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.

As the scale of data continues to grow exponentially, managing resource allocation and energy consumption in big data systems becomes increasingly complex and critical. Moreover, with big data systems, energy efficiency is more important daily. In cloud environments, it can be the determining factor between reduced costs and lowered environmental damage. This paper presents a deep learning-based framework for accurately predicting instant energy consumption in real-time and detecting anomalies of different sizes in big data clusters. We use SmartMonit to gather task execution and real-time infrastructure data. A Feedforward Neural Network (FNN) predicts energy consumption from CPU utilisation, memory usage, and task profiling research. The system will track any deviation from predicted consumption with root cause analysis (RCA) if there are significant anomalies. We also integrate an Autoencoder to identify straggler tasks and inefficient resource utilisation. Userdefined functions are next applied to examine these anomalies and try to detect the underlying reasons, like distributed data processing, locality of computation exploitation, or resource waste. Given the scale and heterogeneity of big data workloads, the system's ability to dynamically adjust and optimise resource usage is essential for handling complex processing tasks. The experimental results prove that the proposed system effectively enhances resource allocation and decreases wasted energy.

6G applications rely on data-intensive AI models for network optimization. These demand a scalable and energyefficient framework to handle massive device networks with stringent latency requirements which current solutions struggle to support. Although reinforcement learning (RL) and split learning have matured to provide commercial solutions elsewhere. Current solutions in 6G have not used them systematically to achieve the sustainability goals. In this paper, we propose a three-layer framework that minimizes energy consumption of the communication system capable of handling large number of devices. The proposed solution uses RL agents at the edge layer to mathematically model the system and communicate to fog layer for aggregation. The aggregated feature maps are further communicated to cloud layer for global model training. We use split learning for communication and training, the learning at each device are communicated for global model creation effectively. Each edge device improves the overall RL model where system matures quickly consuming minimal energy. The proposed framework's efficacy has been tested extensively for accuracy and scalability, in terms of energy consumption, latency and memory utilizations. The simulation results validate the claims of maturity in models across edge, fog and cloud levels.

Vehicular Ad Hoc Networks (VANETs) form the foundational communication framework of intelligent transportation systems, facilitating low-latency, vehicle-to-everything data exchange for enhanced traffic efficiency and safety. Accordingly, ensuring secure, efficient, and scalable authentication is essential to maintain communication trustworthiness, especially in highly dynamic and dense traffic scenarios. While traditional public key cryptography (PKC)-based solutions offer strong security guarantees, they are computationally intensive and struggle to scale under VANET workloads. To address these challenges, this paper proposes a novel lightweight handover authentication scheme that integrates pairing-based cryptography with symmetric key primitives to ensure message integrity, anonymity, and unlinkability. The proposed solution is deployed within a real-world Reconfigurable Intelligent Surface (RIS)-assisted communication environment, enhancing the robustness and feasibility of the authentication process during handover. Furthermore, a comprehensive evaluation is conducted, comparing the computational and communication overhead of the proposed scheme with existing cryptographic protocols. Results demonstrate the superior scalability and efficiency of the proposed approach, making it well-suited for next-generation VANET applications.

Blockchain technology is acclaimed for eliminating the need for a central authority while ensuring stability, security, and immutability. However, its integration into Internet of Things (IoT) environments is hampered by the limited computational resources of IoT devices. Consensus algorithms, vital for blockchain safety and efficiency, often require substantial computational power and face challenges related to security, scalability, and resource demands. To address these critical issues, we propose a novel model that significantly enhances the security and performance of blockchain in IoT environments. Our model introduces three key innovations: (1) a bidirectional-linked blockchain system that strengthens security against long-range attacks by exploiting dual reference points for block validation; (2) the integration of user preferences into the Committee Member Auction (CMA) consensus algorithm, optimizing miner selection to balance resource efficiency with security; and (3) a comprehensive performance and frequency analysis that demonstrates the system’s resilience against double-spend, long-range, and eclipse attacks. The proposed model not only reduces block validation delays but also enhances overall system performance, as evidenced by simulations comparing its effectiveness with existing CMA algorithms. These advancements have the potential to significantly impact the deployment of blockchain in resource-constrained IoT environments, offering a more secure and efficient solution.

With the widespread adoption of Healthcare Internet of Things devices, the need for effective intrusion and anomaly detection has become pivotal in ensuring network security. However, the optimization of machine learning (ML) and deep learning models for these detection tasks frequently necessitates extensive computational resources, adversely affecting both temporal and energy efficiency. This paper introduces an AutoML framework specifically tailored to enhance anomaly detection models, with a strategic focus on energy efficiency throughout the optimization process. The process commences with data preprocessing, followed by feature selection employing a combination of Recursive Feature Elimination and SHapley Additive exPlanations to identify important features for anomaly detection. Subsequently, a baseline Multilayer Perceptron neural network model is trained, and hyperparameter optimization is executed within a constrained search space to mitigate energy consumption. The framework produces optimized models, which are assessed based on accuracy and energy consumption at various checkpoints, with the models demonstrating inferior performance systematically excluded based on predefined accuracy or energy consumption objectives. Experimental outcomes reveal that the pipeline effectively balances detection performance with energy consumption, with certain cases showing minimal accuracy losses (less than 1%) accompanied by substantial energy savings (over 60%), presenting a sustainable and resource-efficient approach to anomaly detection within IoT systems.

