The persistent digital divide, characterized by un-equal access to affordable and reliable mobile connectivity, remains a critical challenge for inclusive global development. The flow of network resources remains a complex task due to multi-vendor policies in beyond 5G networks (B5G) and sparse user distributions in remote areas. Addressing this issue, this paper presents a cost-aware, game-theoretic framework that leverages software-defined network slicing (SDNS) to minimize deployment cost while maintaining service quality across distributed and sparsely connected user entities (UEs). The problem considers multiple mobile infrastructure providers (MIPs) offering network slices at different cost levels and mobile virtual network operators (MVNOs) serving end-users seeking optimal connectivity at reasonable prices. The slice allocation and pricing formulation is cast as a mixed-integer nonlinear optimization problem (MILP) that is NP-hard due to the combinatorial coupling of users, slices, and link capacities. We decompose the problem into tractable subgames using a non-cooperative game-theoretic approach that converges to a near-optimal Nash equilibrium. The equilibrium ensures fair profit distribution among MIPs while providing affordable access to users. Simulation results over sparse network topologies demonstrate improved throughput and SINR characteristics with cost savings up to 25% compared to random allocation. Profitability indices show balanced revenue across providers, validating the efficiency of the proposed distributed algorithm in bridging the digital divide.

The proliferation of interconnected devices in the era of the Internet of Things (IoT) has given rise to the need for robust device identity management and authentication mechanisms in cyber-physical systems (CPSs). Traditional centralized approaches to identity management face challenges of security, scalability, and privacy. Therefore, the paper provides an innovative approach by fusing Self-Sovereign Identity (SSI) with blockchain technology to revolutionize device identity management within CPS environments. In this paper, devices autonomously initiate their identity-creation processes. Each device generates a cryptographic key pair comprising a public key for openly identifying the device and a closely guarded private key used for authentication and decryption purposes. The research also introduces an innovative authentication algorithm within CPS environments that employs secure tokens to validate the authenticity of devices. The proposed framework reduces the risk of unauthorized access and data breaches while empowering devices with control over their identities. Overall, the proposed approach not only enhances security, privacy, and resilience within CPSs but also provides a transformative solution for identity management in dynamic and autonomous device environments.

The smart grid revolution has brought numerous benefits to the energy sector, such as improved efficiency, increased renewable energy integration, and enhanced grid management. The reliance on digital communication within smart grid systems has introduced new security challenges that must be addressed to ensure reliable and secure operation. The existing communication infrastructure often lacks the necessary security measures to protect against cyber threats, which leads to potential vulnerabilities and privacy breaches. This paper presents a novel approach to enhancing smart grid communication by integrating Software-Defined Networking (SDN) and Blockchain technology. Therefore, the proposed work aims to address the specific communication needs of smart grids, which require secure and real-time data exchange between various grid components, including power generation units, substations, distribution networks, and end consumers. The proposed framework provides data integrity, communication and network security, and controller privacy.

The emerging field of smart healthcare has identified emotion detection as a key component in improving patient care, diagnostics, and therapeutic interventions. This paper introduces an innovative approach to emotion detection within the healthcare domain by integrating a Convolutional Neural Network (CNN) with a Maximum A Posterior (MAP) estimator prepared for Magnitude-Squared Spectrum (MSS) analysis. The effectiveness of CNN’s advanced feature extraction capabilities with the statistical strength of MAP estimation offers a promising avenue for interpreting complex physiological signals. The proposed methodology aims to accurately discern and quantify emotional states, thus contributing to the personalization and effectiveness of healthcare services. To validate the efficacy of this approach, the work conducted extensive experiments on a diverse data set composed of physiological signals, demonstrating that the proposed model outperforms existing limitations in emotion recognition tasks. The integration of MSS into CNN frameworks, added with MAP estimation, provides a significant improvement in the detection and analysis of emotions, resulting in more responsive and intelligent healthcare systems. This proposed paper not only presents a novel methodological contribution, but also demonstrates the groundwork for future research toward the intersection of emotional intelligence and healthcare technology.

