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Decentralized Blockchain Technology for the Development of IoT-Based Smart City Applications
Abstract
Most people nowadays are shifting to smart cities for better education, opportunity, and facilities. A smart city is a city that uses modern technology and that improves the facility or quality of the area by using modern technology, devices, or services. Researchers are using blockchain technology for the upgradation of quality of life and services. To improve the services in smart cities, people are using blockchain in various sectors of society. Blockchain is one of the technologies that help in securing sensitive data by encryption. Blockchain technology has the capability to remedy the problems of the health sector with the aid of using bringing more efficiency, privacy, and security to patient health records. The potential of blockchain can be possible because of decentralization, disturbed ledger, and inherited encryption. This paper proverbs information about the contribution of blockchain in smart cities and the use of blockchain in the healthcare sector, enhancing the data securities and also how it can secure the online transaction between users. How services based on blockchain technology can be used in the upgradation and development of smart cities to improve their services and quality of life. It also increases the privacy and security of patients’ medical data. Further, it gives knowledge about the blockchain and its implementation in improving people's quality of life. This paper also discusses the plenty of applications in healthcare in blockchain and the working of blockchain in various sectors.KeywordsHealthcareSmart cityInternet of Things (IoT)Blockchain technologySecurity
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For this study, the researchers conducted a systematic literature review to answer complex questions about the field of blockchain technology. We used an unbiased systematic review process to find works on blockchain-based applications and developed a Python code that searched various online databases. This paper provides an overview of the characteristics, mode of operation, and applications of blockchains in various domains such as transportation, commerce and industry, privacy and security, the financial sector, government, education, healthcare, and the Internet of Things (IoT). The aim was to identify the key research themes addressed in existing articles within each application domain and suggest future research directions for these domains. We analyzed a set of 750 articles published between 2015 and 2021 that dealt with blockchain applications. We found that financial management and security issues have been the main research focus since 2015. However, the use of blockchain in education has become a central research theme in 2021. Healthcare, IoT, and government applications have also grown in popularity. We furthermore analyzed some of the implementations of privacy mechanisms, as well as the challenges and future directions that need to be addressed for effective blockchain deployment. This study contributes to existing research by providing a comprehensive overview of blockchain application themes and their emerging areas for stakeholders in diverse sectors.
We can’t consider blockchain only as a change, but as a fast phenomenon that is already on the move. It is a new wave of disturbances that has come to redesign the interactions that involve any form of exchange of values. It brings a new perspective on security, resilience, and effectiveness of systems. It initially became popular with the emergence of cryptocurrencies. Currently, it is more than just a base for this type of coins, since its materialization it has already made inroads in different areas and industries, which we can highlight the area of health and healthcare. It was originally designed to hold transaction data, however, there is growing interest in providing analytics capabilities. The stored data can be used to respond to organizational needs such as: providing provenance histories, predictive planning, fraud identification, regulatory compliance, etc.
In this article, in section 2, an overview of blockchain technology will be presented. An introduction to the topic Business Analytics will be carried out in section 3, so that it can later be related to the topic under study. The Blockchain Analytics concept will be presented in general in section 4. In the following section we will present several applicability of this technology in the health sector and, finally, a small conclusion and the future work that we intend to follow will be presented.
The healthcare sector is suffering from inefficiencies in handling its data. Many patients and healthcare organisations are frustrated by the numerous hurdles to obtaining current, real-time patient information. Patients are also frustrated at trying to schedule appointments at health organisations that have outdated contact information. The healthcare sector’s attention has been drawn to blockchain technology as a part of the solution, especially since this technology has been successfully applied in the financial sector to improve the security of transactions. The aspect of interoperability is resolved adequately by blockchain technology, because it has the potential to store, manage and share EMRs safely in the healthcare community. Therefore, the technology is having a positive impact on healthcare outcomes for various stakeholders. Interoperability in healthcare eases the exchange of health-related data, such as EMRs, between healthcare entities so that records may be shared and distributed among clinical systems. To handle data in this sector without violating privacy is a challenge, whether in the collection, storage, or analysis. Poor security, which increases data breaches, endangers patients both mentally, socially, and financially. A lack of data-sharing in the healthcare sector is considered a significant issue worldwide. This research focuses on this gap by investigating the benefits of using blockchain at the Ministry of Health in Saudi Arabia, providing a detailed analysis of the healthcare sector, and evaluating how blockchain technology improves data-sharing security. This research proposes a framework that identifies the factors supporting data-sharing using blockchain among healthcare organisations. It has three categories: healthcare systems factors; security factors; and blockchain factors. A triangulation technique achieved reliable results in three steps: a literature review; an expert review; and a questionnaire. This gave a comprehensive picture of the research topic, validating and confirming the results. To construct the framework, factors were comprehensively extracted from the literature then analysed, cleared of duplicates, and categorised. As a result, the final framework is confirmed as being based on the literature and expert review, and it is supported by the practitioners’ survey.