Drones are sophisticated machines that can hover over extreme locations, conduct aerial surveillance, collect surveillance data, and disseminate it to the distributed edge for processing and analysis. The distributed edge deploys advanced artificial intelligence (AI) models to detect any unwarranted activity or object based on surveillance data. However, these lightweight and low-power unmanned aerial vehicles (UAVs) may experience faults due to unprecedented workload when deployed in extreme surveillance domains. In this article, we have designed an AI framework to detect any safety concerns with drones deployed for aerial surveillance in extreme locations based on real-time drone critical parameters. We also propose a MapReduce-based object recognition and classification module to process large-scale images captured by drones efficiently. However, conventional AI systems behave like black box systems, leading to a lack of trust and transparency. Thus, we convert the traditional framework of AI into an explainable edge AI framework using Shapley additive explanations (SHAPs) that opens Pandora’s black box. The experimental results show the effectiveness of the proposed framework in detecting drone safety concerns through explainable health status tracking alongside ensuring an effective object detection mechanism.

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.

In healthcare IoT networks, network anomalies can disrupt the flow of reliable data, potentially compromising healthcare data’s security and integrity. To address this challenge, several anomaly detection methods have been developed using artificial intelligence (AI) algorithms. However, finding an optimal AI model with the best tuning parameters for effective anomaly detection is a time-consuming and resource-intensive task. To address this issue, we propose an Automated AI (AutoAI) approach to optimize the tuning of hyperparameters in healthcare data anomaly detection. By leveraging the power of AutoAI, our goal is to streamline the anomaly detection process, making it more accurate and efficient. Our method is designed to adapt dynamically to the ever-changing nature of healthcare data, ensuring robustness against emerging anomalies. The proposed AutoAI method was validated in a realistic scenario and the outcomes depict the superiority of the proposed approach as compared to existing schemes on various performance evaluation metrics.

In drone swarms, where multiple drones collaborate closely to achieve shared objectives within constrained spatial domains, the intricacies of these interrelated actions can lead to potential issues. Despite rigorous pre-deployment planning, the inherent probability of complications persists. These compli-cations stem from onboard computational resources, hardware failures, and network communication disruptions. While the malfunction of an individual drone may seem inconsequential, it can escalate into a substantial predicament when it disrupts the seamless coordination of the entire swarm. Therefore, the need to proactively monitor drones for predictive failure analysis and the subsequent examination of failed drones to mitigate future occurrences becomes imperative. This paper introduces a comprehensive framework for systematically collecting and processing data within drone swarms. The framework gathers critical information about onboard characteristics and commu-nication metrics. These data points are subjected to advanced analysis using Complex Bayesian Networks to probabilistically uncover complex and hidden relationships between random features. The results demonstrate exceptional accuracy, with influences ranging from 99 % to 79 %, that ensures the reliability and effectiveness of the predictive capabilities in enhancing drone safety and network performance.

There is a massive growth in the rate of heterogeneous devices configured in the Internet of Things (IoT) environment for efficient communication. The IoT devices are limited in resources, and there are no defined protocols in terms of security during communication in the IoT-based platforms. Several solutions are framed to make communication secure in the IoT ecosystem. However, the existing schemes need to be more reliable to handle the cyber threats and unwarranted incidents (such as intrusions, anomalies and attacks) coming from IoT endpoints owing to the unstructured patterns of IoT data and dynamic network conditions. Moreover, heavy cryptographic primitives have their deployment challenges due to the resource constraints of the IoT ecosystem. The dynamic nature of IoT traffic requires flexible and varied rules to handle the threats in different deployment scenarios. Therefore, a programmable interface enabled through Software-defined Networking (SDN) can handle heterogeneous threats and incidents in the IoT cyber world. Thus, in this paper, we have designed a novel framework, SecureFlow, an intrusion detection and dynamic rule configuration system based on the knowledge-based and data-driven ensemble. The proposed framework is robust and fault tolerant owing to dual-layer Intrusion Detection System (IDS) and rule configuration modules that can work without one of them. SecureFlow validated through several experiments performed through emulations in Mininet. The results depict that the proposed framework is effective and promising.

The recognition of capabilities supplied by cloud systems is presently growing. Collecting or sharing healthcare data and sensitive information especially during the Covid-19 pandemic has motivated organizations and enterprises to leverage the upsides coming from cloud-based applications. However, the privacy of electronic data in such applications remains a significant challenge for cloud vendors to adapt their solutions with existing privacy legislation standards such as general data protection regulation (GDPR). This article first proposes a formal model and verification for data usage requests of providers in a cloud composite service using a model checking tool. A cloud pharmacy scenario is presented to illustrate the connectivity of providers in the composite service and the stream of their requests for both collection and movement of patient data. A set of verifications is then undertaken over the pharmacy service in accordance with three significant GDPR obligations, namely user consent, data access, and data transfer. Following that, the article designs and implements a cloud container virtualization based on the verified formal model realizing GDPR requirements. The container makes use of some enforcement smart contracts to only proceed with the providers’ requests that are compliant with GDPR. Finally, several experiments are provided to investigate the performance of our approach in terms of time, memory, and cost.

Financial Technology have revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. However, the massive amount of data (often called as big data) generated seamlessly across different geographic locations can end up as a bottleneck for the underlying network infrastructure. To mitigate this challenge, software-defined network (SDN) has been leveraged in the proposed approach to provide scalability and resilience in multicontroller environment. However, in case if one of these controllers fail or cannot work as per desired requirements, then either the network load of that controller has to be migrated to another suitable controller or it has to be divided or balanced among other available controllers. For this purpose, the proposed approach provides an adaptive recovery mechanism in a multicontroller SDN setup using support vector machine-based classification approach. The proposed work defines a recovery pool based on the three vital parameters, reliability, energy, and latency. A utility matrix is then computed based on these parameters, on the basis of which the recovery controllers are selected. The results obtained prove that it is able to perform well in terms of considered evaluation parameters.

