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

The great progress of wireless communications breeds the prevalence of connected Internet of Vehicles (CIoV). Naturally, the internal connections among active vehicles also act as an indispensable factor in traffic forecasting. Although much related research work has been conducted during the past few years, they mainly designed and/or developed a single specific forecasting model for this purpose. Existing work may acquire ideal effects in some specific scenarios, yet lacking good robustness to dynamic change of situations. To deal with such a challenge, a graph neural networks-driven traffic forecasting model for CIoV is proposed in this work, deduced as Gra-TF for short. In this paper, we regard the dynamics of traffic data as the situation of temporal evolution. With the assistance of ensemble learning, three typical graph-level prediction methods are employed to construct an integrated and enhanced the forecasting model. Such a design is able to take advantage of several methods to minimize the uncertainty in CIoV. Finally, we select a real-world dataset to build the experimental situation for further assessment. Numerical results indicate that the proposed Gra-TF improves the prediction accuracy by 20% to 30% compared with several baseline methods.

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

In this article, we analyzed a Handover Mobile IPv6 (HMIPv6) algorithm under conditions of 5G millimeter wave (mmWave) single and dual connectivity. A single connectivity enables handover between 5G base stations. Meanwhile, a dual connectivity is a handover occurred from a 4G base station to a 5G base station. In sending data, TCP has a congestion control algorithm that allows TCP to handle any congestion problems on the network. In this article, we analyzed the HMIPv6 performance using TCP YeAH and TCP CUBIC with various user equipment (UE) speeds. It showed that each TCP had a fairly stable delay and throughput under the condition of the single connectivity handover with an average delay of 192.8196 ms and average throughput of 52.8812 Mb/s. Whereas, in the dual connectivity handover condition, each TCP had a fluctuating delay and throughput directly proportional to the increasing UE speed. Therefore, TCP YeAH dominated the stability in dual connectivity with an average delay of 221.4226 ms and average throughput of 44.4004 Mb/s. Overall, this article found that TCP YeAH performed better than TCP CUBIC in both the single connectivity and dual connectivity handovers.

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

The continuous urbanization with extensive dynamic situations on evolving cities, urban, and suburban areas, it is not feasible to categorize the navigation as fastest route, toll-free, and other variants. Metropolitan areas are more prone to traffic congestion, lane blocking, accidents, etc., due to the overcrowding and dynamic change of commuters’ arrival rates. In the metropolitan areas, most of the commuters’ use Google map to reach their desired destinations. It is quite often that route specified by navigation will not be reliable because sometimes due to the inability to update the sudden occurrence of incidents on the routes. Currently, Google map and GPS provide the time required to cover the distance and shortest route to reach the destination. The main issues with the existing Google map are it does not considers the impact of sudden occurrence of incidents, does not show the type of incidents that occurred, clearance time, and optimal routes. These issues are solved by designing an efficient context-aware vehicle incidents route service management for an intelligent transport system. The proposed system takes the context information of incidents, vehicles, weather conditions, roadside units, roads, and so on. This context information will be collected and shared with the nearby vehicles and roadside units using both mobile agents and dedicated short-range communication protocols. The proposed system suggests alternative routes with minimal delay and traffic clearance time and severity of incidents to the commuters. Also, it provides the incident information to the neighborhood vehicles, roadside units, nearby hospitals, ambulance, and members of the victims. The proposed system is exhaustively simulated in objective modular network testbed in C++, simulation of urban mobility, and Veins with different simulation parameters. The proposed system’s simulation results reduce the travel time (7 min) compared to the without the context information system (25 min), least collision rate (0.785%) compared to the existing system, minimizes the traffic clearance time in the incident zone, and uniform distribution of vehicle traffic on the estimated routes.