Blockchain technology (BT) has a wide range of built-in features, such as decentralization, transparency, data provenance, security, immutability, and has moved beyond the hype to practical applications in the healthcare industry. Telehealth turns out to be the most efficient and effective way of dispensing healthcare services even in remote areas. Though telehealth has the proven potential to improve the quality of healthcare, its implementation and adoption remain far from ideal. There is a need for a telehealth system that not only ensures the privacy and security of the users but also provides authentic services that enhance the trust to the highest level. Existing applications are leveraging BT and providing limited telehealth services. These applications are focused on a few services; hence do not cover all aspects of healthcare. To unveil the true potential of telehealth, this paper aims to develop an effective telehealth framework-BlockHeal which integrates all essential healthcare services under one platform and ensures a full-fledged trusted environment. The methodology employed includes survey of existing telehealth system, identifying their weaknesses and critical reasons behind their lack of widespread adoption. The proposed framework addresses the limitations and includes all stakeholders of the healthcare system instigating a consolidated platform to ensure authentic, safe, and timely healthcare facilities. Additionally, as the proposed framework is based on BT, it ensures the provision of secure, fault-tolerant, transparent, and tamper-proof data. Moreover, it offers decentralized storage via hyperledger fabric and a collection of decentralized applications (DApps). Finally, the effectiveness of the BlockHeal framework is validated by demonstrating several use-cases.
Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.
Blockchain technology is a highly regarded technology that possesses a plethora of exciting features. This paper analyzes trends and highlights the potential benefits of blockchain deployment in IoT and healthcare. According to the literature, blockchain technology is mostly utilized for data management operations in healthcare and IoT, specifically to improve data security, which includes data integrity, access control, and privacy preservation. In both areas, six distinct types of data security preservation strategies are applied. Additionally, publications highlight how blockchain and IoT, including health IoT, can be used in an integrative way. Three integration mechanisms were seen to accomplish this goal. These solutions range from fully integrating blockchain into data exchanges between IoT devices to using it solely for metadata storage. The most frequently covered area of IoT is a smart city, where blockchain is utilized to improve real-time data sharing, and electricity trading, and so on. Additionally, it is learned that, despite the numerous benefits of blockchain in healthcare, authors typically use it for drug supply chain management and data management purposes in order to avoid counterfeiting and empower patients with regard to their data, respectively.
Blockchain is an emerging technology being applied for creating innovative solutions in various sectors, including
healthcare. A Blockchain network is used in the healthcare system to preserve and exchange patient data through
hospitals, diagnostic laboratories, pharmacy firms, and physicians. Blockchain applications can accurately identify
severe mistakes and even dangerous ones in the medical field. Thus, it can improve the performance, security, and
transparency of sharing medical data in the health care system. This technology is helpful to medical institutions
to gain insight and enhance the analysis of medical records. In this paper, we studied Blockchain technology and
its significant benefits in healthcare. Various Capabilities, Enablers, and Unified Work-Flow Process of Blockchain
Technology to support healthcare globally are discussed diagrammatically. Finally, the paper identifies and debates
fourteen significant applications of Blockchain for healthcare. Blockchain plays a decisive part in handling
deception in clinical trials; here, the potential of this technology offer is to improve data efficiency for healthcare.
It can help avoid the fear of data manipulation in healthcare and supports a unique data storage pattern at the
highest level of security. It provides versatility, interconnection, accountability, and authentication for data access.
For different purposes, health records must be kept safe and confidential. Blockchain helps for the decentralised
protection of data in healthcare and avoids specific threats.
Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottom-up governance approach, citizens play an active role in the process of policy-making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this paper, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.
Decentralized Autonomous Organization (DAO) is believed to play a significant role in our future society governed in a decentralized way. In this article, we first explain the definitions and preliminaries of DAO. Then, we conduct a literature review of the existing studies of DAO published in the recent few years. Through the literature review, we find out that a comprehensive survey towards the state-of-the-art studies of DAO is still missing. To fill this gap, we perform such an overview by identifying and classifying the most valuable proposals and perspectives closely related to the combination of DAO and blockchain technologies. We anticipate that this survey can help researchers, engineers, and educators acknowledge the cutting-edge development of blockchain-related DAO technologies.
Blockchain is a revolutionary technology that is making a great impact on modern society due to its transparency, decentralization, and security properties. Blockchain gained considerable attention due to its very first application of Cryptocurrencies e.g., Bitcoin. In the near future, Blockchain technology is determined to transform the way we live, interact, and perform businesses. Recently, academics, industrialists, and researchers are aggressively investigating different aspects of Blockchain as an emerging technology. Unlike other Blockchain surveys focusing on either its applications, challenges, characteristics, or security, we present a comprehensive survey of Blockchain technology’s evolution, architecture, development frameworks, and security issues. We also present a comparative analysis of frameworks, classification of consensus algorithms, and analysis of security risks & cryptographic primitives that have been used in the Blockchain so far. Finally, this paper elaborates on key future directions, novel use cases and open research challenges, which could be explored by researchers to make further advances in this field.
Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people’s daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain’s proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.
Cloud-assisted Internet of Medical Things (IoMT) is becoming an emerging paradigm in the healthcare domain, which involves collection, storage and usage of the medical data. Considering the confidentiality and accessibility of the outsourced data, secure and fine-grained data sharing is a crucial requirement for the patients. Attribute-based encryption (ABE) is a promising solution to deal with this issue, but considering its property of each attribute sharing with multiple users, how to flexibly and efficiently update access privileges of certain users without affecting others is still a serious challenge. In this paper, we propose a secure and fine-grained data sharing scheme with flexible user access privilege update in cloud-assisted IoMT environment. Specifically, we take ABE as the basic building block, and utilize proxy re-encryption and key blinding techniques to empower the cloud server to re-encrypt the ciphertext affected by revocation and update keys for unrevoked users. In addition, adding attributes for users to extend their access rights is realized only based on few key components stored in cloud without entirely re-computing and re-issuing keys for them. As a result, the patients are able to flexibly and efficiently share their data and manage users' privileges. Formal proof and detailed performance evaluation demonstrate the security and efficiency of the proposed scheme.
The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.
The world health organization (WHO) formally proclaimed the novel coronavirus, called COVID-19, a worldwide pandemic on March 11 2020. In December 2019, COVID-19 was first identified in Wuhan city, China, and now coronavirus has spread across various nations infecting more than 198 countries. As the cities around China started getting contaminated, the number of cases increased exponentially. As of March 18 2020, the number of confirmed cases worldwide was more than 250,000, and Asia alone had more than 81,000 cases. The proposed model uses time series analysis to forecast the outbreak of COVID-19 around the world in the upcoming days by using an autoregressive integrated moving average (ARIMA). We analyze data from February 1 2020 to April 1 2020. The result shows that 120,000 confirmed fatal cases are forecasted using ARIMA by April 1 2020. Moreover, we have also evaluated the total confirmed cases, the total fatal cases, autocorrelation function, and white noise time-series for both confirmed cases and fatalities in the COVID-19 outbreak.
The urbanization in Delhi NCR has led to a rapid increase in the vehicle count concerning the rise in population and mobilization. The emissions from the vehicles are currently counted amongst the main sources of air pollution in Delhi. This affects the quality of air. The emission criterion of various pollutants that are emitted from vehicles is evaluated through various International models, which include various vehicles, their modes of pollutants emitted while driving and other factors that are affecting the weather. The approximate emission of pollutants such as Carbon Monoxide (CO) and/or Particulate Matter (PM), from a variety of vehicles and different fuel types, has undergone diurnal variation over the years, depending on the time of the day. This study presents the emission factor of gaseous pollutants Hydrocarbons (HC), Carbon Monoxides (CO) and Carbon Dioxide (CO2) of 181 four-wheeler cars from different companies containing different types of fuels. The measurement of gaseous pollutants is performed for Delhi, the most polluted city in India. The various facts and data were calculated and analyzed with reference to the standard values set by the national schemes of the Pollution and Environment. Based on this statistical data obtained and analyzed, the scenarios regarding future vehicle growth rate and its impact on air quality are mentioned to overcome emission problems. Therefore, it is important to develop and deploy methods for obtaining real-world measurements of vehicle emissions, to estimate the pollutants. The analysis shows that few parameters need to be a concern for reducing the pollutants emission by vehicles. These major parameters are the high survival rates, decrease in annual mileage and major enforcement for three-to-five-year-old vehicles. This study shows that many old vehicles are used in different regions of the country, regardless of many notifications of banning old vehicles by the Government of India. These old vehicles are the major source of vehicle pollutants. The analysis stated that the diesel engine would emit less CO2/km than a petrol engine if having an almost similar engine capacity.
Many effective tools in fuzzy soft set theory have been proposed to handle various complicated problems in different fields of our real life, especially in decision making. Molodtsov’s soft set theory has been regarded as a newly emerging mathematical tool to deal with uncertainty and vagueness. Lattice ordered multi-fuzzy soft set (LMFSS) has been applied in forecasting process. However, similarity measure is not used in this application. In our research, similarity measure of LMFSS is proposed to calculate the similarity between two LMFSSs. Moreover, some of its properties are introduced and proved. Finally, an application of LMFSS in decision making using similarity measure is analysed.
The COVID-19 pandemic has brought into sharp focus the need to harness and leverage our digital infrastructure for remote patient monitoring. As current viral tests and vaccines are slow to emerge, we see a need for more robust disease detection and monitoring of individual and population health, which could be aided by wearable sensors. While the utility of this technology has been used to correlate physiological metrics to daily living and human performance, the translation of such technology toward predicting the incidence of COVID-19 remains a necessity. When used in conjunction with predictive platforms, users of wearable devices could be alerted when changes in their metrics match those associated with COVID-19. Anonymous data localized to regions such as neighborhoods or zip codes could provide public health officials and researchers a valuable tool to track and mitigate the spread of the virus, particularly during a second wave. Identifiable data, for example remote monitoring of cohorts (family, businesses, and facilities) associated with individuals diagnosed with COVID-19, can provide valuable data such as acceleration of transmission and symptom onset. This manuscript describes clinically relevant physiological metrics which can be measured from commercial devices today and highlights their role in tracking the health, stability, and recovery of COVID-19+ individuals and front-line workers. Our goal disseminating from this paper is to initiate a call to action among front-line workers and engineers toward developing digital health platforms for monitoring and managing this pandemic.
Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.
Healthcare data management has been gaining a lot of attention in recent years because of
its high potential to provide more accurate and cost-ef cient patient care. The traditional client-server and
cloud-based healthcare data management systems suffer from the issues of single point of failure, data
privacy, centralized data stewardship, and system vulnerability. The replication mechanism, and privacy
and security features of blockchain have a promising future in the healthcare domain as they can solve
some of the inherent issues of the health management system. However, most of the recent research works
on blockchain in the healthcare domain have primarily focused on the permission-less Bitcoin network that
suffers from drawbacks such as high energy consumption, limited scalability, and lowtransaction throughput.
Consequently, there is a need for a scalable, fault-tolerant, secure, traceable and private blockchain to suit the
requirements of the healthcare domain. We propose a lightweight blockchain architecture for the healthcare
data management that reduces the computational and communication overhead compared to the Bitcoin
network by dividing the network participants into clusters and maintaining one copy of the ledger per
cluster. Our architecture introduces the use of canal, that allows secure and con dential transactions within
a group of network participants. Furthermore, we propose a solution to avoid forking which is prevalent in
the Bitcoin network. We demonstrate the effectiveness of our proposed architecture in providing security
and privacy compared to the Bitcoin network by analyzing different threats and attacks. We also discuss
how our proposed architecture addresses the identi ed threats. Our experimental results demonstrate that
our proposed architecture generates 11 times lower network traf c compared to the Bitcoin network as the
number of blocks increases. Our ledger update is 1.13 times faster. Our architecture shows a speedup of 67%
in ledger update and 10 times lower network traf c when the number of nodes increases.
As an emerging computing mode, urban computing is mainly used to integrate, analyze and reuse urban resources by using perceptual computing, data mining and intelligent extraction to eliminate the phenomenon of data islands and provide wisdom for people to make decisions. But in the era of big data, the security and privacy leakage of users has become a major obstacle in urban computing. Taking medical big data as an example, this paper analyzed the risk of security and privacy leakage in the collection, transmission, storage, use and sharing of medical big data, and established a medical big data security and privacy leakage risk indicator system with 4 primary indicators and 35 secondary indicators. In addition, the weight of each indicator was calculated by GI method and entropy weight method. Then the fuzzy comprehensive evaluation model was established to verify the risk of medical big data security and privacy disclosure in urban computing. The results show that the risk of medical big data security and privacy leakage in the Grade II Level A hospitals is higher than that in the Grade III Level A hospitals, and in the life cycle of medical big data, the two stages of data storage, data use and sharing may cause more prominent problems of data security and privacy disclosure, while the data collection and data transmission are slightly less. Finally, the comparison of performance further proved the scientificity and effectiveness of this method.
India is one of the most polluted countries in the world, where several major cities are facing serious environmental consequences as a result of rapid pollution growth. The objective of this research is to analyze air pollution trends with respect to various geographical locations, in order to have a global view of the damage caused, so that appropriate actions can be developed in the future to prevent air pollution. In this regard, the polluted database was established based on the data provided by the Central Pollution Control Board; Ministry of Environment, Forest, and Climate Change (India). These data demonstrate the annual growth of SO2, NOx, and particulate matter (PM) 2.5 from 2015 to 2018 and were recorded at various monitoring stations in three cities, namely, Delhi, Bengaluru, and Chennai. The results show that SO2, NOx, and PM 2.5 were from different transport modes, both small or large-scale power generations (from diesel, coal and gas plant), industries, constructions, and domestic cooking. Overall, there was an increasing trend, day by day, in India. The result categorized the considered areas into the following four classes: critically polluted (CP), highly polluted (HP), moderately polluted (MP), and low polluted (LP). The results will assist in the assessment of pollution for the cities investigated in this research.
Software maintainability predicts changes or failures that may occur in software after it has been deployed. Since it deals with the degree to which an application may be understood, repaired or enhanced, it also takes into account the overall cost of the project. In the past several measures have been taken into account for predicting metrics that influence software maintainability. However, deep learning is yet to be explored for the same. In this paper, we perform Deep Learning for Software Maintainability Metrics Prediction on a large number of datasets. Unlike the previous research works, we have relied on large datasets from 299 software’s and subsequently applied various metrics and functions to the same. Twenty-nine object-oriented metrics have been considered along with their impact on software maintainability of open source software. Five Machine Learning algorithms namely Ridge Regression with Variable Selection, Decision Tree, Quantile Regression Forest, Support Vector Machine and Principal Component Analysis have been applied to the original datasets, as well as to the refined datasets. It was found that our study provides results in form of metrics that may be used in the prediction of software maintenance and the proposed Deep Learning model outperforms all of the other methods that were considered. Further, the results of experiment affirm the efficiency of the proposed Deep Learning model for software maintainability prediction.
