ArticlePublisher preview available

Artificial intelligence and blockchain: A review

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

It is irrefutable that blockchain and artificial intelligence (AI) paradigms are spreading at an incredible rate. The two paradigms have distinctive level of innovative nature and multidimensional business propositions. Blockchain innovation can robotize instalments to grant a way for exchanging personal records, information, and logs in a secure, and decentralized manner and can be revealed digitally in the digital currency era. As of late, blockchain and AI are two of the most trending technologies. Blockchain can administer connections among members with no mediator via smart contracts. AI, then, offers insight and dynamic capacities for machines just like people. In this survey, we provide a comprehensive overview about the applications of AI in blockchain. We audit, and sum up the rise of blockchain applications, and stages explicitly focusing on the AI research area. We likewise recognize and summarize open challenges in using blockchain and AI techniques. We also classify the effect of the cloud with these two innovations with respect to the computerized economy, which includes Blockchain as a Cloud and Blockchain as a Service. We moreover survey difficulties and issues identified while provisioning these technologies. It has been found that the integration of AI and blockchain is trusted to make various prospects. Such techniques provide scientists and authorities with an accuracy of up to 90% when taken properly into consideration. Emerging trends worldwide.
This content is subject to copyright. Terms and conditions apply.
Received: 7 November 2020 Revised: 23 December 2020 Accepted: 8 March 2021
DOI: 10.1002/ett.4268
RESEARCH ARTICLE
Artificial intelligence and blockchain: A review
Adedoyin A. Hussain1,2 Fadi Al-Turjman2,3
1Computer Engineering Department,
Near East University, Nicosia, Mersin 10,
Turkey
2Research Centre for AI and IoT, Near East
University, Nicosia, Mersin 10, Turkey
3Artificial Intelligence Engineering Dept.,
Near East University, Nicosia, Mersin 10,
Turkey
Correspondence
Adedoyin Ahmed Hussain, Computer
Engineering Department, Near East
University, Nicosia, Mersin 10, Turkey.
Email: hussaindoyin@gmail.com
Abstract
It is irrefutable that blockchain and artificial intelligence (AI) paradigms are
spreading at an incredible rate. The two paradigms have distinctive level of inno-
vative nature and multidimensional business propositions. Blockchain innova-
tion can robotize instalments to grant a way for exchanging personal records,
information, and logs in a secure, and decentralized manner and can be revealed
digitally in the digital currency era. As of late, blockchain and AI are two of
the most trending technologies. Blockchain can administer connections among
members with no mediator via smart contracts. AI, then, offers insight and
dynamic capacities for machines just like people. In this survey, we provide a
comprehensive overview about the applications of AI in blockchain. We audit,
and sum up the rise of blockchain applications, and stages explicitly focusing
on the AI research area. We likewise recognize and summarize open challenges
in using blockchain and AI techniques. We also classify the effect of the cloud
with these two innovations with respect to the computerized economy, which
includes Blockchain as a Cloud and Blockchain as a Service. We moreover sur-
vey difficulties and issues identified while provisioning these technologies. It has
been found that the integration of AI and blockchain is trusted to make various
prospects. Such techniques provide scientists and authorities with an accuracy
of up to 90% when taken properly into consideration.
1INTRODUCTION
The blockchain is one of the many advertised advancements nowadays, and it has been increasing a great deal of foothold
as a flat innovation to be broadly received in different fields.1-3 Since its commencement in the ’20s, blockchain kept
on rising as a problematic development that will change how we collaborate, robotize installments, follow, and track
exchanges.4Blockchain can be exceptionally practical in taking out the requirement for an incorporated position to
administer and check cooperations and exchanges among a few members. In the blockchain, each exchange is marked
cryptographically and confirmed by all mining hubs. This cannot be changed and it makes a synchronized, safe, and
shared timestamped records.5A similar conspicuous field that is increasing tremendous foothold in artificial intelligence
(AI) that permits a machine in having psychological capacities to understand, surmise, and adjust dependent on infor-
mation it gathers. Ongoing statistical surveying foresees that the AI industry will grow as much as 13 trillion U.S. dollars
constantly in 2030.