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

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

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

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

Currently, there is a huge production in the automotive industries and energy consumption resulted into spike in the cost and emissions of greenhouse gases. Hence, an efficient energy utilisation mechanism has become a prime most important factor. Energy and its conservation problem also have raised and become one of the prominent critical factors needed to address. Therefore, the development of smart grid infrastructure is one of the solutions to address the above issue.This article discusses different methods and mechanisms require to manage energy efficiently within the smart grid network using communication technologies and protocols and proposed an integration method of electric vehicles and smart grid using communication networks for charging or discharging electricity and exchanging relevant information. In addition, this paper discusses different integration strategies and multi‐agent system implementation in the smart grid network. The proposed multi‐agent system shows the efficient method for monitoring power flow and maintaining the stability of the grid. The paper also presents the optimal scheduling of charging of electric vehicles in the smart grid network. Finally, the authors investigated and presented many standard communication protocols and their comparisons with respect to different scenarios. This paper discuss different methods and concepts which would require to manage energy efficiently by using recent communication technologies, communication protocols, and sensor technologies.

The aim of this study is to present the numerical solutions of the higher order singular nonlinear differential equations using an advanced intelligent computational approach by manipulating the Morlet wavelet (MW) neural networks (NNs), global approach as genetic algorithm (GA) and quick local search approach as interior-point method (IPM), i.e., GA-IPM. MWNNs is applied to discretize the higher order singular nonlinear differential equations to express the activation function using the mean square error. The performance of the designed MWNNs using the GA-IPM is observed to solve three different variants based on the higher order singular nonlinear differential model to check the significance, efficacy and consistency of the designed MWNNs using the GA-IPM. Furthermore, statistical performances are provided to check the precision, accuracy and convergence of the present approach.

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

In a smart grid, efficient load management can help balance and reduce the burden on the national power grid and also minimize local operational electricity cost. Robust optimization is a technique that is increasingly used in home energy management systems, where it is applied in the scheduling of household loads through demand side control. In this work, interruptible loads and thermostatically controlled loads are analyzed to obtain optimal schedules in the presence of uncertainty. Firstly, the uncertain parameters are represented as different intervals, and then in order to control the degree of conservatism, these parameters are divided into various robustness levels. The conventional scheduling problem is transformed into a deterministic scheduling problem by translating the intervals and robustness levels into constraints. We then apply Harris’ hawk optimization together with integer linear programming to further optimize the load scheduling. Cost and trade-off schemes are considered to analyze the financial consequences of several robustness levels. Results show that the proposed method is adaptable to user requirements and robust to the uncertainties.

Content-centric networking (CCN) has emerged as a promising future Internet architecture that brings effective content retrieval mechanism. In CCN, the contents are accessed using their names instead of searching for the host location in the network. To improve Quality-of-Service for the users, CCN offers the in-network caching capability that places the incoming contents in the intermediate on-path nodes. During content retrieval, the in-network caching reduces network delay and bandwidth requirements as the requester access the content from the nearest node having a copy of the required content. Therefore, it is crucial to control the content caching decisions in the CCN as the efficiency of the caching scheme largely affects the performance of the network. In this context, a novel content placement scheme is proposed that considers node degree centrality and content provider distance based on the network bandwidth parameters for effective content placement decisions. A heuristic approach has been proposed that investigates the compound effect of these parameters to minimize average delay and network traffic during retrieval of the requested content. The proposed caching scheme has been implemented on the Abilene network topology and evaluated for different content access patterns and caching capacities. Extensive simulation results demonstrate the superiority of the proposed scheme on the existing peer schemes for various performance parameters such as average network hop-count, delay, and network traffic load.

In current digital era, users of various social media upload photos which usually contain tremendous amount of private information on daily basis. Though the private information contained within photos can assist enterprises to provide users with better services, it is also at the risk of being disclosed. Especially, with deep learning techniques developed for object detection tasks, users’ privacy can be extracted with no difficulty. Therefore, we propose an approach to prevent DNN detectors from detecting private objects, especially human body. An algorithm is developed by exploiting an inherent vulnerability of deep learning models known as the adversarial sample problem, and is integrated under a general framework which is also proposed in this work. We evaluate our method on the task of reducing the performance of DNN detectors on PASCAL VOC dataset. Our proposed algorithm can reduce the recall of human detection from 81.1% to 18.0%, while having few effects on pixel value. The results show that our proposed method performs remarkably well on preventing privacy from being exposed by DNN detectors, while causing very limited degradation to the visual quality of images.