Despite a recent wealth of data and information, the healthcare sector is lacking in actionable knowledge. The healthcare industry faces challenges in essential areas like electronic record management, data integration, and computer-aided diagnoses and disease predictions. It is necessary to reduce healthcare costs and the movement towards personalized healthcare. The rapidly expanding fields of deep learning and predictive analytics has started to play a pivotal role in the evolution of large volume of healthcare data practices and research. Deep learning offers a wide range of tools, techniques, and frameworks to address these challenges. Health data predictive analytics is emerging as a transformative tool that can enable more proactive and preventative treatment options. In a nutshell, this paper focus on the framework for deep learning data analysis to clinical decision making depicts the study on various deep learning techniques and tools in practice as well as the applications of deep learning in healthcare.
Originally conceived as a mechanism to enable a trustless cryptocurrency – Bitcoin, blockchain has since unbound itself from its original purpose as an increasing number of industries and stakeholders’ eye the technology as an attractive alternative to solve existing business solutions as well as disrupt mature industries. This paper presents a systematic literature review of blockchain technology, tracking its increase in popularity in relation to similar technologies such as cryptocurrencies as well as Bitcoin. The objective of the paper is to identify the current standing of blockchain technology within the literature while also identifying the major fields of study and areas of application for which blockchain offers a valuable solution. The paper finds that unique features to the blockchain such as privacy, security, anonymity, decentralization, and immutability provide valuable benefits to various fields and subjects. The paper also finds that exploring the application of blockchain has only begun with some limited studies in areas such as the Internet of Things, Energy, Finance, Healthcare, and Government, that also stand to benefit disproportionately from its implementation.
This work provides a systematic literature review of blockchain-based applications across multiple domains. The aim is to investigate the current state of blockchain technology and its applications and to highlight how specific characteristics of this disruptive technology can revolutionise "business-as-usual" practices. To this end, the theoretical underpinnings of numerous research papers published in high ranked scientific journals during the last decade, along with several reports from grey literature as a means of streamlining our assessment and capturing the continuously expanding blockchain domain, are included in this review. Based on a structured, systematic review and thematic content analysis of the discovered literature, we present a comprehensive classification of blockchain-enabled applications across diverse sectors such as supply chain, business, healthcare, IoT, privacy, and data management, and we establish key themes, trends and emerging areas for research. We also point to the shortcomings identified in the relevant literature, particularly limitations the blockchain technology presents and how these limitations spawn across different sectors and industries. Building on these findings, we identify various research gaps and future exploratory directions that are anticipated to be of significant value both for academics and practitioners.
Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain–Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time.
This paper describes how Distributed Ledger Technologies can be used to enforce social contracts and to orchestrate the behaviour of agents trying to access a shared resource. The first part of the paper analyses the advantages and disadvantages of using Distributed Ledger Technologies architectures to implement certain control systems in an Internet of Things (IoT) setting, and then focuses on a specific type of DLT based on a Directed Acyclic Graph. In this setting we propose a set of delay differential equations to describe the dynamical behaviour of the Tangle, an IoTinspired Directed Acyclic Graph designed for the cryptocurrency IOTA. The second part proposes an application of Distributed Ledger Technologies as a mechanism for dynamic deposit pricing, wherein the deposit of digital currency is used to orchestrate access to a network of shared resources. The pricing signal is used as a mechanism to enforce the desired level of compliance according to a predetermined set of rules. After presenting an illustrative example, we analyze the control system and provide sufficient conditions for the stability of the network.
While users enjoy the convenience of data outsourcing in the cloud, they also face the risks of data modification and private information leakage. Searchable encryption technology can perform keyword searches over encrypted data while protecting their privacy and guaranteeing the integrity of the data by verifying the search results. However, some associated problems are still encountered, such as the low efficiency of verification and uncontrollable query results. Accordingly, this paper proposes a Privacy-Preserving Searchable Encryption (PPSE) scheme based on public and private blockchains. First, we store an encrypted index in a private blockchain while outsourcing corresponding encrypted documents to a public blockchain. The encrypted documents are located through the encrypted index. This method can reduce the storage overhead on the blockchains, and improve the efficiency of transaction execution and the security of stored data. Moreover, we adopt a smart contract to introduce a secondary verification access control mechanism and restrict data users' access to the private blockchain through authorization for the purpose of guaranteeing data privacy and the correctness of access control verification. Finally, the security analysis and experimental results indicate that compared with existing schemes, the proposed scheme can not only improve the security of encrypted data but also guarantee the efficiency of the query.
