introduction
Decentralized Physical Infrastructure Network (DePIN) is a cutting-edge concept that combines blockchain technology with the Internet of Things (IoT), and is gradually attracting widespread attention from both inside and outside the industry. DePIN redefines the management and control mode of physical devices through a decentralized architecture, showing the potential to trigger disruptive changes in traditional infrastructure fields such as power grids and waste management systems. Traditional infrastructure projects have long been centrally controlled by governments and large enterprises, and are often faced with high service costs, inconsistent service quality, and limited innovation. DePin provides a new solution that aims to achieve decentralized management and control of physical devices through distributed ledger and smart contract technology, thereby improving 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 ledgers and smart contracts of blockchain technology, enabling device owners, users and related stakeholders to verify the status and operation of devices through a consensus mechanism. This not only improves the safety and reliability of the equipment, but also ensures the operational transparency of the system. For example, in the field of Virtual Power Plant (VPP), DePIN can make the traceability data of sockets public and transparent, allowing users to clearly understand the production and circulation process of data.
Risk dispersion and system continuity: By distributing physical devices to different geographical locations and multiple participants, DePIN effectively reduces the centralization risk of the system and avoids the impact of single point failure 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.
Smart contract automation: DePIN uses smart contracts to automate device operations, thereby improving operational efficiency and accuracy. The execution process of smart contracts is fully traceable on the blockchain, and every step of the operation is 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 are usually highly centralized, DePIN (Decentralized Physical Infrastructure Network) successfully simulates centralized cloud computing functions through the design of a multi-layer modular technology stack. Its architecture includes application layer, governance layer, data layer, blockchain layer and infrastructure layer, and each layer plays a key role in the entire system to ensure the efficient, secure and decentralized operation of the network. The following will analyze these five layers in detail.
Application Layer
Function: The application layer is the user-facing part of the DePIN ecosystem, 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 Internet of Things (IoT) applications, distributed storage, decentralized finance (DeFi) services, etc.
importance:
User experience: The application layer determines how users interact with the DePIN network, which directly affects the user experience and the popularity of the network.
Diversity and innovation: This layer supports a variety of applications, which contributes to the diversity and innovation of the ecosystem and attracts developers and users from different fields to participate.
Value realization: The application layer transforms the network's technical advantages into actual value, promoting the sustainable development of the network and the realization of users' interests.
Governance Layer
Function: The governance layer can operate on-chain, off-chain, or in a hybrid mode, and is responsible for formulating and enforcing network rules, including protocol upgrades, resource allocation, and conflict resolution. Decentralized governance mechanisms such as DAO (decentralized autonomous organization) are usually adopted to ensure transparency, fairness, and democracy in the decision-making process.
importance:
Decentralized decision-making: By decentralizing decision-making power, the governance layer reduces the risk of single point control and improves the network's censorship resistance and stability.
Community participation: This layer encourages active participation of community members, enhances users’ sense of belonging, and promotes the healthy development of the network.
Flexibility and adaptability: Effective governance mechanisms enable the network to respond quickly to changes in the external environment and technological advances, and remain competitive.
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 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 mechanism supports network expansion, handles a large number of concurrent data requests, and ensures system performance and stability.
Data transparency: Open and transparent data storage increases the trust of the network and enables 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 ensure the security and consistency of the network.
importance:
Decentralized trust: Blockchain technology eliminates the reliance on centralized intermediaries and establishes a trust mechanism through distributed ledgers.
Security: Strong encryption and consensus mechanisms protect the network from attacks and fraud, maintaining the integrity of the system.
Smart Contracts: The blockchain layer supports automated and decentralized business logic, improving the functionality and efficiency of the network.
Infrastructure Layer
Function: The infrastructure layer includes the physical and technical infrastructure that supports the operation of the entire DePIN network, such as servers, network equipment, data centers, and energy supply. This layer ensures the high availability, stability, and performance of the network.
importance:
Reliability: A solid infrastructure ensures the continuous operation of the network and avoids service unavailability due to hardware failure or network interruption.
