Blockchain-Based Task Placement and Resource Management in Edge Computing: A Survey

Abstract

As the metaverse progresses, it requires real-time, low-latency, and secure computing capabilities that conventional cloud-based systems cannot fully support. Multiaccess edge computing (MEC) addresses this demand by moving processing closer to the user; however, it also introduces new challenges in task placement, resource management, security, and trust. The blockchain is a promising enabler to address the limitations of trust, transparency, and centralized control in these systems. This survey systematically reviews 61 high-quality studies that explore blockchain-based solutions for task offloading and resource allocation in MEC. This work identifies the prevailing trends and research gaps using a structured method. The analysis reveals that over 60% of studies employ artificial intelligence-based techniques. Additionally, this work quantifies the adoption of various performance metrics from the literature and examines the case study distribution across application domains. Finally, this work outlines the technical challenges and opportunities for future research toward scalable, efficient, and trustworthy blockchain-enabled MEC frameworks.

1. Introduction

The metaverse has emerged as a transformative paradigm for the next generation of the internet, offering a fully immersive, interactive, and persistent environment where users can socialize, work, learn, and play in real time [1]. Enabled by advancements in augmented, virtual, and mixed reality, this environment requires ultra-low latency, high bandwidths, and massive computational resources to deliver seamless and immersive experiences [2].

Next-generation networks, including the fifth generation (5G) and beyond, are pivotal in supporting the metaverse by providing the infrastructure for high-speed data transmission, ultra-reliable low-latency communication, and massive machine-type communication [3]. However, centralized cloud computing, which supports such applications, has encountered substantial challenges in meeting the stringent latency and scalability requirements. This problem has led to the adoption of multiaccess edge computing (MEC), bringing computation and storage resources closer to end users, thus reducing the latency and improving the service quality [4].

Despite its potential, MEC faces challenges. Dynamic and heterogeneous edge environments complicate task placement [5] and resource management [6]. Tasks must be efficiently distributed across edge nodes to minimize latency and energy consumption while ensuring load balancing. Moreover, the reliance of applications on sensitive user data (e.g., biometric information and behavioral patterns) raises security and privacy concerns (e.g., personal information leakage, eavesdropping, unauthorized access, phishing, data injection, broken authentication, insecure design, etc.) [7]. Furthermore, traditional centralized systems are susceptible to single points of failure, data breaches, and unauthorized access [8]. Finally, the lack of trust among stakeholders (e.g., service providers, users, and developers) in the edge ecosystem hampers collaboration and resource sharing [9].

Blockchain technology has emerged as a promising solution to address these challenges. The blockchain can enhance security, privacy, and trust in MEC systems by offering a decentralized, transparent, and tamper-proof ledger [10]. For example, the blockchain can ensure secure and verifiable task placement by recording task assignments and execution results on an immutable ledger [11]. Furthermore, blockchain-based smart contracts can automate resource allocation and ensure fair and transparent resource sharing among stakeholders [12]. The decentralized nature of the blockchain aligns well with the distributed architecture of MEC, rendering it optimal for supporting heterogeneous applications [13].

1.1. Role of the Blockchain in Edge Computing

Blockchain technology offers higher standards for security and privacy in MEC [14] and has unique features (e.g., decentralization, immutability, transparency, and cryptographic security), rendering it suitable for addressing task placement and resource management challenges [15]. The blockchain offers several benefits by applying these features.

• Enhanced security and privacy: Cryptographic blockchain algorithms ensure that data stored on the ledger are secure and tamper-proof, which is critical for applications where sensitive user data must be shielded from unauthorized access and malicious attacks. The blockchain can employ privacy-preserving techniques, such as zero-knowledge proofs, to ensure that user data remain confidential yet verifiable [16].
• Decentralized trust: In a multistakeholder environment, trust is crucial for collaboration and resource sharing [12]. The blockchain eliminates the need for a central authority by fostering decentralized trust via consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS) [17]. This approach ensures that all transactions and interactions are transparent and verifiable by all parties.
• Efficient task placement and resource allocation: The blockchain can facilitate efficient task placement [18] and resource allocation [19] by ensuring a transparent and auditable record of resource availability and usage. Smart contracts can automate allocation, ensuring that resources are fairly and efficiently allocated based on predefined rules and conditions [12].
• Scalability and interoperability: Recent advances (e.g., sharding and layer-2 solutions) have substantially improved blockchain scalability [20], enabling the blockchain to support large-scale and dynamic MEC environments. The interoperability features of the blockchain enable it to integrate with existing MEC frameworks and protocols, allowing seamless collaboration across systems and platforms.

Figure 1 presents a blockchain application framework for the MEC system.

1.2. Contributions

Numerous surveys have assessed the application of blockchain technology in MEC. For example, Moghaddasi and Rajabi [21] focused on blockchain-based offloading methods in MEC settings, evaluating task placement strategies. Iqbal et al. [14] examined vehicular networks powered by the blockchain with an emphasis on task placement to enhance secure and efficient edge services. In addition, Baranwal et al. [19] surveyed blockchain-enabled resource allocation techniques in cloud and MEC environments, assessing decentralized resource management. Furthermore, Naren et al. [22] analyzed the allocation of computing resources for vehicular MEC enabled by the Internet of Things (IoT), providing information on blockchain-assisted resource distribution. Xue et al. [23] explored the integration of blockchain and MEC for IoT applications, examining resource management challenges. Furthermore, Liao et al. [10] discussed security and forensics management in MEC for the IoT, highlighting the role of the blockchain in resource management and security.

