A Decoupled Blockchain Approach for Edge-Envisioned IoT-Based Healthcare Monitoring

Aujla, Gagangeet Singh; Jindal, Anish

doi:10.1109/jsac.2020.3020655

Cited by 131 publications

(64 citation statements)

References 30 publications

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“…Hence, the proposed framework is secured against the 51% attack and selfish mining attack. [58], and Islam and Shin [59] do not provide required security and functionality features like "provides session-key agreement," "provides session key security under CK adversary model," "availability of password update phase," "availability of biometric update phase," "availability of dynamic controller node (personal server) addition phase," "availability of dynamic smart healthcare device addition," "provides formal security verification using AVISPA/ SCYTHER tool," and "provides formal security analysis under Real-or-Random (RoR) model." Moreover, Garg et al' scheme [23] does not support feature like AI-based data analysis.…”

Section: Prevention Of 51% Attack and Selfishmentioning

confidence: 99%

“…A comparative study of the proposed framework and other closely related frameworks is conducted. Various frameworks, for example,Liu et al's framework [54], Garg et al's framework [23], Saha et al's framework [55], Xiang et al's framework [56], Xu et al's framework[57], Aujla and Jindal's framework[58], and Islam and Shin's framework[59] are analysed and compared. The details of comparisons are given in Table2.…”

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confidence: 99%

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Blockchain‐Envisioned Secure Authentication Approach in AIoT: Applications, Challenges, and Future Research

Wazid

Das

2021

Wireless Communications and Mobile Computing

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The Artificial Intelligence of Things (AIoT) is the amalgamation of Artificial Intelligence (AI) methods and the Internet of Things (IoT) infrastructure, which are deployed there to improve the overall performance of the system. AIoT can be deployed to achieve more efficient IoT operations; thereby can improve human-machine interactions and provide better data analysis. AI methods can be used to transform IoT data into useful information for the better decision-making processes, and it further increases the overall usability of the system. AIoT frameworks are very useful and applicable in a variety of applications, like security and surveillance system, smart home, intelligent transportation system, smart farming, secure and safe healthcare monitoring, industrial automation and control, eCommerce, logistics operations and control, and many more. However, AIoT frameworks may have issues related to data security and privacy as they are vulnerable to various types of information security-related attacks. These issues further cause the serious consequences, like the unauthorized data leakage and data update. Blockchain is a specific type of database. It is a digital ledger of transactions, which is duplicated and distributed across the entire network of computer systems. It stores data in the form of some blocks, which are then chained together. Blockchain is tamper proof and provides more security as compared to the traditional security mechanisms. Hence, blockchain can be integrated in various AIoT applications to provide more security. A generalized blockchain-envisioned secure authentication framework for AIoT has been proposed. The adversary model of blockchain-envisioned secure authentication framework for AIoT is also highlighted that covers most of the potential threats of a kind of communication environment. Various applications of the proposed framework are also discussed. Furthermore, different issues and challenges of the proposed framework are highlighted. In the end, we also provide some future research directions relevant to the proposed framework.

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Section: Prevention Of 51% Attack and Selfishmentioning

confidence: 99%

mentioning

confidence: 99%

Blockchain‐Envisioned Secure Authentication Approach in AIoT: Applications, Challenges, and Future Research

Wazid

Das

2021

Wireless Communications and Mobile Computing

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“…For instance, Volkswagen is building a Blockchain-based tracking system to avoid the manipulation of odometer done by the sellers to increase the price of their cars [7]. With the introduction of edge computing in intelligent driving, the vehicle movement, the time-sensitivity at which data is processed, and the allocation of the resource of the EC server have become an essential factor in intelligent driving [56,57]. To remove dependencies on third-party platforms, the authors of [37] have resource transaction architecture that is based on Blockchain.…”

Section: Blockchainmentioning

confidence: 99%

A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren

Gaurav

Sahu

et al. 2021

Complex Intell. Syst.

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The number of vehicles is increasing at a very high rate throughout the globe. It reached 1 billion in 2010, in 2020 it was around 1.5 billion and experts say this could reach up to 2–2.5 billion by 2050. A large part of these vehicles will be electrically driven and connected to a vehicular network. Rapid advancements in vehicular technology and communications have led to the evolution of vehicular edge computing (VEC). Computation resource allocation is a vehicular network’s primary operations as vehicles have limited onboard computation. Different resource allocation schemes in VEC operate in different environments such as cloud computing, artificial intelligence, blockchain, software defined networks and require specific network performance characteristics for their operations to achieve maximum efficiency. At present, researchers have proposed numerous computation resource allocation schemes which optimize parameters such as power consumption, network stability, quality of service (QoS), etc. These schemes are based on widely used optimization and mathematical models such as the Markov process, Shannon’s law, etc. So, there is a need to present an organized overview of these schemes to help in the future research of the same. In this paper, we classify state-of-the-art computation resource allocation schemes based on three criteria: (1) Their optimization goal, (2) Mathematical models/algorithms used, and (3) Major technologies involved. We also identify and discuss current issues in computation resource allocation in VEC and mention the future research directions.

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“…Such systems can be highly useful for early screening and monitoring of easily transmitted diseases such as COVID-19. Block-chain based approaches have also been proposed for edge based healthcare monitoring [7]. Use of deep learning based image analysis has shown tremendous impact in classic medical problems like brain tumor classification [66] and endoscopy [67].…”

Section: Introductionmentioning

confidence: 99%

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

et al. 2021

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The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.

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A Decoupled Blockchain Approach for Edge-Envisioned IoT-Based Healthcare Monitoring

Cited by 131 publications

References 30 publications

Blockchain‐Envisioned Secure Authentication Approach in AIoT: Applications, Challenges, and Future Research

Blockchain‐Envisioned Secure Authentication Approach in AIoT: Applications, Challenges, and Future Research

A survey on computation resource allocation in IoT enabled vehicular edge computing

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

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