Unmanned Aerial Vehicle (UAV) networks have emerged as a transformative technology with applications ranging from surveillance to disaster response. These networks rely on a decentralized architecture where UAVs communicate with each other and with ground stations. But because UAV networks are naturally weak, especially at the edges, strong security measures are needed to keep private data safe and make sure operations run smoothly. The dynamic and distributed nature of UAV networks poses unique security concerns, including data breaches, unauthorized access, and tampering. To address the security challenges prevalent in UAV networks, we propose a Federated Learning-based Adaptive Security (FLAS) Framework using fully homomorphic encryption (FHE). In the FLAS framework, federated learning (FL) allows UAVs to share knowledge while preserving data privacy to defend against security threats. Its integration ensures that collaborative learning occurs in edge-driven UAV environments without exposing raw data. FHE enables secure computations on encrypted data. The proposed FLAS approach ensures that security measures evolve dynamically to counter emerging threats. Our proposed method not only enhances the security posture of UAV networks but also optimizes resource utilization by distributing the learning process across the network's edge.

In the vehicular networks (VN) assisted by the integration of sensing and communication (ISAC), rapid processing of data from sensors is a necessary condition to ensure safe driving and enhance user experience. Utilizing the computational resources of the roadside unit (RSU) can effectively reduce the task processing delay. However, in some areas of the road, uneven distribution of task-vehicles can lead to severe load imbalance in neighbouring RSUs, and these tasks often have different delay requirements. The tasks in the high-load area can be offloaded to the low-load area to balance the load. We use the idle-vehicles in the low-load RSU area that are close to the task-vehicles as relays to hop and offload the tasks to the low-load RSUs. On the other hand, in order to satisfy the delay requirements of the heterogeneous tasks, this paper proposes the priority ordering of the heterogeneous tasks, the more delay-sensitive tasks require more resources to meet their delay requirements, i.e., the higher the priority. In order to both satisfy the delay requirements of heterogeneous tasks and maintain a small average system delay, we establish the optimization problem of minimizing the weighted average system delay and solve it by using the Relay Hopping and Differentiated Task Prioritization (RHATP) algorithm. Simulation results show that under the condition of guaranteeing the delay requirement of high-priority tasks, the strategy can achieve lower system delay and effectively reduce the processing delay in high-load areas. And it still maintains stable performance in different scenarios.

Internet of Medical Things (IoMT) has surfaced as the emerging era of the Internet of Things (IoT), drawing the attention of researchers given its broad operations in Smart Healthcare Systems (SHS) [1]. Since it is extremely risky for an individual to communicate with doctors in the hospital for every minor issue in the present pandemic scenario, we may check our day-to-day health records using IoMT devices and take precautionary measures on our own. In order to improve the delicacy, thickness, and outturn of electronic outfits, IoMT is essential to the healthcare sectors [2], [3].

The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. The proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.

Cyber-Physical Systems (CPS) have been growing in the evolution of interaction with the physical world. CPS can control and manages applications of the physical world around us. However, most traditional CPS-based systems have been designed and developed within the centralized system, which can violate security, trust, and privacy (STP). Blockchain is a potential solution to realizing CPS. It can provide data security and privacy through block hash generation and transaction validation schemes. Blockchain applications can enhance the performance of CPS through the peer-to-peer (P2P) communication mechanism. In this paper, we have described several challenges of CPS applications (i.e., smart grids and connected vehicles) and provided blockchain-based solutions to address STP challenges. The benefits of blockchain in CPS have been discussed in this paper. We also proposed a blockchain-enabled CPS and discussed the integration process of blockchain in several components of CPSs. The proposed solutions enhance the performance of CPS concerning security, privacy, and trust management.

A digital twin (DT) is a virtual replica of a physical system that allows simulation, optimization, and predictive maintenance. Its challenges include the need for accurate and up-to-date data as well as the complexity of integrating different systems and technologies. This paper explores the potential of combining digital twin and blockchain technologies to create next-generation transportation systems that are more efficient, secure, and sustainable. DTs can be used to simulate and optimize transportation operations and maintenance, while blockchain can enhance security and transparency in data exchange and transaction verification. By integrating these technologies, transportation systems can become more resilient, adaptable, and responsive to changing demands and challenges. This paper provides an overview of the key concepts and applications of DTs and blockchain in transportation, including use cases such as autonomous vehicles, smart logistics, and mobility as a service. It also discusses the technical and organizational challenges of implementing these technologies and suggests potential solutions and research directions. Specifically, this paper argues that DT and blockchain technologies have the potential to transform transportation systems into more efficient, sustainable, and equitable systems that can meet the needs of present and future generations.

