of smart homes (SHs) and smart grids (SGs), this burden can be reduced to some extent. To address this issue, we propose an energy management system in this paper which manages the power requirements of SHs automatically according to the utility constraints and user priorities. The proposed system is based on a heuristic technique, which considers the user's priority and power available from the grid as well as distributed energy resources for scheduling of appliances. It works by dividing the appliance scheduling problem in an SH into subproblems for different time slots. Then, a heuristic solution is designed for each subproblem. The instantaneous load demands are handled in real time to comply with the available power from the grid/utility. The data from different SHs is gathered to test the performance of the proposed scheme in real time. Results show that the proposed scheme efficiently manages the load demand of the SH with respect to power available from the utility, battery energy storage system, and user preferences.

Surface detection of small defects plays a vital role in manufacturing and has attracted broad interest. It remains challenging primarily due to the small size of the defect relative to the large surface and the rare occurrence of defects. To address this problem, in this article we propose a novel machine vision approach for automatically identifying the tiny flaws that may appear in a single image. First, the presented defect exaggeration approach produces both the flawless image and the corresponding exaggerated version of the defect by taking the variations in the image as regularization terms. Second, a generative adversarial network (GAN) in conjunction with a convolutional neural network (CNN) is proposed to guarantee the accuracy of tiny surface defect detection by producing exaggerated defect image samples. Furthermore, the limited dataset of the training samples for defect detection is enlarged by exploiting the GAN technique with the variation exaggerated images. To evaluate the performance of our proposed method, we conduct comparison experiments between the state-of-the-art techniques with and without the proposed algorithm as well as comparison experiments between the state-of-the-art techniques and our method. The experimental results on different types of surface image samples demonstrate that the proposed method can significantly improve the performance of the state-of-the-art approaches while achieving a defect detection accuracy of 99.2%.

Workflow scheduling is one of the most difficult tasks due to the variation in the traffic flows generated from diverse cloud applications. Hence, in this article, a container-based virtualization is used to design an energy-efficient workflow scheduling in software-defined data centers. The containers provide the flexibility to the applications to access the underlying resource as per their requirements. Moreover, a runtime scheduler is responsible to handle all the scheduling decisions in the proposed workflow scheduling scheme. Even more, a doubly linked list-based access mechanism is used to provide access to the servers and virtual machines by traversing both ways. Finally, a hashing scheme is used to select an ideal location for the allocation of the containers. The proposed scheme is evaluated with respect to different performance metrics (makespan, execution time, fault tolerance, energy consumption, etc.) on the real data traces. The results obtained depict the superiority of the proposed scheme in comparison to the other existing schemes of its category.

Software-defined networking (SDN) provides an efficient way of managing traffic load by shifting complex and rigid computing tasks to the centralized controller. It reduces the burden on the switches, task of which is to perform the routing based upon the rule-action pair. However, the flow table storage capacity of switches is limited. It may have to face performance bottlenecks, which, in turn, can cause serious security breaches and performance degradation. Hence, in this article, BloomStore, which is a dynamic bloom-filter-based secure rule-space management scheme in SDN, is proposed. BloomStore handles the data traffic dynamically by managing network resources. A twofold security check is used for secure data transfer using double hashing, i.e., two independent hash functions are used to generate k hash functions. Moreover, partitioned hashing is proposed to have insertion and query in a bucket of bloom array. The result analysis demonstrates that BloomStore outperforms its competing variants with respect to various performance parameters.

Internet-of-Things (IoT)-based wireless body area networks (WBANs) play an important role in modern medical systems for patient-health monitoring. WBANs have the capability to collect real-time biological information from the patients' body using intelligent sensors and then send the collected information to the remote doctors or medical experts using the Internet. In recent years, numerous anonymous authentication schemes were proposed to provide security in WBANs. However, many of these schemes are not computationally efficient during anonymous authentication. Moreover, the previous schemes did not provide location privacy for both doctors and patients. In order to overcome these limitations, in this article, we propose an efficient and secure anonymous authentication framework with location privacy preservation for IoT-based WBANs. The comprehensive analysis section shows that the proposed scheme overcomes the security weaknesses in the existing schemes and also provides low computation cost during anonymous authentication.

