Introduction
The Decentralized Physical Infrastructure Network (DePIN) is an emerging concept that combines blockchain technology with the Internet of Things (IoT), gradually attracting widespread attention both inside and outside the industry. DePIN redefines the management and control mode of physical devices through a decentralized architecture, demonstrating the potential to bring disruptive changes to traditional infrastructure fields such as the power grid and waste management systems. Traditional infrastructure projects have long been subject to centralized control by governments and large enterprises, often facing high service costs, inconsistent service quality, and limited innovation. DePin provides a new solution aimed at achieving decentralized management and control of physical devices through distributed ledgers and smart contract technology, thereby enhancing the transparency, credibility, and security of the system.
Functions and Advantages of DePin
Decentralized Management and Transparency: DePIN achieves decentralized management of physical devices through distributed ledger and smart contracts, allowing device owners, users, and relevant stakeholders to verify the status and operations of devices through a consensus mechanism. This not only enhances the security and reliability of devices but also ensures the transparency of system operations. For example, in the field of Virtual Power Plants (VPP), DePIN can publicly and transparently trace the origin data of sockets, enabling users to clearly understand the production and flow of data.
Risk Diversification and System Continuity: By distributing physical devices to different geographical locations and involving multiple participants, DePIN effectively reduces the centralized risk of the system, avoiding the impact of single point failures on the entire system. Even if a node fails, other nodes can continue to operate and provide services, ensuring the continuity and high availability of the system.
Automation of Smart Contract Operations: DePIN utilizes smart contracts to automate device operations, thereby improving operational efficiency and accuracy. The execution process of smart contracts is fully traceable on the blockchain, with each operation recorded, allowing anyone to verify the execution of the contract. This mechanism not only improves the efficiency of contract execution but also enhances the transparency and credibility of the system.
Analysis of DePIN's Five-Layer Architecture
Overview
Although cloud devices typically have highly centralized characteristics, DePIN (Decentralized Physical Infrastructure Network) successfully simulates centralized cloud computing functionality through the design of a multi-layer modular technology stack. Its architecture includes the application layer, governance layer, data layer, blockchain layer, and infrastructure layer, each playing a critical role in ensuring the network's efficient, secure, and decentralized operation. The following will provide a detailed analysis of these five layers.
Application Layer
Function: The application layer is the part of the DePIN ecosystem that directly faces users, responsible for providing various specific applications and services. Through this layer, the underlying technology and infrastructure are transformed into functions that users can directly use, such as IoT applications, distributed storage, and decentralized finance (DeFi) services.
Importance:
User Experience: The application layer determines the interaction between users and the DePIN network, directly impacting user experience and the network's popularity.
Diversity and Innovation: This layer supports a variety of applications, contributing to the diversity and innovative development of the ecosystem, attracting developers and users from different fields to participate.
Value Realization: The application layer translates the technical advantages of the network into practical value, driving the network's continuous development and user benefit realization.
Governance Layer
Function: The governance layer can operate on-chain, off-chain, or in a hybrid mode, responsible for formulating and enforcing network rules, including protocol upgrades, resource allocation, and conflict resolution. It typically adopts decentralized governance mechanisms, such as DAO (Decentralized Autonomous Organization), ensuring the transparency, fairness, and democracy of the decision-making process.
Importance:
Decentralized Decision-Making: By decentralizing decision-making power, the governance layer reduces the risk of single-point control, enhancing the network's resistance to censorship and stability.
Community Participation: This layer encourages active participation from community members, enhancing user belonging and promoting the healthy development of the network.
Flexibility and Adaptability: Effective governance mechanisms enable the network to quickly respond to changes in the external environment and technological advancements, maintaining competitiveness.
Data Layer
Function: The data layer is responsible for managing and storing all data in the network, including transaction data, user information, and smart contracts. It ensures the integrity, availability, and privacy protection of data while providing efficient data access and processing capabilities.
Importance:
Data Security: Through encryption and decentralized storage, the data layer protects user data from unauthorized access and tampering.
Scalability: Efficient data management mechanisms support network expansion, processing a large volume of concurrent data requests, ensuring system performance and stability.