The Internet of Medical Things (IoMT) is a network of interconnected medical devices, wearables, and sensors integrated into healthcare systems. It enables real-time data collection and transmission using smart medical devices with trackers and sensors. IoMT offers various benefits to healthcare, including remote patient monitoring, improved precision, and personalized medicine, enhanced healthcare efficiency, cost savings, and advancements in telemedicine. However, with the increasing adoption of IoMT, securing sensitive medical data becomes crucial due to potential risks such as data privacy breaches, compromised health information integrity, and cybersecurity threats to patient information. It is necessary to consider existing security mechanisms and protocols and identify vulnerabilities. The main objectives of this paper aim to identify specific threats, analyze the effectiveness of security measures, and provide a solution to protect sensitive medical data. In this paper, we propose an innovative approach to enhance security management for sensitive medical data using blockchain technology and smart contracts within the IoMT ecosystem. The proposed system aims to provide a decentralized and tamper-resistant platform that ensures data integrity, confidentiality, and controlled access. By integrating blockchain into the IoMT infrastructure, healthcare organizations can significantly enhance the security and privacy of sensitive medical data.

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.

The network slicing (NS) technique is comprehensively reshaping the next-generation communication networks (e.g., 5G and 6G). Software-defined networking (SDN) and network functions virtualization (NFV) predominantly control the flow of service functions on NS to incorporate versatile applications as per user demands. In the virtualized-SDN (vSDN) environment, a chain of well-defined virtual network functions (VNFs) are installed on service function chains (SFCs) by multiple Internet service providers (ISPs) concurrently. Generation, allocation, reallocation, release, and destroying associative VNFs on SFC is an extremely difficult task while keeping high selection accuracy. Toward solving this fundamental issue, in this work, we have proposed a multilayered SFC formation for adaptive VNF allocation on dynamic slices. We have formulated an ILP to address the VNF-embedding and allocation problem (VNF-EAP) over real network topology (AT&T Topology). Leveraging machine learning techniques we have shown an intelligent VNF selection mechanism to optimize resource utilization. The performance evaluation shows remarkable efficiency on ML-driven dynamic VNF selections over static allocations on SFCs by halving resource usage. Further, we have also studied a VNF typecasting technique for service backup on outage slices in the field of disaster management activities.

Blockchain technology has the potential to revolutionize the way intelligent transportation systems (ITSs) operate in smart cities. By providing a secure and decentralized platform for data exchange and storage, blockchain can enhance the security, privacy, and interoperability of ITS systems. Blockchain technology can be used for various applications in ITS, including secure data exchange between vehicles, infrastructure, and service providers, smart contracts for autonomous vehicles, and decentralized marketplaces for transportation services. However, implementing blockchain in ITS comes with its own set of challenges, including scalability and high computational power requirements. Despite the challenges, blockchain technology offers significant opportunities for ITS in smart cities, enabling new business models and promoting innovation in transportation services. In this article, we study existing challenges, applications, and future requirements for ITS. We discuss the challenges of the ITS and their impact on smart cities. Blockchain-enabled applications are provided with performance analysis based on the critical parameters of ITS. We also derive the security requirements for future ITS. Finally, we provide some opportunities and possible research areas within the ITS to develop smart cities.

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation; 2) object detection; and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework’s usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.

The purpose of the present study is to comprehensively evaluate the present status of the Internet of Things (IoT) in fintech ecology and ahead. The study finds that the concept of communication between the devices and financial technology is not new yet only a considerable number of studies are done on IoT in fintech. The study highlights that both the IoT and fintech show an upward trend in the marketspace. Then, the study identifies that the increase in the demand for blockchain, Internet, mobile network, cloud storage, and IoT devices among the organization are the key drivers of IoT in fintech. The study also shows that the issues related to sustainable energy, digital payments, and security are the current area of applications and challenges for IoT in Fintech. Thereafter, the study concludes that the growth of neurotech will influence the next-generation IoT in fintech as the catalytic agent. Neurotech-enabled IoT (NIoT) in Fintech will enhance the man, machine, and memory (3Ms) relationship for business process innovation.