Although these studies provide valuable perspectives on blockchain applications in MEC, they tend to focus on task placement or resource management individually. In addition, these studies do not adequately address critical aspects, including privacy, scalability, and comprehensive support for emerging applications. This survey fills these gaps by providing a comprehensive review of blockchain-based approaches that unify secure and decentralized task placement and resource management in MEC systems. Unlike previous studies, this survey organizes the literature by the methods and techniques applied to solve these problems.

Table 1. Summary of related surveys and reviews on the blockchain for MEC and their focus.

Reference	Main Topic	Task Placement	Resource Management
[21]	Blockchain-based offloading methods for various MEC settings	✓	✗
[14]	Blockchain-empowered vehicular network	✓	✗
[19]	Blockchain resource allocation in cloud computing and MEC	✗	✓
[22]	Computational resource allocation in IoT-enabled vehicular MEC	✗	✓
[23]	Blockchain and MEC integration for IoT	✗	✓
[10]	Security and forensics management in MEC for IoT	✗	✓
Our work	Blockchain-based task placement and resource management in MEC	✓	✓

✓ = Included; ✗ = Not included.

summarizes the related surveys and reviews on the blockchain for MEC.

This survey offers a comprehensive overview of the role of the blockchain in task placement and resource management in MEC. The contributions of this paper are listed below.

• This work provides a comprehensive background on MEC and blockchain technology, highlighting their architectural components, operational models, and relevance to emerging applications.
• This work offers an in-depth analysis of the challenges in task placement and resource management in edge environments and assesses how the blockchain can address these problems via secure task offloading, decentralized resource control, and privacy-preserving mechanisms.
• This work presents a comprehensive review of recent blockchain-based approaches, critical research gaps in the existing solutions, and future research directions.

2. Research Methodology

The research methodology comprises a systematic literature review, where a rigorous protocol is defined and applied to extract information that answers the research questions. This methodology allows for impartial results and an auditable process. This section details the methodology applied in this review.

2.1. Research Questions

Defining the research questions is the most crucial part of a systematic literature review because the questions guide all further steps of the review. The goal is to formulate questions that can be answered by reviewing primary studies from the literature. A thorough search was conducted to list all subjects addressed regarding blockchain applications for MEC to define the research questions. Then, research gaps not addressed by other surveys were identified. The questions in

Table 2. Research questions (RQs).

RQ	Research Question	Goal
RQ1	How does the blockchain enhance task offloading and resource management in MEC systems?	To understand the role of the blockchain in improving security, decentralization, and trust in task offloading and resource allocation at the network edge.
RQ2	What are the existing frameworks and techniques designed for blockchain-enabled task offloading and resource management in MEC?	To categorize and analyze the methods, techniques, and solutions derived from the literature.
RQ3	What are the current limitations, open challenges, and future research opportunities regarding combining the blockchain with MEC for task placement and resource management?	To identify gaps in the current research and outline potential directions for future studies.

were formulated based on this subject list.

2.2. Search Process

Research articles were gathered from well-known databases, including IEEE Xplore, Scopus, and Google Scholar, focusing on papers published between 2020 and 2025. The search terms “blockchain”, “edge computing”, “MEC”, “task offloading”, “task placement”, “resource allocation”, “resource management”, and others were employed. The references and "cited by" sections of the most relevant articles were checked to ensure that no important or additional aspects were missed. The search returned a high number of articles; hence, criteria for the inclusion and exclusion of the initial studies were necessary to select those that should be the focus of a more in-depth analysis. The inclusion and exclusion criteria for this work are presented in

Table 3. Inclusion criteria (ICs).

	Inclusion Criteria
IC1	The study presents or discusses opportunities or challenges in running the blockchain in edge computing.
IC2	The study proposes or evaluates blockchain-based solutions in the context of MEC.
IC3	The study addresses task placement, resource management, or both in blockchain-enabled MEC environments.

and

Table 4. Exclusion criteria (ECs).

	Exclusion Criteria
EC1	The study is a previous version of a more complete study on the same research.
EC2	The study is unrelated to the blockchain in MEC.
EC3	The study is not written in English.

, respectively.

2.3. Study Selection Procedure

The survey selection process followed a structured multiphase methodology inspired by best practices in systematic literature reviews. The procedure was divided into three phases: identification, screening, and inclusion.

In the identification phase, precise research questions were formulated (Section 2.1), and a comprehensive search strategy was developed (Section 2.2). Using carefully selected keywords and Boolean expressions, 1874 articles were retrieved. After removing 331 duplicate records, the predefined exclusion criteria (Table 4) were applied, resulting in the removal of 1010 articles. The remaining 533 articles were compiled into a list for screening.

The initial screening reviewed titles and abstracts, and, based on their relevance to blockchain integration in MEC, 180 papers were shortlisted. This first filtering round excluded 353 studies that did not make substantial contributions to task placement or resource management.

In the second screening round, a different reviewer conducted a deeper, full-text assessment to ensure objectivity, eliminating 85 papers. Based on expert recommendations and backward citation tracking, eight additional studies were included due to their importance and relevance after a manual search of Google Scholar.