Data Transparency: Publicly transparent data storage increases the network's trustworthiness, allowing users to verify and audit the authenticity of data.
Blockchain Layer
Function: The blockchain layer is the core of the DePIN network, responsible for recording all transactions and smart contracts, ensuring the immutability and traceability of data. This layer provides decentralized consensus mechanisms, such as PoS (Proof of Stake) or PoW (Proof of Work), to safeguard the security and consistency of the network.
Importance:
Decentralized Trust: Blockchain technology eliminates the reliance on centralized intermediaries, establishing a trust mechanism through distributed ledgers.
Security: Strong encryption and consensus mechanisms protect the network from attacks and fraud, maintaining system integrity.
Smart Contracts: The blockchain layer supports automated and decentralized business logic, enhancing the functionality and efficiency of the network.
Infrastructure Layer
Function: The infrastructure layer includes the physical and technical infrastructure that supports the entire DePIN network operation, such as servers, network equipment, data centers, and energy supply. This layer ensures the high availability, stability, and performance of the network.
Importance:
Reliability: Robust infrastructure ensures the continuous operation of the network, avoiding service unavailability due to hardware failures or network interruptions.
Performance Optimization: Efficient infrastructure improves the network's processing speed and responsiveness, enhancing user experience.
Scalability: Flexible infrastructure design allows the network to expand according to demand, supporting more users and more complex application scenarios.
Connection Layer
In some cases, a connection layer is added between the infrastructure layer and the application layer to handle communication between smart devices and the network. The connection layer can be a centralized cloud service or a decentralized network, supporting various communication protocols such as HTTP(s), WebSocket, MQTT, CoAP, etc., to ensure reliable data transmission.
How AI Changes DePin
Intelligent Management and Automation
Device Management and Monitoring: AI technology makes device management and monitoring more intelligent and efficient. In traditional physical infrastructure, device management and maintenance often rely on regular inspections and passive repairs, which are not only costly but also prone to undetected equipment failures. By introducing AI, the system can achieve the following optimizations:
Fault Prediction and Prevention: Machine learning algorithms can predict potential equipment failures by analyzing historical operational data and real-time monitoring data. For example, AI can detect potential failures in transformers or power generation equipment in the power grid through sensor data analysis, allowing for early maintenance to prevent larger-scale power outages.
Real-time Monitoring and Automatic Alerts: AI can conduct 24/7 real-time monitoring of all devices in the network and immediately issue alerts upon detecting anomalies. This includes not only the hardware status of devices but also their operational performance, such as abnormal changes in parameters like temperature, pressure, and current. For instance, in a decentralized water treatment system, AI can monitor water quality parameters in real-time and promptly notify maintenance personnel when contaminants exceed the standard.
Intelligent Maintenance and Optimization: AI can dynamically adjust maintenance plans based on device usage and operational status to avoid over-maintenance and under-maintenance. For example, by analyzing operational data of wind turbines, AI can determine the optimal maintenance cycle and measures to improve power generation efficiency and equipment lifespan.
Resource Allocation and Optimization: AI's application in resource allocation and optimization can significantly improve the efficiency and performance of the DePin network. Traditional resource allocation often relies on manual scheduling and static rules, making it difficult to cope with complex and changing real-world situations. AI can dynamically adjust resource allocation strategies through data analysis and optimization algorithms to achieve the following goals:
Dynamic Load Balancing: In decentralized computing and storage networks, AI can dynamically adjust task allocation and data storage locations based on node load and performance metrics. For example, in a distributed storage network, AI can store high-frequency access data on high-performance nodes and distribute low-frequency access data to lightly loaded nodes, improving the overall storage efficiency and access speed of the network.
Energy Efficiency Optimization: AI can optimize energy production and usage by analyzing equipment energy consumption data and operational patterns. For example, in a smart grid, AI can optimize the start-stop strategy of power generation units and power distribution schemes based on user electricity consumption habits and demand, reducing energy consumption and carbon emissions.