In the modern era, the Cognitive Internet of Things (CIoT) in conjunction with IoT evolves which provides the intelligence power of sensing and computation for next-generation IoT (Nx-IoT) networks. The data scientists have discovered a large amount of techniques for knowledge discovery from processed data in CIoT. This task is accomplished successfully and data proceeds for further processing. The major cause for the failure of IoT devices is due to the attacks, in which Web spam is more prominent. There seems a requirement of a technique which can detect the Web spam before it enters into a device. Motivated from these issues, in this article, a cognitive spammer framework (CSF) for Web spam detection is proposed. CSF detects the Web spam by fuzzy rule-based classifiers along with machine learning classifiers. Each classifier produces the quality score of the webpage. These quality scores are then ensembled to generate a single score, which predicts the spamicity of the webpage. For ensembling, the fuzzy voting approach is used in CSF. The experiments were performed using a standard data set WEBSPAM-UK 2007 with respect to accuracy and overhead generated. From the results obtained, it has been demonstrated that CSF improves the accuracy by 97.3%, which is comparatively high in comparison to the other existing approaches in the literature.

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data, and things. The RPL is one of the unique standardized routing protocols that enable efficient use of smart devices energy, compute resources to address the properties and constraints of LLN networks. The authors investigate the RPL-AODV routing protocol's performance in combining the advantages of both RPL and AODV routing protocol, which works together in a low power resource-constrained network. The main challenging issue is collaborating the AODV and RPL routing protocol in the LLN network. This paper also models the collaborative attacks such as wormhole, blackhole attack for AODV, and rank and sinkhole attacks to exploit the vulnerability of RPL protocol. Finally, the cooperative IDS combining specification-based and signature-based IDS is proposed to detect the collaborative attacks against the RPL-AODV routing protocol that effectively monitors and provides security to the LLN networks.

Social Media has been growing and has provided the world with a platform to opine, debate, display, and discuss like never before. It has a major influence in research areas that analyze human behavior and social groups, and the phenomenon of social interactions is even being used in areas such as Internet of Things. This constant stream of data connecting individuals and organizations across the globe has had a tremendous impact on the functioning of society and even has the power to sway elections. Despite having numerous benefits, social media has certain issues such as the prevalence of fake news, which has also led to the rise of the hate speech phenomenon. Due to lax security throughout these social media platforms, these issues continue to exist without any repercussions. This leads to cyberbullying, defamation, and presents grave security concerns. Even though some work has been done independently on native scripts, hate speech detection, and code-mixed data, there exists a lack of academic work and research in the area of detecting hate speech in transliterated code-mixed data and in-text containing native language scripts. Research in this field is inhibited greatly due to the multiple variations in grammar and spelling and in general a lack of availability of annotated datasets, especially when it comes to native languages. This article comes up with a method to automate hate speech detection in code-mixed and native language text. The article presents an architecture containing a Tabnet classifier-based model trained on features extracted using MuRIL from transliterated code-mixed textual data. The article also shows that the same model works well on features extracted from text in Devanagari despite being trained on transliterated data.

In recent years, the corporate and industrial sectors have been experiencing significant transformations in vehicle-to-vehicle (V2V) communication. It can improve vehicle safety by giving signals to other vehicles wirelessly. The latest software, hardware, and technologies are applied to develop trusted applications that make V2V communication more believable. Today, various technologies are incorporated into vehicles to remove the barrier to existing challenges. The connected vehicles in V2V communication use sensors, data storage, and communication devices. Vehicles can communicate using the latest secure and trusted Cellular Vehicle-to-Everything (C-V2X) technology using direct and network communication modes. Even connected vehicles suffer from data security, user privacy, reliable environment, and vehicle security. Blockchain can help to eliminate those issues in V2V communication systems. Herein, a secure blockchain-enabled V2V communication system (BVCS) is proposed to enhance the security of vehicles and secure data sharing and communication among vehicles. The developed smart contracts in this paper can authenticate users and their vehicles automatically. In this paper, the proposed algorithms can authenticate users, detect unauthorized access, and establish secure communication between vehicles. The proposed system can enhance data security, user privacy, and vehicle security and provide a trusted environment in V2V communication systems.