Performance optimization: Efficient infrastructure improves network processing speed and responsiveness, improving user experience.
Scalability: Flexible infrastructure design allows the network to scale as needed, supporting more users and more complex application scenarios.
Connection Layer
In some cases, people add a connection layer between the infrastructure layer and the application layer, which is responsible for handling the communication between smart devices and the network. The connection layer can be a centralized cloud service or a decentralized network, supporting multiple communication protocols such as HTTP(s), WebSocket, MQTT, CoAP, etc. to ensure reliable data transmission.
How AI is changing DePin
Intelligent management and automation
Equipment management and monitoring: AI technology makes equipment management and monitoring more intelligent and efficient. In traditional physical infrastructure, equipment management and maintenance often rely on regular inspections and passive repairs, which is not only costly but also prone to equipment failures that are not discovered in time. By introducing AI, the system can achieve the following optimizations:
Fault prediction and prevention: Machine learning algorithms can predict possible equipment failures by analyzing historical equipment operation data and real-time monitoring data. For example, by analyzing sensor data, AI can detect possible failures of transformers or power generation equipment in the power grid in advance, arrange maintenance in advance, and avoid larger-scale power outages.
Real-time monitoring and automatic alarm: AI can monitor all devices in the network in real time 24/7 and issue an alarm immediately when an abnormality is detected. This includes not only the hardware status of the device, but also its operating performance, such as abnormal changes in parameters such as temperature, pressure, and current. For example, in a decentralized water treatment system, AI can monitor water quality parameters in real time and immediately notify maintenance personnel to handle it once pollutants exceed the standard.
Intelligent maintenance and optimization: AI can dynamically adjust maintenance plans based on the usage and operating status of equipment to avoid over-maintenance and under-maintenance. For example, by analyzing the operating data of wind turbines, AI can determine the optimal maintenance cycle and maintenance measures to improve power generation efficiency and equipment life.
Resource allocation and optimization: The application of AI 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, which is difficult to cope with complex and changeable actual 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 indicators. For example, in a distributed storage network, AI can store data with higher access frequencies on nodes with better performance, while distributing data with lower access frequencies on nodes with lighter loads, thereby improving the storage efficiency and access speed of the entire network.
Energy efficiency optimization: AI can optimize energy production and use by analyzing the energy consumption data and operation mode of equipment. For example, in smart grids, AI can optimize the start and stop strategies of generator sets and the distribution of electricity according to users' electricity consumption habits and electricity demand, thereby 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 plans based on real-time traffic conditions, vehicle locations, and cargo demand, thereby 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 will continue to generate large amounts of data, including sensor readings, device status information, network traffic data, etc. AI technology shows significant advantages in data collection and processing:
Efficient data collection: Traditional data collection methods may face problems such as data dispersion and low data quality. Through smart sensors and edge computing, AI can collect high-quality data locally in real time on the device and dynamically adjust the frequency and scope of data collection according to demand.
Data preprocessing and cleaning: Raw data usually contains noise, redundancy, and missing values. AI technology can improve data quality through automated data cleaning and preprocessing. For example, machine learning algorithms can be used to detect and correct abnormal data and fill in missing values, thereby ensuring the accuracy and reliability of subsequent analysis.
Real-time data processing: The DePin network needs to process and analyze massive amounts of data in real time to quickly respond to changes in the physical world. AI technology, especially streaming processing and distributed computing frameworks, makes real-time data processing possible.
Intelligent decision-making and prediction: In the decentralized physical infrastructure network (DePin), intelligent decision-making and prediction is one of the core areas of AI application. Through deep learning, machine learning and prediction models, AI technology can achieve intelligent decision-making and accurate prediction of complex systems, and improve the autonomy and response speed of the system:
Deep learning and predictive models: Deep learning models can handle complex nonlinear relationships and extract potential patterns from large-scale data. For example, by analyzing the operation data and sensor data of equipment through deep learning models, the system can identify potential signs of failure, perform preventive maintenance in advance, reduce equipment downtime, and improve production efficiency.
