In-depth research report | Feasibility and challenges of distributed computing networks for heterogeneous devices

Key points

Why is “any device is a computing power provider” still far away?

This report deeply explores the challenges faced by heterogeneous distributed computing networks (DePIN) composed of PCs, mobile phones, edge devices, etc. in moving from "technical feasibility" to "economic feasibility". From the inspiration of volunteer computing from BOINC and Folding@home to the commercialization attempts of DePIN projects such as Golem and Akash, the report sorts out the history, current situation and future of this track.

• Heterogeneous network challenges : differences in device performance, high network latency, and large node fluctuations. How to schedule tasks, verify results, and ensure security?
• Supply is in excess, demand is scarce : Cold start is easy, but real paying users are hard to find. How can DePIN transform from a mining game into a real business?
• Security and compliance : data privacy, cross-border compliance, responsibility attribution... Who will cover these hard issues that cannot be avoided by "decentralization"?

The report is about 20,000 words and is expected to take 15 minutes to read (This report is produced by DePINOne Labs. Please contact us for reprinting)

1. Introduction

1.1 Definition of Heterogeneous Device Distributed Computing Network

A distributed computing network refers to a network composed of geographically dispersed and diverse computing devices (such as personal computers, smart phones, IoT edge computing boxes, etc.), which aims to aggregate the idle computing resources of these devices through Internet connections to perform large-scale computing tasks.

The core idea of this model is that modern computing devices usually have powerful processing power, but most of the time their utilization is very low (for example, ordinary desktop computers only use 10-15% of their capacity). Distributed computing networks try to integrate these underutilized resources to form a large virtual computing cluster.

Unlike traditional supercomputers (High-Performance Computing, HPC) or centralized cloud computing, the most notable feature of this type of distributed network is its heterogeneity .

The devices participating in the network vary greatly in terms of hardware (CPU type, GPU model, memory size), operating system (Windows, macOS, Linux, Android), network connection quality (bandwidth, latency), and availability patterns (devices may be online or offline at any time).

Managing and effectively utilizing this highly heterogeneous and dynamically changing resource pool is one of the core technical challenges facing such networks.

1.2 Historical Background: Volunteer Computing

Despite many challenges, the technical feasibility of using distributed heterogeneous devices for large-scale computing has been fully demonstrated through decades of volunteer computing (VC) practice.

BOINC (Berkeley Open Infrastructure for Network Computing)

BOINC is a typical success story. It is an open source middleware platform that uses a client/server architecture. The project runs the server to distribute computing tasks, and volunteers run the BOINC client software on their personal devices to perform these tasks. BOINC has successfully supported many scientific research projects, covering astronomy (such as SETI@home, Einstein@Home), biomedicine (such as Rosetta@home), climate science and other fields, using volunteers' computing resources to solve complex scientific problems. The computing power of the BOINC platform is amazing. Its aggregated computing power was several times that of the top supercomputers at the time , reaching the PetaFLOPS level, and this computing power came entirely from the idle resources of personal computers contributed by volunteers. BOINC was originally designed to handle a network environment composed of heterogeneous, intermittently available and untrusted nodes. Although it takes a certain amount of technical investment to establish a BOINC project (about three man-months of work, including system administrators, programmers and web developers), its successful operation proves the technical potential of the VC model.

Folding@home (F@h)

F@h is another well-known volunteer computing project. Since its launch in 2000, it has focused on helping scientists understand disease mechanisms and develop new treatments by simulating biomolecular dynamics processes such as protein folding, conformational changes, and drug design. F@h also uses volunteers' personal computers (even PlayStation 3 game consoles in the early days) for large-scale parallel computing. The project has achieved remarkable scientific achievements and has published more than 226 scientific papers. Its simulation results are in good agreement with experimental data. Especially during the COVID-19 pandemic in 2020, the public's enthusiasm for participation was high, and the computing power aggregated by Folding@home reached the ExaFLOP level (100 trillion floating-point operations per second) , becoming the world's first computing system to reach this scale, which strongly supported the research of the SARS-CoV-2 virus and the development of antiviral drugs.

Long-running projects such as BOINC and Folding@home have proven irrefutably that it is technically feasible to aggregate and utilize the computing power of a large number of distributed, heterogeneous, volunteer-provided devices to handle certain types of parallelizable, computationally intensive tasks (especially scientific computing) . They have laid an important foundation for task distribution, client management, and handling unreliable nodes.

1.3 The rise of business models: Golem and DePIN computing

Based on the technical feasibility verified by volunteer computing, projects attempting to commercialize this model have emerged in recent years, especially the DePIN (Decentralized Physical Infrastructure Networks) computing project based on blockchain and token economy.

Golem Network is one of the early explorers in this field and is considered a pioneer of the DePIN concept. It has built a decentralized computing power market that allows users to buy or sell computing resources (including CPU, GPU, memory and storage) in a peer-to-peer (P2P) manner using its native token GLM. There are two main types of participants in the Golem network: Requestors, users who need computing power; and Providers, users who share idle resources in exchange for GLM tokens. Its target application scenarios include CGI rendering, artificial intelligence (AI) calculations, cryptocurrency mining and other tasks that require a lot of computing power. Golem achieves scale and efficiency by splitting tasks into smaller subtasks and processing them in parallel on multiple provider nodes.

DePIN computing is a broader concept that refers to the use of blockchain technology and token incentives to build and operate various physical infrastructure networks including computing resources. In addition to Golem, there are also projects such as Akash Network (providing decentralized cloud computing services), Render Network (focusing on GPU rendering), io.net (aggregating GPU resources for AI/ML), and many other projects. The common goal of these DePIN computing projects is to challenge traditional centralized cloud computing service providers (such as AWS, Azure, GCP) and provide lower-cost and more flexible computing resources in a decentralized manner. They try to use token economic models to incentivize hardware owners around the world to contribute resources, thereby forming a huge, on-demand computing network.