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.

Smart grid (SG) is an emerging technology which provides many services to the end users and utilities, such as load management, frequency regulation, and grid stability. Although many solutions exist to provide these services in a secure manner, but these solutions are not adequate keeping in view of the heavy cryptographic primitives execution on these devices. Hence, in this article, GUARDIAN, a blockchain-based secure demand response management scheme is presented so as to take energy trading decisions securely for managing the overall load of residential, commercial, and industrial sectors. In GUARDIAN, the miner nodes, which are block verifiers, are selected using their power consumption and processing power. These nodes are responsible for authenticating the energy transactions in SG. The energy transaction is initialized by an end user which creates the block of transaction to trade the energy. The miner nodes then validate these blocks and adds these in the blockchain. The successful energy trade occurs only for the blocks which are in the blockchain. The proposed scheme is lightweight in terms of communication and computation costs. Moreover, the results obtained demonstrate the effectiveness of proposed scheme for secure demand response management in the SG.

With the popularization of wireless communication and smart devices in the medical field, mobile medicine has attracted more and more attention because it can break through the limitations of time, space, and objects and provide more efficient and quality medical services. However, the characteristics of a mobile smart medical network make it more susceptible to security threats such as data integrity damage and privacy leakage than those of traditional wired networks. In recent years, many digital signature schemes have been proposed to alleviate some of these challenges. Unfortunately, traditional digital signatures cannot meet the diversity and privacy requirements of medical data applications. In response to this problem, this paper uses the unique security attributes of sanitizable signatures to carry out research on the security and privacy protection of medical data and proposes a data security and privacy protection scheme suitable for smart mobile medical scenarios. Security analysis and performance evaluation show that our new scheme effectively guarantees data security and user privacy while greatly reducing computation and communication costs, making it especially suitable for mobile smart medical application scenarios.

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator. The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals.

Stock exchanges around the world are exploring the best possible solution that can improve trading efficiency, lower the risks and tighten secu- rity levels. The working and functioning of a stock exchange involves very hectic and cumbersome pro- cedures which are time consuming, cost inefficient and can be prone to numerous risks. Machine learning and Blockchain are most popular upcoming technologies. In this paper we present a novel secure and de- centralized intelligent stock market prediction model. We present a blockchain based solution for stock exchange model that uses machine learning accessible smart contracts. The machine learning model makes a prediction on the future of the stock market providing an intelligent solution for secure stock market.

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.

Recommender systems based on collaborative filter- ing technique generate the accurate and reliable predictions for customers by using their preferences about various products. Usage of user ratings has been a prevalent and successful practice for various e-commerce based websites, but launching a new e-commerce site will not benefit from this, as new site has no database of customers. A combined effort by existing companies and newly launched companies for recommendation generation can be beneficial for both companies and customers, if confidential data is protected. In literature, most of the existing techniques for secure prediction generation are based on data distortion and homomorphic encryption techniques, which may cause accuracy loss and high computation cost, respectively. To overcome these issues, this paper proposes a prediction generation scheme for the horizontally distributed data among different companies, which can help new entrants and existing sites, while preserving the privacy of the customer data. Analysis of the proposed scheme is done for parameters: privacy, accuracy, coverage and performance. The proposed scheme is secure, and the accuracy and coverage are improved due to collaboration of multiple parties. Moreover, the computation complexity of the proposed scheme is also low.

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.