With the increasing number of Internet of Things (IoT) devices being deployed and used in daily life, the load on computational devices has grown exponentially. This situation is more prevalent in smart cities where such devices are used for autonomous control and monitoring. Smart cities have different kinds of applications that are aided through IoT devices that collect data, send it to computational processing and storage devices, and get back decisions or actuate the actions based on the input data. There has been a stringent requirement to reduce the end-to-end delay in this process owing to the remote deployment of cloud data centres. This eventually led to the revolution of edge computing, wherein nano–micro-processing devices can be deployed closer to the premises of the smart application and process the data generated with a lower turnaround time. However, due to the limited computational power and storage, controlling the workload diverted to the edge devices has been challenging. The workload scheduling policies and task allocation schemes often fail to consider the run time health of the edge devices due to a lack of proper monitoring infrastructure. Thus, in this article, we proposed a health monitoring and diagnosis framework for geo-distributed edge clusters processing big data generated by smart city applications. This framework is built over the Map-Reduce approach for distributed processing of big data on edge clusters deployed across the smart city. Within this framework, SmartMonit (a monitoring agent) is deployed that collects the health statistics of edge devices and predicts the potential failures using an artificial neural network-based self-organising maps approach. The proposed framework is deployed over different clusters to test the efficacy concerning failure detection.

In the 5G era, the problem of data islands in various industries restricts the development of artificial intelligence technology, so data sharing is proposed. High-quality data sharing directly affects the effectiveness of machine learning models, but data leakage and abuse will inevitably occur in the process. As a consequence, in order to solve this problem, federated learning is proposed. This method uses the personalized data of multiple edge devices to train the model. The central server collects the training results of the edge devices and updates the global model, and then iteratively tests and updates the model through the edge devices. However, edge devices may have problems, such as unbalanced load and exit from the training process, which makes the training time of the model long and the effect is poor. Therefore, in the process of federated learning, the selection of reliable and high-quality edge devices becomes crucial. On this basis, in this article, we introduce reinforcement learning (RL) to preselect edge devices and obtain a set of candidate devices and then determine reliable edge devices through social attribute perception. The simulation experiment data analysis demonstrates that this scheme can improve the reliability of federated learning and complete the training process in a shorter time, the efficiency of federated learning increased by approximately 10.3%.

Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision-making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this article, we propose a proof-of-learning consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge’s reputation opinion by executing a smart contract, and partly adopts the winner’s learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts.

Although modern Maritime Transportation Systems (MTS) have been extensively benefited from Internet of Things (IoT) technology, but still the risks and challenges in safety and reliability have increased substantially. The involvement of different maritime parties in the marine transportation flow scheduling and management further escalates these challenges. Thus, we need an IoT-based collaborative processing system that unifies the modular structure and integrates multiple modules involved in MTS. Moreover, the need for a shared and controlled access mechanism that cannot be manipulated or tampered by unauthorized parties is also essential requirement in MTS. Blockchain, as an emerging technology, has become a key tool in data security protection because of its non-tampering and non-forgery characteristics. Keeping in view of this aspect, in this paper, an IoT-based collaborative processing system based on blockchain is proposed for marine transportation flow scheduling and management. In addition, we propose a novel consensus mechanism based on Verifiable Random Function (VRF) and reputation voting to reduce the communication cost in blockchain consensus communication process. The proposed scheme has been validated in a simulated environment and the results illustrate that the scheme has obvious effect in resisting replay attack and camouflage attack. Furthermore, the optimized consensus mechanism improves the security by 8% and the transaction processing speed by 6% on the premise that the communication cost is basically unchanged.

The global escalation in the road traffic density alleviates the harmful emissions and fuel bills due to congestion and misaligned traffic control. The conventional traffic monitoring schemes (camera or sensor-based) are not able to cover every nook and corner and thus miss various vital traffic parameters that can otherwise be very useful for traffic density and pattern analysis. Internet of Drones (IoD) has been widely adopted to resolve various related challenges and has strong potential in traffic monitoring even in the areas where scarcity of fixed infrastructure is witnessed. Thus, in this paper, we have proposed an IoD-based traffic monitoring system to avoid the congestion on the roads within the available infrastructure. Moreover, to deal with the dynamic network typologies, an software-defined networking (SDN)-based centralized controller is configured to generate the flow rules for end to end data transmission. However, the drones communicate with each other through an open channel (now controlled through a programmable SDN architecture). Thus, the integrity of data collected by drones must be protected through a robust security mechanism. So, we have adopted a blockchain technology to secure the proposed system against unauthorised access and maintain data integrity. However, maintaining the entire blockchain on the drones can lead to several resource bottlenecks. Thus, we have used a referenced blockchain architecture that decouples the data from the blockchain part and stores it in the off-chain. The proposed scheme has been validated using simulated environments that validates its the superiority in contrast to the existing variants.

Edge intelligence propelled federated learning as a promising technology for embedding distributed intelligence in the Internet of Things (IoT) ecosystem. The multidimensional data generated by IoT devices is enormous in volume and personalized in nature. Thus, integrating federated learning to train the learning model for performing analysis on source data can be helpful. Despite the above reasons, the current schemes are centralized and depend on the server for aggregation of local parameters. So, in this paper, we have proposed a model that enables the sensor to be part of a defined cluster (based on the type of data generated by the sensor) during the registration process. In this approach, the aggregation is performed at the edge server for sub-global aggregation, which further communicates the aggregated parameters for global aggregation. The sub-global model is trained by selecting an optimal value for local iterations, batch size, and appropriate model selection. The experimental setup based on the tensor flow federated framework is verified on MNSIT-10 datasets for the validity of the proposed methodology.