Even though there are many contending advancements that attempt to insusceptible information in shrewd homes
over assaults.6Blockchain development as presumably the most encouraging for guaranteeing the home system over
control assaults on locked information and granting a protected stage to all the gadgets in the system to communicate with
Trans Emerging Tel Tech. 2021;32:e4268. wileyonlinelibrary.com/journal/ett © 2021 John Wiley & Sons, Ltd. 1of26
https://doi.org/10.1002/ett.4268
... Since the past decade, blockchain has been widely used in a variety of fields, including various IoT applications. 10,11 By maintaining a safe, shared, and distributed ledge at each connected entity, blockchain ensures secure communication in a peer-to-peer decentralized network. 12 On the blockchain network, smart contracts can run user software programs autonomously in response to triggered events. ...
... The discount operator is used among the subject, object, and each neighbor between the IoT and FSP which is followed by the consensus operator to evaluate the aggregated indirect trust value. Therefore, the total cumulative indirect trust of device D k on F w is calculated by given in Equation (11). ...
Article
Full-text available
With the recent advancements in the Internet of Things, cloud computing has emerged as an important industrial technology that assists in various data analysis operations. However, the remote locality of cloud servers and scalability issues of cloud computing make it unsuitable for real‐time computing‐intensive applications. Fog computing strives to support cloud computing in meeting scalability demands by providing location‐sensitive services closer to end devices. With decentralized heterogeneous resource capabilities, fog architecture can handle several computation‐intensive and delay‐sensitive user requests. Although deploying service providers in an untrustworthy environment makes it challenging to assess the trustworthy acquired services. Conspicuously, in this article, we present a trusted task offloading and resource allocation using blockchain technology. To start with, we analyze direct and indirect trust with a subjective logical aggregation approach using a distributed trust assessment approach. Additionally, we examined the various quality of service parameters and constructed a smart contract that utilizes the state‐of‐the‐art deep reinforcement learning algorithm, namely Deep Deterministic Policy Gradient, to maximize fog revenue while serving as many user requests as possible. The entire process from task generation to results calculation is assisted by blockchain and offloading task transactions are stored in the secure, immutable, and tamper‐resistant ledger. To assess the effectiveness of our proposed scheme, we compared the simulation results with other baseline schemes over different performance metrics in terms of reward, service latency, energy consumption, task drop ratio, and transaction success rate. The results suggest that enabling trust computation improves transaction success by 21%.
... Through this procedure, they have picked a feasible way in two phases. This is gained to successfully pick the genuine specialist and assemble the resources [54][55][56][57][58][59]. Yet it has been discussed in the literature that there are still various areas that need tending to, and this work proposed here aimed to settle these issues. ...
Article
Full-text available
Task scheduling for the cloud is one of the main advances in IoMT stage, which impacts the whole execution of the cloud resource. Cloud is a proficient headway for computation, and it encompasses data storage, management, and manipulation in large volumes. Thus, a proposition is being made a better approach to proffer task scheduling in the cloud. In this case, a new hybrid genetic algorithm (HGA) is proposed. The proposed HGA method will be justified by contrasting it with the previous researches and approaches. The CloudSim is utilized to quantify their effect on various metrics like timing factors and resource utilization. The proposed HGA technique enhanced the viability of task scheduling with a better execution rate of 32.57 ms. Thus, the experimented outcomes show that the HGA also reduces cost profoundly.
... Business and corporate monetary administration look simple, yet this is a mindboggling issue on the off chance that somebody does not have the foggiest idea about a successful, effective, and sound working system (Hussain & Al-Turjman, 2021). Since it is so mind-boggling, it is not is business as usual. ...