In the aftermath of disasters, localization of trapped victims is imperative to ensure their safety and rescue. This article presents a novel localization and path planning approach that uses unmanned aerial vehicles (UAVs). The UAVs can extract one-hop neighbor information from the devices that may have run out of power by using directed wireless power transfer (WPT). The one-hop neighbor information corresponds to range measurements, which may or may not contain noise. For the noiseless case, we present a customized online graph traversal approach that minimizes the search energy of the UAV and the number of unlocalized nodes. The lower limits on the various performance aspects of this joint approach are presented. For a noiseless case, the results of UAV travel distance and cells searched show a decreasing trend with an increase in the number of maximum neighbors. These curves approximately approach their corresponding lower limits when the number of maximum neighbors is increased beyond 9. For the case of noisy range measurements, using the same objective function and graph traversal algorithm, the probabilistic region for search is determined that gives the least probability of flip errors. To this end, we further optimize the UAV flight path and its search energy in the probabilistic region through clustering. The proposed method is able to achieve linear scaling of the area searched with respect to the noise level. For a given noise level and increasing number of nodes, the UAV search energy with clustering can reduce the energy cost to 70%.

With the global outbreak of COVID-19, the Internet of Medical Things (IoMT), as an extension of the Internet of Things (IoT), has received increasing attention due to its ability to remotely monitor the main symptoms. In addition, based on widely collected IoMT data, the spread and even the origin of a pandemic can be traced effectively. However, spectrum resources become scarce with the increasing number of patients. Therefore, the effective allocation of communication, computing, and cache (3C) resources becomes increasingly important. In this article, we first study the unique characters of IoMT and construct a distributed 3C resource framework. Interactions of gateways in IoMT are specified and formed as an infinitely repeating game, where each gateway performs strategy selection based on probability learning at the network edge to reach a Nash equilibrium. A case study is provided to demonstrate the high efficiency of the designed 3C resource allocation strategy. Finally, we discuss the challenges and open issues of edge learning with IoMT from three aspects: real-time edge learning, 3C resource management, and privacy.

The fifth generation (5G) aims to connect massive amounts of devices with higher reliability, lower latency, and faster transmission speed, which are vital for implementing e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from the physical layer, upper layer, and cross-layer perspectives on designing 5G e-health systems. We then elaborate two use cases according to our implementations (i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment). We finally envision the future research trends and challenges of 5G e-health systems.

Vehicular crowdsensing has been widely leveraged in the intelligent transportation systems to collect real-time traffic data. However, the data security and privacy preservation issues have not been well investigated for vehicular crowdsensing. This article proposes a blockchain-enabled vehicular crowdsensing system to protect user privacy and data safety in 5G Internet of Vehicles (IoV). In order to maximize the security and minimize the latency of blockchain, a deep reinforcement learning (DRL)-enabled algorithm is proposed to select proper active miners and transactions. Then, a two-sided matching-based algorithm is put forwarded to allocate the nonorthogonal multiple access subchannels to minimize the maximum uploading delay of all users to guarantee the freshness of messages. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analysis demonstrates that our system can protect user privacy, guarantee data security as well as integrity, and defend against common attacks. With the development of information communication techniques, vehicular crowdsensing is widely leveraged in the Intelligent Transportation Systems (ITSs) to collect real-time traffic information (e.g., traffic accidents and jams). Recently, vehicles have begun to be regarded as the consumer electronics product, due to the advancements in the development of autonomous vehicles, and users in vehicular crowdsensing focus more and more on their privacy protection and data security. In order to guarantee the connectivity and communication of users participating in the vehicular crowdsensing, intelligent management of network traffic and offering services to vehicles has become a vital need, especially for the highly dynamic topology and untrusty communication environment in 5G Internet of Vehicles (IoV).1 However, the vehicular crowdsensing-based data collection and transmission in 5G IoVs face the following major challenges