With the rapid increase in the energy demands from different sectors across the globe, there is lot of pressure on the power grid to maintain a balance between the demand and supply. In this context, smart grid (SG) may play a vital role as it provides the bidirectional energy flow between utilities and end users. Contrary to the traditional power grid, it has advanced switching and sensing devices (for example, sensors and actuators) for load balancing and peak shaving. In SG systems, various smart devices and electrical appliances which are placed in the smart buildings regularly generate data related to energy usage, occupancy patterns, or movements of the end users. By applying an efficient data pre-processing and data analytics technique, this data can be analyzed to extract important energy patterns which can be used in demand response management, load forecasting, and peak shaving. But, one of the main challenges in SG systems is to have an integrated approach to pre-process and analyze the data with minimum error rates and higher accuracy. To tackle the aforementioned challenges, an unified scheme based upon the deep learning and recurrent neural networks (RNN) is proposed in this paper. The data collected from smart homes is pre-processed and decomposed using high-order singular value decomposition (HOSVD) and then long short-term memory (LSTM) model is applied on it. As the data collected from SG is time series-based data so LSTM based regression model gives minimum root mean square (RMSE) and mean absolute percentage error (MAPE) values as compared to the other techniques reported in the literature. A case study of 112 smart homes with hourly basis data is considered for evaluation of the proposed scheme in which energy patterns are predicted with least RMSE and MAPE. The results obtained clearly show that the proposed scheme has superior performance in comparison to the other existing schemes.

Demand side load prediction is one of the most challenging tasks in smart grid environment due to uncertainties between demand and supply. Hence, in order to overcome this issue, this paper presents a scheme based on machine learning and deep learning for energy load forecasting by considering the weather condition of the area. We propose a two-tier Ensemble model, which ensembles the results of machine learning model (Support Vector Machine) and deep learning models (Convolu- tional Neural Network One Dimensional and Long Term Short memory) with a simple neural network to predict the load and demand gap. Then, we train and test the model with the UMass Smart* Dataset - 2017 release by taking the readings of appliances and weather conditions. The experimental results demonstrate that the proposed scheme has a significant improvement over the existing load forecasting methods having short-term and long- term load prediction models with an overall accuracy of 95.6%.

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.

Data Dissemination is the distribution of data/statistics to the end users. With the adoption of Internet of Drones (IoD) environment for data dissemination, an efficient scheme is proposed which provides data integrity, identity anonymity, authentication, authorization, accountability (AAA) to the system model. We propose a system model having Ethereum based public blockchain distributed network in order to secure drone communication for the data collection and transmission. The proposed model provides secure communication between the drones and the users in a decentralized way. In this paper, blockchain technology is used for the storage of collected data from the drones and update the information into the distributed ledgers to reduce the burden of drones. It also provides integrity, authentication, and authorization to the collected data by the drones in the system model. Motivated by this consideration, the goal of this paper is threefold. First, we select a forger node from the number of drones. Second, we create blocks and validate their processes. Third, we provide secure data dissemination by applying Proof-of-Stake consensus mechanism. Afterward, we evaluate the security of the presented system model compared against the corresponding ones of some state-of-the-art in terms of communication time/cost. The results confirm that our system model is reliable and scalable for data dissemination in the IoD environment.

Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 4.0 ecosystem. In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move. However, there are various challenges for the design of patient driven healthcare recommender systems. Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods. For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem. DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health. On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes.

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.

Energy management of mobile devices is a crucial issue in fifth generation (5G) network due to their limited battery capacity. Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging technique which allows mobile devices to harvest energy from radio frequency (RF) signals. Moreover, Non-Orthogonal Multiple Access (NOMA) serves multiple users simultaneously using the same subchannel inter-user interference mitigation. By considering the aforementioned issues, in this paper, we propose a subchannel assignment scheme for SWIPT-NOMA based pico base station/femto base station with macro-cellular networks. The energy-efficient subchannel assignment is a probabilistic mixed non-convex optimization problem by considering imperfect channel state information (CSI). To address this problem, many-to-many matching theory is used in the proposal. Numerical results show that the proposed algorithm performs better in terms of numbers of PUs/FUs, average energy efficiency (EE) of the Picocells/Femtocells, in comparison to the orthogonal frequency division access scheme and conventional NOMA.