Audit trails are critical components in enterprise business applications, typically used for storing, tracking, and auditing data. Entities in the audit trail applications have weak trust boundaries, which expose them to various security risks and attacks. To harden the security and develop secure by design applications, blockchain technology has been recently introduced in the audit trails. Blockchains take a consensus-driven clean slate approach to equip audit trails with secure and transparent data processing, without a trusted intermediary. On a downside, blockchains significantly increase the space-time complexity of the audit trails, leading to high storage costs and low transaction throughput. In this article, we introduce
BlockTrail
, a novel blockchain architecture that fragments the legacy blockchain systems into layers of codependent hierarchies, thereby reducing the space-time complexity and increasing the throughput. BlockTrail is prototyped on the “practical Byzantine fault tolerance” protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is secure and efficient, with low storage footprint.
The Internet of Medical Things is developing rapidly in recent years. However, the timeliness and security of healthcare applications challenge its adoption. In this article, we propose a blockchain-assisted edge computing platform that timely and securely processes time-sensitive healthcare applications. We propose a blockchain-assisted framework that leverages distributed edge servers to achieve fast data processing. We design smart contracts to verify the identity and data credibility of network entities. We formulate the problem as a directed acyclic graph organized scheduling model and develop online orchestrating algorithms to meet the timeliness requirement. We implement the blockchain prototype and evaluate the performance of the proposed algorithm under extensive configurations. Results show that fast blockchain-assisted edge computing platform for healthcare achieves a significant timeliness guarantee, and at the same time outperforms conventional schemes from the latency perspective.
Current trends have diagnosed the emergence of two technologies by integrating them. The two technologies are “Blockchain” and “Internet of Things(IoT).” The IoT stands for providing “smart” features to all the products where it steps into it. For example, IoT Smart City, Smart E-Healthcare system, Smart Home, Smart domestic appliances, Smart Agriculture exist. The next technology is called blockchain, which establishes the P2P (peer-to-peer) network, decentralized and non-tampering technique exists. Thus the marriage between blockchain and the IoT poses many advantages. This paper reviews the intersection of these two recent research areas for the past three years. We hope this paper supports the new researchers and engineers interested in blockchain to build future blockchain–IoT systems.KeywordsBlockchainIoTBlockchain_IoT Architecture
The perpetual evolution of IoT continues to make cities smart beyond measure with the abundance of data transactions through expansive networks. Healthcare has been a foremost pillar of settlements and has gained particular focus in recent times owing to the pandemic and the deficiencies it has brought to light. There is an exigency to developing smart healthcare systems that make smart cities more intelligent and sustainable. Therefore, this paper aims to present a study of smart healthcare in the context of a smart city, along with recent and relevant research areas and applications. Several applications have been discussed for early disease diagnosis and emergency services with advanced health technologies. It also focuses on security and privacy issues and the challenges posed by technologies such as wearable devices and big healthcare data. This paper briefly reviews some enhanced schemes and recently proposed security mechanisms as countermeasures to various cyber-attacks. Recent references are primarily used to present smart healthcare privacy and security issues. The issues are laid out briefly based on the different architecture layers, various security attacks, and their corresponding proposed solutions along with other facets of smart health such as Wireless Body Area Network (WBAN) and healthcare data.
The Internet of Things (IoT) devices possessed by individuals produce massive amounts of data. The private data onto specific IoT devices can be combined with intelligent platform to provide help for future research and prediction. As an important digital asset, individuals can sell private data to get rewards. Problems, such as privacy, security, and access control prevent individuals from sharing their private data. The blockchain technology is widely used to build an anonymous trading system. In this article, we construct a blockchain-based privacy-preserving and rewarding private data-sharing scheme (BPRPDS) for IoT. A privacy issue worth considering is that the malicious cloud server may establish a behavior profile database of data users (DUs). In the case of anonymity, the transactions of private data sharing are easy to cause disputes. When anonymous DUs are framed, it is hard to protect their rights. With the help of the deniable ring signature and Monero, we realize the behavior profile building prevention and nonframeability of BPRPDS. At the same time, we utilize the licensing technology executed by smart contracts to ensure flexible access control of multisharing. The proposed BPRPDS is provably secure. Performance analysis and experimental results show that BPRPDS is efficient and practical.
Industrial Internet of Things (IIoT) is a convincing stage by interfacing different sensors around us to the Internet, giving incredible chances for the acknowledgment of brilliant living. It is a fast growing technology in the present scenario. IIoT has its effect on almost every advanced field in the society. It has impact not only on work, but also on the living style of individual and organization. Due to high availability of internet, the connecting cost is decreasing and more advanced systems has been developed with Wi-Fi capabilities. The concept of connecting any device with internet is “IIoT”, which is becoming new rule for the future. This manuscript discusses about the applications of Internet of Things in different areas like — automotive industries, embedded devices, environment monitoring, agriculture, construction, smart grid, health care, etc. A regressive review of the existing systems of the automotive industry, emergency response, and chain management on IIoT has been carried out, and it is observed that IIoT found its place almost in every field of technology.