This represents a paradigm shift from volunteer computing, which relies primarily on altruism or community reputation (points) as incentives, to DePIN’s adoption of tokens for direct economic incentives. DePIN seeks to create an economically sustainable and more general distributed computing network that can go beyond specific areas such as scientific computing and serve a wider range of market needs.

However, this shift also introduces new complexities, especially in terms of market mechanism design and the stability of token economic models.

Preliminary assessment: observations on oversupply and insufficient demand

The core dilemma currently faced by the DePIN computing field is not to allow users to participate in the network and contribute computing power, but how to supply computing power to the network to truly undertake and provide services for various computing power needs .

• Supply is easy to bootstrap : Token incentives are very effective in bootstrapping supply to join the network.
• Demand is hard to prove : Generating real, paid demand is much harder. DePIN projects must provide competitive products or services that solve real problems, not just rely on token incentives.
• Volunteer computing has proven technical feasibility, but DePIN must prove economic feasibility, which depends on effectively solving the demand-side problem. Volunteer computing projects (such as BOINC, F@h) are successful because the "demand" (scientific computing) is intrinsically valuable to the researchers running the projects, while the supply side is motivated by altruism or interest.

DePIN builds a market where suppliers expect to receive financial rewards (tokens), while demanders must perceive the value of the service to exceed its cost. It is relatively straightforward to guide supply with tokens, but creating real paid demand requires building services that can compete with or even surpass centralized services (such as AWS). Current evidence shows that many DePIN projects still face huge challenges in the latter.

2. Core technical challenges of heterogeneous distributed networks

Building and operating a heterogeneous distributed computing network consisting of mobile phones, personal computers, IoT devices, etc., faces a series of severe technical challenges. These challenges arise from the physical dispersion of network nodes, the diversity of devices themselves, and the unreliability of participants.

2.1 Device Heterogeneity Management

Devices in the network vary greatly in hardware (CPU/GPU type, performance, architecture such as x86/ARM, available memory, storage space) and software (operating systems such as Windows/Linux/macOS/Android and their versions, installed libraries and drivers). This heterogeneity makes it extremely difficult to deploy and run applications reliably and efficiently across the network. A task written for a specific high-performance GPU may not run on a low-end mobile phone, or may run very inefficiently.

BOINC’s response

BOINC handles heterogeneity by defining "platforms" (combinations of operating systems and hardware architectures) and providing specific "application versions" for each platform. It also introduces a "Plan Class" mechanism that allows for more refined task distribution based on more detailed hardware characteristics (such as specific GPU models or driver versions). In addition, BOINC supports running existing executables using wrappers, or running applications in virtual machines (such as VirtualBox) and containers (such as Docker) to provide a unified environment across different hosts, but this will incur additional performance overhead.

DePIN’s response

Many DePIN computing platforms also rely on containerization technology (e.g. Akash uses Docker) or specific runtime environments (e.g. Golem's gWASM, which may also support VM/Docker) to abstract the differences in underlying hardware and operating systems and improve application compatibility. However, fundamental performance differences between devices still exist. Therefore, the task scheduling system must be able to accurately match tasks to nodes with corresponding capabilities.

Device heterogeneity significantly increases the complexity of application development, deployment, task scheduling (matching tasks to appropriate nodes), performance prediction, and result verification. Although virtualization and containerization provide a certain degree of solution, they cannot completely eliminate the performance differences. To efficiently utilize the diverse hardware resources in the network (especially specialized accelerators such as GPUs and TPUs), complex scheduling logic is required, and even different optimized application versions may need to be prepared for different types of hardware, which further increases the complexity. Relying solely on general-purpose containers may result in the performance of specialized hardware not being fully utilized.

2.2 Network Delay and Bandwidth Limitation

Network latency refers to the time required for data to be transmitted between network nodes. It is mainly affected by physical distance (the speed of light limits the propagation delay), network congestion (causing queuing delays), and device processing overhead. High latency can significantly reduce the system's response speed and throughput, affect user experience, and hinder the execution of tasks that require frequent interactions between nodes. In high-bandwidth networks, latency often becomes a performance bottleneck.

Bandwidth refers to the maximum amount of data that can be transmitted per unit time over a network connection. Insufficient bandwidth can cause network congestion, further increasing latency and reducing the actual data transfer rate (throughput). Volunteer computing and DePIN networks often rely on participants' home or mobile Internet connections, which may have limited and unstable bandwidth (especially upload bandwidth).

High latency and low bandwidth greatly limit the types of workloads suitable for running on such networks . Tasks that require frequent communication between nodes, transfer large amounts of input/output data relative to the amount of computation, or require real-time responses are often impractical or inefficient in this environment. Network limitations directly affect task scheduling strategies (data locality becomes critical, i.e., computation should be close to the data) and the efficiency of transmission of results. Especially for tasks that require large amounts of data transfer and synchronization, such as AI model training, the bandwidth of consumer-grade networks can become a serious bottleneck.

Network limitations are the result of the combined effects of physical laws (latency is constrained by the speed of light) and economic factors (bandwidth costs). This makes distributed computing networks naturally more suitable for "embarrassingly parallel" tasks that are computationally intensive, communication sparse, and easy to parallelize. Compared with centralized data centers with high-speed internal networks, such network environments usually have poor communication efficiency and reliability, which fundamentally limits the scope of applications and market size that they can effectively serve.

2.3 Node Dynamics and Reliability

The devices (nodes) participating in the network are highly dynamic and unreliable. Nodes may join or leave the network at any time (known as "churn" or Churn), and devices may unexpectedly lose power, disconnect from the network, or be turned off by the user. In addition, these nodes are generally untrustworthy and may return incorrect results due to hardware failure (such as instability caused by overclocking) or malicious behavior.

