Computecoin Network: Di Infrastructure of Web 3.0 and Di Metaverse
Abstract
Web 3.0, wey be evolution of Web 2.0, dey refer to decentralized applications (dAPP) wey dey run for blockchain. Na dis applications dey allow anybody to join with dia personal data well protected and controlled by demsef. But, plenty challenges dey for di development of Web 3.0 like accessibility (e.g., less accessible to many users like for modern web browsers) and scalability (e.g., high cost and long learning curve for using decentralized infrastructure).
For example, although non-fungible token (NFT) dey stored for blockchain, di content of many NFTs still dey stored for centralized clouds like AWS or Google clouds. Dis one dey put high risk for user NFT assets, and e no agree with di nature of Web 3.0.
Di metaverse, wey Neal Stephenson first talk for 1992, dey refer to one infinite large patchwork of persistent virtual worlds wey people fit travel freely, socialize and work inside. But, metaverse applications and platforms like Fortnite and Roblox dey face big wahala: dia growth dey limited by small supply of low-cost and instantaneous computing power from centralized clouds.
To summarize, to build next-generation applications for di current centralized infrastructure (wey dem build since 1990s) don become di bottleneck for di road to our dream world.
We don start dis project, di Computecoin network together with im native token CCN, to solve dis matter. Our aim be to build di next-generation infrastructure for all-purpose applications for Web3 and di metaverse. For another word, we wan do for web 3.0 and di metaverse wetin centralized cloud providers don do for Web 2.0.
Di basic idea of our system be to first gather decentralized clouds like Filecoin and data centers around di world (instead of to build new infrastructure like AWS do 20 years ago) and then offload computation to one proximity network of di nearby gathered decentralized clouds to empower end users' data processing tasks like AR/VR 3D rendering and real-time data storage with low cost and instantaneous manner.
Computecoin network contain two layers: PEKKA and di metaverse computing protocol (MCP). PEKKA na aggregator and scheduler wey dey seamlessly join decentralized clouds and dynamically offload computation to one proximity network. PEKKA im abilities include to deploy web3 and metaverse applications to decentralized clouds within minutes, and to provide one unified API for easy data storage and retrieval from any decentralized cloud, like Filecoin or Crust.
Di MCP na layer-0.5/layer-1 blockchain wey get original consensus algorithm, proof of honesty (PoH), wey dey guarantee say di results of outsourced computation for di decentralized cloud network na original. For another word, PoH dey establish trust for computation tasks wey dem outsource to trustless decentralized clouds, building di foundation for web 3.0 and di metaverse ecosystem.
I. INTRODUCTION
Many people dey agree say Web 3.0 na di key to actualize one more decentralized and interactive experience for di metaverse. So, we dey usually see Web 3.0 and related technologies as di building blocks for di metaverse. Therefore, for wetin dey follow, we dey focus our discussion on di metaverse, di ultimate goal wey computecoin dey target.
A. Introduction to metaverse
Imagine every activity and experience for your daily life dey take place within arm’s reach of each other. Imagine seamless transit between each space, each node, wey you dey inhabit and di people and things wey you dey interact with inside dem. Dis vision of pure connectivity na di beating heart of di metaverse.
Di metaverse, as im name suggest, dey refer to one infinite vast patchwork of persistent virtual worlds wey people fit travel freely between dem. Dem dey usually credit Neal Stephenson with to lay out di first description of di metaverse for im seminal 1992 science fiction novel Snow Crash. Since dat time, plenty projects — everything from Fortnite and Second Life to CryptoKitties and Decentraland — don push humanity closer to di metaverse.
When e finally take shape, di metaverse go offer im inhabitants one online experience wey rich like, and intimately linked with, dia lives for di physical realm. Indeed, dese bold pioneers go fit immerse demsef for di metaverse through all manner of devices, including VR headsets and 3D-printed wearables, as well as technological standards and networks like blockchain and 5G. Meanwhile, di metaverse im smooth functioning and capacity to expand boundlessly go depend on one durable base of computing power.
Di metaverse im development don take one bifurcated path. For one hand, centralized metaverse experiences, like Facebook Horizon and Microsoft Mesh, dey aim to build standalone worlds wey dia territory dey entirely for proprietary ecosystems. For di other hand, decentralized projects dey seek to equip dia users with di tools to create, exchange and own digital goods, secure dia data, and interact with each other outside di confines of corporate systems.
For both cases, though, di metaverse no be just platform, game, or social network; e potentially be every online platform, game and social network wey people around di world dey use all bundled together for one landscape of virtual worlds wey no one user own and every user own at di same time.