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

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

In this paper, an Internet of Energy (IoE) solution for providing optimal charging and control of resources in the era of electric vehicles (EVs) with energy trading and security is considered. A blockchain based security solution is presented by considering the IoE as a distributed energy system with internet of things (IoT). However, by inheriting the security concerns of IoTs, IoE also faces the security challenges and is considered here. Hierarchical key generation is used for cryptographic keys, based on lattice constructions. Along with a consensus tailored with Practical Byzantine Fault Tolerance (PBFT), an additional smart contract is also applied for utilization of energy transfer. The presented solution provides transparency, reliability, availability and failure recovery inheriting from the blockchain aspects. To the best of our knowledge, the present study is the first attempt in this direction of IoE-EV interface solutions. The experimental results, comparative analysis and security validation affirm the superiority of the proposed solution over the existing approaches.

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

In recent times, wireless communication systems have been considered by a fixed spectrum allocation policy, where administrative agencies provide wireless spectrum to licensees on a long-term basis for high geographical areas. Cognitive radio networks (CRN) will provide a large bandwidth to mobile users. However, CRN networks impose challenges due to security issues and spectrum management issues. Hence, in this paper, blockchain based security enhancement and spectrum sensing method is developed for managing the spectrum as well as identify the malicious user in the CRN. In the CRN, spectrum sensing is a fundamental requirement which affected by the malicious user. The malicious user is attacking the general signal detection of network and disturbs the accuracy of the system performance. The occurrence of a malicious user in CRN sends false sensing data which decreases the presentation of the system. Blockchain-based security and spectrum sensing is achieved in the CRN network which empowers the system performance. The blockchain-based method is utilized to identify the malicious user in the CRN by an Adaptive threshold spectrum energy detection algorithm. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, sensing performance gain, total error probability, missed detection probability, number of selected sensing nodes, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm.

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

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

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

Software-defined networking (SDN) has predominantly become the defacto network standard for next generation networking due to its flexibility features; through a single-phase full deployment is unlikely owing to obvious tedious substitution and economic reasons. This has paved the path for hybrid SDN (h-SDN) as a practical alternative with a centralized SDN controller that connects a slow of OpenFlow switches (OFSs) and Legacy switches (LSs). However, such h-SDN imposes fresh challenges since the existing SDN solutions for key functions like topology discovery are rendered futile since LSs do not respond to the controller in the same way OFSs do. State-of-the-art SDN topology protocols, hence, leave out LSs undetected resulting in suboptimal performance. This article presents a novel indirect controller to legacy forwarding (ICLF) scheme that sets out by the controller sending a solitary PACKET_OUT ( P $_{\text{out}}$ ) to an OpenFlow device, which then percolates through the slow of OFSs and LSs; by exploiting the way OFSs and LSs react to the forwarded P $_{\text{out}}$ , the controller can discover the network topology, except for terminal LSs configurations if any. Experiments performed on Mininet shows that the proposed scheme shows an improvement amounted between 34.6%–97.9% in the number of ports detected and reduction in number of messages of about 25%–58.9% under varying degree of legacy device densities, albeit at a slightly overhead of messages forwarded.

Internet of Things (IoT) is an expanded application of Internet, which are used to provide various services for users. Up to now, IoTs have received more attention, because it can provide ubiquitous connectivity for various devices. With the development of IoTs, its security has become a main issue. Attackers use various techniques to implement cyber attacks for the IoT, which threats the users’ privacy seriously. As a security mechanism, intrusion detection techniques can detect various illicit behaviors before attackers invade the network. An intrusion detection system can implement effective defense functions to keep the network away from attacks. This paper proposes an intrusion detection algorithm based on deep reinforcement learning, which pursued the trends of traffic flows by extracting statistical features of prior network traffic for traffic prediction at first. Then, we use traffic predictors to employ intrusion detection. The evaluations verify the effectiveness of our algorithm in detecting Distributed Denial of Service attacks (DDoS).