This dynamic nature may cause task execution to be interrupted, resulting in a waste of computing resources. Unreliable nodes will affect the correctness of the final result. High churn rates make it difficult to complete tasks that require long-term execution and bring difficulties to task scheduling. Therefore, the system's fault tolerance becomes crucial.

Generally speaking, there are several strategies for dealing with node instability:

• Redundancy/Replication : Assign the same task to multiple independent nodes for execution, and then compare their calculation results. Only when the results are consistent (or within the allowed error range) are they accepted as valid. This can effectively detect errors and malicious behavior and improve the reliability of the results, but at the cost of increased computing overhead. BOINC also uses an adaptive replication strategy based on the historical reliability of the host to reduce overhead.
• Checkpointing : Allows applications to periodically save their intermediate states. When a task is interrupted, it can be resumed from the most recent checkpoint instead of starting from the beginning. This greatly reduces the impact of node loss on task progress.
• Deadlines & Timeouts : Set a deadline for each task instance to complete. If a node fails to return a result before the deadline, the instance is assumed to have failed and the task is reassigned to another node. This ensures that the task will eventually complete even if some nodes are unavailable.
• Work Buffering : The client pre-downloads enough tasks to ensure that the device can remain working when it temporarily loses network connection or cannot obtain new tasks, maximizing resource utilization.

Dealing with unreliability is a core principle of distributed computing network design, not an additional feature. Since nodes cannot be directly controlled and managed like in a centralized data center, the system must rely on statistical methods and redundancy mechanisms to ensure the completion of tasks and the correctness of results. This inherent unreliability and its coping mechanisms increase the complexity and overhead of the system, thus affecting the overall efficiency.

2.4 Task Management Complexity: Segmentation, Scheduling, and Verification

Segmentation : First, a large computational problem needs to be decomposed into many small work units that can be executed independently. This requires the problem itself to be highly parallelizable, preferably with an "easy to parallelize" structure, that is, there is almost no dependency or communication requirement between subtasks.

Task Scheduling : Effectively allocating these task units to appropriate nodes in the network for execution is one of the most core and challenging problems in distributed computing. In a heterogeneous and dynamic network environment, the task scheduling problem is usually proven to be NP-complete, meaning that there is no known polynomial time optimal solution. The scheduling algorithm must take into account a variety of factors:

• Node heterogeneity : Differences in nodes’ computing power (CPU/GPU), memory, storage, architecture, etc.
• Node dynamics : node availability, online/offline patterns, and churn rates.
• Network conditions : latency and bandwidth between nodes.
• Task characteristics : computational effort, memory requirements, data volume, dependencies (if there are dependencies between tasks, they are usually represented as directed acyclic graphs (DAGs)), and deadlines.
• System policies : resource share allocation (such as BOINC's Resource Share), priority.
• Optimization goals : may include minimizing the total completion time (Makespan), minimizing the average task turnaround time (Flowtime), maximizing throughput, minimizing costs, ensuring fairness, improving fault tolerance, etc. There may be conflicts between these goals.

Scheduling strategies can be static (one-time allocation before the task starts) or dynamic (adjusting allocation according to the real-time status of the system, and divided into online mode and batch mode). Due to the complexity of the problem, heuristics, meta-heuristics (such as genetic algorithms, simulated annealing, ant colony optimization, etc.) and methods based on artificial intelligence (such as deep reinforcement learning) have been widely studied and applied. BOINC clients use local scheduling strategies (including work acquisition and CPU scheduling) to try to balance multiple goals such as deadlines, resource shares, and maximizing points acquisition.

Result Verification : Since the node is untrusted, the correctness of the returned result must be verified.

• Replication-based verification : This is the most commonly used method, which is to let multiple nodes calculate the same task and then compare the results. BOINC uses this method and provides "homogeneous redundancy" for tasks that require completely consistent results, ensuring that only nodes with the same hardware and software environment participate in the replication calculation of the same task. Golem also uses redundant verification and may adjust the verification frequency (probabilistic verification) based on the reputation of the provider, or use spot-checking. This method is simple and effective, but the cost is high (the amount of calculation is doubled or more).
• Non-determinism : For some computing tasks, especially AI reasoning performed on GPUs, even if the input is the same, the output under different hardware or operating environments may be slightly different (computational non-determinism). This makes the replication verification method based on exact result matching invalid. New verification methods are needed, such as comparing the semantic similarity of the results (for AI outputs), or using statistical methods (such as the SPEX protocol) to provide probabilistic correctness guarantees.
• Cryptographic method : Verifiable Computation technology provides a way to verify the correctness of a calculation without repeated execution.
• Zero-Knowledge Proofs (ZKPs) : Allows the prover (computing node) to prove to the verifier that a certain calculation result is correct without revealing any input data or intermediate processes of the calculation. This is very promising for protecting privacy and improving verification efficiency, but generating ZKP itself usually requires a lot of computing overhead, limiting its application in complex calculations.
• Fully Homomorphic Encryption (FHE) : allows arbitrary calculations to be performed directly on encrypted data, and the encrypted result is the same as the result calculated on the plaintext after decryption. This can achieve extremely high privacy protection, but the current FHE scheme has extremely low computational efficiency and high cost, and is far from being widely used.
• Trusted Execution Environments (TEEs) : Use hardware features (such as Intel SGX, AMD SEV) to create an isolated and protected memory area (enclave) to ensure the confidentiality and integrity of the code and data running in it, and to provide proof to remote parties (remote attestation). TEE provides a relatively efficient verification method, but it relies on specific hardware support, and its security also depends on the security of the hardware itself and the related software stack.