Noninvasive continuous blood pressure estimation is a promising alternative to minimally invasive blood pressure measurement using cuff and invasive catheter measurement, because it opens the way to both long-term and continuous blood pressure monitoring in ecological situation. The most current estimation algorithm is based on pulse transit time measurement where at least two measured signals need to be acquired. From the pulse transit time values, it is possible to estimate the continuous blood pressure for each cardiac cycle. This measurement highly depends on arterial properties which are not easily accessible with common measurement techniques; but these properties are needed as input for the estimation algorithm. With every change of input arterial properties, the error in the blood pressure estimation rises, thus a periodic calibration procedure is needed for error minimization. Recent research is focused on simplified constant arterial properties which are not constant over time and uses only linear model based on initial measurement. The elaboration of continuous calibration procedures, independent of recalibration measurement, is the key to improving the accuracy and robustness of noninvasive continuous blood pressure estimation. However, most models in literature are based on linear approximation and we discuss here the need for more complete calibration models.
The lack of intrinsic security technologies in the current Internet of Things (IoT) systems brings forth numerous security vulnerabilities and privacy risks. To this end, a distributed and decentralized technology named blockchain comes out as a viable solution. This paper investigates the integration trends of blockchain technology with IoT and discusses the insights of this new paradigm. In particular, this paper presents a comprehensive survey on security improvements achieved in IoT systems using blockchain and the challenges that originate during this integration. Further, the paper highlights the most relevant blockchain based IoT applications and outlines some future research directions.
The Interplanetary File System (IPFS) seeks to build a decentralized, fast and efficient file system able to connect all devices worldwide. In particular, its decentralized nature makes it viable to be applied to other decentralized applications, such as Decentralized Online Social Networks. Several Blockchain Online Social Networks adopted IPFS for storing larger resources, such as videos, letting them claim to be censorship free platforms. In this paper we inspect whether IPFS is a good choice as data storage for Decentralised Social Applications, discussing its strengths and weaknesses related to our scenario. We face the problem of data storage and persistence thanks to the so-called "pinning" services implemented on top of IPFS. Additionally, we provide a set of analyses concerning physical location, protocols, and identity of the IPFS nodes discovered by our crawling node.
Blockchain is a distributed, decentralized public ledger that has gained massive momentum recently. Currently, security services such as privacy, confidentiality, resource provenance, access control, authentication and integrity assurance are managed by centralized controllers. However, this centralization faces numerous security and privacy challenges. Blockchain solve such challenges as it helps to create an attack resistant, digital data storage as well as sharing platform by employing linked block structures for data verification and trusted consensus mechanism for data synchronization. The goals of this paper are to provide an in-depth survey of blockchain technology, to provide insights into the blockchain security threats, to highlight the privacy necessities for current applications, to outline their challenges and give an insight on how these challenges can be resolved by the blockchain technology. Furthermore, we summarize the future research challenges associated with the usage of blockchain based security services to spur further investigation in this field.
In current years, Internet of Things has come a long way and is well emerged within many organizations and fields covering the sector of healthcare. The continuous execution of IoT within the field of healthcare will direct to a fast rise in productivity and examination of data. In reference to medical gadgets, developments in technology will enhance the results of patients with superior analytics. Thus, this chapter introduces Internet of Health Things with wearable healthcare systems in detail and shows the inter-association of interaction allowed medical gadgets and their combination to broader scale networks of healthcare to enhance the health of patients, and because of this sensitive behavior of systems related to health. Still, It meets various issues, specifically in regards of security, privacy and scalability. This work also presents an overview of approaches based on IoT for healthcare and healthcare aided living as well. Moreover, this chapter illustrates IoT networks for healthcare and the different characteristics of IoT confidentiality and safety including security needs. Also, how distinct technologies such as augmented reality, big data, cloud computing and many more can be implemented in the reference of healthcare are described and ultimately, some pathways for forthcoming work on healthcare based on IoT based on a collection of challenges and open issues are presented.
Increased longevity and people's concern about aging with quality has led to increased health and wellness data. Much of the data is not interoperable because of its divergent structures and semantics, being little used and little protected. Existing standards are complex and lack the adherence of the agents involved to ensure their application. Blockchain technology offers alternatives to unify the standards and their application by consensus algorithm, which considers the validation and secrecy in the insertion of the blocks of transactions in the chain. However, smart contracts can ensure secrecy and the rules of data sharing in blocks in the chain. In this paper, we propose a blockchain architecture with a consensus algorithm that considers data collected in the health and wellness ecosystem, including those obtained by IoT devices and persisted in middleware platforms. It is intended that this architecture be able to answer the questions and establish the concepts for the full and secure sharing of health and wellness data.