Article
Full-text available
Running a business is a job that requires high efficiency and productivity. This productivity can also come from the company's bookkeeping or accounting work efficiency. The use of digital technology is one of the goals to gain efficiency towards profitable and sustainable company productivity. For this reason, we have carried out this study intending to get the benefits and benefits of using digital technology to ease the company's accounting work. In order to be able to answer the questions and hypotheses of this study, we have conducted a series of data searches on accounting and technology databases from several international publications. The data we collect we examine using a phenomenological approach to search for as much data as possible to get plant parts that can answer this royal question. The process has involved data coding, data interpretation, and critical evaluation in obtaining relevant answers to questions. Based on the study and discussion of the data, we can conclude that several benefits and advantages are obtained when companies, especially the accounting department, can utilize digital applications in handling the company's financial work tasks, all of which are useful for improving the company's accounting performance. Hopefully, this new finding will be a significant input for efforts to develop a similar study in digital financial applications towards improving the accounting performance of both public and private companies.
... 27,28 In particular, the optimization process must be cost effective, providing stable and productive outcomes to ensure an effective system and to improve an immense measure of information. 29 Considering the limitations in conflicting multiple features and constrains, there is a growing consensus for determining accurate and fast multi-objective optimization methods. To tackle these challenges, the study of various multi-objective optimizations applied to 4G and 5G technologies has become a necessity for the present and future multi-mode terminals 30 and future 6G networks. ...
Article
Full-text available
The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high‐performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks. Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra‐lower latency, ultra‐security, high speed, and reliable connectivity. Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi‐objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks.
... Research communities have shown keen interests to study the exclusive characteristics and research potential of federated learning. 47,48 Although the review study works 49,50 reveal the idea of convergence of blockchain with AI. Authors explore the possibility of using blockchain to do away the key challenges associated with AI. ...
Article
Full-text available
The prominent achievement of blockchain technology stimulates exceptional innovation. The major component of blockchain is the consensus mechanism. The standard consensus mechanisms specifically Proof‐of‐Work (PoW) rely on mining procedures and stake‐based mechanisms such as Proof‐of‐Stake (PoS) rely on massive stake investment as the sole criteria for selection of leader nodes. However, PoW impose huge computational power requirements and latter may incorporate malicious nodes as leader nodes in anonymous blockchain. These issues might fuel the way for distrust among the participants in blockchain. Henceforth, a novel game theory based reliable PoS mechanism for blockchain has been proposed. Federated learning has been used to compute trust_score for each node. The nodes are trained on locally generated dataset. Further, a game theoretic approach has been proposed that uses a reward and punishment scheme to ensure threshold level of trust_score maintenance by each node. Finally, a crop insurance use case has been developed with the consensus mechanism and blockchain coded in python. The insurance claims are made to operate through smart contract based mobile app system to impart more authenticity. The system is tested and results show an intrusion accomplishment rate reduced by approximate 40% when compared to the standard PoS mechanism and by approximately 33% for algorand, 29% for ouroboros and 20% for tendermint. The mean absolute error also decreases by 30% within specific time. Furthermore, the proposed federated learning‐based system is compared with basic neural network‐based machine learning model and the results reveal that a significant reduction in average training time amounting to 8.35 second is achieved. Test accuracy has also been analyzed for various learning mechanisms.
... As a common feature, blockchain ledgers provide an audit trail, which can be used to check the accountability of all transactions occurring in the metaverse. A zero-knowledge evidence system enables individuals to identify critical facts in the metaverse while also protecting their privacy and retaining ownership of their resources from deepfakes [97]. This will prevent AI from exploiting resources in the metaverse. ...
Preprint
Full-text available
Since Facebook officially changed its name to Metaverse in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users' digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments towards the use of blockchain in the metaverse in the future.