Task management, especially scheduling and verification, is much more complex in heterogeneous, unreliable, and untrusted distributed networks than in centralized cloud environments. Scheduling is an active research area (NP-complete), while verification faces fundamental challenges such as non-determinism and verification cost, which limit the types of computational tasks that can be reliably and economically executed and verified.

2.5 Security and privacy protection across devices

Threat environment : Distributed computing networks face security threats from multiple levels:

• Node level : Malicious nodes may return forged results or falsely report the amount of computation to defraud rewards. Nodes controlled by attackers may be used to run malicious code (if the project server is compromised, attackers may try to distribute viruses disguised as computing tasks). Nodes may attempt to access sensitive data on the host system or other nodes. Internal threats from volunteers or providers should not be ignored.
• Network level : The project server may be attacked by denial of service (DoS), such as being flooded by a large amount of invalid data. Network communications may be eavesdropped (Packet Sniffing), resulting in the leakage of account information (such as keys, email addresses). Attackers may perform man-in-the-middle attacks or IP spoofing.
• Project level : Project owners may intentionally or unintentionally release applications that contain vulnerabilities or malicious functions, damaging participants' devices or privacy. The project's input or output data files may be stolen.
• Data privacy : Processing data on untrusted nodes inherently presents privacy risks, especially when it comes to personally identifiable information (PII), commercially sensitive data, or regulated data such as medical information. Data can also be intercepted during transmission. Complying with data protection regulations such as GDPR and HIPAA is extremely challenging in a distributed environment.

Mitigation mechanisms :

• Result and reputation verification : Verify the correctness of the results and detect malicious nodes through redundant calculations. Establish a reputation system (such as Golem) to score and screen nodes based on their historical behavior.
• Code signing : The project party digitally signs the application it releases. The client verifies the signature before running the task to ensure that the code has not been tampered with and prevent the distribution of malicious code (BOINC uses this mechanism).
• Sandbox and isolation : Run computing tasks in a restricted environment (such as low-privileged user accounts, virtual machines, containers) to prevent tasks from accessing sensitive files or resources on the host system. TEE provides strong hardware-based isolation.
• Server security : Take traditional server security measures such as firewalls, encrypted access protocol (SSH), disabling unnecessary services, and regular security audits. BOINC also provides upload certificates and size limit mechanisms to prevent DoS attacks against data servers.
• Authentication and encryption : Use strong authentication methods (e.g. multi-factor authentication MFA, tokens, biometrics). Use mTLS encryption (e.g. Akash) for inter-node communications. Encrypt data in transit and at rest.
• Network security : Use network segmentation, zero-trust architecture, continuous monitoring, and intrusion detection systems to protect network communications.
• Trusted Providers : Allow users to select providers that have been audited and certified by a trusted third party (such as Akash’s Audited Attributes).
• Privacy-preserving technologies : Although costly, technologies such as FHE and ZKP can theoretically provide stronger privacy protection.

Security is a multi-dimensional issue that requires protecting the integrity and privacy of project servers, participant nodes, network communications, and the computing process itself. Despite the existence of multiple mechanisms such as code signing, redundant computing, sandboxing, etc., the inherent untrustworthiness of participants requires system designers to remain vigilant and accept the additional overhead that comes with it. For commercial applications or scenarios involving sensitive data, how to ensure data privacy on untrusted nodes remains a huge challenge and a major adoption barrier.

3. DePIN Dilemma: Matching Computing Power Supply and Demand

This section will delve into the difficulties of matching supply and demand, especially in terms of workload allocation, service discovery, service quality assurance, and market mechanism design.

3.1 Why is demand more difficult than supply?

In the DePIN model, it is relatively easy to use token incentives to attract computing resource suppliers (nodes) to join the network. Many individuals and organizations have idle computing hardware (especially GPUs), and connecting them to the network in the hope of receiving token returns is generally seen as a low-threshold, low-friction way to participate. The potential value of the token is sufficient to drive early growth on the supply side, forming a so-called "cold start".

However, the generation of demand follows a completely different logic and faces greater challenges. Simply having a large supply of computing power does not mean that the network has economic value. Sustainable demand must come from users who are willing to pay to use this computing power. This means that the computing services provided by the DePIN platform must be attractive enough to solve users' real problems and be better or at least not worse than existing centralized solutions (such as AWS, GCP, Azure) in terms of cost, performance or specific functions.

Token incentives alone cannot create this real demand; they can only attract supply.

The current market situation also confirms this. The decentralized storage sector (such as Filecoin) has already seen obvious problems of oversupply and low utilization, and its token economic activities are more centered around miners and speculation rather than meeting the storage needs of end users. In the computing sector, although scenarios such as AI and 3D rendering bring potential huge demand, the DePIN platform still faces challenges in actually meeting these needs. For example, io.net aggregates a large number of GPUs, but the bandwidth and stability of consumer-grade GPUs may not be sufficient to support large-scale AI training, resulting in low actual utilization. Although Render Network benefits from OTOY's user base, its token burn rate is much lower than the issuance rate, indicating that actual application adoption is still insufficient.

Therefore, the DePIN model naturally tends to promote supply through tokenization. However, the generation of demand requires the traditional "Product-Market Fit" process, which requires overcoming strong market inertia and competing with mature centralized service providers, which is essentially a more difficult business challenge. This asymmetry in the supply and demand generation mechanism is the core economic dilemma currently faced by the DePIN computing model.

3.2 Challenges of Workload Distribution and Service Discovery

In the DePIN computing network, effectively allocating users' computing tasks (demand) to appropriate computing resources (supply) in the network is a complex process involving service discovery and workload matching.

Complexity of matching : Demanders often have very specific requirements, such as specific GPU models, minimum number of CPU cores, memory size, storage capacity, specific geographic locations (to reduce latency or meet data sovereignty requirements), and even specific security or compliance certifications. The resources provided by suppliers are highly heterogeneous. It is a difficult task to accurately match each demand with a cost-effective provider that meets all conditions in a large and dynamically changing supply pool.