Improved Resource Utilization: AI can maximize resource utilization through deep learning and optimization algorithms. For example, in a decentralized logistics network, AI can dynamically adjust delivery routes and vehicle scheduling based on real-time traffic conditions, vehicle locations, and cargo demands, improving delivery efficiency and reducing logistics costs.
Data Analysis and Decision Support
Data Collection and Processing: In the Decentralized Physical Infrastructure Network (DePin), data is one of the core assets. Various physical devices and sensors in the DePin network continuously generate a large amount of data, including sensor readings, device status information, and network traffic data. AI technology demonstrates significant advantages in data collection and processing:
Efficient Data Collection: AI can collect high-quality data in real-time at the device level through intelligent sensors and edge computing, dynamically adjusting data collection frequency and scope as needed.
Data Preprocessing and Cleaning: Raw data typically contains noise, redundancy, and missing values. AI technology can enhance data quality through automated data cleaning and preprocessing. For example, machine learning algorithms can detect and correct abnormal data, fill in missing values, ensuring the accuracy and reliability of subsequent analysis.
Real-time Data Processing: The DePin network requires real-time processing and analysis of massive data to quickly respond to changes in the physical world. AI technology, especially streaming processing and distributed computing frameworks, enables real-time data processing.
Intelligent Decision-Making and Prediction: Intelligent decision-making and prediction are core areas of AI application in the Decentralized Physical Infrastructure Network (DePin). Through deep learning, machine learning, and prediction models, AI can achieve intelligent decision-making and precise prediction of complex systems, improving system autonomy and response speed:
Deep Learning and Prediction Models: Deep learning models can handle complex nonlinear relationships and extract latent patterns from large-scale data. For example, by analyzing equipment operational data and sensor data using deep learning models, the system can identify potential signs of failure and proactively perform preventive maintenance, reducing equipment downtime and improving production efficiency.
Optimization and Scheduling Algorithms: Optimization and scheduling algorithms are another important aspect of AI's intelligent decision-making in the DePin network. By optimizing resource allocation and scheduling schemes, AI can significantly improve system efficiency and reduce operational costs.
Security
Real-time Monitoring and Anomaly Detection: Security is a crucial factor in the Decentralized Physical Infrastructure Network (DePin). AI technology can timely detect and respond to various potential security threats through real-time monitoring and anomaly detection. Specifically, AI systems can analyze network traffic, device status, and user behavior in real-time to identify abnormal activities. For example, in a decentralized communication network, AI can monitor data packet flow, detect abnormal traffic, and malicious attack behavior. Through machine learning and pattern recognition techniques, the system can quickly identify and isolate infected nodes, preventing further spread of attacks.
Automated Threat Response: AI can not only detect threats but also automatically take response measures. Traditional security systems often rely on human intervention, while AI-driven security systems can take action immediately upon threat detection, reducing response time. For example, in a decentralized energy network, if AI detects abnormal activity in a node, it can automatically disconnect the node's connection and activate backup systems to ensure network stability. Additionally, through continuous learning and optimization, AI can improve the efficiency and accuracy of threat detection and response.
Predictive Maintenance and Protection: Through data analysis and prediction models, AI can predict potential security threats and equipment failures, taking preventive measures in advance. For example, in a smart transportation system, AI can analyze traffic flow and accident data to predict potential high-risk areas for accidents and deploy emergency measures in advance to reduce the probability of accidents. Similarly, in a distributed storage network, AI can predict the risk of storage node failures and perform maintenance in advance to ensure data security and availability.
How DePin Changes AI
Advantages of DePin's Application in AI
Resource Sharing and Optimization: DePin allows different entities to share computing resources, storage resources, and data resources. This is particularly important for scenarios where AI training and inference require a large amount of computing resources and data. Decentralized resource sharing mechanisms can significantly reduce the operational costs of AI systems and improve resource utilization.
Data Privacy and Security: In traditional centralized AI systems, data is often stored centrally on a specific server, leading to data leakage and privacy issues. DePin ensures data security and privacy through distributed storage and encryption technologies. Data owners can share data with AI models for distributed computing while retaining data ownership.
