Search

Currently, all document processes in developing countries require third-party (human) verification; this makes said documents susceptible to human error. Blockchain technology makes the need for a central authority redundant as the devices needing to download digital assets are connected to one another via a peer-to-peer network. It allows for the exchange and storage of digital assets without the need for third-party oversight. The main objective of this article is to develop a blockchain solution in an attempt to mitigate corruption. The contribution details a document verification process as a solution to lessen third-party access to sensitive information and ensure that it is safe and untampered with. The solution proposed to this issue was the introduction of blockchain technology to aid in document verification. This makes the entire system decentralized and reduces the amount of third-party access to sensitive information. Using blockchain makes documents immutable thus reducing corrupt activity such as identity theft and ghost accounts. A blockchain implementation in the field of document verification also dissolves the need for certified copies, which then allows for better use of state funds and public service workers. This work can prove to be a major enabler to achieve sustainable development goal-16 (Peace, Justice, and Strong Institutions).

Artificial intelligence (AI) is gaining lots of interest across multiple domains, such as consumer electronics, the Internet of Things (IoT), blockchain, edge computing, remote sensing, etc. Bringing adaptiveness to AI-based learning makes the overall system autonomous and robust. In parallel, Blockchain is one of the vital solutions of current age computing to make the end-to-end process decentralize. It is unavoidable to integrate AI with Blockchain when the system designing focuses on decentralized approaches, such as decentralized storage, security, and processing. Blockchain is the only solution in the current age to make the end-to-end system decentralized. This article gives a close view of Blockchain properties, data structures, IoT use cases, AI integration, and the potential future aspects.

Nonfungible tokens (NFTs) have recently received widespread attention as their market has experienced massive growth since early 2021. NFTs are blockchain-based tokens used to represent ownership of digital and physical assets in a manner that is transparent, traceable, tamper-proof, secure, and trustworthy. Although NFTs have enabled new applications in the gaming and art industries, their full extent and potential are yet to be explored. In this article, we outline the vision of using NFTs in smart cities. We describe the main components of NFTs and how smart city applications, such as smart governance, smart services, smart economy, smart industry, smart environment, and smart mobility and transportation, can benefit from them. In addition, we show how leveraging NFTs can add value to the metaverse. Finally, we discuss research challenges that hinder the adoption of NFTs in smart cities.

Globalisation has facilitated different industries to eliminate geographical boundaries and equipped organisations to work collectively to produce goods. Pharmaceuticals is one such industry which has majorly benefited from globalisation to reduce costs and increase profitability at different aspects of research, development, manufacturing of drugs and finally distribution of drugs. Globalisation not only reduces costs and increases profits for the industry, it also makes essential medication available to all individuals even at remote areas of undeveloped, developing and developed countries. As the number of entities or participants grew in the Pharmaceutical Supply Chain (PSC), the complexity and abstractness of the drug delivery from manufacturer to consumer has increased, which raised new concerns like counterfeit medication, incorrect, incomplete or no information about the drug reaching the consumer thus undermining customer confidence and most importantly distribution delays which can cause serious impact on the life of the consumer as well as business growth. Considering all these challenges, there is a dire need for a robust PSC which can eliminate blind parties and provide a transparent chain. A transparent PSC will eliminate data fragmentation between different participating entities by creating a trail of secure, single source of truth for the entire life cycle of a medicine, thereby eliminating the introduction of counterfeits and ensuring the consumer safety. This article proposes a novel Distributed Ledger Technology (DLT) based transparent supply chain for PSC and proof‐of‐concept is implemented to analyse the scalability and efficiency of the proposed architecture. Distributed Ledger Technology (DLT) is one of the technologies which can provide such transparent PSC and Smart Contracts (SC) are the most commonly used component in such systems to implement business logic and access control mechanisms. The proposed PharmaChain model investigates all the interactions between the main entities in PSC and also addresses smart contract issues such as re‐entrance, randomness and lacking trustworthy data feed. Results are compared with other proposed solutions for PSC based on DLT.

Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients’ home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients’ information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.

Nonfungible tokens (NFTs) have emerged as one of the most popular digital assets in recent years. They represent ownership of unique items that are stored on a blockchain and cannot be changed, replaced, or copied. Digital exchanges sell and trade NFT-minted assets that can only have one official owner at a time. NFTs are entering all sectors, from art to real estate, and healthcare is the next sector. In this article, we envision using NFTs in healthcare. We present the main components along with key features of NFTs and their benefits. We discuss the potential opportunities brought about by NFTs in different healthcare areas, such as supply chain management, patient-centric data management, digital twins, clinical trial management, and genomics. We discuss some important challenges hindering the adoption of NFTs in healthcare, serving as future research directions.

Federated learning collects data from various devices, analyzes it locally, aggregates it, and then finds meaningful insights from it. Data sampling works the same way by dividing the larger data set into smaller parts and applying computation to those data sets, which reduces the time taken to do the work. Data sampling in federated learning aims to find the ideal mixture of selecting data sets for training purposes to improve training accuracy while staying within the maximum capability of the device and network. In this article, we present an overview and analysis of recent data sampling techniques for federated learning. The list includes sampling approaches suitable for federated learning environments such as clustering, dynamic sampling, adaptive sampling, probabilistic sampling, and many more. The feature analysis is comprised of a description of the procedure, the criteria, and other relevant parameters for sampling. The efficiency of the sampling technique is analyzed via comparison of claimed accuracy and convergence rate with respect to the used dataset.

Propelled by the growth of automotive industry, and the ubiquity of smart sensors, intelligent transport systems such as the Internet of Vehicles (IoV) have seen significant research interest in recent times. The emerging distributed IoV networks support real-time vehicular applications through on-device computing, communication-efficient data processing, edge computing, and cloud aggregation. While enriching the user experience by minimizing the end-to-end latency through efficient energy management, IoV deployments face the fundamental challenge of security attacks. In this article, we discuss various security attack modes in an edge-centric intelligent IoV framework, consisting of distributed smart vehicles, and remote processing units. We highlight various attack detection and mitigation mechanism for the proposed IoV framework, to address the security challenges. Finally, we shed light on several future research directions to ensure security of sensory data in edge-centric IoV systems.

In recent times, communication and connectivity requirements have been evolving rapidly, and it is evident with the recent deployment of fifth-generation (5G) cellular networks worldwide. Similarly, unmanned aerial vehicles (UAVs) have gained interest in several applications, including surveillance, monitoring, geographic mapping, and logistics, among others. Therein, 5G cellular network support for UAVs is gaining both attention and momentum within various regulatory bodies and consortiums. While 5G is gaining momentum to establish necessary communication technologies for UAV operations, we provide a state-of-the-art overview emphasizing the technical challenges of applying millimeter-wave technology to support UAV communication. This article provides a closer look at the key enabling technologies and their potential issues impacting UAV operation under a unifying 5G network and beyond. In addition, this article also discusses the security issues impacting network privacy and safety vulnerabilities of UAVs. To conclude, we identify several open research issues that drive UAV deployment and operations and demand further study.

The quantity of IoT gadgets is consistently expanding and these gadgets are delay-sensitive and require a speedy reaction. These gadgets are connected with a cloud for the computation of requests yet there may be a delay in computation. To beat the present circumstance, fog nodes are kept at the edge of the IoT gadgets to perform the speedy calculation for delay-delicate applications. IoT devices generate a tremendous number of tasks and if the task number is increased then the number of fog nodes needs to be increased for immediate processing. Here, QoS-aware Fog Node (QSFN) provisioning algorithm has been proposed in which the number of fog nodes is automatically increased based on CPU utilisation, queue length and the number of available resources. We evaluate the performance of QSFN provisioning with Bee Swarm and Concurrent algorithm based on makespan, average execution, flowtime, success execution rate and average response time.