Service discovery mechanism : How do users find providers that meet their needs? DePIN platforms usually adopt a market-based approach to solve the service discovery problem:

• Marketplace/Order Book : The platform provides a market where providers publish their resources and quotations, and demanders publish their needs and the prices they are willing to pay. For example, Akash Network adopts this model and combines it with a reverse auction mechanism.
• Task Templates & Registry : The Golem network allows demanders to use predefined or customized task templates to describe computing needs, and use the application registry to find providers who can perform these template tasks.
• Auction Mechanisms : Akash’s reverse auction (demanders set a maximum price and providers bid) is a classic example, which aims to lower prices through competition.

Pricing mechanism : Prices are usually determined by market supply and demand dynamics, but may also be affected by factors such as provider reputation, resource performance, service level, etc. 26 For example, Render Network adopts a multi-layer pricing strategy that considers speed, cost, security, and node reputation.

Current limitations

Existing matching mechanisms may not be optimal. It is not enough to simply find “available” resources; the key is to find “suitable” resources. As mentioned earlier, consumer-grade hardware may not be able to handle AI training tasks due to insufficient bandwidth, even if its GPU computing power itself is sufficient. Finding providers that meet specific compliance (such as HIPAA) or security standards may also be difficult because providers in the DePIN network have different backgrounds.

Effective load distribution requires much more than a simple resource availability check. It requires sophisticated discovery, matching, and pricing mechanisms that accurately reflect the capabilities and reliability of providers and the specific requirements of demanders. These mechanisms are still evolving and improving in the current DePIN platform. If the matching process is inefficient or the results are poor (for example, assigning bandwidth-intensive tasks to low-bandwidth nodes), the user experience will be greatly compromised and the value proposition of DePIN will be weakened.

3.3 The Problem of Quality of Service (QoS) Guarantee

In traditional centralized cloud computing, service providers usually promise a certain quality of service through service level agreements (SLAs), such as guaranteeing specific uptime, performance indicators, etc. Although the execution of these SLAs may sometimes be biased towards the provider, they at least provide a formal quality expectation framework.

In a DePIN network consisting of a large number of unreliable and uncontrolled nodes, it is much more difficult to provide similar QoS guarantees.

• Lack of centralized control : No single entity can fully control and manage the performance and reliability of all nodes.
• Difficulty in verifying off-chain events : The blockchain itself cannot directly observe and verify real-world events that occur off-chain, such as whether a computing node has actually achieved the promised computing speed, or whether its network connection is stable. This makes it difficult to implement automated QoS based on blockchain.
• Individual default risk : In a decentralized market, any participant (provider or demander) may breach the agreement. The provider may not be able to provide the promised QoS, and the demander may refuse to pay.

In order to establish trust in a decentralized environment and try to guarantee QoS, several mechanisms have emerged:

• Witness Mechanisms : Introduce independent third-party "witnesses" (usually incentivized community members) to monitor the quality of off-chain services and report to the network when SLA violations occur. The effectiveness of this mechanism depends on reasonable incentive design to ensure that witnesses perform their duties honestly.
• Reputation Systems : Establish reputation scores by tracking the provider's historical performance (such as task success rate, response time, reliability). Demanders can choose providers based on reputation, and providers with poor reputations will find it difficult to obtain tasks. This is one of the key mechanisms adopted by Golem.
• Audited Providers : Rely on trusted auditing organizations to review and certify the hardware, security standards, and operational capabilities of providers. Demanders can choose to use only audited providers, thereby increasing the credibility of service quality. Akash Network is promoting this model.
• Staking/Slashing : Providers are required to pledge a certain amount of tokens as a deposit. If a provider behaves improperly (such as providing false resources, failing to complete tasks, malicious behavior) or fails to meet certain service standards, their pledged tokens will be "slashed". This provides an economic constraint for providers to be honest and trustworthy.

Overall, QoS guarantees in DePIN networks are often weaker and less formalized than traditional cloud SLAs, currently relying more on the reputation of the provider, audit results, or basic redundancy mechanisms rather than strict, enforceable contractual guarantees.

The lack of strong and easy-to-implement QoS guarantees is a major obstacle to the adoption of DePIN by enterprise users and critical business applications. How to establish reliable service quality expectations and trust without centralized control is a key issue that DePIN must solve to mature. Centralized clouds implement SLAs by controlling hardware and networks, while DePIN relies on indirect mechanisms based on economic incentives and community supervision. The reliability of these mechanisms still needs to be tested by the market in the long term.

3.4 Market Mechanisms: Pricing, Reputation, and Provider Selection

An effective market mechanism is the key to the DePIN platform’s successful matching of supply and demand and building trust.

DePIN typically adopts a market-driven pricing approach, aiming to provide lower costs than the fixed prices of centralized clouds through competition. Common pricing mechanisms include:

• Auction/Order Book : Like Akash’s reverse auction, demanders set a price cap and providers bid.
• Negotiated pricing : For example, Golem allows providers and demanders to negotiate prices to a certain extent.
• Tiered pricing : For example, Render offers different price tiers based on factors such as speed, cost, security, reputation, etc. The price discovery process can be complex and requires balancing the interests of both supply and demand.

In a decentralized market full of anonymous or pseudonymous participants, reputation is an integral part of building trust. The Golem network uses an internal reputation system to score providers and demanders based on factors such as task completion, payment timeliness, and result correctness. The reputation system helps identify and exclude malicious or unreliable nodes.