Enhanced Reliability and Availability: Through a decentralized network structure, DePin improves the reliability and availability of AI systems. Even if a node fails, the system can continue to operate. Decentralized infrastructure reduces the risk of single point failures, enhancing system resilience and stability.
Transparent Incentive Mechanism: The token economy in DePin provides a transparent and fair incentive mechanism for transactions between resource providers and users. Participants can contribute computing resources, storage resources, or data to receive token rewards, creating a virtuous cycle.
Potential Application Scenarios of DePin in AI
Distributed AI Training: AI model training requires a large amount of computing resources. Through DePin, different computing nodes can collaborate to form a distributed training network, significantly accelerating the training speed. For example, a decentralized GPU network can provide training support for deep learning models.
Edge Computing: With the proliferation of Internet of Things (IoT) devices, edge computing has become an important direction for AI development. DePin can allocate computing tasks to edge devices close to the data source, improving computing efficiency and response speed. For example, smart home devices can utilize DePin to achieve localized AI inference, enhancing user experience.
Data Marketplace: The performance of AI models depends on a large amount of high-quality data. DePin can establish a decentralized data marketplace, allowing data providers and users to trade data while ensuring privacy. Through smart contracts, the data trading process is transparent and trustworthy, ensuring the authenticity and integrity of the data.
Decentralized AI Service Platform: DePin can serve as infrastructure to support decentralized AI service platforms. For example, in a decentralized AI image recognition service platform, users can upload images, and the platform processes them through distributed computing nodes and returns results. This platform not only improves service reliability but also incentivizes developers to continuously optimize algorithms through token mechanisms.
AI + DePin Projects
In this section, we will explore several DePin projects related to AI, focusing on the decentralized file storage and access platform Filecoin, the decentralized GPU computing power leasing platform Io.net, and the decentralized AI model deployment and access platform Bittensor. These three play important roles in data storage access, computing power support for training, and model deployment and usage in the field of AI.
Filecoin
Filecoin is a decentralized storage network that uses blockchain technology and a cryptocurrency economic model to achieve distributed data storage on a global scale. Developed by Protocol Labs, Filecoin aims to create an open and public storage market where users can purchase storage space in the network by paying Filecoin tokens (FIL) or earn FIL by providing storage services.
Features
Decentralized Storage: Filecoin stores data in a decentralized manner, avoiding the centralization drawbacks of traditional cloud storage, such as single point of failure and data censorship risks.
Market-Driven: Filecoin's storage market is determined by supply and demand, and storage prices and service quality are dynamically adjusted through free market mechanisms, allowing users to choose the optimal storage solution based on their needs.
Verifiable Storage: Filecoin ensures the effective storage and backup of data at storage providers through mechanisms such as Proof-of-Spacetime (PoSt) and Proof-of-Replication (PoRep).
Incentive Mechanism: Through mining and transaction reward mechanisms, Filecoin encourages network participants to provide storage and retrieval services, thereby increasing the network's storage capacity and availability.
Scalability: Filecoin's network supports large-scale data storage and fast access through technologies such as sharding, meeting the demands of future massive data growth.
Addressing Pain Points
High Data Storage Costs: Through Filecoin's decentralized storage market, users can flexibly choose storage providers, reducing data storage costs.
Data Security and Privacy Issues: Decentralized storage and encryption technologies in Filecoin ensure data privacy and security, reducing the risk of data leakage associated with centralized storage.
Data Storage Reliability: Filecoin's time-space proof and replication proof mechanisms ensure the integrity and verifiability of data during storage, enhancing data storage reliability.
Trust Issues with Traditional Storage Platforms: Filecoin achieves storage transparency through blockchain technology, eliminating the monopoly and manipulation of data by third-party institutions, enhancing user trust in storage services.
Target Users
Storage Providers: By providing access to idle disk space, storage providers respond to user storage requests and earn tokens. Storage providers need to stake tokens, and if they fail to provide valid storage proofs, they may be penalized and lose part of their staked tokens.
File Retrievers: When users need to access files, file retrievers locate the file's storage location to earn tokens. File retrievers do not need to stake tokens.
Data Storers: Through market mechanisms, data storers submit the price they are willing to pay and match with storage providers to send data. Both parties sign the transaction order and submit it to the blockchain.