The creation of non-invasive Neuro-headset technology has resulted in a variety of applications such as control, automation, and vehicular control. Unmanned Aerial Vehicles (UAVs), mostly used in aerial communication, is a growing field of study that may be applied to both defense and commercial applications. As the need for drone control grows, recent advancements in the BCI-based drone control system have been made. This paper proposes a unique prototype to achieve smooth and stable control of a UAV by the human brain based on a motor imagery signal for semi-autonomous navigation. In such cases the accuracy and signal detection and response time get the utmost priority to get a decent performance of the vehicle. Currently, it is very common to use CSP for the classification of motor imagery data. However, this algorithm needs improvement in the low quantity of training samples and noisy data. Because in that scenario the over-fitting may be a problem. To overcome those drawbacks this study proposed Stacked Ensemble Common Spectral Pattern (SECSP). Comparing this method with Common spatial pattern (CSP), Filter Bank CSP (FBCSP), and Common Spatio-Spectral Patterns (CSSP) it has been proved that our model outperforms those popular methods in terms of accuracy, sensitivity, robustness, and precision for motor imagery Electroencephalogram data. The publicly available data-set for motor imagery BCI Competition IV, Data-set 1, BCI Competition III, Data-set IVA have all been tested with Stacked Ensemble CSP (SECSP). Performance is enhanced when compared to previous approaches, with an average accuracy of 87.74 percent for all subjects and 95.64 percent for the first and second data sets, respectively.

The rapid developments in hardware, software, and communication technologies have facilitated the spread of interconnected sensors, actuators, and heterogeneous devices such as single board computers, which collect and exchange a large amount of data to offer a new class of advanced services characterized by being available anywhere, at any time, and for anyone. This ecosystem is widely referred to as the Internet of Things (IoT). In the past years, the number of deployments both for sensor networks and the IoT grew significantly. This continuous and exponential growth is facilitated by investments and research activities originating from industry, academia, and governments while the penetration of these technologies is also driven by the high technology acceptance rates of both consumers and technologists across disciplines. Such networks collect, store, and exchange a large volume of heterogeneous data. Nevertheless, their rapid and widespread deployment, along with their participation in the provisioning of potentially critical services (e.g., safety applications, healthcare, and manufacturing) raise numerous issues related to the security, data analysis, and energy awareness of the performed operations and provided services.

TO BUILD a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives.

To build a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare develop products for specialized medical applications. Using IoT-Health for data collection and workflow automation is a great way to reduce waste and minimize human errors. However, the security of healthcare information is a major concern, and cybersecurity has become a significant threat for healthcare providers as well as governments to achieve sustainable city milestones. IT professionals must continually resolve health data security issues to help patients and the damage that healthcare security breaches can have on their lives.

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

Obtaining timely and accurate traffic information is one of the most important problems in intelligent transportation system, which will make vehicles run smoothly, avoid road congestion, save road running time and reduce vehicle energy consumption. In the current Internet of Vehicles system, the traffic management center can learn from the feedback information of all vehicles to improve the ability of decision-making and traffic command. However, the existing feedback mechanism does not respond to the spatial-temporal characteristics of data in time, due to the lack of communication capability of the current equipment. So, it cannot meet the requirements of ultra-low delay, high reliability and high security in the Internet of Vehicles. To solve this problem, this paper proposes a blockchain-based proxy vote and revocation scheme for decision feedback in Internet of Vehicles, which allows the intelligent system to ignore the unevenness and heterogeneity in the 6G technology. In addition, blockchain technology notarizes the vote data of vehicles and outsources microservices. Secondly, we use the attributes of decision-related nodes instead of their identities to enable anonymous vote. Smart contracts can automatically expand the scalability of outsourced microservices. Finally, the security proof of the proposed scheme ensures the security and consistency of outsourced microservices. The simulation results also show that our scheme greatly improves the efficiency of voting feedback.

The ongoing pandemic of COVID-19 has shown the limitations of our current medical institutions. There is a need for research in automated diagnosis for speeding up the process while maintaining accuracy and reducing computational requirements. In this work, an IoT and edge computing based framework is proposed to automatically diagnose COVID-19 from CT scans of the patients using Deep Learning techniques. The proposed method requires less computational power and uses ensemble learning to increase the models’ overall predictive performance. In the simulation, it was found that each model performs better in some areas than the other. The proposed scheme uses ensemble learning to take advantage of such an occurrence and achieved an accuracy of 86.2% and an AUC score of 89.8% on the COVIDCT-Dataset. This accuracy is achieved keeping the hardware accessibility in mind by training the models using a labeled dataset of CT-scans of the patients. Unlike other works, we were able to train models on a single enterprise-level GPU. It can easily be provided on the edge of the network, which reduces communication overhead and latency. This work aims to demonstrate a less hardware-intensive approach for COVID19 detection with excellent performance combined with medical equipment and help ease the examination procedure.