In the ever-evolving landscape of technology, the rapid proliferation of emerging computing paradigms has opened new frontiers of innovation, transforming the way we interact with and perceive the world. This relentless march of progress has ushered in the era of “Security and Privacy-Aware Emerging Computing,” a paradigm that places the fundamental values of security and privacy at its core. It seeks to develop innovative solutions that safeguard sensitive data, protect user privacy, and fortify the security of emerging computing systems.

The questions “When do we use the blockchain?” or “Where do we use the blockchain?” or “Why do we use the blockchain?” look simple, but these questions become complicated due to the increasing utilization of the blockchain. These days, it is being explored as a standard security solution. The blockchain has unique properties, and these are entirely different from other standard security or cryptography solutions. This article gives a summary of the uses of the blockchain, while answering the above three important questions.

The Internet of Things (IoT) is an emerging technology whose popularity and use cases grow with every passing day. Although the domain of IoT has proven to be transformative, as the deployment size of the network increases, it becomes a huge challenge to secure such a large number of heterogeneous devices connected by a complex network having a variety of access protocols. Software defined networking (SDN), with its unique capabilities of decoupling control plane from data plane and maintaining a centralized programmable controller, has been gaining a lot of prominence in solving the security challenges faced by the IoT domain. This work first analyzes the security threats faced by the IoT domain and the inherent architectural flaws for these vulnerabilities. It then goes on to understand how solutions presented by the modified architecture of SDN-IoT can help to tackle these challenges. Lastly, the work draws a realistic image of bringing in SDN-IoT by showcasing the critical issues faced by this domain and the current efforts attempting to solve these issues. Thus, it serves as a good primer for researchers looking to delve into the topic for tackling prevalent challenges.

This article presents the first-ever blockchain that can simultaneously handle device and data security, which is important for the emerging Internet-of-Everything (IoE). It presents a unique concept of blockchain that integrates hardware security primitives called physical unclonable functions (PUFs) to solve scalability, latency, and energy requirement challenges and is called PUFchain. This article also introduces a new consensus algorithm called “Proof of PUF-Enabled Authentication” (PoP) for deployment in PUFchain. PoP is 1000 times faster than the well-established proof-of-work (PoW) and 5 times faster than proof-of-authentication (PoAh).

The use of Internet-of-Things (IoT) devices in homes and the immediate proximity of an individual communicates to create Personal IoT (PIoT) networks. The exploratory study of PIoT is in its infancy, which will explore the expansion of new use cases, service requirements, and the proliferation of PIoT devices. This article provides a big picture of PIoT architecture, vision, and future research scope.

A variety of technologies including Internet-of-Things (IoT), IoT-edge/fog computing, and embedded systems are gaining more importance in designing smart applications (e.g., smart healthcare) with minimal human interventions. All the devices and datacenters in these applications are connected to the Internet. Considering the Internet properties, cybersecurity attackers get an easy chance to become part of the system and participate in the data communication process. This article presents thoughts on paradigm-shift next generation cryptosystems to overcome the vulnerabilities of the omnipresent conventional cryptosystems.

The perception of fog computing is to bring a virtual presence into day-to-day objects. The lowest layer of the fog architecture is the Internet of Things (IoT), which created a revolution by changing ordinary objects into smart objects that automatically sense and process data. In the IoT, smart objects connected over the Internet communicate with each other and exchange data with the fog server to improve services to customers. There are some challenges to achieving the benefits of the IoT. This article discusses a three-layered fog architecture and highlights potential security threats and solutions at each layer. Finally, open research issues are discussed at all three layers of the fog hierarchy.

The Internet of Things (IoT) is an emerging technology that has gained a huge user base by facilitating Internet-connected devices being used in numerous applications including smart vehicular infrastructure. In this context, we focus on the traditional vehicular ad hoc network that has evolved into a new perception called the Internet of Vehicles (IoV), and is expected to soon transform into the Internet of Autonomous Vehicles (IoAV). IoAV hopes to facilitate smart vehicular infrastructure and autonomous driving without the need for human involvement. However, as the number of connected vehicles keeps increasing, so does the need for autonomous decision making. Hence, the IoAV must provide robust, secure, seamless, and scalable communication among the vehicles as well as the roadside units. This article provides an overview of autonomous vehicle communication layers, its associated properties, and security threats. Further, this article also briefly discusses the current research trends and future research issues.