Data Users: Users submit a unique file identifier and payment to retrieve the file. File retrievers locate the file's storage location, respond to the storage request, and provide the data.
Token Economic System
FIL Token Circulation: FIL is the native cryptocurrency of the Filecoin network, used to pay for storage fees, reward miners, and conduct transactions within the network. The circulation of FIL tokens maintains the normal operation of the Filecoin network.
Rewards for Storage Miners and Retrieval Miners: Storage providers earn FIL tokens by providing storage space and data retrieval services. Miner rewards are related to the storage space provided, data access frequency, and participation in network consensus.
Network Fees: Users need to pay FIL tokens to purchase storage and retrieval services. The fees are determined by the supply and demand relationship in the storage market, allowing users to freely choose service providers in the market.
Token Issuance and Inflation: The total supply of Filecoin is 2 billion, and new FIL tokens are gradually issued through mining rewards. As the number of miners increases, the network's inflation rate gradually decreases.
Io.net
Io.net is a distributed GPU computing platform that collects and clusters idle computing power to provide market-based computing power scheduling and temporary supplementation, rather than replacing existing cloud computing resources. The platform allows suppliers to deploy supported hardware for users to rent through simple Docker commands to meet the needs of task distribution and processing. Io.net aims to provide an effect close to that of cloud computing platforms through distributed computing power sharing, significantly reducing service costs.
Features
Easy Deployment: Suppliers can easily deploy hardware through Docker commands, and users can conveniently rent hardware clusters through the platform to obtain the required computing power.
Clustered Computing Power: By clustering idle computing power, the platform serves as a market-based computing power scheduler and temporary supplement, improving the overall utilization of computing resources.
Secure Transmission and On-chain Storage: The platform uses end-to-end encryption to ensure the security of user data. Additionally, task execution information is stored on the blockchain, achieving transparent and permanent log storage.
Node Health Monitoring: The platform records and publicly discloses the health status of each node, including offline time, network speed, and task execution, to ensure system stability and reliability.
Addressing Pain Points
Insufficient Computing Power: With the rise of large models, there is a surge in demand for GPU computing power for training. Io.net fills this computing power gap by integrating idle GPU resources from the public.
Privacy and Compliance: Large cloud platform service providers such as AWS and Google Cloud have strict KYC requirements for users, while Io.net circumvents compliance issues through decentralized means, allowing users to flexibly choose resource usage.
High Costs: Service prices on cloud computing platforms are high, while Io.net significantly reduces costs through distributed computing power sharing, achieving service quality close to that of cloud platforms through clustering technology.
Target Users
Computing Power Providers: Provide idle GPUs to the platform for others to use. Depending on the performance and stability of the provided devices, token rewards can be obtained.
Computing Power Users: Rent GPUs or GPU clusters using tokens for task submission or large model training.
Stakers: Stakers support the long-term stable operation of the platform by staking platform tokens and earn staking rewards from equipment leasing, which helps improve the ranking of excellent equipment.
Token Economic System
Token Usage: All transactions within the platform use the native token $IO to reduce transaction friction in smart contracts. Users and suppliers can use USDC or $IO for payment, but using USDC incurs a 2% service fee.
Total Token Supply: The maximum supply of $IO is 800 million, with 500 million issued at launch, and the remaining 300 million used to reward suppliers and stakers. Tokens will be gradually released over 20 years, starting at 8% of the total supply in the first year and decreasing by 1.02% monthly.
Token Burning: A portion of the platform's income will be used to repurchase and burn $IO, with funding sources including a 0.25% bid fee and a 2% service fee for payments made using USDC.
Token Allocation: Tokens will be allocated to seed round investors, Series A investors, the team, ecosystem and community, and supplier rewards.
Bittensor (TAO)
Bittensor is a decentralized peer-to-peer AI model marketplace designed to promote the production and circulation of AI models by allowing different intelligent systems to evaluate and reward each other. Through a distributed architecture, Bittensor has created a market that continuously produces new models and rewards contributors for their information value. The platform provides researchers and developers with a platform to deploy AI models to earn income, while users can access various AI models and functionalities through the platform.