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

Computational Intelligence (CI) is a more efficient paradigm for solving real-world problems in uncertain conditions. The traditional CI approaches are not capable to provide the complete and suffi...

The outbreak of coronavirus COVID-19 not only brings great disaster to the people of the world, but also brings heavy burden to the medical and health network system. Massive network data traffic and resource optimization requests make traditional network architectures unable to calmly deal with the impact of COVID-19. Artificial intelligence (AI) can effectively raise the upper limit of the medical and health network, as evidenced by the ever-increasing restorative clinical data. In addition, the development of next-generation network (NGN) technologies based on machine learning (ML) has created unlimited possibilities for the emergence of emerging medical methods. In order to reflect the effective results of the current application of AI technologies in the fight against the COVID-19 epidemic and provide a reliable guarantee for subsequent diagnosis and treatment of COVID-19 epidemics, a series of AI technologies which can be used in the diagnosis and treatment of COVID-19 are systematically summarized and analyzed. Based on various AI technologies and methods, we try to propose an AI-based medical network architecture. The architecture uses AI technologies to quickly and effectively realize the monitoring, diagnosis and treatment of patients. Finally, we rationally analyzed the technical challenges and practical problems that may be faced in implementing the architecture. The purpose of this article is to inspire scholars and medical researchers to carry out the latest research in response to the COVID-19 epidemic and make breakthrough medical technology progress.

The massive growth of the Internet of Things (IoT) applications and the challenges of Cloud computing have increased the importance of Fog networks for timely processing the requests from delay-sensitive applications. A Fog network provides local aggregation, analysis, and processing of IoT requests that may or may not be time-critical. One of the major issues of Fog is its capacity planning considering the traffic load of time-critical requests. The response time can be huge if a time-critical request is processed on Cloud. The response time of a time-critical request can be big on the Fog layer if it is not prioritized. Hence, there is a need to handle the time-critical traffic on a priority basis at the Fog layer. In this paper, a priority queuing model with preemption has been proposed considering the mixed types of requests at the Fog layer. The proposed approach determines the required number of Fog nodes in order to satisfy the desired Quality of Service (QoS) requirements of IoT requests. The proposed mechanism is evaluated through simulations using the iFogSim simulator. The work can be used in the capacity planning of Fog networks.

In this paper, we propose a region-based pricing scheme, named as RegPrice, for provisioning safety-related decisions dynamically to the end-users. Typically, heterogeneous type of sensor nodes are present in the device layer of Safe-aaS. Considering the case of safety in road transportation, we compute the fixed and variable costs incurred in procurement, deployment, and maintenance for each of these different types of sensor nodes. We introduce the concept of tariff cost, which varies with the type of road in different regions and presence of similar homogeneous sensor nodes deployed in that region. Finally, we estimate the utility of a sensor node, which is a function of the sensing area, ratio of the fixed cost to total cost incurred, responsiveness factor, and rating given by an end-user for that sensor node. The SSPs provide rent to the sensor/vehicle owners for taking their sensor nodes on lease. In order to formulate the interactions among the SSPs and sensor owners, we apply first-price, sealed-bid auction-based game theoretic approach, where SSPs act as bidders. Exhaustive simulation results depict that the proposed pricing scheme, RegPrice, is capable of reducing the expenses of a SSP by 7.51% and 9.71% compared to the existing pricing schemes [1] and [2] .

This article highlights the importance of Internet of Things (IoT) and 5G networks in sustainable cities, focusing on disaster management systems. It also discusses suitable 5G communication technologies in disaster affected and communications outage areas. The article surveys various potential emerging communication technologies such as IoT, device-to-device communications, vehicular networks, cloud and fog computing, unmanned aerial vehicles, and sensor networks for disaster situations. The presented work offers insight in collaborative solutions using diverse technologies, which benefit from ever rising wireless connected devices to support communications in natural catastrophes. The article also presents extensive taxonomy for disaster communication systems and shares key results in selected areas. The key requirements for successful deployment of a robust communications system are also presented.