Edge of Things (EoT) technology enables end-users participation with smart sensors and mobile devices (such as smartphones and wearable devices) to the smart devices across the smart city. Trust management is the main challenge in EoT infrastructure to consider the trusted participants. The Quality of Service (QoS) is highly affected by malicious users with fake or altered data. In this article, a robust trust management (RTM) scheme is designed based on Bayesian learning and collaboration filtering. The proposed RTM model is regularly updated after a specific interval with the significant decay value to the current calculated scores to update the behavior changes quickly. The dynamic characteristics of edge nodes are analyzed with the new probability score mechanism from recent services’ behavior. The performance of the proposed trust management scheme is evaluated in a simulated environment. The percentage of collaboration devices is tuned as 10%, 50%, and 100%. The maximum accuracy of 99.8% is achieved from the proposed RTM scheme. The experimental results demonstrate that the RTM scheme shows better performance than the existing techniques in filtering malicious behavior and accuracy.

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 emergence of advanced technologies has triggered a sweeping digital transformation in the industrial ecosystem. The cutting-edge technologies (like, Internet of things, big data, artificial intelligence, drones, cyber-physical systems, and augmented reality, and computer vision) are key enablers of this industrial revolution. Industry 4.0 has reshaped the conventional manufacturing and production processes into automated operations and workflows. This industrial transition is fueled by advanced computing (cloud and edge computing), analytic (big data analytics and computational analytics), intelligent (machine and deep learning), and communication (programmable and intelligent networks) infrastructure and technologies. The collection, aggregation, analysis, and processing of big data generated from the industrial periphery (like manufacturing equipment and maintenance systems) enable real-time decision-making and autonomous opportunities. However, the voluminous size, variability, and frequency of this data bring a wide array of disputes and oppositions in the resource-limited Industrial systems. Moreover, the continuous decision-making workflow in production and manufacturing segments increases the sharing of data across different functions, systems, and organizational boundaries. For this reason, cloud computing and big data technologies (Hadoop and Map-Reduce) can improve the anticipated response and reaction times. Industry 4.0 will lead toward more devices enriched with embedded computing platforms which boost the capabilities of the overall workflow. But, this also leads towards an increased communication and interaction between these devices which can end up in various challenges for the underlying network infrastructure. However, the conventional communication protocols may end up in various performance bottlenecks which in turn can increase the threat from different kinds of attacks and security challenges. Concluding the above discussion, the industrial ecosystem would rely on two entities: (1) users or infrastructure (physical world) and (2) cloud-enabled algorithms and autonomous systems (virtual world) that are connected through advanced and autonomous communication technologies. The driving force behind the success of these industrial ecosystems relies on the efficient gathering/collection, analysis, and storage of data generated by smart devices and sensors. Under this domain, big data analytics is set to be driving predictive manufacturing and provide timely detection of anomalies and system failures to predict product quality. In this way, big data is bound to play a prominent role in driving the industrial ecosystem. Even more, the only reason for this concern is not limited to the volume of data but the major concern is the contribution of this data for the design and implementation of efficient industrial processes and policies. The interpretation and understanding of the available data help to the design of efficient processes and policies related to industrial systems. The focus of this special issue is to present novel and seminal contributions around the important issues and challenges related to big data management and analytics for industrial 4.0 ecosystems. It provides ground-breaking research from academia and industry, that emphasizes the novel solutions, applications, tools, software, and algorithms designed to handle the industrial big data. A substantial number of submissions were received for the special issue. The papers were reviewed by at least three reviewers and underwent a rigorous two rounds of reviews. After the completion of the peer review process, we have accepted 10 seminal contributions related to big data analytics for Industry 4.0. All the accepted papers either discuss the recent solutions related to big data analytics or proposes an innovative way of handling big data across diverse infrastructure deployments. The outline of these contributions discussed below. The first paper titled "An Efficient Scheme for Secure Feature Location using Data Fusion and Data Mining in IOT Environment" by Balaji et al.1 proposes a secure feature location approach based on data fusion and data mining to overcome the challenges of the existing textual and dynamic approaches. The first step in this approach involves the removal of repeated test cases followed by the selection of important attributes. The artificial flora optimization algorithm was used to remove the repeated test cases. After this, the Caesar Cipher-RSA algorithm was used to encrypt the selected attributes, and thereafter a score value was assigned to them. This score value acts as an input to the K-mean algorithm to normalize it using the min-max approach. The evaluation results show that the proposed approach is superior in comparison to existing variants. The second paper titled "Data Dimensionality Reduction Techniques for Industry 4.0: Research Results, Challenges, and Future Research Directions" by Chhikara et al.2 provides a comprehensive survey on dimensionality reduction techniques. This survey discussed various data dimensionality techniques, analyzed them, and provided a thorough comparison based on different factors and parameters. The survey provided an understanding of the applicability of dimensionality reduction techniques in group or stand-alone use cases. The third paper titled "Deep-Q Learning-based Heterogeneous Earliest Finish Time Scheduling Algorithm for Scientific Workflows in Cloud" by Kaur et al.3 proposes a workflow scheduling approach wherein a deep-Q learning mechanism is used. This deep-Q mechanism was based on a heterogeneous earliest-finish-time algorithm was designed to amalgamate the deep learning approach with the heuristic approach for task scheduling. The evaluations were performed on a workflow simulator and the results depict the superiority of the proposed approach in contrast to the existing algorithms in terms of makespan and speed. The fourth paper titled "A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0" by Zhang et al.4 proposes a multidomain virtual network embedding algorithm to improve the performance and reduce the computational delay. This algorithm is based on centralized hierarchical architecture and avoids local optimum by improvising the particle swarm optimization algorithm to include a genetic variation factor. However, as the problem is composed of multiple objectives, the proposed work simplifies the same by decomposing it into a single-objective problem using a weighted summation method. According to the obtained results, the proposed approach converges to an optimal solution quickly. Further, a candidate selection algorithm was proposed to reduce the cost associated with mapping. In this algorithm, the physical domain calculates the mapping cost for all nodes and selects the one with the lowest mapping cost. The results show the efficiency of the proposed approach in terms of delay, cost, and several other performance indicators. The fifth paper titled "A Community-based Hierarchical User Authentication Scheme for Industry 4.0" by Sinha et al.5 proposes a community-based hierarchical approach that is used to decide the way to provide access rights of the smart end devices to the users in the Industry 4.0 ecosystem. The hierarchical structure helps to ensure that only the legitimate users get access rights after clearing the multilevel authorization process. This approach also ensures identity leakage as the legitimate parties coordinate closely with each other for the authentication process. The validation shows that the proposed approach is susceptible to various types of attacks. The sixth paper titled "PSSCC: Provably Secure Communication Framework for Crowdsourced Industrial Internet of Things Environments" by Dharminder et al.6 proposes an identity-based signcryption method in the provably secure communication framework. During signcryption, the end-user performs pairing-free computation that proves to be computationally efficient. Based on the modified bilinear Diffie-Hellman inversion and strong Diffie-Hellman problems, the framework is proved to be secure in the Industrial Internet of Things environment. The evaluation was performed based on communication and computation cost and the results look very promising. The seventh paper titled "Applying Artificial Bee Colony Algorithm to the Multi-depot Vehicle Routing Problem" by Gu et al.7 utilizes an artificial bee colony algorithm in multidepot Vehicle Routing Problem to manage the vehicular routes among multiple depots in an optimized and time-efficient manner. Initially, the multidepot Vehicle Routing Problem is decomposed single-depot problem using depot clustering. After this, a modified artificial bee colony algorithm is used to generate solutions for each depot. In the end, a coevolution strategy is proposed to realize a complete solution to the multi-depot Vehicle Routing Problem. The proposed algorithm was validated through extensive experiments and the results were compared with greedy and genetic algorithms based on different parameters. The results depict a performance enhancement to the tune 70% over the greedy algorithm and 3% over the genetic algorithm. The eighth paper titled "An IoT-enabled Decision Support System for Circular Economy Business Model" by Mboli et al.8 proposes a decision support system based on the Internet of Things for a circular economy business model. This system is based on an ontological model that effectively allows to predict, track, and monitor the residual value of the product. This allows businesses to utilize circularity decisions complemented by a semantic decision support system to create a first of its kind, semantic ontological model. The proposed model was validated based on real-world use case scenario to understand viability and applicability. The ninth paper titled "Security Analytics for Real-Time Forecasting of Cyberattacks" by Javed et al.9 proposes a pattern identification framework for cyberthreats. After identification of the cyber patterns a forecasting model suggests the pattern of growth in an emerging network threat. This framework predicts the maximum threat intensity and its occurrence over the period thereby suggesting the likelihood of maximum intensity. The framework involves four steps, (1) continuous activity monitoring, (2) behavior forecasting, (3) estimating the intensity of a potential cyberattack, and (4) predicting the potential risk of cyber attacks over a predefined time window. The validation depicts an average lead time of 1.75 h good enough to limit the potential impact of the attack. The tenth paper titled "An Efficient Hadoop based Brain Tumor Detection Framework using Big Data Analytic" by Chahal et al.10 proposes a brain tumor segmentation approach based on a hybrid weighted fuzzy mechanism. This approach works in tandem with the Matlab Distributed Computing Server and Hadoop to fuzify the pixel values to create meaningful clusters of large data. The approach is validated based on huge MR brain data across clusters of varying sized DICOM datasets using hybrid fuzzy clustering in MapReduce on Hadoop. The experiments performed compared the read, write, and processing time on each node. The outcomes show an elevation in the read and write operation time with an increase in the data size to multinode. The processing time comes out to be 35 min and 3.4 min on single and three-node clusters, respectively. Further increasing the data size to 7.3 GB, the proposed approach process the data in 235.4 min and 2085.2 min for the three-node cluster and single node, respectively. We hope that the seminal research contributions and findings presented in this special issue would benefit the readers to enhance their knowledge base and encourage them to work on various aspects of big data analytics. We express our sincere gratitude and thanks to the editor-in-chief for allowing us to organize this special issue. The support from the editorial office staff was excellent and we thank them for the same. We are also thankful to all the authors who submitted their ideas and finding in this special issue and made it possible, and to the reviewers for their thoughtful and critical suggestions to improve the quality of the submitted papers.