Features
Distributed Marketplace: Bittensor has established a decentralized AI model marketplace, allowing engineers and small AI systems to monetize their work directly, breaking the monopoly of large companies on AI.
Standardization and Modularity: The network supports multiple modes (such as text, image, and speech), allowing different AI models to interact and share knowledge, and can be extended to more complex multimodal systems.
Node Ranking: Each node is ranked based on its contribution to the network, including the effectiveness of task execution, evaluations from other nodes on its output, and the trust it gains in the network. Nodes with higher rankings will receive more network weight and rewards, incentivizing nodes to continuously provide high-quality services in the decentralized market. This ranking mechanism ensures fairness and improves the overall computing efficiency and model quality of the network.
Addressing Pain Points
Centralization of Intelligent Production: The current AI ecosystem is concentrated in a few large companies, making it difficult for independent developers to monetize. Bittensor provides a decentralized peer-to-peer market, offering independent developers and small AI systems the opportunity to profit directly.
Low Utilization of Computing Resources: Traditional AI model training relies on single tasks and cannot fully utilize diverse intelligent systems. Bittensor allows different types of intelligent systems to collaborate, improving the efficiency of computing resource utilization.
Target Users
Node Operators: Integrate computing power and models into the Bittensor network to earn token rewards by participating in task processing and model training. Node operators can be independent developers, small AI companies, or individual researchers, and can improve their rankings and earnings in the network by providing high-quality computing resources and models.
AI Model Users: Users in need of AI computing resources and model services can rent computing power and intelligent models in the Bittensor network by paying tokens. Users can be enterprises, research institutions, or individual developers who use high-quality models in the network to perform specific tasks such as data analysis and model inference.
Stakers: Users holding Bittensor tokens support the long-term stable operation of the network through staking and earn staking rewards. Stakers not only benefit from network inflation but also indirectly influence the overall computing efficiency and reward distribution of the network through staking to improve the ranking of supported nodes.
Token Economic System
Token Usage: All transactions and incentives within the Bittensor network are conducted using the native token, reducing friction in the transaction process. Users can use tokens to pay for computing resources and model services, while node operators earn tokens by providing services.
Token Generation: One TAO token is generated every 12 seconds, distributed based on the performance of the subnet and its nodes. The token allocation ratio is 18% to subnet owners, and 41% each to subnet miners and validators. The maximum token supply is 21 million.
Challenges and Conclusion for DePin
DePin, as an emerging network architecture, has achieved decentralized management of physical infrastructure by combining blockchain technology. This innovation not only addresses the issues of data privacy, service interruptions, and high scalability costs faced by traditional infrastructure but also empowers network participants with more control and involvement through token incentive mechanisms and self-organizing models. Despite demonstrating strong potential, DePin still faces some challenges.
Scalability: DePin's scalability issues stem from its reliance on the decentralized nature of blockchain technology. As the number of users and network scale increases, the transaction volume on the blockchain network also increases, especially when DePin applications are connected to the physical world, requiring higher information transmission. This can lead to longer transaction confirmation times and increased transaction fees, affecting the overall efficiency and user experience of the network.
Interoperability: The DePin ecosystem is built on multiple blockchains, requiring DePin applications to support homogeneous or heterogeneous state transitions and achieve seamless interoperability with other blockchain networks. However, current interoperability solutions are often limited to specific blockchain ecosystems or come with high cross-chain costs, making it difficult to fully meet the needs of DePin.
Regulatory Compliance: As part of the Web 3.0 ecosystem, DePin faces multiple regulatory challenges. Its decentralized and anonymous nature makes it difficult for regulatory authorities to monitor fund flows, potentially leading to an increase in illegal fundraising, pyramid schemes, and money laundering activities. Additionally, in terms of tax regulation, the anonymity of accounts makes it challenging for governments to collect the evidence needed for tax collection, posing a challenge to the existing tax system.
In the future, the development of DePin will depend on the resolution of these key issues and is expected to play an important role in a wide range of application scenarios, reshaping the operational mode of physical infrastructure.
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