Optimization and scheduling algorithm: Optimization and scheduling algorithm is another important aspect of AI realizing intelligent decision-making in the DePin network. By optimizing resource allocation and scheduling solutions, AI can significantly improve system efficiency and reduce operating costs.
Security
Real-time monitoring and anomaly detection: In decentralized physical infrastructure networks (DePin), security is a crucial factor. AI technology can detect and respond to various potential security threats in a timely manner 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 the flow of data packets and detect abnormal traffic and malicious attack behaviors. Through machine learning and pattern recognition technology, the system can quickly identify and isolate infected nodes to prevent further spread of attacks.
Automated threat response: AI can not only detect threats, but also automatically respond. Traditional security systems often rely on human intervention, while AI-driven security systems can take action immediately after a threat is detected, reducing response time. For example, in a decentralized energy network, if AI detects abnormal activity at a node, it can automatically cut off the connection to the node and start the backup system to ensure the stable operation of the network. In addition, AI can improve the efficiency and accuracy of threat detection and response through continuous learning and optimization.
Predictive maintenance and protection: Through data analysis and prediction models, AI can predict potential security threats and equipment failures and take protective measures in advance. For example, in intelligent transportation systems, AI can analyze traffic flow and accident data, predict possible high-incidence areas of traffic accidents, deploy emergency measures in advance, and reduce the probability of accidents. Similarly, in distributed storage networks, AI can predict the failure risk of storage nodes and perform maintenance in advance to ensure data security and availability.
How DePin is changing AI
Advantages of DePin in AI
Resource sharing and optimization: DePin allows computing, storage and data resources to be shared between different entities. This is especially important for scenarios where AI training and inference require large amounts of computing resources and data. The decentralized resource sharing mechanism can significantly reduce the operating costs of AI systems and improve resource utilization.
Data privacy and security: In traditional centralized AI systems, data is often stored on a central server, which may lead to data leakage and privacy issues. DePin ensures data security and privacy through distributed storage and encryption technology. Data holders 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 and improves the resilience and stability of the system.
Transparent incentive mechanism: The token economics in DePin provides a transparent and fair incentive mechanism for transactions between resource providers and users. Participants can obtain token rewards by contributing computing resources, storage resources or data, forming a virtuous circle.
Potential application scenarios of DePin in AI
Distributed AI training: AI model training requires a lot of computing resources. Through DePin, different computing nodes can work together to form a distributed training network, significantly speeding up training. For example, decentralized GPU networks can provide training support for deep learning models.
Edge computing: With the popularity of Internet of Things (IoT) devices, edge computing has become an important direction for the development of AI. DePin can distribute computing tasks to edge devices close to data sources to improve computing efficiency and response speed. For example, smart home devices can use DePin to achieve localized AI reasoning and improve user experience.
Data Market: The performance of AI models depends on a large amount of high-quality data. DePin can establish a decentralized data market, enabling data providers and users to trade data while protecting privacy. Through smart contracts, the data transaction process is transparent and credible, ensuring the authenticity and integrity of the data.
Decentralized AI service platform: DePin can serve as an infrastructure to support decentralized AI service platforms. For example, a decentralized AI image recognition service platform, users can upload images, the platform processes them through distributed computing nodes and returns the results. This platform not only improves the reliability of the service, but also encourages developers to continuously optimize the algorithm through the token mechanism.
AI + DePin Project
In this section, we will explore several AI-related DePin projects, focusing on the decentralized file storage and access platform Filecoin, the decentralized GPU computing power rental platform Io.net, and the decentralized AI model deployment and access platform Bittensor. These three play an important role in data storage access, computing power support training, and model deployment in the field of AI.
Filecoin
Filecoin is a decentralized storage network that enables distributed data storage worldwide through blockchain technology and cryptocurrency economic models. 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.
Function
Decentralized storage: Filecoin stores data in a decentralized manner, avoiding the centralized drawbacks of traditional cloud storage, such as single point failure and data censorship risks.