For our opinion, di metaverse contain five layers wey dey stacked on top of each other. Di most elemental layer na infrastructure — di physical technologies wey dey support di metaverse im functioning. Dese include technological standards and innovations like 5G and 6G networks, semiconductors, tiny sensors wey dem dey call MEMS and Internet data centers (IDCs).
Di protocol layer dey come next. Im components na di technologies, like blockchain, distributed computing and edge computing, wey dey ensure di efficient and effective computing power distribution to end users and individuals’ sovereignty over dia own online data.
Human interfaces make up di third layer of di metaverse. Dese include devices — like smartphones, 3D-printed wearables, biosensors, neural interfaces, and AR/VR enabled headsets and goggles — wey dey serve as our entry points into wetin go one day be one collective of persistent online worlds.
Di creation layer of di metaverse dey stack on top of di human interface stratum, and e dey made up of top-down platforms and environments, like Roblox, Shopify and Wix, wey dem design to give users tools wey dem go use create new things.
Finally, di aforementioned experience layer dey complete di metaverse stack, dey lend di metaverse im working parts one social, gamified exterior. Di components of di experience layer dey range from non-fungible tokens (NFTs) to e-commerce, e-sports, social media and games.
Di sum of dese five layers na di metaverse, one agile, persistent, and interconnected patchwork of virtual worlds wey dey stand shoulder-to-shoulder for one contiguous universe.
B. Limitations of di metaverse development
Today, di world im most popular online worlds, like Fortnite and Roblox, no fit support di radical accessibility, connectivity and creativity wey go define di metaverse of tomorrow. Metaverse platforms dey face big wahala: Constricted by one limited supply of computing power, dem dey fall short of to deliver one true metaverse experience to dia users.
Although high profile projects — such as Facebook im upcoming Horizon project and Mesh, Microsoft im foray into di world of holoporting and virtual collaboration — get di backing of leading cloud services, di virtual worlds wey dem dey offer users go still dey covered with red tape, highly centralized and lacking interoperability.
For example, Roblox, wey get more than 42 million daily active users, fit only support few hundred concurrent users for one single virtual world. Dis one far cry from di metaverse vision of thousands or even millions of users wey dey interact simultaneously for di same virtual space.
Another limitation na di high cost of computing power. Centralized cloud providers dey charge premium prices for di computing resources wey dem need to run metaverse applications, making am hard for small developers and startups to enter di space. Dis one dey create one barrier to innovation and dey limit di diversity of experiences wey dey available for di metaverse.
Furthermore, di current infrastructure no be dem design to handle di unique demands of metaverse applications. Dese applications require low latency, high bandwidth, and real-time processing capabilities wey dey beyond di reach of many existing systems. Dis one dey result to one subpar user experience, with lag, buffering, and other performance issues.
C. Our solution: di computecoin network
Computecoin network na dem design to address dese limitations by providing one decentralized, high-performance infrastructure for di metaverse. Our solution dey leverage di power of decentralized clouds and blockchain technology to create one more accessible, scalable, and cost-effective platform for metaverse applications.
Di key innovation of Computecoin network na im ability to aggregate computing resources from one global network of decentralized clouds and data centers. Dis one dey allow us to provide one virtually unlimited supply of computing power at one fraction of di cost of centralized providers.
By offloading computation to one proximity network of nearby decentralized clouds, we fit minimize latency and ensure real-time performance for metaverse applications. Dis one critical for immersive experiences like AR/VR, where even small delay fit break di illusion of reality.
Di two-layer architecture of Computecoin network — PEKKA and MCP — dey provide one comprehensive solution for di metaverse. PEKKA dey handle di aggregation and scheduling of computing resources, while MCP dey ensure di security and authenticity of computations through im innovative Proof of Honesty consensus algorithm.
D. Paper organization
Di remainder of dis paper na dem organize as follows: For Section II, we dey provide one detailed overview of PEKKA, including im architecture, resource aggregation capabilities, and computation offloading mechanisms. Section III dey focus on di Metaverse Computing Protocol (MCP), with one in-depth explanation of di Proof of Honesty consensus algorithm. Section IV dey discuss how AI-powered self-evolution go enable Computecoin network to continuously improve and adapt to changing demands. For Section V, we dey describe di tokenomics of CCN, including token allocation, stakeholder rights, and di mining and staking mechanisms. Section VI dey list our publications related to Computecoin network. Finally, Section VII dey conclude di paper with one summary of our vision and future plans.
