PaperlayerView exact revision on arXiv

Source-linked AI summary

Integration of Blockchain and Edge Computing in Internet of Things: A Survey

He Xue, Dajiang Chen, Ning Zhang, Hong-Ning Dai, Keping Yu

arXiv:2205.13160v1cs.CReess.SP

TL;DR

IoT data growth strains traditional cloud capacity, while edge computing and blockchain are presented as complementary technologies for IoT services. This survey reviews their integration, architecture, and solution areas, and summarizes challenges that hinder large-scale deployment.

  • Problem

    IoT data growth creates real-time storage, processing, and exchange demands beyond traditional cloud capacity, while integration research spans several issues and application areas.

    Source: §I · p. 1

  • Method

    The survey presents an IBEC architecture and classifies studies and solutions across resource management, data management, computation offloading, security, and privacy.

    Source: §I · p. 2, C. IBEC · p. 5, §VII · p. 18

  • Results

    The survey identifies research challenges that hinder large-scale IBEC deployment and discusses performance optimization and potential solutions.

    Source: §VII · p. 18

  • Takeaways & Limitations

    The survey describes blockchain and edge computing as complementary technologies used for secure and efficient resource management, computation offloading, and data sharing in IoT.

    Source: §VII · p. 18

    Resource limits, network heterogeneity, and dynamic environments constrain full use of existing data-security techniques in edge computing.

    Source: §I · p. 1

Abstract

from arXiv · show

As an important technology to ensure data security, consistency, traceability, etc., blockchain has been increasingly used in Internet of Things (IoT) applications. The integration of blockchain and edge computing can further improve the resource utilization in terms of network, computing, storage, and security. This paper aims to present a survey on the integration of blockchain and edge computing. In particular, we first give an overview of blockchain and edge computing. We then present a general architecture of an integration of blockchain and edge computing system. We next study how to utilize blockchain to benefit edge computing, as well as how to use edge computing to benefit blockchain. We also discuss the issues brought by the integration of blockchain and edge computing system and solutions from perspectives of resource management, joint optimization, data management, computation offloading and security mechanism. Finally, we analyze and summarize the existing challenges posed by the integration of blockchain and edge computing system and the potential solutions in the future.

I. INTRODUCTION

IoT data growth strains cloud capacity, while edge computing and blockchain offer complementary resources and security functions. The survey examines their reciprocal integration, its architecture, and the issues and solutions it brings.

I. INTRODUCTION

  • IoT data growth raises real-time demands beyond traditional cloud capacity, with latency and bandwidth pressure limiting cloud use in scenarios such as smart grids and IoV.Edge computing moves computing and storage toward IoT data sources, but edge-resource limits and network heterogeneity constrain some existing data-security techniques.Source: §I · p. 1
  • Blockchain can support edge security through critical-data storage and smart-contract mechanisms, while edge computing can supply computing resources for blockchain mining.The introduction also describes smart-contract uses for access control, authentication, privacy preservation, resource allocation, task offloading, and pricing.Source: §I · p. 1
  • The survey treats blockchain and edge computing as complementary technologies, covering blockchain-enabled edge computing, edge computing-enabled blockchain, and their integration through existing issues.It also discusses other technologies when they contribute to IBEC architecture or solutions.Source: §I · p. 2

II. AN OVERVIEW OF BLOCKCHAIN AND EDGE COMPUTING

  • The paper’s overview section reviews blockchain, edge computing, and the opportunities created by their integration.The section roadmap places these technology overviews before the discussion of IBEC opportunities.Source: §II · p. 2

A. Blockchain

  • Blockchain stores transaction lists in block bodies and prior-block hashes and timestamps in headers, linking blocks chronologically and cryptographically as a distributed ledger.Its properties include decentralization, tamper-proofing through encryption and consensus, traceability, cryptography, and distributed storage.Source: A. Blockchain · pp. 2–3

B. Edge Computing

Edge computing brings services closer to IoT data sources to address cloud latency and bandwidth burdens. Its architecture distributes devices, edge resources, and cloud capabilities across three tiers.