Mobile-edge computing (MEC) is expected to provide reliable and low-latency computation offloading for massive Internet of Things (IoT) with the next generation networks, such as the sixth-generation (6G) network. However, the successful implementation of 6G depends on network densification, which brings new offloading challenges for edge computing, one of which is how to make offloading decisions facing densified servers considering both channel interference and queuing, which is an NP-hard problem. This article proposes a distributed-two-stage offloading (DTSO) strategy to give tradeoff solutions. In the first stage, by introducing the queuing theory and considering channel interference, a combinatorial optimization problem is formulated to calculate the offloading probability of each station. In the second stage, the original problem is converted to a nonlinear optimization problem, which is solved by a designed sequential quadratic programming (SQP) algorithm. To make an adjustable tradeoff between the latency and energy requirement among heterogeneous applications, an elasticity parameter is specially designed in DTSO. Simulation results show that compared to the latest works, DTSO can effectively reduce latency and energy consumption and achieve a balance between them based on application preferences.

Long distance communication links may severely affect the cyber-physical systems (CPSs) in 5G (and future 6G) networks and degrade its reliability and resilience by disrupting the quality index of network latency. Further, centralized network architectures have low fault tolerance and are prone to security threats. Virtualized software defined network (vSDN)-enabled 5G networks closely monitor these facts and redefine the existing network topology to find potential locations for deploying controller and hypervisor instances. In this article, we propose an approach of dynamically deploying controller-hypervisor (C-H) pair(s) to provide a variety of network functions like differentiation between control and data signals, various translation functions, etc., with ultra low latency (ULL). The system model deals with real network topology and four well-defined network latency matrices with a mixed integer linear programming model to optimize latency objectives. A reverse path-flow mechanism (RPFM) has been proposed to provide feasible solutions by keeping the network load, and controller capacity under a tolerance limit. We have further minimized the H-plane load by distributing the network resources based on the arrival time of SERVICE_IN requests from the users. Simulation results show that our proposed technique achieves significant reduction in latency and an evolved-ULL (e-ULL) experience, where all real-time user demands are handled efficiently. The proposed approach can also be used for similar critical localization problems like service chain mapping in 5G-NR, baseband unit deployment in 5G C-RAN and firewall deployment in distributed CPS.

With the proliferation of information and communication technology in every walks of the society, including healthcare services, digitization, and increased sophistication have been gaining pace, digital healthcare alternatives such as electronic healthcare record (EHR) have gained prominence with increased patients' data volume. However, traditional EHR-based systems are plagued by data loss risks, security and immutability consensus over health records, gapped communication among constituted hospitals, and inefficient clinical data retrieval systems, among others. Blockchain has been developed as a decentralized technology that holds the promise to address the aforesaid facilities in EHR-based systems. This article presents a patient-centric design of a decentralized healthcare management system with blockchain-based EHR using javascript-based smart contracts. A working prototype based on hyperledger fabric and composer technology has also been implemented which guarantees the security of the proposed model. Experiments with the hyperledger caliper benchmarking tool provide performance such as latency, throughput, resource utilization, and so on under varied scenarios and control parameters. The results affirm the efficacy of the proposed approach.

introduction Introduction to the Special Issue on Decentralized Blockchain Applications and Infrastructures for Next Generation Cyber-Physical Systems Share on Editors: Kim Kwang Raymond Choo University of Texas at San Antonio University of Texas at San AntonioView Profile , Uttam Ghosh Vanderbilt University Vanderbilt UniversityView Profile , Deepak Tosh University of Texas El Paso University of Texas El PasoView Profile , Reza M. Parizi Kennesaw State University Kennesaw State UniversityView Profile , Ali Dehghantanha University of Guelph University of GuelphView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 2June 2021 Article No.: 38epp 1–3https://doi.org/10.1145/3464768Online:15 June 2021Publication History 0citation68DownloadsMetricsTotal Citations0Total Downloads68Last 12 Months68Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.

Internet-of-Things has emerged to develop smart communities so that real time sensing and decision can improve operational efficiency and quality of lives. However, establishing such infrastructure can be exceptionally intricate due to the vast variety of devices and the implemented technologies. Also, it poses several unique challenges, such as heterogeneity of the infrastructure, type, and scale of deployment, security, privacy, and inter-operability. One primary concern in a smart city environment is the capabilities of typically end-IoT devices which are vulnerable to security threats and prone to confidentiality and integrity breach of data. Blockchain can potentially address these security challenges due to the distributed ledger's inherent properties. In this paper, we propose a decentralized architecture using the Blockchain to provide a secure and resilient smart city infrastructure that can run the ledger service over a distributed network. We consider a permissioned Blockchain, Hyperledger Sawtooth, and to automate and to overcome the infrastructural challenges concerning the smart city deployment, and we provide a systematic methodology that automates the deployment process and saves a significant amount of time. We simulate and deploy a Blockchain-integrated smart city environment using the proposed seamless deployment strategy using our automation module. With the proposed deployment scheme, we improve the Blockchain-based infrastructure development time by 82% compared to the traditional deployment approach.