II. PEKKA
A. Overview
PEKKA (Parallel Edge Computing and Knowledge Aggregator) na di first layer of di Computecoin network. E dey serve as one aggregator and scheduler wey dey seamlessly integrate decentralized clouds and dynamically offload computation to one proximity network. Di primary goal of PEKKA na to provide one unified interface for accessing and utilizing computing resources from various decentralized cloud providers.
PEKKA na dem design to address di fragmentation of di decentralized cloud ecosystem. Currently, plenty decentralized cloud providers dey, each with im own API, pricing model, and resource specifications. Dis fragmentation dey make am hard for developers to leverage di full potential of decentralized computing.
By aggregating dese resources into one single network, PEKKA dey simplify di process of deploying and scaling metaverse applications. Developers fit access one global network of computing resources through one unified API, without to worry about di underlying infrastructure.
B. Aggregation of decentralized clouds
PEKKA dey aggregate computing resources from variety of decentralized cloud providers, including Filecoin, Crust, and others. Dis aggregation process involve several key steps:
1. Resource discovery: PEKKA dey continuously scan di network to identify available computing resources from various providers. Dis include information about di type of resources (CPU, GPU, storage), dia location, and dia current availability.
2. Resource validation: Before to add resources to di network, PEKKA dey validate dia performance and reliability. Dis one dey ensure say only high-quality resources dey included for di network.
3. Resource indexing: Validated resources dey indexed for one distributed ledger, wey dey serve as one transparent and immutable record of all available resources for di network.
4. Pricing normalization: PEKKA dey normalize di pricing models of different providers, making am easy for users to compare and select resources based on dia needs and budget.
5. Dynamic resource allocation: PEKKA dey continuously monitor di demand for computing resources and dey adjust di allocation accordingly. Dis one dey ensure say resources dey used efficiently and say users get access to di resources wey dem need when dem need am.
Di aggregation process na dem design to be decentralized and trustless. No single entity dey control di network, and all decisions dey made through one consensus mechanism. Dis one dey ensure say di network remain open, transparent, and resilient.
C. Computation offloading to one proximity network
One of di key features of PEKKA na im ability to offload computation to one proximity network of nearby decentralized clouds. Dis one critical for metaverse applications, wey require low latency and real-time processing.
Computation offloading involve to transfer computational tasks from one user's device to one nearby node for di network. Dis one dey reduce di burden on di user's device and dey ensure say tasks dey processed quickly and efficiently.
PEKKA dey use one sophisticated algorithm to determine di optimal node for each task. Dis algorithm dey take into account several factors, including di node im proximity to di user, im current load, im performance capabilities, and di cost of using di node.
Di offloading process dey transparent to di user and di application developer. Once one task offload, PEKKA dey monitor im progress and dey ensure say di results dey returned to di user for one timely manner.
C1. Offloading function 1
Di first offloading function na dem design for latency-sensitive tasks, such as real-time rendering and interactive applications. For dese tasks, PEKKA dey prioritize proximity and speed over cost.
Di algorithm dey work as follows: When one latency-sensitive task dey received, PEKKA dey identify all nodes within one certain geographic radius of di user. E then dey evaluate dese nodes based on dia current load and processing capabilities. Di node with di lowest latency and sufficient capacity na dem select to process di task.
To minimize latency further, PEKKA dey use predictive analytics to anticipate future demand. Dis one dey allow di network to pre-position resources for areas wey dem expect demand to high, ensuring say low-latency processing dey always available.
C2. Offloading function 2
Di second offloading function na dem design for batch processing tasks, such as data analysis and content rendering. For dese tasks, PEKKA dey prioritize cost and efficiency over speed.
Di algorithm dey work as follows: When one batch processing task dey received, PEKKA dey identify all nodes for di network wey get di necessary resources to process di task. E then dey evaluate dese nodes based on dia cost, availability, and historical performance. Di node wey offer di best combination of cost and efficiency na dem select to process di task.
For large batch processing tasks, PEKKA fit split di task into smaller sub-tasks and distribute dem across multiple nodes. Dis parallel processing approach dey significantly reduce di time wey e require to complete large tasks.
III. Metaverse Computing Protocol
A. Overview
Di Metaverse Computing Protocol (MCP) na di second layer of di Computecoin network. E na one layer-0.5/layer-1 blockchain wey dey provide di security and trust infrastructure for di network. MCP na dem design to ensure say di results of computations wey dem perform on di decentralized cloud network na authentic and reliable.
One of di key challenges for decentralized computing na to ensure say nodes dey perform computations correctly and honestly. For one trustless environment, no guarantee dey say one node no go tamper with di results of one computation or claim say e don perform work wey e no do.