After the final selection, 61 high-quality papers were retained for detailed analyses and classification. These studies were the basis for the comparative evaluation and discussion. Figure 2 illustrates the complete flow of the selection process, summarizing the number of articles included and excluded at each step.

The remainder of this article is organized as follows. Section 3 introduces the enabling technology for secure and decentralized edge computing. Next, Section 4 discusses the existing work on blockchain-enabled task placement and resource allocation in edge computing. Then, Section 5 outlines the open research challenges and future research directions. Finally, Section 6 concludes with a summary of the insights and contributions of this work.

3. Enabling Technology for Secure and Decentralized Edge Computing

3.1. MEC Infrastructure

The MEC infrastructure is the backbone for the deployment and management of decentralized computing resources close to end users [24], enabling low-latency, high-performance applications for next-generation services. By reducing the reliance on centralized cloud servers, MEC addresses the limitations concerning latency, bandwidth constraints, and data privacy. The infrastructure comprises the hardware, system software, and middleware, each with a role in ensuring efficient, secure, and scalable MEC systems [25]. In addition, MEC is a natural complement to the blockchain, enabling secure and trustworthy coordination between distributed entities without requiring centralized control.

3.2. Blockchain Overview

The blockchain is a decentralized digital ledger that securely records transactions on a computer network, ensuring that, once information is documented, it becomes immutable and transparent [26]. This decentralized approach eliminates the need for a central authority because the network participants verify each transaction using consensus mechanisms. Satoshi Nakamoto [27] introduced the concept of the blockchain in 2008 as the foundational technology for Bitcoin, authorizing peer-to-peer transactions without intermediaries.

In Figure 3, the blockchain comprises a series of blocks, each with a transaction list, timestamp, and cryptographic hash of the previous block, creating an unalterable chain. This structure ensures that, once the data are recorded, they cannot be changed without altering all following blocks and gaining consensus from the network, providing a high level of security and trust [28]. Transactions are broadcast to the network and, after validation, are appended to a new block by miners or validators, depending on the consensus protocol.

Beyond its initial application in cryptocurrency, the blockchain has expanded to diverse sectors, including finance [29], networking [30], and other domains, providing solutions that allow secure, transparent, and tamper-proof recordkeeping. The decentralized nature of the blockchain reduces the risk of centralized points of failure and improves data integrity, rendering it a promising technology for applications demanding high security and trust.

3.2.1. Blockchain Architecture and Components

The multilayered blockchain architecture is designed to facilitate decentralized and secure digital transactions in a peer-to-peer network. The blockchain design provides transparency, immutability, and trust without a central authority. The core elements of the blockchain architecture are listed below [31].

Data layer: The foundational layer of the blockchain architecture stores crucial blockchain information, including block timestamps, transaction details, and Merkle roots. Blocks comprise a header and body, with the headers containing the previous block hash, timestamp, and Merkle root, ensuring a tamper-proof chronological order.

Network layer: This layer propagates transactions and blocks between the nodes in the network, ensuring that all nodes have the latest ledger version by managing communication and data exchange protocols. The network layer is essential in maintaining a decentralized blockchain by facilitating peer-to-peer interactions.

Consensus layer: The blockchain applies consensus mechanisms to maintain agreement on the ledger state. These protocols (e.g., PoW and PoS) define how nodes validate transactions and agree on adding new blocks to the chain. Consensus mechanisms are critical to ensure the security and reliability of the blockchain, averting fraudulent activities and double spending.

Incentive layer: This layer offers rewards to participants who contribute resources to the network, such as miners or validators. Incentives are typically cryptocurrency tokens and are designed to promote honest behavior and active participation in maintaining network security and functionality.

Application layer: The topmost layer of the blockchain architecture is the application layer, where decentralized applications and smart contracts function. This layer enables developers to create and deploy applications using blockchain technology for diverse use cases, including finance, supply chain management, and healthcare.

3.2.2. Blockchain Types

Blockchain technology is classified based on the decentralization level, access control, and governance structure. The primary blockchain types are public, private, consortium (or federated), and hybrid. Each type is designed to meet specific requirements concerning transparency, security, and scalability [27].

Public blockchain: The public blockchain is a fully decentralized, open network where anyone can participate in transaction validation and ledger maintenance. Bitcoin [27] and Ethereum [32] are public blockchains operating without a central authority. Transactions are validated via consensus mechanisms (e.g., PoW or PoS), guaranteeing immutability and transparency. However, public blockchains encounter scalability challenges due to their high computational costs and slow transaction speeds [33].

Private blockchain: In a private blockchain, only authorized entities can access and validate transactions [34]. These blockchains are often employed in enterprise applications, where organizations require controlled access, high efficiency, and security. Hyperledger Fabric is a widely applied private blockchain framework for business applications, offering role-based permissions and fast transaction processing [35]. The principal limitation of private blockchains is lower decentralization, as a single entity often controls access and governance [36].

3.2.3. Why Use the Blockchain?

Securing task placement and resource management in MEC is critical due to the vulnerabilities of centralized task management, including unauthorized data modifications, single points of failure, and a lack of transparency. Researchers have integrated blockchain technology into MEC to address these challenges, applying decentralization, immutability, and cryptographic security to guarantee tamper-proof task execution and fair resource distribution.