Users need effective tools to screen and select reliable providers that meet their needs. Golem relies mainly on reputation scores to help users filter providers; Akash Network introduces the concept of "Audited Attributes". Users can specify in their deployment description language (SDL) files that only bids from providers that have been audited by trusted entities (such as the Akash core team or other possible future auditors) will be accepted. In addition, the community is also discussing the introduction of a user rating system (Tier 1) and the integration of a wider range of third-party audits (Tier 2). Akash also attracts high-quality, professional providers who are committed to long-term services to join the network through the Provider Incentives Program.

The biggest challenge facing reputation systems is the possibility of manipulation (score manipulation). The effectiveness of the audit mechanism depends on the credibility of the auditor and the rigor of the audit standards. Ensuring that there are a sufficient number and variety of high-quality providers in the network and that these providers can be easily discovered by demanders remains an ongoing challenge. For example, although the utilization rate of A100 GPUs on the Akash network is high, their absolute number is still insufficient to meet all demand.

Effective market mechanisms are critical to the success of DePIN. While mechanisms such as auctions help price competition, reputation and audit systems are critical complementary layers to control quality and reduce risk. The maturity, reliability, and resistance to manipulation of these mechanisms directly affect user confidence in the platform and willingness to adopt. If users cannot reliably find high-quality providers that meet their needs through these mechanisms, the efficiency and attractiveness of the DePIN market will be greatly reduced.

4. Economic Viability: Incentives and Token Economics

One of the core innovations of DePIN is that it attempts to solve the incentive problem in the construction and operation of distributed infrastructure through token economics. This section will explore the evolution of incentive mechanisms from volunteer computing to DePIN, the design challenges of computing network token economic models, and how to strike a balance between contributor rewards and consumer value.

4.1 Evolution of incentive mechanism: from BOINC points to DePIN tokens

Volunteer computing projects such as BOINC rely primarily on non-economic incentives. BOINC has established a system of "credits" to quantify the contributions of participants based on the amount of computation they have completed (usually based on FLOPS or CPU time for benchmarks). The main purpose of these credits is to provide reputation, satisfy the competitive psychology of participants (for example, through team rankings), and gain recognition within the community. The credits themselves usually have no direct monetary value and cannot be traded. BOINC's credit system is designed to be fair, difficult to forge, and support cross-project credit tracking (achieved through third-party websites).

The DePIN project uses crypto tokens (such as Golem's GLM, Akash's AKT, Render's RNDR/RENDER, Helium's HNT, Filecoin's FIL, etc.) as its core incentive mechanism. These tokens usually have multiple functions:

• Medium of exchange : Serves as a means of payment for purchasing services (such as computing, storage, and bandwidth) within the platform.
• Incentives : Rewarding participants who contribute resources (such as computing power, storage space, network coverage) is a key tool for supply-side bootstrapping.
• Governance : Token holders can usually participate in the decision-making process of the network, such as voting on protocol upgrades, parameter adjustments, fund usage, etc.
• Staking : used to ensure network security (for example, Akash's verification nodes need to stake AKT), or may be a condition for providing or accessing services.

This is a fundamental shift from BOINC's non-financial, reputation-based points system to DePIN's direct financial, token-based incentive system. DePIN aims to attract a wider range of more commercially motivated resource providers by providing direct economic rewards. However, this also introduces a series of new complex issues such as cryptocurrency market volatility, token valuation, and economic model sustainability. The value of token rewards is no longer a stable point, but is linked to market prices, which makes the incentive effect unstable and poses challenges to designing a sustainable economic cycle.

4.2 Designing a sustainable token economic model for computing networks

The ideal DePIN token economic model aims to create a positive cycle, namely the "flywheel effect". Its logic is: token incentives attract resource supply → the formed resource network provides services → valuable services attract paying users (demand) → user payment (or consumption of tokens) increases the value or utility of tokens → token value increase or utility enhancement further incentivizes suppliers to join or stay → increased supply enhances network capabilities and attracts more demand .

Core Challenges

• Balancing supply and demand incentives : How to find a balance between rewarding the supply side (usually through token issuance/release, i.e. inflation) and driving the demand side (through token destruction/locking/use, i.e. deflation or utility) is the core difficulty of the design. Many projects face the problem of high inflation rate and insufficient token consumption on the demand side, making it difficult to maintain the value of tokens.
• Rewards should be tied to value creation : Incentive mechanisms should be tied to real, valuable contributions to the network (such as successfully completing computing tasks and providing reliable services) as much as possible, rather than simple participation or online time.
• Long-term sustainability : As early token release decreases or market conditions change, the model needs to be able to continue to incentivize participants to avoid network shrinkage due to insufficient incentives.
• Managing price volatility : The sharp fluctuations in token prices will directly affect the income expectations of providers and the usage costs of demanders, posing a huge challenge to the stability of the economic model. Akash Network introduced the USDC payment option in part to solve this problem.

Model Examples

• Golem (GLM) : Mainly positioned as a payment token, used to settle computing service fees. Its value is directly related to the usage of the network. The project migrated from GNT to the ERC-20 standard GLM token.
• Render Network (RNDR/RENDER) : Adopts the "Burn-and-Mint Equilibrium" (BME) model. Demanders (rendering task submitters) burn RENDER tokens to pay for services, while providers (GPU node operators) are rewarded by minting new RENDER tokens. In theory, if the demand (burn amount) is large enough to exceed the reward minting amount, RENDER may become a deflationary token. The project has migrated its tokens from Ethereum to Solana.
• Akash Network (AKT) : AKT tokens are primarily used for network security (validator staking), governance voting, and are the default settlement currency within the network (although USDC is now also supported). The network collects a portion of the fee (Take Fee) from each successful lease to reward AKT stakers. The AKT 2.0 upgrade aims to further optimize its token economics.