IoT has revolutionized the modern way of life with the advent of intelligent systems. The traditional network architecture employed in the IoT domain is unable to define sufficient solutions of these challenges for a cost-effective and seamless workflow. The intelligent IoT system ensures scalability in challenging or hostile environments. With the emergence of the SDN domain offering programming ability of the control plane, many of these challenges seemed surmountable. This article presents a synergized overview of the challenges faced by the traditional domain and how they can be overcome by the upcoming domain of SDN-IoT. A thorough analysis of the practical adoptions showcase the feasibility of the solution in a real-time environment. The article examines the state of the art and highlights some of the key open points in the domain, based on shortcomings of the current state of SDN-IoT, that can be taken up for future research.

AI security is vital; however, it should be designed carefully by considering the suitable assurance processes based on system requirements. Data should be secure throughout the process of an AI system. The use case3 of self-driving cars is briefly considered.

The Internet of Things (IoT) is a framework built as a network of trillions of devices (called things) communicating with each other to offer innovative solutions to real-time problems. These devices monitor the physical environment and disseminate collected data back to the base station. In many cases, the sensor nodes have limited resources like energy, memory, low computational speed, and communication bandwidth. In this network scenario, sensors near the data collector drain energy faster than other nodes in the network. A mobile sink is a solution in sensor networks in which the network is balanced with node energy consumption by using a mobile sink in the sensing area. However, the position of the mobile sink instigates packet overhead and energy consumption. This article discusses a novel data-routing technique to forward data toward a base station using a mobile data collector, in which two data collectors follow a predefined path to collect data by covering the entire network. The proposed technique improves the network performance, including energy consumption and sensing area lifetime.

In 2008, the emergence of the blockchain as the foundation of the first-ever decentralized cryptocurrency not only revolutionized the financial industry but proved a boon for peer-to-peer (P2P) information exchange in the most secure, efficient, and transparent manner. The blockchain is a public ledger that works like a log by keeping a record of all transactions in chronological order, secured by an appropriate consensus mechanism and providing an immutable record. Its exceptional characteristics include immutability, irreversibility, decentralization, persistence, and anonymity.

The blockchain is emerging as one of the most propitious and ingenious technologies of cybersecurity. In its germinal state, the technology has successfully replaced economic transaction systems in various organizations and has the potential to revamp heterogeneous business models in different industries. Although it promises a secure distributed framework to facilitate sharing, exchanging, and the integration of information across all users and third parties, it is important for the planners and decision makers to analyze it in depth for its suitability in their industry and business applications. The blockchain should be deployed only if it is applicable and provides security with better opportunities for obtaining increased revenue and reductions in cost. This article presents an overview of this technology for the realization of security across distributed parties in an impregnable and transparent way.

Due to the wide variety of devices used in computer network systems, cybersecurity plays a major role in securing and improving the performance of the network or system. Although cybersecurity has received a large amount of global interest in recent years, it remains an open research space. Current security solutions in network-based cyberspace provide an open door to attackers by communicating first before authentication, thereby leaving a black hole for an attacker to enter the system before authentication. This article provides an overview of cyberthreats, traditional security solutions, and the advanced security model to overcome current security drawbacks.