A study [37] suggested a content caching and computation strategy based on the blockchain to enhance MEC security and efficiency. Their method prevents unauthorized content requests by validating the authenticity of the cached data using PoS consensus. The authors integrated a deep Q network (DQN)-based solution to optimize caching decisions, enabling intelligent content placement and mitigating malicious manipulation.

Li et al. [38] designed a blockchain-enabled digital twin (DT) vehicular edge network to improve task offloading and resource management in vehicular MEC (VEC). The DTs monitor network communication, computation, and caching resources, whereas the blockchain guarantees secure and decentralized transactions.

Similarly, Wang et al. [39] introduced a vehicular consortium blockchain for secure resource sharing in VEC. They designed multistep smart contracts to prevent selfish, malicious behavior using a BFT-based PoS consensus for efficient verification.

In addition, Iqbal et al. [40] analyzed security risks in blockchain systems, particularly Sybil and double-spending attacks, employing a security risk management framework. They evaluated Ethereum-based healthcare applications, identifying vulnerabilities and proposing countermeasures to improve security. They also discussed the role of permissioned blockchains in mitigating security and implementation challenges in industry applications.

Li et al. [41] designed an enhanced PoS algorithm, advancing blockchain security and decentralization. Their strategy splits nodes to reduce centralization risks and allows witness nodes to report malicious activity. This validation approach detects long-range attacks by comparing the consensus transactions with the longest chain.

Table 5. Comparison of conventional edge computing and blockchain-based edge computing.

Characteristic	Conventional Edge Computing	Blockchain-Based Edge Computing
Control and coordination	Centralized orchestration by cloud or edge servers	Decentralized coordination via consensus and smart contracts
Trust model	Relies on a central trusted entity for validation	Trust is distributed and enforced using cryptographic consensus
Security	Vulnerable to tampering and single-point failures	High tamper resistance and security via the distributed ledger
Transparency	Limited visibility of system operations	Transparent, immutable logs of all operations
Fault tolerance	Low fault tolerance due to central dependencies	High fault tolerance using distributed nodes
Incentivization	No built-in incentives for collaboration	Token-based rewards for participation and validation
Scalability challenges	Limited by centralized resources	Scalability supported by distributed participation
Application suitability	Best for trusted, homogeneous environments	Ideal for untrusted, multiparty, dynamic environments

provides an overview of the characteristics of conventional edge computing versus blockchain-based edge computing.

4. Blockchain-Enabled Task Placement and Resource Allocation in Edge Computing

This section reviews studies that apply the blockchain to task placement and resource allocation in MEC, categorized based on the approach. Each section summarizes related papers, highlighting their methods, contributions, and limitations.

4.1. Machine Learning-Based Schemes

Integrating artificial intelligence (AI) with blockchain technology has led to innovative solutions for task offloading in diverse computing environments. For example, Liu and Sun [42] applied a deep reinforcement learning (DRL) algorithm based on the actor–critic architecture to enhance the task allocation efficiency, permitting multiple agents to share a global memory pool. This approach allows collaborative learning, where the agents benefit from each other’s experiences, accelerating convergence and enhancing decision making.

Chu [43] introduced a blockchain-based task offloading technique integrated with neural networks. These neural networks predict and dynamically adjust task offloading decisions based on the network conditions, minimizing offloading delays and reducing the computational costs.

Similarly, Nguyen et al. [44] presented a blockchain-enabled AI-driven task offloading and mining framework that optimizes resource allocation in MEC. By applying multiagent DRL, the system jointly optimizes task offloading decisions, channel selection, and computational resource distribution for efficient and adaptive task execution. In addition, Fan [45] employed a hybrid DRL-based algorithm to optimize task processing delays and energy consumption in blockchain-secured cloud–edge–end cooperative networks.

Researchers have also integrated the blockchain into federated learning (FL) for task offloading. For example, Nguyen et al. [46] applied FL in blockchain-based multiserver MEC to advance distributed machine learning model training and secure task offloading. They integrated peer-to-peer blockchain communications to guarantee trustworthy model aggregation and protection against poisoning attacks. Another study [47] combined FL with the blockchain to improve task scheduling and security in microservice-based mobile cloud computing by allowing privacy-preserving task offloading without exposing raw user data.

Wang et al. [48] designed a framework that integrates the blockchain, FL, and MEC to optimize task offloading and ensure low latency and data privacy. The study addressed the challenges of insufficient blockchain throughput and the dynamic nature of IoT and MEC systems with high-dimensional features and large-scale actions. These frameworks present significant promise in handling dynamic, complex task offloading in MEC environments. In addition, DRL and FL are the most commonly applied techniques due to their adaptability and decentralized learning capabilities.

Various studies have explored the integration of the blockchain with AI-based resource allocation techniques in MEC. The decentralization of the blockchain guarantees that resource transactions are immutable and verifiable, diminishing risks associated with fraudulent resource claims and unauthorized access. For example, Luong et al. [49] recommended an optimal auction-based edge resource allocation mechanism exercising deep learning by designing a multilayer neural network to optimize the revenue of the MEC service provider and ensure incentive compatibility and individual rationality for miners.

Furthermore, He et al. [50] designed a blockchain-based framework to increase trust between IoT devices and MEC nodes. The authors employed the asynchronous advantage actor–critic reinforcement learning algorithm to optimize edge resource allocation in a smart contract on a private blockchain network. They defined a step-by-step transaction process between an IoT device and ECN for decentralized, trustless, and immutable interactions.