Article
Agrochemicals are products that, due to their hazardous nature and high cost, need to be monitored. The management of agrochemical packaging is generally precarious, and the supply chain needs more control due to its reverse characteristic. Traceability in the supply chain usually uses one sensor and not a combination of multiple sensors. The proposed model allows one to trace agrochemicals with reliable and immutable information coming from various sensors, solving problems of unreliable traceability, product theft, and product tampering. Unlike related work, this model contributes with a proposal segmented in modules that focus on security and scalability in controlling used packaging. In this case, the proofs of concept indicate detection of the opening movement of the safe cabinet from 5 lux (unit of illuminance) and movement of packages after a radius of 2 cm, and the data sending time between the model layers was around 1 second. The positive aspects are benefits for detecting intact products and packages openly within the production process and persistently; other benefits include better use of assets and management of the production chain, real‐time production data collection, classification, grouping, and prediction of events. Monitoring the packaging reverse chain and the possibility of transaction auditing. Benefits include better use of assets and management of the production chain, real‐time production data collection, classification, grouping, and prediction of events generated by the production operation, and persistence of this information for future transactions and audits. Farmers and society benefit from making production and supply chains cleaner and safer and reducing the risk of costly and environmental accidents. This article is protected by copyright. All rights reserved.
Chapter
Participatory sensing and collaborative learning has proliferated the emergence of the Internet of Things (IoT) to collect and analyze the grid data in a service-based cloud framework. The decentralized framework prefers multiple participants to train the local or global model accurately. The real-time participant's and cloud computing services play crucial roles such as semi-honesty and maliciousness to address the challenge of privacy-preservation. Moreover, privacy-preservation utilizes machine learning (ML) and data aggregation to discover derived statistics to investigate state-of-the-art techniques and applications. As a result, privacy-preserving ML includes decentralized collaboration, human activity, and collaborative anomaly detection to perform an independent component analysis. It is one of the most widely recognized classifiers for grid applications around the globe. Hence, the capacity of artificial intelligence and IoT frameworks to recognize conceivable distributed differential privacy is significant. It is essential to have a framework that permits the power sector to quickly and precisely identify cyber threats. Cyber threats are the most well-known reason for any offensive maneuver around the world. The objective of this chapter is to design a prescient module with the capacity to foresee if the cyber threat is benign or harmful during the diagnosis and give a predictable outcome. This chapter presents a mechanized diagnosis and prediction of cyber threats utilizing AI and IoT. To additionally boost the model's accuracy and general performance, various learning procedures are applied, such as system robustness, privacy leakages, and parameter optimizer. To do this, the study aims to assemble and test a prescient model that smooth out the various mix of ML specifically, support vector machine with the best classifier, the K-nearest neighbors with the best K value, and Decision Tree (DT) and deep learning algorithms called convolutional neural networks, as well as IoT. These models are assessed and compared to recognize the model with the best performance. To start, the performance of various top-notch AI strategies was assessed utilizing the transmission network dataset. With respect to precision, least-square errors and learning time were assessed in the trial results. The performance proportion of the system is the absolute performance precision acquired from the test results of the models. The accuracy was up to 0.97%. It is trusted that this undertaking can build up a prescient model with high and solid precision to help power grid practitioners in the erection and commissioning of power plant services.
Article
Full-text available
The data publishing platform based on blockchain has advantages of decentralization, anti‐tampering function, and distributed storage. However, privacy leakage, untraceability, and the data tampering cannot be addressed. In order to solve these problems, this article proposes an aggregation signature scheme based on the bilinear mapping, and proposes a traceable privacy‐aware data publishing platform by applying the aggregation signature scheme to the data publishing platform on permissioned blockchain. Performance analysis shows that the traceable privacy‐aware data publishing platform reduces the communication cost, improves the efficiency of signature verification and transmission, provides the anonymity and traceability for the users in the platform. In order to solve some problems of the data publishing platform based on permissioned blockchain, this article proposes an aggregation signature scheme based on the bilinear mapping, and proposes a traceable privacy‐aware data publishing platform by applying the aggregation signature scheme to the data publishing platform on permissioned blockchain.