Summary The ever increasing number of Android malware has always been a concern for cybersecurity professionals. Even though plenty of anti‐malware solutions exist, we hypothesize that the performance of existing approaches can be improved by deriving relevant attributes through effective feature selection methods. In this paper, we propose a novel two‐step feature selection approach based on Rough Set and Statistical Test named as RSST to extract refined system calls, which can effectively discriminate malware from benign apps. By refined set of system call, we mean the existence of highly relevant calls that are uniformly distributed thought target classes. Moreover, an optimal attribute set is created, which is devoid of redundant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on three datasets resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel, and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Moreover, the empirical analysis of RSST derived system calls outperformed other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes, and network traces.

With the ever increasing adoption rate of Internet-enabled devices [also known as Internet of Things (IoT) devices] in applications such as smart home, smart city, smart grid, and healthcare applications, we need to ensure the security and privacy of data and communications among these IoT devices and the underlying infrastructure. For example, an adversary can easily tamper with the information transmitted over a public channel, in the sense of modification, deletion, and fabrication of data-in-transit and data-in-storage. Time-critical IoT applications such as healthcare may demand the capability to support external parties (users) to securely access IoT data and services in real-time. This necessitates the design of a secure user authentication mechanism, which should also allow the user to achieve security and functionality features such as anonymity and un-traceability. In this paper, we propose a new lightweight anonymous user authenticated session key agreement scheme in the IoT environment. The proposed scheme uses three-factor authentication, namely a user's smart card, password, and personal biometric information. The proposed scheme does not require the storing of user specific information at the gateway node. We then demonstrate the proposed scheme's security using the broadly accepted real-or-random (ROR) model, Burrows-Abadi-Needham (BAN) logic, and automated validation of Internet security protocols and applications (AVISPAs) software simulation tool, as well as presenting an informal security analysis to demonstrate its other features. In addition, through our simulations, we demonstrate that the proposed scheme outperforms existing related user authentication schemes, in terms of its security and functionality features, and computation costs.

Due to its widespread popularity and usage in many applications (smart transport, energy management, e-healthcare, smart ecosystem, and so on), the Internet of Things (IoT) has become popular among end users over the last few years. However, with an exponential increase in the usage of IoT technologies, we have been witnessing an increase in the number of cyber attacks on the IoT environment. An adversary can capture the private key shared between users and devices and can launch various attacks, such as IoT ransomware, Mirai botnet, man-in-the-middle, denial of service, chosen plaintext, and chosen ciphertext. To mitigate these security attacks on the IoT environment, the traditional public key cryptographic primitives are inadequate because of their high computational and communication costs. Therefore, lattice-based public-key cryptosystem (LB-PKC) is a promising technique for secure communication. We discuss the taxonomy of two major problems, namely, the shortest path and the closest path problems with respect to the applicability of lattice-based cryptographic primitives for IoT devices. Moreover, we also discuss various LB-PKC techniques, such as NTRU, learning with errors (LWEs), and ring-LWE (R-LWE) which are often used to solve shortest path and lattice NP-hard problems in a polynomial time. We further classify the R-LWE into three categories, namely identity-based encryption, homomorphic encryption, and secure authentication key exchange. We describe the operations and algorithms adopted in each of these encryption mechanisms. Finally, we discuss the challenges, open issues, and future directions for applying LB-PKC in the IoT environment.

The maturity of cloud computing, the Internet of Things technology, and intelligent transportation system has promoted the rapid development of vehicular ad-hoc networks (VANETs). To keep pace with real-world demands (mobility, low latency, etc.) in a practical VANETs deployment, there have been attempts to integrate fog computing with VANETs. To facilitate secure interaction in fog-based VANETs, we design a new authenticated key agreement protocol without bilinear pairing. This protocol achieves mutual authentication, generates a securely agreed session key for secret communication, and supports privacy protection. We also give a strict formal security proof and demonstrate how the proposed protocol meets the security requirements in the fog-based VANETs. We then evaluate the efficiency of the proposed protocol, and it shows the practicality of the protocol.