In recent trends, Cyber-Physical Systems (CPS) and Internet of Things interpret an evolution of computerized integration connectivity. The specific research challenges in CPS as security, privacy, data analytics, participate sensing, smart decision making. In addition, The challenges in Wireless Sensor Network (WSN) includes secure architecture, energy efficient protocols and quality of services. In this paper, we present an architectures of CPS and its protocols and applications. We propose software related mobile sensing paradigm namely Mobile Sensor Information Agent (MSIA). It works as plug-in based for CPS middleware and scalable applications in mobile devices. The working principle MSIA is acts intermediary device and gathers data from a various external sensors and its upload to cloud on demand. CPS needs tight integration between cyber world and man-made physical world to achieve stability, security, reliability, robustness, and efficiency in the system. Emerging software-defined networking (SDN) can be integrated as the communication infrastructure with CPS infrastructure to accomplish such system. Thus we propose a possible SDN-based CPS framework to improve the performance of the system.

Cloud data centers are critical infrastructures to deliver cloud services. Although security and performance of cloud data centers have been well studied in the past, their networking aspects are overlooked. Current network infrastructures in cloud data centers limit the ability of cloud provider to offer guaranteed cloud network resources to users. In order to ensure security and performance requirements as defined in the service level agreement (SLA) between cloud user and provider, cloud providers need the ability to provision network resources dynamically and on the fly. The main challenge for cloud provider in utilizing network resource can be addressed by provisioning virtual networks that support information centric services by separating the control plane from the cloud infrastructure. In this paper, we propose an sdn based information centric cloud framework to provision network resources in order to support elastic demands of cloud applications depending on SLA requirements. The framework decouples the control plane and data plane wherein the conceptually centralized control plane controls and manages the fully distributed data plane. It computes the path to ensure security and performance of the network. We report initial experiment on average round-trip delay between consumers and producers.

In this poster we have proposed a novel network virtualization framework for information centric data center networks that decouples the control plane and data plane, where the fully distributed data plane is controlled and managed by a well-defined and centralized control plane. Exploiting some network virtualization notion, the proposed framework provides an easily and fast deployable solution to information centric data center networks to provide several virtual networks on same physical network and efficiently handle client requests. It also deals with reliable access to virtual networks as well as mapping of virtual and physical network elements. The proposed framework utilizes the efficiency of information centric networking by providing in-network caching and faster access to requested data.

In this paper, we propose a low-overhead identity-based distributed dynamic address configuration scheme for secure allocation of IP addresses to authorized nodes of a managed mobile ad hoc network. A new node will receive an IP address from an existing neighbor node. Thereafter, each node in a network is able to generate a set of unique IP addresses from its own IP address, which it can further assign to more new nodes. Due to lack of infrastructure, apart from security issues, such type of networks poses several design challenges such as high packet error rate, network partitioning, and network merging. Our proposed protocol takes care of these issues incurring less overhead as it does not require any message flooding mechanism over the entire MANET. Performance analysis and simulation results show that even with added security mechanisms, our proposed protocol outperforms similar existing protocols.

Secure distributed IP addressing is a prime requirement in mobile ad hoc networks (MANETs) for unicast communication. Several types of security threats have been observed in most of the proposed dynamic addressing approaches as they rely on broadcasting for address solicitation and/ or duplicate address detection. In this paper, we propose an ID based distributed dynamic IP configuration scheme to securely allocate IP addresses to the authorized hosts for a MANET without broadcasting over the entire network. Each host can generate unique IP addresses from its own IP address and can assign those addresses to the new nodes. In addition, we propose a novel signature scheme that authenticates and lessens the security threats associated with dynamic IP configuration. Proof of correctness of the proposed signature scheme verifies that the scheme is secure against any forgery attack. Extensive simulation results show that the proposed addressing scheme has low overhead and fairly good addressing latency with added security mechanisms as compared to similar existing dynamic configuration schemes. Moreover, the proposed scheme is robust and can efficiently solve the problem of network partitions and mergers.