Machine-to-machine (M2M) communication networks foster direct communication between machine-type communication devices without human intervention. However, M2M networks encounter challenges concerning data integrity, low-latency processing, and secure interactions due to their reliance on centralized infrastructures. Li et al. [51] designed a blockchain-enabled M2M framework with MEC and a dueling DQN to optimize caching, computation, and security for efficient and secure interactions. Industrial IoT (IIoT) environments face limitations in secure resource sharing, device cooperation, and trust management due to dynamic and distributed infrastructures. Moreover, Iqbal et al. [52] developed a blockchain-enabled IIoT framework that applies a DQN and social-aware incentive mechanism to support adaptive and secure multihop task offloading between nearby devices.

Similarly to task placement, studies have integrated FL and the blockchain for resource management in MEC. For example, Muazu et al. [53] designed an edge-empowered blockchain FL system for resource management on the Internet of Medical Things (IoMT). Gradient parameters are encrypted using Paillier encryption before sharing with federated clients to enhance security and privacy. The blockchain guarantees the secure cataloging and auditing of IoMT transactions, whereas MEC manages complex computational tasks for resource-constrained IoMT devices.

In addition, Ayepah-Mensah et al. [54] explored the blockchain and federated DRL for dynamic resource allocation in radio-access network slicing. Traditional centralized controllers can result in inefficient resource use due to strategic tenant behavior and the computational overhead. They proposed a peer-to-peer resource trading framework to address this problem, where the slice tenants directly communicate and negotiate resource allocation.

The reviewed studies, summarized in

Table 6. Summary of blockchain studies on task offloading in MEC (machine learning-based schemes).

Reference	Technique	Contributions	Evaluation Parameters
[42]	Multiagent DRL	Optimizes task offloading efficiency in blockchain-enabled MEC	Reward, delay, energy, privacy
[43]	DL	Reduces computational costs and data propagation delay in mobile environments	Processing time, execution, transaction rate
[44]	Multiagent DRL	Offers cooperative task offloading and block mining in blockchain-based MEC	Utility, latency, bandwidth
[45]	Hybrid DRL	Optimizes task offloading and energy efficiency in blockchain-enabled MEC	Reward, offloading proportion, delay, energy
[46]	FL, DRL	Minimizes latency in blockchain-based FL	Accuracy, loss, reward, latency
[47]	FL	Enhances security and resource optimization in mobile cloud computing	Service overhead, boot time, CPU utilization, failure rate
[48]	FL, DDPG	Optimizes 5G resource utilization and task offloading in IoT systems	Reward
[55]	DRL	Secures MEC task offloading with blockchain and DRL	Cost, memory usage, throughput, latency, CPU utilization
[56]	CRL	Optimizes task offloading efficiency in heterogeneous edge networks	Reward, energy, time
[57]	DRL	Increases computation and throughput of blockchain systems	System rewards
[58]	DNN	Optimizes IoT blockchain networks with MEC and DNNs	Energy, loss
[59]	DL	Offers deep learning for vehicle detection with a two-phase authentication mechanism for secure device verification	Time, energy, throughput, successful transactions
[60]	FL	Provides a blockchain-enabled FL system with a reputation mechanism for smart home manufacturers	Test accuracy, reward value, reputation value
[61]	DDPG	Offers an intelligent computing offloading model for the Internet of Vehicles	Loss, reward, cost, success rate
[62]	Multiarmed bandit RL	Optimizes task offloading policies and resource allocation in blockchain-enabled MEC systems	Delay, cost
[49]	DL	Maximizes the revenue of the MEC service provider in mobile blockchain networks	Revenue, probability
[50]	RL	Enhances secure and intelligent resource allocation in edge-centric IoT	Loss, reward, delay, task drop rate
[51]	DQN	Maximizes system rewards in blockchain-enabled M2M communications	Reward, latency
[53]	FL	Minimizes computing costs in IoMT with encrypted gradient sharing and secure transaction auditing	Accuracy, time, memory usage
[54]	FL, DRL	Enhances resource utilization and quality of service in radio-access network slicing	Loss, reward, time
[63]	FL, DRL, MDP	Enhances trust and energy-efficient model training in IoT-based FL	Accuracy, reward, loss, energy
[64]	FL, ADMM	Minimizes service cost in the MEC-enabled blockchain	Energy, bandwidth, latency
[65]	A3C, MDP	Minimizes offloading costs in healthcare systems for secure and efficient medical resource allocation	Delay, utility, energy
[66]	DDQN, MDP	Optimizes resource allocation for task offloading in IoT-based MEC systems	Reward, energy, latency
[67]	DRL, CMDP	Enhances data security and resource utilization in decentralized MEC networks	Latency, probability

, demonstrate that machine learning-based approaches are increasingly favored owing to their adaptability and scalability in blockchain-enabled MEC systems. However, these methods often require considerable computational resources and prudent coordination to balance security, latency, and energy consumption.

4.2. Game Theory Schemes

The mathematical game theory framework models and analyzes strategic interactions between rational decision makers. Guo et al. [68] introduced a game-theoretical approach to optimize task offloading in blockchain-based MEC. Due to the limited computational capacity of IoT mobile devices, miners can offload mining tasks to nonmining devices on a collaborative mining network or to edge cloud resources. The resource competition between nonmining devices is modeled as a double auction game, where the Bayes–Nash equilibrium controls the optimal auction price for resource allocation. Moreover, the Stackelberg game models the interactions between the edge cloud operator and collaborative mining networks, providing optimal resource pricing and demand allocation.