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.

Smart Grid (SG) has emerged as one of the most powerful technologies of the modern era for an efficient energy management by integrating information and communication technologies (ICT) in the existing infrastructure. Among various ICT, cloud computing (CC) has emerged as one of the leading service providers which uses geo-distributed data centers (DCs) to serve the requests of users in SG. In recent times, with an increase in service requests by end users for various resources, there has been an exponential increase in the number of servers deployed at various DCs. With an increase in the size, the energy consumption of DCs has increased many folds which leads to an increase in overall operational cost of DCs. However, efficient resource allocation among these geo-distributed DCs may play a vital role in reducing the energy consumption of DCs. Moreover, with an increase in harmful emissions, the use of renewable energy sources (RES) can benefit DCs, SG, and society at large. Keeping focus on these points, in this paper, an energy-aware resource allocation scheme is proposed using a Stackelberg game for energy management in cloud-based DCs. For this purpose, a cloud controller is used to receive the requests of users which then distributes these requests among geo-distributed DCs in such a way that the energy consumption of DCs is sustained by RES. However, if energy consumption of DCs is not sustained by RES then the energy is drawn from the grid. The requests of users are routed to the DC which is offered lowest energy tariff from the grid. For this purpose, a Stackelberg game for energy trading is also proposed to select the grid offering lowest energy tariff to DCs. The proposed scheme is evaluated using various performance metrics using Google workload traces. The results obtained show the effectiveness of the proposed scheme.

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

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.

Industrial wireless sensor network (IWSN) is an emerging class of a generalized WSN having constraints of energy consumption, coverage, connectivity, and security. However, security and privacy is one of the major challenges in IWSN as the nodes are connected to Internet and usually located in an unattended environment with minimum human interventions. In IWSN, there is a fundamental requirement for a user to access the real-time information directly from the designated sensor nodes. This task demands to have a user authentication protocol. To satisfy this requirement, this paper proposes a lightweight and privacy-preserving mutual user authentication protocol in which only the user with a trusted device has the right to access the IWSN. Therefore, in the proposed scheme, we considered the physical layer security of the sensor nodes. We show that the proposed scheme ensures security even if a sensor node is captured by an adversary. The proposed protocol uses the lightweight cryptographic primitives, such as one way cryptographic hash function, physically unclonable function, and bitwise exclusive operations. Security and performance analysis shows that the proposed scheme is secure, and is efficient for the resource-constrained sensing devices in IWSN.

The devices in smart grids (SG) transfer data to a utility center (UC) or to the remote control centers. Using these data, the energy balance is maintained between consumers and the grid. However, this flow of data may be tampered by the intruders, which may result in energy imbalance. Thus, a robust authentication protocol, which supports dynamic SG device validation and UC addition, both in the local and global domains, is an essential requirement. For this reason, ECCAuth: a novel elliptic curve cryptography-based authentication protocol is proposed in this paper for preserving demand response in SG. This protocol allows establishment of a secret session key between an SG device and a UC after mutual authentication. Using this key, they can securely communicate for exchanging the sensitive information. The formal security analysis, informal security analysis, and formal security verification show that ECCAuth can withstand several known attacks.

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

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 digitalization and massive adoption of advanced technologies in the automotive industry not only transform the equipment manufacturer's operating mode, but also change the current business models. The increased adoption of autonomous cars is expected to disrupt government regulations, manufacturing, insurance, and maintenance services. Moreover, providing integrated, personalized, and on-demand services have shared, connected, and autonomous cars in the smart city for a sustainable ecosystem. To address these issues in this paper, we propose a blockchain-based distributed framework for the automotive industry in the smart city. The proposed framework includes a novel miner node selection algorithm for the blockchain-based distributed network architecture. To evaluate the feasibility of the proposed framework, we simulated the proposed model on a private Ethereum blockchain platform using captured dataset of mined blocks from litecoinpool.org. The simulation results show the proof-of-concept of the proposed model that can be used for wide range of future smart applications.

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