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.

Emerging multitenant cloud computing ecosystems allow multiple applications to share virtualized pool of computing and networking resources. As a result, such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorized access). While cloud computing providers support robust security and privacy mechanisms (e.g., public key cryptography, firewalls, and virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit, and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation in Europe and the Data Protection Act in the U.K., further emphasizes the need to overcome these privacy limitations. In this article, a novel technique for monitoring, auditing, and verifying the operations carried out on a user’s personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., blockchain and smart contracts) for developing an immutable recording technique, which transparently logs, monitors, and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests ($>$13K) ensuring minimal latency ($\approx$50–60 ms) and overheads for three different service packages varied with respect to the number of actors and operations.

Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g., industrial control systems) that require time-critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things devices, those devices lack computing capabilities to guarantee reasonable performance for time-critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytic tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and quality of services based multilevel queue traffic scheduling system that can undertake semiautomatic bandwidth slicing to process time-critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.

Internet of Things (IoT)-based Healthcare services, which are becoming more widespread today, continuously generate huge amounts of data which is often called big data. Due to the magnitude and intricacy of the data, it is difficult to find valuable information that can be used for decision-making and prediction. Big data systems take on a significant infrastructure service to better serve the purpose of IoT systems and support critical decision making. On the other hand, privacy preservation, data integrity, and identity verification are essential requirements in healthcare big data service management. To overcome these problems, this article offers a scalable computing system that provides verifiable data access mechanism for IoT-enabled health data analytics in the big data ecosystem. There are two primary sub-architectures in the proposed architecture, namely a big data analytics tracking system and a derived blockchain-based data storage/access system. This approach leverages big data systems and blockchain architecture to analyze, and securely store data from IoT-enabled devices and allow verified access to the stored data. The zero-knowledge protocol is used to ensure that no information is accessible to unauthenticated users alongside avoiding data linkability. The results demonstrate the effectiveness of the our method to solve the problems of big data analytics and privacy issues in healthcare.