Similarly, Zhang et al. [69] adopted a prospect-theoretical approach to optimize task offloading decisions in MEC-empowered blockchain networks, addressing the challenges of resource-constrained mobile devices. They also devised a Stackelberg game, where MEC service providers function as leaders, setting resource pricing. In contrast, miner devices act as followers, making offloading decisions based on individual risk preferences.

Lang et al. [70] explored cooperative computational offloading based on game theory in blockchain-enabled VEC networks, where vehicles offload tasks to nearby resource-rich vehicles. They designed the offloading decision-making process as an offloading game based on the Nash equilibrium, where vehicles strategically select offloading partners.

Another study [71] devised the problem of computational offloading and coin lending in blockchain-empowered MEC as a noncooperative game, addressing the competition between resource-constrained mobile equipment. The study demonstrated the existence of a pure-strategy Nash equilibrium and proposed a distributed algorithm enabling the mobile equipment to converge to an optimal resource allocation strategy with low computational complexity. These studies model complex interactions in MEC–blockchain ecosystems, presenting decentralized strategies for pricing, competition, and cooperation. Stackelberg and Nash games are prevalent, allowing layered decisions between service providers and resource seekers.

Game theory is widely applied in MEC and blockchain-based resource allocation to model strategic interactions between multiple entities. This theory fosters optimized decision making in competitive and cooperative environments, guaranteeing efficient resource use and fair task offloading. This section discusses studies that employ game-theoretical approaches to blockchain-enabled resource allocation in MEC.

Guo et al. [72] investigated the integration of game theory into blockchain-based MEC by designing a two-stage Stackelberg game between miners and an edge service provider (ESP). In this model, the ESP provides miners with computational resources, enabling them to strategically determine the optimal amount to purchase for the maximum mining efficiency. The study assessed two mining schemes, deriving the Stackelberg equilibrium to establish the best incentives for ESPs and miners.

In addition, Baranwal et al. [73] addressed resource allocation limitations in IIoT applications by proposing a decentralized auction-based mechanism using the consortium blockchain and smart contracts. The study devised a bidding and allocation process as an auction game, where edge servers are incentivized to bid truthfully to ensure secure, efficient, and responsive resource distribution.

Given the excessive storage overhead and limited capacity of edge devices, Rui et al. [74] designed a two-stage auction mechanism using smart contracts, where the preferred nodes are designated to store blocks based on multidimensional bids, reducing the storage cost and realizing fair resource allocation. Blockchain technology is integrated to augment the security and transparency, maintaining immutable transaction records and averting fraudulent resource allocation.

Similarly, Sun et al. [75] improved the system utility and service satisfaction in VEC. They advocated for a cooperative algorithm integrating bargaining-based intraserver resource allocation with matching-based interserver offloading, facilitating hierarchical collaboration between vehicles, edge networks, and cloud servers. This group of studies indicates that auction-based and Stackelberg games are often employed to balance incentives and fairness between stakeholders. Although these approaches offer transparency and economic efficiency, they may increase the complexity in real-time implementations due to equilibrium computations and the necessity of accurate user behavior modeling.

Table 7. Summary of blockchain studies on task offloading in MEC (game theory-based schemes).

Reference	Technique	Contributions	Evaluation Parameters
[68]	Double auction, Stackelberg	Optimizes blockchain task offloading and maximizes mining utility in mobile IoT networks	Delay, miner density, transaction rate
[69]	Stackelberg	Optimizes blockchain mining task offloading in MEC, accounting for device-specific risk–reward preferences	Utility
[70]	Cooperative game, Nash equilibrium	Offers blockchain-based secure data sharing in vehicular MEC	Latency, probability
[71]	Nash equilibrium	Minimizes the total cost of mobile devices in blockchain-empowered MEC	Cost, time, iterations
[76]	Nash bargaining	Optimizes secure and tamper-resistant task scheduling framework for IoT edge environments	Reward, price, scheduling time, bargaining time, CPU memory usage
[77]	Stackelberg	Maximizes utility for blockchain miners and service providers in a realistic edge-assisted setting	Price, utility, computing demand
[78]	Stackelberg	Optimizes cost efficiency and node utilities through a reputation-based incentive mechanism in consortium blockchain-enabled edge networks	Utility, cost, reputation
[79]	Stackelberg	Minimizes energy consumption and delay in blockchain-enabled multi-UAV MEC networks	Delay, energy
[80]	Stackelberg	Maximizes the utilities of blockchain users and miners in MEC-enabled wireless blockchain networks	Price, transaction rate, utility
[72]	Stackelberg game	Optimizes incentive mechanisms in edge-assisted blockchain networks	Profit
[73]	Auction theory	Enhances secure and fair resource allocation in IIoT MEC	Time, utility, pricing
[74]	Second-price auction	Reduces blockchain storage overhead in resource-constrained edge environments	Benefit
[75]	Bargaining theory	Improves resource utilization and service satisfaction in VEC networks	Utility, time
[81]	Stackelberg game, auction algorithm	Optimizes resource allocation in dynamic UAV-assisted mobile MEC environments	Utility, latency, demand
[82]	Stackelberg game	Optimizes resource allocation and enhances security in MEC-enabled vehicular networks	Utility, power
[83]	Stackelberg game	Maximizes utility and enhances transaction security in blockchain-enabled mobile MEC networks	Price, utility

summarizes the related work.