Market-driven: Filecoin's storage market is determined by supply and demand. Storage prices and service quality are dynamically adjusted through free market mechanisms. Users can choose the best storage solution based on their needs.
Verifiable storage: Filecoin ensures that data is effectively stored and backed up at the storage provider 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 storage capacity and availability of the network.
Scalability: The Filecoin network supports large-scale data storage and fast access by introducing technical means such as sharding to meet the needs of massive data growth in the future.
Pain points solved
High data storage costs: Through Filecoin's decentralized storage market, users can choose storage providers more flexibly and reduce data storage costs.
Data security and privacy issues: Decentralized storage and encryption technology ensure the privacy and security of data, reducing the risk of data leakage caused by centralized storage.
Reliability of data storage: The space-time proof and replication proof mechanisms provided by Filecoin ensure the integrity and verifiability of data during the storage process, improving the reliability of data storage.
Trust issues in traditional storage platforms: Filecoin achieves storage transparency through blockchain technology, eliminates the monopoly and manipulation of data by third-party institutions, and enhances users' trust in storage services.
Target Users
Storage providers: respond to users' storage requests and earn tokens by providing idle disk space access to the platform. Storage providers need to stake tokens. If they fail to provide valid storage proofs, they will be punished and lose some of their staked tokens.
File Retriever: When a user needs to access a file, they can earn tokens by retrieving the file location. File Retrievers do not need to stake tokens.
Data storer: Through the market mechanism, submit the price they are willing to pay, and after matching with the storer, send the data to the storer. Both parties sign the transaction order and submit it to the blockchain.
Data user: The user submits a unique file identifier and pays a price, and the file retriever will find the storage location of the file, respond to the storage request and provide the data.
Token Economic System
Circulation of FIL tokens: FIL is the native cryptocurrency in the Filecoin network, used to pay storage fees, reward miners, and conduct transactions in 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. Miners' rewards are related to the storage space they provide, the frequency of data access, and their contribution to 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. Users can freely choose suitable 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 inflation rate of the network will gradually decrease.
Io.net
Io.net is a distributed GPU computing platform that collects and clusters idle computing power to provide computing power scheduling and temporary supplement for the market, rather than replacing existing cloud computing resources. The platform allows suppliers to deploy supported hardware through simple Docker commands for users to rent to meet task distribution and processing needs. Through the distributed computing power sharing model, io.net hopes to provide effects close to cloud computing platforms while significantly reducing service costs.
Function
Easy deployment: Suppliers can easily deploy hardware through Docker instructions, 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 acts as a dispatcher and temporary supplement of market computing power, thereby improving the overall utilization of computing resources.
Secure transmission and on-chain storage: The platform uses end-to-end encryption technology to ensure the security of user data. At the same time, task execution information will be stored on the chain to achieve transparent and permanent storage of logs.
Node health monitoring: The platform records and discloses the health status of each node, including offline time, network speed, and task execution status, to ensure the stability and reliability of the system.
Pain points solved
Insufficient computing power: Due to the rise of large models, the market demand for GPU computing power required for training has increased dramatically. 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 avoids compliance issues through decentralization, allowing users to choose to use resources more flexibly.
High cost: The service price of cloud computing platform is relatively high, while io.net significantly reduces costs through distributed computing power sharing, and at the same time achieves service quality close to that of cloud platforms through clustering technology.
Target Users
Computing power providers: connect idle GPUs to the platform for others to use. Based on the performance and stability of the equipment provided, you can get token rewards.
Computing power users: rent GPUs or GPU clusters by consuming tokens for task submission or large model training.
Pledgers: Pledgers pledge platform tokens to support the long-term stable operation of the platform and obtain pledge income from equipment leasing, which helps to 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 pay with USDC or $IO, but using USDC requires a 2% service fee.
Total token supply: $IO has a maximum supply of 800 million, 500 million will be issued at launch, and the remaining 300 million will be used to reward suppliers and stakers. The tokens will be gradually released over 20 years, starting with 8% of the total in the first year and decreasing by 1.02% per month.