B. Edge Computing

  • Edge computing places computation resources near terminals, keeping data generation, processing, and use close to the source to reduce response latency.The passage characterizes this as a low-latency service property.Source: B. Edge Computing · p. 3
  • Edge servers can provide local autonomy and self-recovery during network interruptions, while the central cloud dynamically offloads and schedules tasks to edge servers.Source: B. Edge Computing · p. 3
  • Edge devices can process data locally so sensitive information need not travel to remote clouds, improving security to some extent.The architecture also includes heterogeneous and scalable edge devices that supplement cloud resources.Source: B. Edge Computing · p. 3
  • The architecture has device, edge, and core infrastructure layers, with computing and storage capacity increasing toward the cloud.Resource-limited heterogeneous IoT devices collect and upload data; edge servers provide nearby storage, computing, and network resources; tasks beyond edge capacity go to the cloud, which can also adjust edge deployment.Source: B. Edge Computing · p. 3

C. IBEC

IBEC combines blockchain’s security properties with edge computing’s resources in a layered architecture. The survey describes resource enrichment, security and privacy assurance, and participation mechanisms as motivations for integration.

C. IBEC

  • Blockchain’s privacy preservation, immutability, and traceability can improve IoT edge-computing security, while edge computing can provide resources for blockchain operation.The integration is described as applied in IoV, smart grids, and IIoT.Source: C. IBEC · pp. 3–4
  • Edge computing can supply storage, computing, and communication resources for IoT devices that must run consensus, broadcast or verify transactions, and record blockchain history.Edge servers can process computation-intensive tasks and maintain blockchain data for sharing.Source: C. IBEC · p. 4
  • Blockchain consensus and its decentralized peer-to-peer architecture are presented as ways to support data consistency, traceability, tamper-proofing, and privacy amid dynamic, heterogeneous edge networks.Source: C. IBEC · p. 4
  • Design mechanisms can let resource-constrained edge devices participate in mining or share collected data for returns, which the paper says improves system scalability and flexibility.Source: C. IBEC · p. 4
  • The architecture connects resource-limited edge devices to edge servers that generally act as blockchain nodes and miners, with cloud infrastructure handling highly complex tasks.The cloud may store high-overhead raw data such as healthcare X-ray images while metadata remains on the blockchain; edge servers may execute smart contracts or train and update AI models through federated learning.Source: C. IBEC · p. 5

III. BLOCKCHAIN-ENABLED EDGE COMPUTING

The survey reviews how blockchain supports edge computing, including resource management and security. It describes smart-contract resource mechanisms and blockchain-based trust, integrity, and access-control approaches.

III. BLOCKCHAIN-ENABLED EDGE COMPUTING

  • The section reviews blockchain-enabled edge computing studies across resource management, security, data management, and lightweight blockchain for resource-limited devices.Source: §III · p. 5

A. Blockchain-based Resource Management for Edge Computing

  • Blockchain-based resource management uses smart contracts for resource allocation, reputation, and trading, with reviewed work addressing task assignment, spectrum access, and computation offloading.Examples include BCEdge’s proof-of-performance facility selection, auction-based spectrum access, and reputation-based cooperative offloading; the studies are summarized in Table I.Source: A. Blockchain-based Resource… · p. 5

B. Blockchain-based Security Layer for Edge Computing

  • Blockchain-based security-layer studies use ledgers and smart contracts to support trustworthy resource management, data integrity and traceability, strategy synchronization, and edge-service trust.The surveyed approaches also include access control, privacy-preserving trust management, and defenses against specified attacks; the studies are summarized in Table II.Source: B. Blockchain-based Security Layer for… · pp. 5–6

C. Blockchain-based Data Management for Edge Computing

The section surveys blockchain-based data management for edge computing, covering protected data access, knowledge sharing, content caching, and D2D transmission.