4.3. Heuristic Algorithm-Based Schemes

Heuristic algorithms are widely applied to address the computational complexity of task offloading and resource allocation in MEC. These algorithms offer near-optimal solutions with a lower computational overhead.

Aknan et al. [84] designed a blockchain-enabled intelligent framework that employs a metaheuristic AI-based bat algorithm for task offloading. The bat algorithm, inspired by echolocation behavior in bats, is employed for fast convergence in determining an optimal offloading strategy. In collaborative MEC, trust problems between MEC servers from various operators pose significant challenges, rendering incentives for cooperation unreliable. Hence, Wenjing et al. [85] addressed this problem by designing a reputation-based node selection mechanism in a consortium blockchain-enabled collaborative MEC framework. The lack of trust among MEC servers could cause monopolization by certain nodes, resulting in inefficiency and security risks in task offloading.

Heuristic algorithms provide efficient and experience-driven strategies to manage edge communication, computing, and storage resources. For instance, Tian et al. [86] designed a cloud–network–edge collaborative task offloading model with blockchain integration to increase trust among terminals and blockchain nodes, seeking to address the exponential data growth and low-trust environment in the 6G-enabled Internet of Everything. The study applied the wolf fish collaborative search algorithm approach to allocate computational resources efficiently.

Taskou et al. [87] employed the blockchain to decentralize service function chain resource allocation, enhancing system resilience and security to address the single point of failure in network function virtualization systems. Approximation- and Hungarian-based resource allocation algorithms were proposed to achieve near-optimal performance and minimize energy consumption and resource utilization costs.

Wenjing et al. [85] devised a resource allocation problem to minimize the total user delay. They solved the problem by applying the Tammer decomposition method with heuristic algorithms and introduced a reputation-based consensus mechanism to guarantee fair node selection and increase security.

4.4. Other Schemes

Other AI models (e.g., DTs, evolutionary algorithms, etc.) have also been integrated into the blockchain to enhance task offloading. For example, Liu et al. [88] proposed a DT-assisted task offloading scheme that employs the blockchain and channel state information to select cooperative mobile equipment servers. Similarly, Xu et al. [89] addressed task offloading challenges in VEC using DT tools to support seamless vehicle handovers and secure blockchain consensus mechanisms.

In environments with traditional MEC devices (ECDs), disproportionate resource requests often result in inefficient task execution, causing some ECDs to become overloaded, whereas others remain underused. Xu et al. [90] introduced a blockchain-enabled computation offloading method to address this problem by mitigating resource overload and data vulnerability during task offloading. The method integrates the nondominated sorting genetic algorithm III to generate balanced resource allocation strategies and efficient workload distribution among ECDs.

Other mechanisms based on the blockchain have been developed to improve resource allocation in MEC. For example, Islam et al. [91] suggested a decentralized intelligent VEC architecture based on the blockchain, enabling computation verification and introducing a secure federation model that addresses load imbalances and biased resource allocation.

Moreover, Baranwal et al. [92] developed a blockchain-based resource allocation framework featuring a novel consensus mechanism to improve the trust and transparency in MEC. The ESPs compete by submitting efficient allocation strategies, and the best solution is rewarded through a leader election.

Similarly, Li and Ma [93] integrated a trust-based reputation model and a two-phase Byzantine fault-tolerant blockchain protocol to ensure secure task offloading in MEC. They applied a many-objective evolutionary algorithm to optimize resource allocation with complex workflow requirements in dynamic MEC environments.

Figure 4 illustrates the performance metrics considered in the evaluation of these studies. Figure 5 presents the distribution of application domains explored in this field.

5. Open Challenges and Research Perspectives

This survey finds substantial scope for research in blockchain resource allocation and task offloading in the MEC environment. Some problems and future research directions in this area are presented below.

5.1. Incentive Mechanisms

Most blockchain-enabled task offloading models assume that participating nodes are rational and cooperative [44]. However, in practice, nodes may act selfishly or unpredictably drop out unless prudently incentivized [94]. Moreover, users may be selfish and unwilling to contribute resources without sufficient compensation.

Token-based incentive systems implemented via smart contracts are the most often adopted solution [95]. Typically, these mechanisms reward participants based on task submission or confirmation. In some designs, rewards are distributed based on the declared participation or majority vote, which may promote collusion, low-effort execution, or fraudulent task claims [62].

Reputation models have been proposed as an overlay to track past behavior and filter unreliable nodes [78,96]. However, these schemes are often static or slow to adapt and can be manipulated when not linked to objective, verifiable outcomes.

Some systems have incorporated hybrid models, combining delegation-based consensus with auction-based or contribution-weighted incentives [97]. However, these can heavily rely on assumptions of honest behavior or stable node populations, which do not hold in mobile or intermittently connected edge environments. Redundant execution has been suggested to mitigate dishonesty, but this method increases energy consumption and resource waste [98]. Nevertheless, incentive mechanisms accounted for just 13% of the reviewed papers. This indicates that a systematic guideline to select the most suitable incentive mechanisms for different scenarios is yet to be designed.