Token Burning: A portion of the platform revenue will be used to buy back and burn $IO. The fees will come from a bilateral 0.25% reservation fee and a 2% service fee for payments made using USDC.
Token distribution: Tokens will be distributed to seed investors, Series A investors, team, ecosystem and community, and supplier rewards.
Bittensor (TAO)
Bittensor is a decentralized peer-to-peer AI model market that aims 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 can continuously produce new models and reward contributors for information value. The platform provides researchers and developers with a platform to deploy AI models to earn revenue; while users can use various AI models and functions through the platform.
Function
Distributed Marketplace: Bittensor has built 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, images, and voice), allowing different AI models to interact and share knowledge, and can be expanded to more complex multimodal systems.
System ranking: Each node is ranked according to its contribution to the network. The contribution measurement criteria include the node's performance on the task, the evaluation of its output by other nodes, and the amount of time it has gained in the network.
Nodes with higher rankings will receive more network weight and rewards, which motivates them to continue to provide high-quality services in the decentralized market. This ranking mechanism not only ensures the fairness of the system, but also improves the overall computing efficiency and model quality of the network.
Pain points solved
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 independent developers and small AI systems with direct profit opportunities through a peer-to-peer decentralized market.
Low utilization of computing resources: Traditional AI model training relies on a single task and cannot fully utilize diverse intelligent systems. Bittensor allows different types of intelligent systems to collaborate with each other and improve the utilization efficiency of computing resources.
Target Users
Node operators: connect computing power and models to the Bittensor network, and receive token rewards by participating in task processing and model training. Node operators can be independent developers, small AI companies, or even individual researchers, who can improve their ranking and income in the network by providing high-quality computing resources and models.
AI model users: users who need AI computing resources and model services rent computing power and intelligent models in the Bittensor network by paying tokens. Users can be enterprises, scientific research institutions or individual developers who use high-quality models in the network to complete specific tasks, such as data analysis, model reasoning, etc.
Stakers: Users holding Bittensor tokens support the long-term stable operation of the network through staking and receive staking rewards. Stakers can not only benefit from the inflation of the network, but also improve the ranking of the nodes they support through staking, thereby indirectly affecting the overall computing efficiency and income distribution of the network.
Token Economic System
Token usage: All transactions and incentives within the Bittensor network are conducted through native tokens, reducing friction in the transaction process. Users can use tokens to pay for computing resources and model services, and node operators earn tokens by providing services.
Token generation: One block is generated every 12 seconds, generating 1 TAO token, which is distributed based on the performance of the subnet and the performance of the nodes in it. The distribution ratio of tokens is: 18% is allocated to the subnet owner, and the subnet miners and validators each receive 41%. The maximum supply of tokens is 21 million.
Challenges and conclusions of DePin
As an emerging network architecture, DePIN realizes the decentralized management of physical infrastructure by combining blockchain technology. This innovation not only solves the problems of data privacy, service interruption and high expansion cost faced by traditional infrastructure, but also gives network participants more control and participation through token incentive mechanism and self-organization model. Although DePIN has shown great potential, it still faces some challenges.
Scalability: DePIN's scalability problem stems from its reliance on the decentralized nature of blockchain technology. As the number of users and network size increase, the transaction volume on the blockchain network will also increase, especially the connection between DePIN applications and the physical world, which requires higher information transmission requirements. This will lead to longer transaction confirmation times and increased transaction fees, which in turn affects the overall network efficiency and user experience.
Interoperability: The DePIN ecosystem is built on multiple blockchains, which requires DePIN applications to support homogeneous or heterogeneous state transitions and achieve seamless interoperability with other blockchain networks. However, current interoperability solutions are usually limited to specific blockchain ecosystems or accompanied by 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 regulators to monitor the flow of funds, which may lead to an increase in illegal fundraising, pyramid schemes, and money laundering. In addition, in terms of tax supervision, due to the anonymity of accounts, it is difficult for the government to collect evidence required for taxation, which poses 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 and reshape the operation mode of physical infrastructure.