C. Blockchain-based Data Management for Edge Computing

  • BlockTDM combines membership authentication, responsibility tracing, multi-signature PBFT, privacy-preserving data segmentation, and smart-contract controls for sensitive data.Its controls include conditional access and decryption queries for protected transactions.Source: C. Blockchain-based Data Management… · p. 7
  • For knowledge sharing, UCB uses proof-of-popularity, while a consortium knowledge market combines proof-of-trading with game-theoretic pricing.UCB's PoP ranks user-centric proposal values and uses security-aware block generation; the market's PoT combines PoW and PoS to reduce energy consumption.Source: C. Blockchain-based Data Management… · p. 7
  • A blockchain-assisted MEC content-sharing scheme allocates mining tasks to base stations and develops caching strategies to maximize total profit.The strategies address both linear and nonlinear relationships between computing power and shared-data size.Source: C. Blockchain-based Data Management… · pp. 7–8
  • For D2D data transmission, proof-of-reliability restricts transmission to users with sufficient credit and supporters, while blockchain provides tamper-proofing and traceability.Source: C. Blockchain-based Data Management… · p. 8

D. Lightweight Blockchain for Resource-limited Edge Devices

The section surveys lightweight blockchain designs for resource-limited edge devices, addressing blockchain storage and consensus computation demands.

D. Lightweight Blockchain for Resource-limited Edge Devices

  • Blockchain-based resource management requires substantial consensus computation and full-chain storage, making support for blockchain a priority in edge computing.Source: D. Lightweight Blockchain for… · p. 8
  • LiTiChain stores time-limited transactions and blocks in an Endtime Ordering Graph and deletes expired blocks to save edge-node storage.Its k-height insertion and deletion or renewal algorithms maintain the graph's block ordering.Source: D. Lightweight Blockchain for… · p. 8
  • A redesigned blockchain pairs optimal block-storage allocation and recent-block caching with an improved PoS mechanism for battery-limited edge devices.The mechanism motivates nodes to hold more tokens and store more data.Source: D. Lightweight Blockchain for… · p. 8
  • Other proposed consensus designs use expectation maximization with polynomial matrix factorization for PoW puzzles, or trusted-node verification of MAC values in PoAh.In PoAh, nodes package and sign blocks, and selected trusted nodes verify them using the corresponding public keys.Source: D. Lightweight Blockchain for… · p. 8

IV. EDGE COMPUTING-ENABLED BLOCKCHAIN

The paper surveys how edge computing can supply resources for blockchain, then reviews integration issues and resource-management solutions.

IV. EDGE COMPUTING-ENABLED BLOCKCHAIN

  • Edge computing can act as a resource enabler for blockchain, including cloud computational resources that IoT devices purchase through mining as a service.In a UAV-enabled setting, blockchain records resource-trading data for tamper-proofing and traceability, while game-theoretic methods address pricing and resource-demand optimization.Source: §IV · pp. 8–9
  • Edge resources support blockchain mining allocation through pricing and game-theoretic schemes, collaborative mining with idle IoT devices, and immediate or strategic reporting.A hierarchical edge-cloud scheme and an ADMM approach address provider pricing and miner-provider interactions; collaborative mining also models offloading and auction prices.Source: §IV · p. 9
  • An edge-based parallel-processing framework uses edge nodes as validators to process blockchain activity data and improve blockchain scalability.Source: §IV · p. 9

V. EXISTING ISSUES AND THE CORRESPONDING SOLUTIONS IN IBEC

A. Resource Management for IBEC

  • Resource-management studies use blockchain and smart contracts for video-transcoding trades, task and spectrum allocation, and incentive mechanisms with varying truthfulness and budget-balance constraints.Other work applies game-based pricing and learning to resource management for computing and mining tasks in MEC networks.Source: A. Resource Management for IBEC · pp. 9–10
  • IBEC resource management also covers trustworthy edge caching, federated-learning content caching, multi-domain network slicing, and blockchain-supported wireless resource exchange.The cited designs use demand-based caching and fair allocation, gradient compression, blockchain-recorded service agreements, and SDN-integrated architectures.Source: A. Resource Management for IBEC · pp. 10–11

B. Joint Optimization for IBEC

The section reviews joint optimization of blockchain and edge resources, using optimization and learning approaches to address coupled system objectives.