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

The development of smart technology and smart cities has solved the problem of data islands, but it has also brought about information security problems. Federated learning provides solutions to information security problems, which is a new machine learning method that effectively protects the local privacy of edge devices by distributing models to edge devices for training. However, due to malicious attacks from malicious edge devices, the accuracy and efficiency of federated learning are greatly compromised. Therefore, to solve the above problems, this paper proposes a reputation-aware method based on reinforcement learning (RA-RL) to select edge devices to ensure that the federated learning process is not attacked. Specifically, we introduce a reputation measurement scheme to evaluate the reputation of edge devices and use it as one of the features of edge devices. Then extract the feature matrix of candidate edge devices as the RL training environment to calculate the probability of each edge device is selected, and finally use the greedy algorithm to determine the devices that will eventually participate in the federated learning. Simulation experiments show that the RA-RL algorithm can effectively solve the training data security problem in federated learning, and is superior to other algorithms in terms of load balance, efficiency and accuracy.

Edge of Things (EoT) enables the seamless transfer of services, storage, and data processing from the cloud layer to edge devices in a large-scale distributed Internet of Things (IoT) ecosystems (e.g., Industrial systems). This transition raises the privacy and security concerns in the EoT paradigm distributed at different layers. Intrusion detection systems (IDSs) are implemented in EoT ecosystems to protect the underlying resources from attackers. However, the current IDSs are not intelligent enough to control the false alarms, which significantly lower the reliability and add to the analysis burden on the IDSs. In this article, we present a Dew Computing as a Service (DaaS) for intelligent intrusion detection in EoT ecosystems. In DaaS, a deep learning-based classifier is used to design an intelligent alarm filtration mechanism. In this mechanism, the filtration accuracy is improved (or sustained) by using deep belief networks. In the past, the cloud-based techniques have been applied for offloading the EoT tasks, which increases the middle layer burden and raises the communication delay. Here, we introduce the dew computing features that are used to design the smart false alarm reduction system. DaaS, when experimented in a simulated environment, reflects lower response time to process the data in the EoT ecosystem. The revamped DBN model achieved the classification accuracy up to 95%. Moreover, it depicts a 60% improvement in the latency and 35% workload reduction of the cloud servers as compared to edge IDS.

The in-house health monitoring sensors form a large network of Internet of things (IoT) that continuously monitors and sends the data to the nearby devices or server. However, the connectivity of these IoT-based sensors with different entities leads to security loopholes wherein the adversary can exploit the vulnerabilities due to the openness of the data. This is a major concern especially in the healthcare sector where the change in data values from sensors can change the course of diagnosis which can cause severe health issues. Therefore, in order to prevent the data tempering and preserve the privacy of patients, we present a decoupled blockchain-based approach in the edge-envisioned ecosystem. This approach leverages the nearby edge devices to create the decoupled blocks in blockchain so as to securely transmit the healthcare data from sensors to the edge nodes. The edge nodes then transmit and store the data at the cloud using the incremental tensor-based scheme. This helps to reduce the data duplication of the huge amount of data transmitted in the large IoT healthcare network. The results show the effectiveness of the proposed approach in terms of the block preparation time, header generation time, tensor reduction ratio, and approximation error.

SUMMARY With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time, and the required computing and storage resources far exceed the load capacity of vehicle terminals. Mobile edge computing enables data resources to be processed near device terminals, and provides low‐latency and high‐reliability computing services to meet the power and service quality requirements of terminal devices. Therefore, in order to achieve better data resource management, this paper introduces mobile edge computing technology, and mainly researches secure message transmission optimization algorithms based on mobile edge computing. Firstly, we prioritize secure messages through the analytic hierarchy process. This can guarantee that the most urgent messages get the highest transmission level. Secondly, we establish an optimal task offloading model of delay and energy loss by assigning different weight factors to delay and energy loss. The Lagrangian relaxation method is used to transform the nonconvex problem into a convex problem. We use greedy algorithm to solve the main problem. Finally, the vehicle transmits secure messages through the topology of the local network within its defined communication range. Performance evaluation results show that the scheme not only reduces the redundant transmission of messages, but also improves the performance of end‐to‐end delay and message deliver success ratio of secure messages.

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.

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.

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers, which is not only a financial burden but also increases their carbon footprint. The private software defined wide area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this article addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.

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.

Drones are equipped with high-vision cameras, advanced sensors, and GPS receivers to deliver diverse services from high altitude thereby creating an airborne network. In this environment, physical things (drones, sensors, etc.,) are controlled using computational algorithms to form a cyber-physical system for the Internet of drones. Although the drones provide manifold benefits still there are many issues (security, privacy, and data integrity) which must be resolved before the usage of drones in smart cyber-physical systems. So, in this paper, a blockchain-based security mechanism for cyber-physical systems is proposed to ensure secure transfer of information among drones. In this mechanism, the miner node is selected using a deep learning-based approach, i.e., a deep Boltzmann machine, using features like computational resources, the available battery power, and flight time of the drone. The proposed mechanism is evaluated based on different performance metrics and the results obtained show the potential benefits of the proposed scheme.