Mechanisms that tightly couple reward distribution with verifiable task outcomes must be developed, involving the construction of lightweight verification schemes that enable the correctness of the claims to be validated before incentives are released, discouraging fraudulent claims without imposing significant computational or communication overheads. Such mechanisms could improve trust and efficiency, making blockchain-enabled MEC deployments more feasible in real-world scenarios.

5.2. Cross-Chain Support

The existing task offloading frameworks based on the blockchain often assume single-chain environments [48,51], limiting their practicality in multichain edge scenarios where cross-domain interactions are common. Several solutions have been proposed to support cross-chain identity verification and authentication [99], such as notary-assisted schemes. Notary approaches are categorized as single- or multisignature. The former suffers from a single point of failure, whereas the latter remains vulnerable to collusion despite reducing centralization by involving multiple notaries, which may compromise user identity and transaction confidentiality [100].

In relay-based models, the correctness of cross-chain communication relies on trusted relay nodes, and any malicious behavior by these nodes can expose sensitive information. Hash-lock techniques focus on the fairness of atomic swaps, ensuring that assets are exchanged or not, as appropriate. However, this technique is only applicable to cross-chain operations between homogeneous blockchains and lacks compatibility for cross-chain operations between heterogeneous blockchains [101].

Existing cross-chain methods risk privacy leaks, particularly concerning transaction confidentiality and user identity protection, and cross-chain support is absent in over 90% of existing frameworks. Future work should explore privacy-preserving cross-chain protocols designed for dynamic and resource-constrained edge environments. These protocols must balance interoperability, security, and efficiency while preserving transaction confidentiality and user identity in heterogeneous blockchain ecosystems.

5.3. High Network Density

Most blockchain-enabled task offloading frameworks are designed and evaluated for relatively sparse or moderately populated network conditions [68]. In a real-world environment, edge networks are characterized by high node density, comprising numerous users, MEC servers, and blockchain nodes operating nearby. The use of high network density scenarios in the blockchain–edge computing domain has shown a progressive increase, from 4.5% in 2020 to 36% in 2024 when considering the studies reviewed.

High network density introduces scalability limitations that are often neglected. Studies have found that increased transaction loads considerably degrade the throughput and confirmation latency in blockchain-integrated edge systems [102,103]. Various techniques (e.g., adaptive block sizing and hierarchical consensus) have been proposed but they remain underexplored in real-time, high-density environments [104].

Communication interference presents another challenge. In dense networks, overlapping wireless transmissions between base stations and edge servers can severely degrade the signal, affecting the precision and latency of task offloading [105]. These effects are more noticeable when multiple users attempt concurrent access to edge resources. Future frameworks should be developed to support dense, latency-sensitive edge environments, including the development of interference-aware protocols, adaptive consensus strategies, and resource allocation schemes that can sustain low latency and high throughput under extreme network loads.

5.4. Privacy Guarantees

Although the blockchain is often introduced in MEC to improve trust, traceability, and auditability, ensuring privacy for users and their data remains an ongoing challenge [106]. This challenge becomes critical in applications where large volumes of personal and context-sensitive data are continually exchanged between users, devices, and services. Notably, privacy has demonstrated a significant increase in the last two years, with a 16% relative increase from 2023 (38.4%) to 2024 (44.6%).

Privacy-preserving techniques, such as FL, anonymization, and cryptographic methods, have been proposed [60]. However, these approaches rely on a centralized component and are challenging to scale in decentralized, multiuser edge environments. Some frameworks still require users to expose sensitive information, such as identity or task data, during offloading and consensus processes, posing risks in blockchain-enabled systems.

The blockchain helps to verify transactions and interactions, but its transparency can unintentionally reveal patterns about user behavior or data usage [107]. As the edge becomes more integrated with immersive applications, where data concerning user movements, preferences, and interactions are personal, protecting such information is essential. Future frameworks should be designed to ensure strong privacy guarantees across the edge and blockchain layers, without compromising performance or usability. Promising directions include fully decentralized privacy-preserving protocols, integrating zero-knowledge proofs, and applying lightweight encryption methods that protect sensitive data without degrading the system’s performance or usability.

6. Conclusions

This survey comprehensively examines blockchain applications for task placement and resource management in MEC. This work reveals that blockchain integration offers significant enhancements in transparency, trust, and autonomy for decentralized edge environments that support real-time applications. This review highlights prominent approaches, including AI-driven offloading, FL, game-theoretical frameworks, and lightweight consensus protocols.

The study finds that the blockchain improves security and accountability but faces scalability and latency limitations in high-density edge networks. Moreover, energy consumption remains a critical constraint for resource-limited edge devices, and interoperability problems exist due to heterogeneous blockchain and edge computing platforms. Furthermore, current privacy mechanisms partially address confidentiality requirements but do not completely eliminate risks in decentralized settings.

Future research should prioritize the following. First, scalable and energy-efficient consensus and resource allocation algorithms should be developed to be suitable for dense MEC environments. Second, interoperability standards should be designed to facilitate seamless integration across diverse edge and blockchain systems. Finally, decentralized privacy-preserving techniques should be advanced to safeguard user data without compromising performance or transparency.

Although blockchain-enabled MEC is a promising paradigm for emerging applications, such as the metaverse and IoT, realizing its full potential requires further innovation to overcome the identified limitations. This survey offers a foundational reference to guide researchers in constructing secure, efficient, and scalable edge computing architectures based on the blockchain.