Article
Full-text available
Today, blockchain uses a list of various blocks for storing a distributed flat of invariable information that informs of a set of replicated logical things in the Internet of Things (IoT). In the blockchain, a set of blocks includes various transactions containing a cryptographic hash value and a timestamp. Blockchain-as-a-service (BaaS) as a new service in cloud providers presents an infrastructure for accessing users to execute, manage, and monitor blockchain applications without high secured infrastructure requirements. Recently, BaaS concepts widely have used the management of IoT applications in different layers such as network, data, control, and resource. This paper provides a systematic review of recent research studies in BaaS models. The main goal behind this review is to categorize the applied scenarios, trends, evaluated Quality of Service (QoS) factors, new challenges, and open directions on BaaS models in IoT management. To evaluate the existing research studies in this field, five analytical research questions are proposed to analyze the technical aspects of each study. The analytical results based on existing research questions specify that the BaaS models are applied to network layer to manage IoT environment more than other layers. Also, security and privacy are two important factors to evaluate the existing BaaS models in cloud-edge IoT environments. Finally, the integration of BaaS models on IoT environments with interconnections of cloud-edge computing and software defined networks creates great secure opportunities for smart environment applications to monitor, manage, and improve all the atomic services and resources.
Article
Full-text available
The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.
Article
Full-text available
Past international trade practices have been associated with opaque information flows that have hindered traceability and created hurdles in hassle‐free trade. Blockchain and allied technologies have been investigated as a panacea for the problems faced by the Cloud‐Based Manufacturing industry. However, previous literature has focused on limited aspects of a typical Cloud‐Based Manufacturing chain, such as monitoring assets and securing traceability, which is widely neglecting data integrity and data access. To overcome such drawbacks, the current paper proposes secure smart contracts based on the ERC20 interface in a permissioned blockchain with relevant processes and functions to obtain a holistic framework for securing Cloud‐Based Manufacturing operations. The efficacy of the proposed framework was demonstrated on the case study. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework. Additionally, several outlines for future research are outlined. The current paper proposes permission Blockchain with relevant processes and functions to obtain a holistic framework for securing the supply chain and logistic operations. The efficacy of the proposed framework was demonstrated in the case study. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework.
Article
Full-text available
Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.
Article
Full-text available
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial intelligence has elicited much attention from the academia and industry. In our current dynamic capital market, financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a “financial brain.” In this paper, we survey existing studies on financial intelligence. First, we describe the concept of financial intelligence and elaborate on its position in the financial technology field. Second, we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management, risk management, financial security, financial consulting, and blockchain. Finally, we propose a research framework called FinBrain and summarize four open issues, namely, explainable financial agents and causality, perception and prediction under uncertainty, risk-sensitive and robust decision-making, and multi-agent game and mechanism design. We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.
Article
Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2–97.6% overall accuracy without PCA and 97.6–100% with PCA for positive cases identification, respectively.
Article
The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and extraordinary high-speed data transfer. The challenge of 5G is to assure the network performance and different quality of service (QoS) requirements of different services, such as machine type communication (MTC), enhanced mobile broad band (eMBB), and ultra-reliable low latency communications (URLLC) over 5G networks. Unlike the previous ”one size fits all” system, the softwarization, slicing and network capability exposure of 5G provide dynamic programming capabilities for QoS assurance. With the increasing complexity and dynamics of the network behaviors, it is non-trivial for a programmer to develop traditional software codes to schedule the network resources based on expert knowledge, especially when there is no quantitative relationship among the network events and the QoS anomalies. Machine learning is a computer technology that gives computer systems the ability to learn with data and improve performance and accuracy of decision making on a specific task, without being explicitly programmed. The areas of machine learning and communication technology are converging. Supervised learning based QoS assurance architecture for 5G networks was proposed in this paper. The supervised machine learning mechanisms can intelligently learn the network environment and react to dynamic situations. They can learn from the fore passed QoS related information and anomalies, and further reconstruct the relationship between the fore passed data and the current QoS related anomalies automatically and accurately. They, then, can trigger automatic mitigation or provide suggestions. The supervised machine learning mechanisms can also predict future QoS related anomalies with high confidence. In this paper, a case study for QoS anomaly root cause tracking based on decision tree was given to validate the proposed framework architecture.