DePIN token economics is still highly experimental. It is extremely difficult to find a model that can effectively launch the network, continuously incentivize participation, and closely align incentives with real economic activities. Many existing models appear to face inflationary pressures or rely too much on market speculation rather than intrinsic value. If the issuance rate of tokens far exceeds the consumption or purchasing pressure generated by actual use, the price of tokens may fall. Falling prices will reduce incentives for providers and may cause supply to shrink. Therefore, it is critical to the long-term survival of DePIN to strongly link the value of tokens to the actual usage (demand) of network services.

4.3 Balancing Contributor Rewards and Consumer Value Proposition

The DePIN platform must strike a delicate balance between two aspects:

1. Rewards for suppliers : Rewards (primarily tokens) must be attractive enough to incentivize a sufficient number of high-quality providers to join and continue to operate their computing resources.
2. Value to demanders : The price offered to consumers (those who demand computing tasks) must be significantly lower than, or better in performance/functionality than, centralized alternatives (such as AWS, GCP) in order to effectively attract demand.

The DePIN project claims that its asset-lite model (protocol developers do not directly own the hardware) and ability to leverage underutilized resources enables it to provide services at lower operating costs, thereby rewarding providers while also providing lower prices to consumers. In particular, for providers whose hardware has depreciated or has lower operating costs (such as using consumer-grade hardware), the expected rate of return may be lower than that of large data centers.

Challenges in balancing supply and demand

• Token volatility : The instability of token prices makes this balance difficult to maintain. If the token price drops sharply, the actual income of the provider will decrease, which may cause it to exit the network unless it increases the price of the service (denominated in tokens), which in turn weakens its attractiveness to consumers.
• Matching service quality with price : Consumers should not only pay a low price, but also get a matching and reliable quality of service (QoS). Ensuring that providers can continue to provide the performance and stability that meets demand is key to maintaining the value proposition.
• Competitive Pressure : Competition between DePIN projects may lead to a “race to the bottom” in terms of rewards, offering unsustainably high rewards to attract early users, but this will harm long-term economic health.

The economic viability of DePIN depends on finding a sustainable balance point: at this balance point, providers can earn enough revenue (taking into account costs such as hardware, electricity, time, and token value fluctuations), while consumers pay significantly lower prices than cloud giants and receive acceptable services. This balance window can be quite narrow and very sensitive to market sentiment and token prices. Providers have actual operating costs, and token rewards must be able to cover these costs and provide profits, while also considering the value risk of the token itself. Consumers will directly compare DePIN's price and performance with AWS/GCP. DePIN must show a huge advantage in some dimension (mainly cost) to win demand. The fee mechanism within the network (such as transaction fees, lease fees) or token burning mechanism must be able to provide sufficient rewards to providers while maintaining price competitiveness for consumers. This is a complex optimization problem, especially in the context of wild fluctuations in crypto asset prices.

5. Legal and regulatory impact

DePIN projects, especially those involving cross-border distributed computing networks, will inevitably encounter complex legal and regulatory issues in their operations. These issues involve data sovereignty, privacy regulations, cross-border data flows, token characterization, and the attribution of responsibilities for decentralized governance.

5.1 Data sovereignty, privacy regulations and cross-border data flows

Data Sovereignty : Many countries have laws requiring that certain types of data (especially sensitive data or personal data of citizens) must be stored or processed within their borders. The DePIN network is naturally globally distributed, and computing tasks and data may flow between nodes in different countries, which can easily conflict with the data sovereignty regulations of various countries.

Privacy Regulations : Regulations such as the EU’s General Data Protection Regulation (GDPR) set extremely strict rules for the collection, processing, storage, and transfer of personal data. DePIN networks must comply with these regulations if they process data involving personally identifiable information (PII) or user behavior (for example, the input or output of certain computing tasks may contain such information). The GDPR has extraterritorial effect, and even if the DePIN platform or node is located outside the EU, it must comply with the GDPR as long as its service objects or monitoring behaviors involve EU residents. In a distributed network consisting of a large number of anonymous or pseudonymous nodes, it is a huge challenge to ensure that all nodes comply with the requirements of regulations such as the GDPR.

Cross-Border Data Flows : The transfer of data from one jurisdiction to another is subject to strict legal restrictions. For example, GDPR requires that the country receiving the data must provide a level of data protection that is "substantially equivalent" to that of the EU (i.e., "adequacy determination"), otherwise additional safeguards must be taken, such as standard contractual clauses (SCCs) and impact assessments must be conducted. The Clarifying Lawful Overseas Use of Data Act (CLOUD Act) in the United States allows U.S. law enforcement agencies to require service providers headquartered in the United States to provide data stored anywhere in the world, which further exacerbates the legal conflicts of international data transfers. The distribution of input data and the recovery of result data for DePIN computing tasks almost inevitably involve cross-border data flows, making compliance extremely complicated.

These legal requirements are in direct conflict with the decentralized, borderless nature of DePIN. Ensuring compliance may require complex technical solutions, such as geofencing or filtering tasks based on data type and origin, but this increases the complexity of the system and may limit the efficiency and scale of the network. Compliance issues may be a major obstacle for DePIN to handle sensitive data or be used in highly regulated industries (such as finance and healthcare).

5.2 Responsibility and Accountability in Decentralized Systems

In traditional centralized services, the responsible party is usually clear (i.e. the service provider). But in a decentralized network consisting of many independent and even anonymous participants, it becomes very difficult to determine who bears legal responsibility when something goes wrong. For example:

• If a computing node returns an incorrect result, causing the user to suffer financial losses, who should be held responsible? Is it the node provider, the protocol developer, or the user who bears the risk?
• If a provider node is hacked, resulting in a user data leak, how is liability determined?
• If the network is used for illegal activities (such as running malware, computing illegal content), who will bear legal responsibility?

Unclear responsibility not only makes it difficult for users to recover losses, but also exposes providers and developers to potential legal risks. How to deal with disputes between users and providers? How to ensure that providers comply with local laws and regulations (such as content filtering requirements)?