Modern sensing devices play a pivotal role in achieving data acquisition, communication, and dissemination for the Internet of Things (IoT). Naturally, IoT applications and intelligent sensing systems supported by sensing devices, such as wireless sensor networks (WSNs), are closely coupled. Modern intelligent sensing systems generate huge volumes of sensing data, well beyond the processing capabilities of common techniques and tools. As a result, collecting, managing, and processing IoT big sensing data within an acceptable time duration is a new challenge for both research and industrial applications. The massive size, extreme complexity, and high speed of big sensing data bring new technical requirements including data collection, data storage, data organization, data analysis, and data publishing in real time when deploying real-world IoT applications. To better facilitate these IoT applications, the convergent research of WSNs, big data, the IoT, and cloud computing is a natural scientific development trend. In this article, we concentrate on big-sensing-data curation and preparation issues with cloud computing under the theme of the IoT. There are three especially critical issues that need to be addressed: scalable big-sensing-data cleaning, scalable big-sensing-data compression, and cloud-based data curation response for IoT device optimization. Viewed from the IoT side, all IoT sensing devices are integrated together in an adaptive solution and upload their data onto the cloud. The automatic responses from both the cloud and intelligent sensors will change the status or behavior of sensing devices and, therefore, the status of the IoT itself.

In the continuous evolution of the computer age, the next big step in security is being achieved by embracing quantum cryptography. As the unit of information shifts from the currently used bit to the quantum bit (qubit), a new realm of untapped features is offered that can be used to ensure the confidentiality of the information being shared among participants. This system confidentiality is ensured by the use of a quantum channel for exchanging secret keys, which are then used to encrypt the data being shared. The system is a combination of cryptography and quantum computing that is aided by the laws of physics.

A smart system integrates appliances of sensing, acquisition, classification and managing with regard to interpreting and analyzing a situation to generate decisions depending on the available data in a predictive way.Remotely sensed images are an essential tool for evaluating and analyzing land cover dynamics, particularly for forest-cover change. The remote data gathered for this operation from different sensors are of high spatial resolution and thus suffer from high interclass and low intraclass vulnerability issues which retards classification accuracy. To address this problem, in this research analysis, a smart logistic fusion-based supervised multi-class classification (SLFSMC) model is proposed to obtain a thematic map of different land cover types and thereby performing smart actions. In the pre-processing stage of the proposed work, a pair of closing and opening morphological operations is employed to produce the fused image to exploit the contextual information of adjacent pixels. Thereafter quality assessment of the fused image is estimated on four fusion metrics. In the second phase, this fused image is taken as input to the proposed classifiers. Afterward, a multi-class classification model is designed based on the supervised learning concept to generate maps for analyzing and exporting decisions based on any critical climatic situation. In our paper, for estimating the performance of proposed SLFSMC among few conventional classification techniques such as the Naïve Bayes classifier, decision tree, Support vector machine, and K-nearest neighbors, a statistical tool called as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is involved. We have implemented proposed SLFSMC system on some of the regions of Victoria, a state of Australia, after the deforestation caused due to different reasons.

Now we are in an era of technology transformation in our everyday life, where data play a key role in the decision making and bringing the action into reality. These data are collected from many distributed sources. Another important concept in this process is machine learning (ML) and data analytics. Federated learning (FL) is the term coined by Google. It facilitated the distributed learning process and shared the results to the outcomes to the central entity instead of conducting the complete learning process at the centre. In the traditional machine learning approach, data are brought to the model. Whereas FL brings ML techniques to the data used at end devices. In a nutshell, FL, which is also known as distributed learning deals with both centralized and distributed devices. The central entity(server) selects the statistical model which is to be trained. Then, it sends the trained model to multiple decentralized devices or servers. These distributed nodes train the model locally with their own data. Finally, the central entity pools the results from the distributed nodes and prepares a common model without accessing the data from the other nodes.1,2

The articles in this special section focus on artificial intelligence (AI)-driven security solutions for the Internet of Everything (IoE). The IoE integrates the components, such as people, things, data, and process for the smart system building, and where the Internet of Things (IoT) is integrated as one of its architectural components.1 Individual components are connected to the Internet to build an autonomous system.