Article
In recent years, the Internet of Things (IoT) infrastructures are developing in various industrial applications in sustainable smart cities and societies such as smart manufacturing, smart industries. The Cyber-Physical System (CPS) is also part of IoT-oriented infrastructure. CPS has gained considerable success in industrial applications and critical infrastructure with a distributed environment. This system aims to integrate the physical world to computational facilities as cyberspace. However, there are many challenges, such as security and privacy, centralization, communication latency, scalability in such an environment. To mitigate these challenges, we propose a Deep Learning-based IoT-oriented infrastructure for a secure smart city where Blockchain provides a distributed environment at the communication phase of CPS, and Software-Defined Networking (SDN) establishes the protocols for data forwarding in the network. A deep learning-based cloud is utilized at the application layer of the proposed infrastructure to resolve communication latency and centralization, scalability. It enables cost-effective, high-performance computing resources for smart city applications such as the smart industry, smart transportation. Finally, we evaluated the performance of our proposed infrastructure. We compared it with existing methods using quantitative analysis and security and privacy analysis with different measures such as scalability and latency. The evaluation of our implementation results shows that performance is improved.
Article
In the recent year, Internet of Things (IoT) is industrializing in several real-world applications such as smart transportation, smart city to make human life reliable. With the increasing industrialization inIoT, an excessive amount of sensing data is producing from various sensors devices in the Industrial IoT. To analyzes of big data, Artificial Intelligence (AI) plays a significant role as a strong analytic tool and delivers a scalable and accurate analysis of data in real-time. However, the design and development of a useful big data analysis tool using AI have some challenges, such as centralized architecture, security, and privacy, resource constraints, lack of enough training data. Conversely, asan emerging technology, Blockchain supports a decentralized architecture. It provides a secure sharingof data and resources to the various nodes of the IoT network is encouraged to remove centralizedcontrol and can overcome the existing challenges in AI. The main goal of our research is to designand develop an IoT architecture with blockchain and AI to support an effective big data analysis. Inthis paper, we propose a Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligencethat provides an efficient way of converging blockchain and AI for IoT with current state-of-the-art techniques and applications. We evaluate the proposed architecture and categorized into twoparts: qualitative analysis and quantitative analysis. In qualitative evaluation, we describe how touse AI and Blockchain in IoT applications with ‘‘AI-driven Blockchain’’ and ‘‘Blockchain-driven AI.’’ Inquantitative analysis, we present a performance evaluation of the BlockIoTIntelligence architecture tocompare existing researches on device, fog, edge and cloud intelligence according to some parameterssuch as accuracy, latency, security and privacy, computational complexity and energy cost in IoT applications.TheevaluationresultsshowthattheproposedarchitectureperformanceovertheexistingIoT architectures and mitigate the current challenges.
Article
The finite-time tracking control of a class of stochastic quantized nonlinear systems is thought about in this article. Different from the studies on conventional finite-time control of stochastic systems, the quantized control problem is first taken into account and the nonlinear terms may be completely unknown. The quantization error and unknown nonlinearities make the existing finite-time stability criterion unavailable. By adopting the approximation ability of neural network, a novel adaptive neural control strategy is proposed, which removes the linear growth condition assumption for nonlinearities in existing finite-time studies. To be convenient for finite-time stability analysis of stochastic nonlinear systems, an important finite time stability criterion in integral form is first set up. Then, combining Jessen’s inequality and the proposed finite-time stability criterion, the finite-time mean square stability of stochastic nonlinear system is proved.