B. Joint Optimization for IBEC

  • Joint allocation of radio spectrum and computing resources balances MEC energy consumption against delay and blockchain time to finality.The approach jointly considers user association, data rates, CPU-cycle frequencies, and block-producer scheduling, and formulates the problem as MINLP.Source: B. Joint Optimization for IBEC · p. 11
  • For blockchain-enabled MEC and UAV-assisted M2M, learning-based schemes jointly optimize resources and blockchain decisions to improve system performance.The cited approaches use double-dueling DQN for MEC delay, throughput, and confirmation latency, and dueling DQN for transmission requests, computing-node selection, block size, and block interval.Source: B. Joint Optimization for IBEC · p. 12
  • For delay-tolerant M2M data, DQN selects caching servers, computing servers, and blockchain decisions; related work also applies DRL to offloading and caching problems.The delay-tolerant scheme addresses time-varying conditions, while cited consensus designs include partial PBFT and integrated DPoS and aBFT.Source: B. Joint Optimization for IBEC · pp. 12–13

C. Data Management Framework for IBEC

The section surveys how blockchain supports decentralized data management in edge-enabled IoT, including data access, secure storage, sharing, and trading.

C. Data Management Framework for IBEC

  • Blockchain supports decentralized, third-party-free management of edge-generated data, including secure access, sharing, and traceability.Edgence combines decentralized crowdsourcing and AI training without transmitting data to the cloud; a three-layer architecture uses smart contracts for access and orchestration management.Source: C. Data Management Framework for IBEC · p. 13
  • Secure-storage designs combine integrity, adaptability, and anonymity mechanisms, or use regenerative coding and synchronized cloud- and edge-layer blockchains for reliability and integrity.The surveyed approaches include Data Integrity Service, off-chain state channels, hybrid cryptography, and periodic synchronization between local and global blockchains.Source: C. Data Management Framework for IBEC · p. 13
  • Blockchain incentives support data sharing and trading; surveyed systems also use local processing, privacy-preserving aggregation, and block-generation changes for throughput and latency.EdgeBoT processes data locally to reduce upload bandwidth, while BSDA combines sensitive-task decomposition with energy-efficient aggregation.Source: C. Data Management Framework for IBEC · p. 13

D. Computation Offloading for IBEC

The section reviews computation-offloading schemes that use blockchain to record resource use and support trusted allocation, alongside security strategies for IBEC spanning authentication, privacy, messaging, and protected execution.

D. Computation Offloading for IBEC

  • Computation offloading transfers all or part of a task from resource-constrained devices to nearby edge servers, while blockchain can record allocation and offloading status for consistency and integrity.The recorded status data is stored on-chain.Source: D. Computation Offloading for IBEC · p. 13
  • DeTEC uses heuristic task allocation to minimize latency and records edge-server contributions, while BeCome selects offloading strategies by time, energy, and load balance using a VM allocation ledger.BeCome generates candidate strategies with NSGA-III and selects among them using multi-criteria decision making.Source: D. Computation Offloading for IBEC · pp. 13–14
  • Other designs model mobile-equipment offloading versus bank loans as a noncooperative game and use header-only PoW offloading or online DRL with an adaptive genetic algorithm [167].The DRL approach formulates offloading as an MDP and targets policy self-adjustment in dynamic blockchain-enabled MEC.Source: D. Computation Offloading for IBEC · p. 14