Nowadays, the crime rate increases dramatically in every country. Therefore, it is an urgent need for governments and social associations to produce persistent solutions and disincentive penalties to prevent crime issues. Specifically, social media plays an important role in crime rate detection; thus, reducing crime rates significantly. It would be a good medium for the desired task. In this paper, we analyze Twitter data collected from Twitter accounts for seven different locations (Ghaziabad, Chennai, Bangaluru, Chandigarh, Jammu, Gujarat, and Hyderabad) from January 2014 to November 2018 in a case study of India, which is opted to illustrate the efficiency of the proposed work. Sentiment analysis has been used to analyze users’ behavior and psychology through the tweets of people to track crime actions. Twitter part-of-speech tagger, which is a Markov Model of first-order entropy, has been used for part-of-speech in online conversational text. Brown clustering is used for a long set of unlabeled tweets. Comparisons are verified with real crime rates from an authorized source of information according to different locations. We also measure the latest crime trends for the highest (Ghaziabad, Uttar Pradesh) and lowest crime cities (Jammu) in India. It has been found that the latest crime trends have been recorded for the time duration of 7 days (23, January 2019 to 30, January 2019). The analyses demonstrate that the obtained results match with the real crime rate data. We believe that these types of studies will help to detect the real-time crime rate for different locations and detect the crime pattern easily.
Vehicular Named Data Networking (VNDN) has recently emerged as a novel paradigm to facilitate content-centric data sharing for Internet of Vehicles (IoV). However, an information holder can spread fake data to clients for malicious purposes, which may affect the driving decision of the recipient or even worse, cause traffic congestion and accidents. In this paper, we build a data-sharing system that consists of a double-layer blockchain. The nodes at the bottom layer request for service by announcing their requirements in the NDN paradigm. For the upper layer, the nodes submit their demands and supplies to the nearest roadside unit (RSU) for further matching. We model the balance between demand and supply as a matching game. To encourage nodes to provide positive services, a reputation management mechanism that combines negative and positive transaction records is proposed. Simulation results verify the validity of our system, and the data-sharing mechanism fosters a secure information interaction in VNDN.
Healthcare networking and computing infrastructure is rapidly changing from closed environments to open environments that incorporate ubiquitous medical devices and novel application scenarios. For example, home-based healthcare involves the collection and analysis of data from pervasive sensors (often in real time) to guide therapy or to perform medical interventions. In this article, we address the challenges of data interoperability and regulatory compliance when designing and deploying healthcare applications in a heterogeneous home-edge-cloud environment. We propose the ChainSDI framework that leverages the blockchain technique along with abundant edge computing resources to manage secure data sharing and computing on sensitive patient data. We implement programmable ChainSDI application programming interfaces to facilitate the specification of home-based healthcare services running on a software-defined infrastructure (SDI). This article is a collaboration between computer scientists, medical researchers, healthcare IT professionals as well as healthcare providers with the goal of increasing the availability of SDI infrastructures while meeting the performance and regulatory requirements of healthcare applications. Our prototype demonstrates the feasibility of the framework and serves as a testbed for novel experimental studies in the context of emerging healthcare applications.
Blockchain is projected to be the latest revolutionary technology and is gaining increasing attention from academics and practitioners. Blockchain is essentially a distributed and immutable database that enables more efficient and transparent transactions. The consensus-based record validation can eliminate the need for a trusted intermediary. We utilize the transaction cost theory to create a better understanding of how blockchain might influence supply chain relations, specifically in terms of transaction costs and governance decisions. Conceptually developing a set of six propositions, we argue that blockchain limits opportunistic behavior, the impact of environmental and behavioral uncertainty. Blockchain reduces transaction costs, as it allows for transparent and valid transactions. We explore several areas for future research on how blockchain might shape supply chain management in the future.
In recent years, the rapid urbanization of world’s population causes many economic, social and environmental problems, which affect people’s living conditions and quality of life significantly. The concept of “Smart City” brings opportunities to solve these urban problems. The objectives of smart cities are to make the best use of public resources, provide high quality services to the citizens, and improve the people’s quality of life. Information and Communication Technology (ICT) plays an important role in the implementation of smart cities. Blockchain as an emerging technology has many good features, such as trust-free, transparency, pseudonymity, democracy, automation, decentralization and security. These features of blockchain are helpful to improve smart city services and promote the development of smart cities. In this paper, we provide a comprehensive survey on the literature involving blockchain technology applied to smart cities. First, the related works and background knowledge are introduced. Then, we review how blockchain technology is applied in the realm of smart cities, from the perspectives of smart citizen, smart healthcare, smart grid, smart transportation, supply chain management and others. Finally, some challenges and broader perspectives are discussed.
The digitalization and massive adoption of advanced technologies in the automotive industry not only transform the equipment manufacturer's operating mode but also change the current business models. The increased adoption of autonomous cars is expected to disrupt government regulations, manufacturing, insurance, and maintenance services. Moreover, providing integrated, personalized, and on-demand services have shared, connected, and autonomous cars in the smart city for a sustainable ecosystem. To address these issues in this paper, we propose a blockchain-based distributed framework for the automotive industry in the smart city. The proposed framework includes a novel miner node selection algorithm for the blockchain-based distributed network architecture. To evaluate the feasibility of the proposed framework, we simulated the proposed model on a private Ethereum blockchain platform using captured dataset of mined blocks from litecoinpool.org. The simulation results show the proof-of-concept of the proposed model which can be used for wide range of future smart applications.