Amalgamation of blockchain and sixth‐generation‐envisioned responsive edge orchestration in future cellular vehicle‐to‐anything ecosystems: Opportunities and challenges

Bhattacharya, Pronaya; Bodkhe, Umesh; Zuhair, Mohd; Rashid, Mamoon; Liu, Xuan; Verma, Ashwin; Dewangan, Ram Kishan

doi:10.1002/ett.4410

Cited by 9 publications

(9 citation statements)

References 89 publications

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“…Consequently, participants in the supply chain can be ready for transshipment and conduct international business. 40…”

Section: Advantages and Motivation For The Transformative Shift Towar...mentioning

confidence: 99%

“…Thus, consensus mechanisms should deploy a compelling mix of spatial and temporal requirements, as the effective topology varies with time. 40 At the same time, effective machine-learning mechanisms might improve the network's capacity, and machine-learning algorithms might interpret historical data to improve accuracy.…”

Section: Research Gaps Findings and Future Directionsmentioning

confidence: 99%

“…Stakeholders mentioned in the smart contracts fetch real‐time access to the data once it has been recorded on the blockchain. Consequently, participants in the supply chain can be ready for transshipment and conduct international business 40 …”

Section: Introductionmentioning

confidence: 99%

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A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

Trivedi

Rao

Parmar

et al. 2023

Security and Privacy

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Recently, Internet‐of‐Things (IoT) based applications have shifted from centralized infrastructures to decentralized ecosystems, owing to user data's security and privacy limitations. The shift has opened new doors for intruders to launch distributed attacks in diverse IoT scenarios that jeopardize the application environments. Moreover, as heterogeneous and autonomous networks communicate, the attacks intensify, which justifies the requirement of trust as a key policy. Recently, blockchain‐based IoT solutions have been proposed that address trust limitations by maintaining data consistency, immutability, and chronology in IoT environments. However, IoT ecosystems are resource‐constrained and have low bandwidth and finite computing power of sensor nodes. Thus, the inclusion of blockchain requires an effective policy design regarding consensus and smart contract environments in heterogeneous IoT applications. Recent studies have presented blockchain as a potential solution in IoT, but an effective view of consensus and smart contract design to meet the end application requirements is an open problem. Motivated by the same, the survey presents the integration of suitable low‐powered consensus protocols and smart contract design to assess and validate the blockchain‐IoT ecosystems. We present blockchain‐IoT's emerging communication and security aspects with performance issues of consensus protocols, interoperability, and implementation platforms. A case study of a smart contract‐based blockchain‐driven ecosystem is presented with a comparative analysis of mining cost and latency, which shows its suitability in real‐world setups. We also highlight attacks on blockchain IoT, open issues, potential findings, and future directions. The survey intends to drive novel solutions for future consensus and safe, smart contract designs to support applicative IoT ecosystems.

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“…Consequently, participants in the supply chain can be ready for transshipment and conduct international business. 40…”

Section: Advantages and Motivation For The Transformative Shift Towar...mentioning

confidence: 99%

Section: Research Gaps Findings and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

Trivedi

Rao

Parmar

et al. 2023

Security and Privacy

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“…6G technology enables edge intelligence through ML, DL, and FL models. 6G supports 1 Tbps user data rate, roundtrip latency of <0.1 ms, and introduces new services like extremely reliable low latency communications (eRLLC), with a reliability rate of 99.9999999%, mobile broadband reliable low-latency communication (MBRLLC), massive ultra-reliable low-latency communication (mURLLC) and human-centric services (HCSs) [7], [28].…”

Section: ) Beyond 5g Networkmentioning

confidence: 99%

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

et al. 2022

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Recently, unmanned aerial vehicles (UAVs) have gained attention due to increased use-cases in healthcare, monitoring, surveillance, and logistics operations. UAVs mainly communicate with mobile base stations, ground stations (GS), or networked peer UAVs, known as UAV swarms. UAVs communicate with GS, or UAV swarms, over wireless channels to support mission-critical operations. Communication latency, bandwidth, and precision are of prime importance in such operations. With the rise of data-driven applications, fifth-generation (5G) networks would face bottlenecks to communicate at near-real-time, at low latency and improved coverage. Thus, researchers have shifted towards network designs that incorporate beyond 5G (B5G) networks for UAV designs. However, UAVs are resource-constrained, with limited power and battery, and thus centralized cloud-centric models are not suitable. Moreover, as exchanged data is through open channels, privacy and security issues exist. Federated learning (FL) allows data to be trained on local nodes, preserving privacy and improving network communication. However, sharing of local updates is required through a trusted consensus mechanism. Thus, blockchain (BC)-based FL schemes for UAVs allow trusted exchange of FL updates among UAV swarms and GS. To date, limited research has been carried out on the integration of BC and FL in UAV management. The proposed survey addresses the gap and presents a solution taxonomy of BC-based FL in UAVs for B5G networks due to the open problem. This paper presents a reference architecture and compares its potential benefits over traditional BC-based UAV networks. Open issues and challenges are discussed, with possible future directions. Finally, a logistics case study of BC-based FL-oriented UAVs in 6G networks is presented. The survey aims to aid researchers in developing potential UAV solutions with the key integrating principles over a diverse set of application verticals.INDEX TERMS Beyond 5G networks, 6G, blockchain, federated learning, unmanned aerial vehicles. D. ORGANIZATION AND READING MAP

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“…Another specific schemes include low-density parity check schemes (LDPC), preferred in B5G networks, or 6G-uRLLC service. LDPC allows less errors while transmission [52]. Turbo codes are also specifically used along-with convolutional and polar coding schemes.…”

Section: E High Level Overview Of Fl-based Himentioning

confidence: 99%

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel¹,

Bhattacharya

Tanwar

et al. 2022

IEEE Access

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The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient's data which contributes effectively to the training process.The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FLbased electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.

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Amalgamation of blockchain and sixth‐generation‐envisioned responsive edge orchestration in future cellular vehicle‐to‐anything ecosystems: Opportunities and challenges

Cited by 9 publications

References 89 publications

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

A transformative shift toward blockchain‐basedIoTenvironments: Consensus, smart contracts, and future directions

Blockchain-Based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

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