E. Security Strategies for IBEC

  • Security designs span trusted services across the Edge-Fog-Cloud continuum, authentication and anonymity, and blockchain-verified routing intended to preserve multi-domain topology privacy,.The routing approaches use consortium blockchain and bloom filters; the cited privacy-enhancement scheme uses ECC.Source: E. Security Strategies for IBEC · pp. 14–15
  • Anonymous storage and transaction protocols and private/public blockchain messaging with MQTT–CoAP integration, address data anonymity and communication needs across edge groups and the wider network.CoAP is used for resource-limited terminals to communicate with edge nodes.Source: E. Security Strategies for IBEC · p. 15
  • Other security approaches use digital twins with permissioned blockchain for FL communication security and data privacy, dual ledgers for LoRa threats, and SGX for privacy and tamper-proof execution.The LoRa system analyzes DoS, single-point-of-failure, malicious-gateway, and malicious-server threats; the SGX design also supports incentives and sandboxed execution.Source: E. Security Strategies for IBEC · p. 16

VI. CHALLENGES AND SOLUTIONS

This section introduces IBEC challenges and potential solutions.

VI. CHALLENGES AND SOLUTIONS

  • The section presents challenges for IBEC and discusses potential solutions.Source: §VI · p. 16

A. Performance Scalability

The section identifies blockchain scalability and resource management as challenges for IBEC, then summarizes existing expansion approaches and future resource-management directions.

A. Performance Scalability

  • Bitcoin processes only 3 to 7 transactions per second, and blockchain scalability remains a challenge for IBEC amid rising IoT verification demands and emerging 6G applications.The paper links the challenge to large data volumes and many devices, and calls for improved blockchain scalability.Source: A. Performance Scalability · pp. 16–17
  • Blockchain expansion includes on-chain protocol changes –, off-chain approaches –, and zero-layer data-transfer optimization; future IBEC schemes should be lightweight and flexible for heterogeneous, resource-limited edge devices.The paper proposes AI-empowered expansion schemes as a future direction.Source: A. Performance Scalability · p. 17

B. Resource Management

  • IBEC resource management must address heterogeneous, often resource-limited devices, computation-intensive applications, and the substantial resources needed to maintain blockchain.In 6G and beyond, integration across air, space, ground, and sea networks further increases resource-management demands.Source: B. Resource Management · p. 17
  • The paper identifies adaptive resource integration and allocation, AI-based edge intelligence, and realistic bidding and pricing as potential ways to improve IBEC resource management and QoS.It notes that device proliferation, heterogeneity, and frequent online/offline changes make resource integration and management harder.Source: B. Resource Management · p. 17

C. Security

The survey identifies security and privacy-computing challenges in IBEC, including vulnerable smart contracts, device authentication, and protecting data during computation. It reviews proposed responses and summarizes optimization directions and broader deployment challenges.

C. Security

  • Smart-contract vulnerabilities, inalterable errors, malicious code, and limited legal oversight make correctness and effectiveness difficult to ensure in IBEC.Privacy-preserving encrypted-traffic inspection is proposed to detect abnormal or suspicious contract code.Source: C. Security · p. 17
  • Heterogeneous devices, authentication standards, and edge-side computing pressure make lightweight, fast, consistent identification a challenge.Proposed directions include distributed IoT device identification and standardizing identification protocols.Source: C. Security · p. 17

D. Privacy Computing

  • Sensitive data sharing and knowledge discovery require privacy protection during computation, while existing blockchain security and complex traditional privacy mechanisms do not fully address that need.PEC and MPSC applicability must account for edge-resource limits and substantial offloading demands; lightweight, parallel, and modular optimization is identified as a possible trend.Source: D. Privacy Computing · p. 18

VII. CONCLUSION

  • The survey reviews IBEC architecture, research issues, and solutions across resource management, data management, computation offloading, security, and privacy, and identifies challenges hindering large-scale deployment.It discusses performance optimization in scalability, resource management, security, and privacy computing.Source: §VII · p. 18
Loading 2205.13160v1…