Current DePIN projects rely primarily on code-level mechanisms (such as smart contracts that automatically execute payments), reputation systems (to punish bad actors), and possible on-chain or off-chain arbitration mechanisms (although the details are unclear in the materials provided) to handle disputes and regulate behavior. However, the legal effectiveness of these mechanisms is often untested.

The lack of a clear legal framework to define responsibilities in a decentralized system creates legal uncertainty and risks for all participants (users, providers, developers). This uncertainty is one of the important factors that hinder DePIN from being adopted by mainstream enterprises. How to establish an effective accountability mechanism under the premise of decentralization is a major legal and technical challenge facing DePIN. Centralized service providers (such as AWS) are more easily trusted by enterprises because of their clear responsible entities, while DePIN's distributed structure makes the allocation and execution of legal responsibilities unclear, thereby increasing the risks of commercial applications.

5.3 The unclear status of DePIN tokens and network governance

How should the tokens issued by the DePIN project be legally characterized (securities, commodities, or utility tokens)?

This is an unresolved question around the world, especially as regulators such as the U.S. Securities and Exchange Commission (SEC) take a tough stance. The lack of clear, forward-looking guidelines from regulators has led to huge legal uncertainty for both project owners and investors. If tokens are deemed unregistered securities, project owners, developers, and even token holders may face severe penalties. This ambiguity has severely hampered the financing, planning, and development of the DePIN project.

Governance : Many DePIN projects adopt a decentralized governance model, allowing token holders to participate in the formulation of network rules, protocol upgrades, and management of community funds through voting and other means. However, the legal status and responsibility definition of this decentralized governance structure are also unclear. How legally binding are these governance decisions? If governance decisions cause problems in the network or harm the interests of certain participants, who will bear the responsibility? Is it the voting token holders, the core development team, or the protocol itself?

Regulatory Lag : The speed of technological innovation often far exceeds the speed of updating regulatory policies. In the absence of clear rules, regulators often adopt a "regulation by enforcement" approach to punish existing projects, which has a chilling effect on the entire industry and stifles innovation.

Regulatory ambiguity, especially around token classification and governance responsibilities, is a dark cloud hanging over the entire DePIN industry. The industry urgently needs regulators to provide clearer and more technologically responsive rules so that projects can invest resources in technology and product development rather than guessing and responding to legal compliance. This legal fog makes companies hesitant when deciding whether to adopt or invest in DePIN technology.

6. User Experience

Although the DePIN computing network has theoretical advantages such as cost and decentralization, its user experience (UX) — both for providers who contribute resources and consumers who use resources — is often a major barrier to adoption. Compared with mature centralized cloud platforms, participating in the DePIN network usually requires higher technical barriers and more complex operational processes.

6.1 Joining and managing a node: Contributor (provider) perspective

BOINC volunteer experience : One of the design goals of BOINC is to make it easy for the general public to participate, so its client software strives to be simple and easy to use. Volunteers only need to download and install the client program, select the scientific field or specific project of interest, and then the client will automatically download and run the computing tasks in the background, which has little impact on the user's daily use of the computer. This process is relatively simple and has a low technical threshold. However, for researchers running BOINC projects, setting up project servers, porting applications to various platforms, and writing task submission scripts can be quite complicated. Although the introduction of virtual machine technology has alleviated the difficulties of application porting, it has also increased the complexity of configuration.

Golem Provider Experience : Becoming a provider on the Golem network requires installing a specific provider agent software (Linux installation packages are provided). Users need to configure the resources they are willing to share (CPU, memory, disk, etc.). This usually requires some knowledge of Linux system operations. In addition, providers need to understand and manage the receipt and wallet operations of GLM tokens.

Akash Network Provider Experience : Akash's providers are typically data center operators or individuals/organizations that own server resources. They need to set up physical or virtual servers and run Akash's Provider Daemon to access the network. This typically requires high technical skills, such as familiarity with Linux server management, network configuration, and often implies an understanding of container orchestration technologies such as Kubernetes, as Akash primarily runs containerized workloads. Providers also need to manage AKT tokens (for receiving rewards or potential staking), participate in market bidding, and may need to go through an audit process to obtain trusted certification. Some specific DePIN platforms may also have hardware requirements, such as the TEE function of P2P Cloud requires AMD EPYC processors.

DePIN in general : The complexity of provider setup varies greatly between different DePIN projects. Some projects (such as Helium's wireless hotspot) strive for a "plug and play" experience, but computing DePINs generally require providers to have higher technical literacy. Managing cryptocurrency wallets and processing token transactions is an additional learning curve and operational barrier for non-cryptocurrency users.

Compared to BOINC's easy-to-use design for volunteers, the commercial DePIN computing platform generally has higher technical requirements for providers. Providers need to manage their nodes, resources, pricing, and collection like running a small business. This limits the scope of potential providers, making them more inclined to professional technicians or institutions rather than ordinary computer users.

6.2 Accessing and using resources: the consumer (demander) perspective

BOINC "Consumers" : BOINC is primarily designed for research projects that require large-scale computing. Researchers need to set up and maintain project servers, manage the generation and distribution of applications and work units, and the collection and verification of results. It is not intended for ordinary consumers or developers who need general-purpose computing power on demand.

Golem user experience : Users need to define and submit computing tasks through the API or SDK provided by Golem (such as JS API, Ray interface). This usually requires the use of task templates (which can be pre-made or custom created) to describe task logic, resource requirements, and verification methods. Users need to hold and use GLM tokens for payment. They also need to use the reputation system to help select reliable providers. This whole process requires certain programming skills and understanding of the Golem platform.

Akash Network demander experience : Akash users (tenants) need to use its specific "Stack Definition Language" (SDL) to describe the application