Software defined networking has solved many challenging issues in the field of networking industry. It separates the control plane from the data forwarding plane. This makes SDN to be more powerful than traditional networking. However, energy cost enhances the overall network cost. Therefore, this issue needs to be addressed to improve design requirements and boost the networking performance. In this article, several energy efficiency techniques have been discussed. To represent it in more detail, a thematic taxonomy of energy efficiency techniques in SDN is given by considering several technical studies of the past research. These studies have been categorized into three sub categories of traffic aware model, end-host aware model and finally rule placement. These models are provided with detailed objective functions, parameters, constraints and detailed information. Furthermore, useful visions of each approach, its advantages and disadvantages and compressive analysis of energy efficiency techniques are also discussed. Finally, the paper is highlighted with the future directions for energy efficiency in SDN.

The advances in communication, computation, and sensing technologies have led researchers, academics, and aircraft industries to strive to design efficient enabling technologies for unmanned aerial vehicles (UAVs). UAVs are being deployed for surveillance, monitoring, and various other operations in a wide range of military, consumer, and commercial applications. A web of UAVs forming a heterogeneous network offers a dynamic solution for providing wireless and Internet connectivity to devices, sensors, and machines in various situations, such as during natural disasters and over mountainous terrain, among others. Thus, achieving high-speed wireless communication for nextgeneration communication systems (5G) has emerged as one of the applications of UAVs. Remote sensing and surveillance as well as critical infrastructure monitoring and control are further applications that UAV technologies are disrupting.

The modern era of information and technology leverages connectivity and information sharing for enhancing applications and reducing human intervention. The proliferation of Internet of Things (IoT) in almost all aspects of modern life raises major challenges of security and privacy in health care services and the pharmaceutical industry. Blockchain has emerged as a secure technology in a trustless network without central governance. The inherent attributes it provides can solve the security and privacy challenges related to IoT and extend its capabilities. However, traditional instances of Blockchain are not suited for the IoT environment for reasons such as computationally expensive consensus process. Proof of authentication is a lightweight consensus algorithm that can be implemented in the IoT environment. This paper presents an IoT‐friendly Blockchain scheme implemented to evaluate the feasibility of medical supply and drug transportation to aid security and mitigate privacy issues which are fault‐tolerant, transparent, and traceable. The proof of concept implementation on portable single board computer demonstrates proof of authentication consensus algorithm executed with block validation and block addition in 30 and 40 ms while consuming 45 and 60mJ, respectively. Additionally, the network performance of the proposed architecture was presented and evaluated in the application context. Integrated IoT and Blockchain Architecture.

In vehicular networks, vehicles communicate with each other and transfer messages to enhance the safety of both commuters and vehicles. Trust management in these networks has become a key issue because of the selfish behavior that is frequently exhibited by some vehicles. Most of the existing trust management schemes for vehicular networks schemes have adopted cryptographic techniques, which require high computation time and high network resource utilization. We propose a Hierarchical Trust Management System (HTMS) that relies on the trust metric computation for every individual vehicle. We also propose a systematic approach based on trust evaluation, trust propagation and trust aggregation to compute the trustworthiness of vehicles. Initially, the trust evaluation scheme computes a local trust score based on a behavioral analysis. Next, the proposed trust propagation model shares the computed local trust score to neighboring vehicles using a platoon-based dissemination technique. Based on the local trust score and propagated trust opinions, our aggregation technique computes a Global Trust score (GTS) using Dempster-Shafer theory. Finally, based on the computed GTS score, the proposed system invokes an action module to reward or punish vehicles. The simulation results obtained show that the proposed system is highly resilient to trust-based attacks and enables trustworthy peers to generate a higher number of data transfers within the network.

The Internet of Things (IoT) is focused on architectures and protocols for the efficient interconnection of heterogeneous things, deployment of infrastructure, and creation of value-added services. The IoT has become an emerging technology, where more than 50 billion things will be connected to the Internet by 2020, and data in the network will grow by another 1,000 times. The framework of the IoT is the backbone for several applications, such as smart cities and smart homes, where most of the data move to the cloud for further evaluation because of the resource poverty of IoT devices.