Cloud computing is currently playing an essential role in supporting emerging sectors such as smart energy, intelligent transportation, and large-scale distributed systems. The scalability and adaptability features of cloud computing are efficiently enable resource utilization and continuous data exchange in dynamic and heterogeneous environments. However, the increasing demand for cloud services has raised the energy consumption of cloud data centers, which has a critical environmental impact. Therefore, sustainable resource management tactics have become crucial in today's world. Dynamic Virtual Machine (VM) consolidation is one of the major tactics for sustainable resource management in the green cloud computing environment. Herein, a novel dynamic Energy-Aware Cosine Similarity Learning Network (ECSLN) is proposed to predict the overutilized host. Further, an Impact Factor-Based VM Selection (IFBVMS) method is proposed to select VMs for migration, and an ECSLN-Packed Placement method for VM placement into hosts. The main aim of the proposed VM consolidation is to maximize energy efficiency while preserving compliance with Service Level Agreements (SLAs) for sustainable environments. The experimental evaluation using real-world traces like PlanetLab, Bitbrains, and Alibaba Cluster 2020 workload validates that the proposed VM consolidation methods significantly reduce energy consumption and SLA violation compared to the existing methods for sustainable environments. The evaluation shows that the proposed ECSLN-based consolidation framework enables green cloud infrastructure by intelligently balancing energy consumption, SLA violation, and performance of cloud data centers.

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 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%.

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.

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.

Manufacturing of integrated circuits at the smaller technology nodes leads to several defects in them that must be screened and appropriately diagnosed for minimization of cost overruns. A substantial portion of the functional failures during the process of manufacturing test is often attributed to the defects inside the scan chains. With the advancements in the digital test technologies, almost every chip is manufactured with in-built pattern compression infrastructure. This exacerbates the problem of scan chain diagnosis from the collected failure traces. In this work, an automated methodology to perform this diagnosis in the presence of multiple faults is proposed. Deep learning is utilized to predict the probable candidate locations given the compressed scan chain response. Experiments have been performed on different fault models. Experimental results indicate that the proposed methodology is able to perform the diagnosis with a success rate of approximately 80-100%.

The concept behind the Internet of Things (IoT) is taking everything and connecting to the internet so that all devices would be able to send and receive data online. Internet of Vehicles (IoV) is a key component of smart city which is an outcome of IoT. Nowadays the concept of IoT has plaid an important role in our daily life in different sectors like healthcare, agriculture, smart home, wearable, green computing, smart city applications, etc. The emerging IoV is facing a lack of rigor in data processing, limitation of anonymity, privacy, scalability, security challenges. Due to vulnerability IoV devices must face malicious hackers. Nowadays with the help of blockchain (BC) technology energy system become more intelligent, eco-friendly, transparent, energy efficient. This paper highlights two major challenges i.e. scalability and security issues. The flavor of edge computing (EC) considered here to deal with the scalability issue. A BC is a public, shared database that records transactions between two parties that confirms owners through cryptography. After a transaction is validated and cryptographically verified generates “block” on the BC and transactions are ordered chronologically and cannot be altered. Implementing BC and smart contracts technologies will bring security features for IoV. It plays a role to implement the rules and policies to govern the IoV information and transactions and keep them into the BC to secure the data and for future uses.

The objective of this paper is to develop a methodology for generating probabilistic risk maps for unobserved quantities of interests such as aflatoxin. Aflatoxin is a naturally occurring carcinogenic and it is a serious global issue and an emerging risk for crop producers. The production of aflatoxin is highly dependent on environmental conditions such temperature and water activity, and it can contaminate grains before harvest or during storage. The focus of this paper is to develop a procedure to account for spatial dependencies and uncertainties in risk calculations, to provide various stakeholders with situational awareness to better understand, communicate, and mitigate the aflatoxin risk before harvest. The proposed probabilistic model is obtained in two stages: the production of aflatoxin with quantified uncertainties is modeled under various temperature and water activity conditions within a controlled environment (wet-lab), and then the predictive aflatoxin model is linked with environmental conditions obtained on a regular basis to generate regional probabilistic risk maps. Since both aflatoxin production and environmental data are modeled using Gaussian processes, the resulted probabilistic model is a stacked Gaussian process, where the environmental Gaussian process model governs the input space of the aflatoxin Gaussian process model. The regional prediction of aflatoxin is obtained by marginalizing over the latent space provided by the environmental variables. The methodology is applied to calculate the aflatoxin levels of corn lands in South Carolina in the drought year 2012, where few field measurements are available for an initial comparison with our aflatoxin predictions.

BACKGROUND: Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley's K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites. RESULTS: Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type. CONCLUSIONS: Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.