With the surge in the demand for online services and multimedia applications, the traffic on the underlying network infrastructure has escalated (multi-folded) in recent years. To meet the strict latency requirements, Software-defined Networking (SDN) provides flexible network control (and possible intelligence) that can act as an enabler for application-oriented service industry. However, the crippling gap between the business needs and the network delivery potential necessitates the underlying network to constantly (and consistently) adapt, protect, and inform across all strands of the service-oriented landscape. Intent-based network has emerged as a recent solution to the cover the above gap by capturing business intent and thereafter activating and assuring it networkwide. Motivated from these facts, in this article, an Intent-based network control framework has been designed over the SDN architecture for data dissemination in the vehicular edge computing ecosystem. In this framework, a tensor-based mechanism is used to reduce the dimensionality of the incoming elephant-like traffic and then classifying the specific-attribute data traffic according to the defined priority requirement of the underlying applications. Here, the network policies are configured using the intent-based controller according to the application requirement and then forwarded to the SDN controller to enable intelligent data dissemination (through an optimal route) at the data plane. Convolution Neural Network is used to train the flow table to allocate the route dynamically for the classified traffic queues. The proposed framework has been evaluated through extensive simulations and the results supports the claims in terms of the quality of service requirements.

Internet of Things (IoT) allows the creation of virtually endless connections into a global array of distributed intelligence. However, the design, development, and deployment of IoT applications are complex and complicated due to various unwarranted challenges. For instance, addressing the IoT application users' subjective and objective opinions with IoT workflow instances remains a challenge for the design of a more holistic approach. Moreover, the complexity of IoT applications increased exponentially due to the heterogeneous nature of the Edge/Cloud services, utilised with the aim of lowering latency in data transformation and increase re-usability. Hence, in this paper, we present an IoT workflow composition system (IoTWC) to allow IoT users to pipeline their workflows with proposed IoT workflow activity abstract patterns. IoTWC leverages the analytic hierarchy process (AHP) to compose the multi-level IoT workflow that satisfies the requirements of any IoT application. Moreover, the users are befitted with recommended IoT workflow configurations using an AHP based multi-level composition framework. The proposed IoTWC is validated on a user case study to evaluate the coverage of IoT workflow activity abstract patterns and a real-world scenario for smart buildings. The comprehensive analysis shows the effectiveness of IoTWC in terms of IoT workflow abstraction and composition.

Recently, novel tactile and flexible network services and applications emerge, along with their explosive growth of mobile data traffic. However, current internet cannot fulfill the demands of these network services. The upcoming 5G network, including the tactile internet, is designed by adopting softwarization and virtualization technologies, aiming at removing the rigidity of dedicated network hardware and implementing various network services in a flexible manner. One key technical issue in 5G network is the virtual resource allocation, known as virtual network embedding (VNE). However, existing studies focus on allocating virtual resource in the fixed underlying network, ignoring the effect of the mobile end nodes. As unmanned aerial vehicles (UAVs) will play an important role in 5G era, we incorporate UAVs into the 5G network in order to expand the coverage and agility of novel network services. In this paper, we conduct a research on the virtual resource allocation in UAVs-integrated 5G networks. The formal problem model for UAVs-integrated 5G networks is involved. A novel profit model, quantifying the UAV mobility for telecommunication service provider (TSP), is proposed. Then, we propose one virtual resource allocation algorithm, labeled as UAV-5G-VNE. Our UAV-5G-VNE consists of initial allocation part (UAV-5G-VNE-Ini) and re-allocation part (UAV-5G-VNE-Re). Our UAV-5G-VNE enables to predict all possible connecting access nodes of virtual UAV and makes implemented network services continued. In order to validate our UAV-5G-VNE efficiency, we conduct the experiments. Experiment results demonstrate that UAV-5G-VNE outperforms two benchmark algorithms, in terms of TSP profit and virtual service acceptance.

Internet of Things (IoT) has brought major changes in the way the workload is processed closer to the location of the data source. The need for near-to-real time provisioning of IoT workload has necessitated the emergence of Edge Computing. However, it is not entirely possible to shit the entire workload on to the edge layer due to the computational limitations of the edge devices. Hence, this challenge ended up with the amalgamation of IoT-Edge-Cloud ecosystem. But, one of the major challenges in this ecosystem is workload management in a self-organized manner (or according to the nature of the workload). This article tries to overcome this challenge by utilizing the benefits of Self Organized Map (SOM). This article comprises of three strands, 1) a SOM-based workload classification approach to handle the IoT workloads in a flexible manner, 2) an energy-efficient workload scheduling scheme using container-based virtualization, and 3) a workload migration mechanism based on secure caching technique. The proposed strands are evaluated using a simulated environment and the outcomes seem promising in contrast to generalized container-based workload scheduling.

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.

At present, the traditional heuristic method to solve the problem of virtual network embedding (VNE) is still the mainstream. In the environment of network virtualization (NV), a more efficient VNE algorithm is needed to serve the construction of smart city. Using heuristic algorithm to solve the problem of VNE does not meet its development requirements. In this paper, a VNE algorithm based on node probability is proposed by using reinforcement learning (RL) algorithm. The algorithm extracts three attributes of each substrate node to form a feature matrix, which is used as the input of the policy network to train the agent. The purpose is to deduce the mapping probability of each node and rank the base nodes according to this probability, then embed the virtual nodes in this order. Finally, the breadth first search (BFS) strategy is used to map the links. Simulation results show that our algorithm is superior to a representative algorithm based on node ranking in terms of the acceptance rate of virtual network requests (VNR), long-term revenue consumption ratio and long-term average revenue.

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.

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

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

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.

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.

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.

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.

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.

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.

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.

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

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

With the enormous growth of number of nodes in a peer-to-peer (P2P) network, establishing a direct connection between two communicating nodes is a challenging task. It is mandatory to have a strong connection between different nodes for data sharing among P2P networks. Hence, this paper presents a modified data structure, named as Adaptive Skip Graph, a variant of a probabilistic data structure, skip list, which establishes a direct connection between two frequently communicating nodes by changing the membership vector of the caller node once the threshold is crossed. The proposed approach reduces the search time complexity of skip graph (O(log n)) substantially for the scenario where a node 'x' is repeatedly queried by a certain node 'y'. The results obtained using the proposed scheme are validated by experiments performed on a large set of nodes.