Internet of Drones (IoDs) is getting growing interest of researchers due to its applicability in wide range of applications for transportation, weather monitoring, emergency monitoring for flood, earth quake, healthcare and road hazards. To update the data about emergency situation, a real-time data sharing is mandatory. However, regular message transmission by various drones may not only overwhelm a central server but it also causes congestion on the network. It is mandatory to reduce messaging cost and congestion. This paper presents a fog-assisted congestion avoidance approach for Smooth Message Dissemination (SMD). We present a message forwarding algorithm for congestion avoidance to select the appropriate next-hop node using layered model. This model is based on various layers having drones. In first phase, it looks for an appropriate drone in a layer near the fog server for message forwarding. In next step, the drone is identified in nearby layers to forward the emergency message to next-hop to further locate the group head as per priority. It is a drone that has less distance towards fog server and inform in its one-hop circle. It can stop forwarding message after delivering it to fog server. Finally, the fog server disseminates information timely towards upper layers for necessary actions for emergency situations. The performance of the proposed approach is validated through extensive simulations using NS 2.35. Results prove the dominance of SMD over counterparts in terms of messaging overhead, packet delivery ratio, throughput, energy consumption and average delay. Proposed SMD improves PDR by 85% and message overhead cost by 91% as compared to counterparts.

Vision sensors in Internet of Things (IoT)-connected smart cities play a vital role in the exponential growth of video data, thereby making its analysis and storage comparatively tough and challenging. Those sensors continuously generate data for 24 hours, which requires huge storage resources, dedicated networks for sharing with data centers, and most importantly, it makes browsing, retrieval, and event searching a difficult and time-consuming job. Video summarization (VS) is a promising direction toward a solution to these problems by analyzing the visual contents acquired from a vision sensor and prioritizing them based on events, saliency, person's appearance, and so on. However, the current VS literature still lacks focus on resource-constrained devices that can summarize data over the edge and upload it to data repositories efficiently for instant analysis. Therefore, in this article, we carry out a survey of functional VS methods to understand their pros and cons for resource-constrained devices, with the ambition to provide a compact tutorial to the community of researchers in the field. Further, we present a novel saliency-aware VS framework, incorporating 5G-enabled IoT devices, which keeps only important data, thereby saving storage resources and providing representative data for immediate exploration. Keeping privacy of data as a second priority, we intelligently encrypt the salient frames over resource-constrained devices before transmission over the 5G network. The reported experimental results show that our proposed framework has additional benefits of faster transmission (1.8~13.77 percent frames of a lengthy video are considered for transmission), reduced bandwidth, and real-time processing compared to state-of-the-art methods in the field.

Recent advances in Low Power Wide Area Network (LPWAN) are expected to augment the already prodigious proliferation of Industrial Internet of Things (IIoT). However, this unrepresented growth is tinged by the uncertainty of possible challenges in security and privacy. In this work, we propose a novel blockchain-envisioned fine grained user access control scheme for data security and scalability in IIoT environment. The proposed scheme supports multiple attribute authorities and also a constant size key and ciphertext. The data gathered by the IoT smart devices are encrypted using the cipher-policy attribute based encryption (CP-ABE) and sent to their nearby gateway nodes. Later, the gateway nodes form the transactions from the encrypted data from the smart devices which are used to form partial blocks. The partial blocks are then forwarded to the cloud server(s) in the peer-to-peer (P2P) network to convert them into full blocks, which are verified, mined and added into the blockchain using the voting-based practical Byzantine fault tolerance (PBFT) consensus algorithm. The proposed scheme also allows a user to access the secure data stored in the blocks into the blockchain using the CP-ABE mechanism. The security analysis demonstrates the robustness of the proposed scheme against various attacks, and the comparative study with related relevant schemes also highlights the advantage of the proposed scheme over existing approaches. Finally, a blockchain implementation of the presented scheme summarizes the computational costs for a varied number of transactions per block, and also for a varied number of blocks mined in the blockchain.

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