Demand response management in smart cities is one of the most challenging tasks to be performed due to the continuous changes in the load profile of the home users. The existing proposals in the literature fail to observe the hidden patterns in the load profile of these users. So, to fill these gaps, the concept of deep learning has been used in this paper for smart energy management in a smart city. The consumption data from smart homes (SHs) is gathered and taken as an input to the deep learning model, convolution neural network (CNN). The CNN model learns the hidden patterns in the data and outputs different load curves. These load curves are then used to train a support vector regression (SVR) model, which predicts the overall load consumption of all SHs in the smart city. This prediction is then compared with the power generation from the grid and consequently the demand response (DR) of the connected SHs is managed so as to minimize the gap between predicted demand and supply. The proposed scheme has been evaluated on the dataset collected from PJM and open energy information with respect to load demand prediction and DR management. The results obtained prove the efficacy of the proposed scheme. The prediction errors, i.e., root mean squared error and mean absolute percentage error are observed less in comparison to the cases when CNN and SVR are used individually.

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

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

With the evolution of Internet of Things (IoT), there has been an overwhelming increase in the number of connected devices in recent years. Due to this, generation of massive amounts of data is inevitable from these enormous number of devices in IoT environment, especially in Internet of Vehicles (IoV). In such an environment, there is a need of a paradigm shift from traditional host-centric approach to a more flexible content-centric networking approach. The existing TCP/IP-based congestion control mechanisms can not be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput which most of the existing TCP variants (Tahoe, Reno, NewReno and TCP Vegas) may not be able to provide. So, in this paper, a deep learning- based content centric data dissemination approach for IoV is presented by taking into account the mobility of vehicles and type of content shared among vehicles. The proposed scheme works in three phases: 1) In the first phase, an energy estimation scheme is designed to identify the vehicles which can participate in data dissemination. 2) In the second phase, connection probability of these vehicles is computed to identify stable and reliable connections using Weiner process model. 3) In the last phase, a convolutional neural network (CNN)-based scheme for estimating the social relationship score among vehicle-to-vehicle pairs is designed. CNN is used to identify the ideal vehicle pairs, which can share data to ensure minimum delay and high data availability. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained proves the efficacy of the proposed scheme with respect to performance metrics such as-content disseminated, energy, and social score.

In a multi-cloud environment, consumers can access multiple cloud services using a single heterogeneous computing architecture. In such an environment, multiple instances of the same cloud service and its component may be geographically dispersed. So, cloud service broker (CSB) exploits the heterogeneity of multi-cloud environment to provide high performance at a low price to its consumers. The consumer tasks are allocated to the geo-dispersed cloud service components for execution of various services. For this purpose, an optimal service components identification and task allocation are major concerns keeping in view of the heterogeneity in multi-cloud environment. For this purpose, a scheduling algorithm, which takes care of location, price, and performance is required. Therefore, in this paper, SLOPE: A Self Learning Optimization and Prediction Ensembler for Task Scheduling in Multi-cloud Environment is proposed. SLOPE works in two phases, 1) In first phase, Bayes theorem is used to design a self-learning algorithm, to compute the conditional probability (strength) of each service component in order to select the probable rule string, and 2) a roulette wheel method is used to select an optimal scheduling policy for a given service request. SLOPE helps to identify the best possible service component from the pool of resources on the basis of dynamic factors and then schedule a service request to the selected component. Unlike most of the other existing approaches, SLOPE builds an efficient schedule for service selection. Experimental results demonstrate that SLOPE performs better in comparison to other competing schemes of its category.

With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services such as e-commerce, e-health, e-banking, etc., to the end users. These services are hosted on massive geodistributed data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multi-edge-cloud environment involves huge data migrations across the underlying network infrastructure, which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedge-cloud environment using software-defined networks (SDN). To handle the inter-DC migrations efficiently, an SDN-based control scheme is presented, which provides energy-aware network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide cost-effective inter-DC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.

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

Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may lead to various challenges for the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for the data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, first the Frobenius norm is applied on high-order-tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks for minimization of the network load and effective bandwidth utilization. The proposed scheme minimizes the transmission delay incurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces using `R' programming and Matlab. The big data traces considered for evaluation consist of more than two million entries (2,075,259) collected at one minute sampling rate having hetrogenous features such as-voltage, energy, frequency, electric signals, etc. Moreover, a comparative study for different data traces and a real SG testbed is also presented to prove the efficacy of the proposed scheme. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such asnetwork delay, accuracy, and throughput.

Cloud computing (CC) has emerged as a leading technology for providing on-demand services such as, network access, data storage, computation to end users for smooth execution of various applications. Such services are provided over physical servers hosted by large data centers (DCs) which may be geographically located. In recent times, with an increase in service requests for various resources, DCs have expanded drastically in terms of number of servers. With such an increase in high-end servers, the energy consumption of DCs has escalated many folds which may lead to additional burden on the grid. Moreover, the escalation in energy consumption of DCs has an impact on carbon footprints in the environment. Hence, the integration of renewable energy sources (RES) with DCs may ease the load of grid to a great extent. However, due to intermittent nature of RES, it is a difficult task to sustain DCs using RES. Hence, to sustain the energy consumption of DCs using RES, the penetration of electric vehicles (EVs) can be a major leap. To resolve these issues, a software-defined network (SDN)-based DC energy management system using RES and EVs is designed in this paper. In the proposed scheme, a charging- discharging mechanism for penetration of EVs is formulated to cope with the intermittent nature of RES. The results obtained clearly depict that the penetration of EVs played a major role to manage the energy consumption of DC using RES.