MCP dey address dis challenge through im innovative Proof of Honesty (PoH) consensus algorithm. PoH na dem design to incentivize nodes to act honestly and to detect and punish nodes wey dey act maliciously.
In addition to providing security and trust, MCP also dey handle di economic aspects of di network. E dey manage di creation and distribution of CCN tokens, wey dem dey use to pay for computing resources and to reward nodes for dia contributions to di network.
B. Consensus: Proof of Honesty (PoH)
Proof of Honesty (PoH) na one novel consensus algorithm wey dem design specifically for di Computecoin network. Unlike traditional consensus algorithms like Proof of Work (PoW) and Proof of Stake (PoS), wey dey focus on validating transactions, PoH na dem design to validate di results of computations.
Di core idea behind PoH na to create one system where nodes dey incentivized to act honestly. Nodes wey consistently provide accurate results dey rewarded with CCN tokens, while nodes wey provide inaccurate results dey penalized.
PoH dey work by periodically sending "phishing tasks" to nodes for di network. Dese tasks na dem design to test di honesty of di nodes. Nodes wey correctly complete dese tasks dey demonstrate dia honesty and dey rewarded. Nodes wey fail to complete dese tasks or provide incorrect results dey penalized.
B1. Algorithm overview
Di PoH algorithm consist of several key components: di phishing-task repository, di task scheduler, di result verifier, di judgment system, and di incentive protocol.
Di algorithm dey work as follows: Di task scheduler dey select nodes from di network to perform computational tasks. Dese tasks include both real user tasks and phishing tasks from di phishing-task repository. Nodes dey process dese tasks and dey return di results to di result verifier.
Di result verifier dey check di results of both real tasks and phishing tasks. For real tasks, di verifier dey use one combination of cryptographic techniques and cross-validation with other nodes to ensure accuracy. For phishing tasks, di verifier don already know di correct result, so e fit immediately detect if one node don provide incorrect result.
Di judgment system dey use di results from di verifier to determine which nodes dey act honestly and which no dey. Nodes wey consistently provide correct results dey rewarded with CCN tokens, while nodes wey provide incorrect results dey penalized by to confiscate dia stake.
Over time, di algorithm dey adapt to di behavior of nodes. Nodes wey get history of honesty dey trusted with more important tasks and dey receive higher rewards. Nodes wey get history of dishonesty dey given fewer tasks and fit eventually dem exclude from di network.
B2. Phishing-task repository
Di phishing-task repository na one collection of precomputed tasks with known results. Dese tasks na dem design to test di honesty and competence of nodes for di network.
Di repository contain one wide variety of tasks, including simple calculations, complex simulations, and data processing tasks. Di tasks na dem design to be representative of di types of tasks wey nodes go encounter for di real network.
To ensure say nodes no fit distinguish between phishing tasks and real tasks, di phishing tasks dey formatted identically to real tasks. Dem also cover similar range of difficulty levels and computational requirements.
Di repository dey continuously updated with new tasks to prevent nodes from to memorize di results of existing tasks. New tasks dey added by one decentralized group of validators, wey dem dey reward with CCN tokens for dia contributions.
Di selection of tasks from di repository dey done randomly to ensure say nodes no fit predict which tasks go be phishing tasks. Dis random selection process na dem design to make am hard for malicious nodes to game di system.
B3. Task scheduler
Di task scheduler na responsible for distributing tasks to nodes for di network. E dey play critical role for ensuring say tasks dey processed efficiently and say di network remain secure.
Di scheduler dey use one reputation system to determine which nodes dey eligible to receive tasks. Nodes with higher reputation (i.e., history of providing correct results) dey more likely to receive tasks, especially high-value tasks.
When dey distribute tasks, di scheduler dey take into account several factors, including di node im reputation, im processing capabilities, im location, and im current load. Dis one dey ensure say tasks dey assigned to di most appropriate nodes.
For real user tasks, di scheduler fit assign di same task to multiple nodes to enable cross-validation. Dis one dey help to ensure say di results accurate, even if some nodes act maliciously.
For phishing tasks, di scheduler typically dey assign each task to one single node. Dis one be because di correct result don already known, so no need for cross-validation.
Di scheduler dey continuously monitor di performance of nodes and dey adjust im task distribution algorithm accordingly. Dis one dey ensure say di network remain efficient and responsive to changing conditions.
B4. Result verification
Di result verification component na responsible for checking di accuracy of di results wey nodes return. E dey use one combination of techniques to ensure say di results both correct and authentic.
For phishing tasks, verification dey straightforward: di verifier dey simply compare di result wey di node return with di known correct result. If dem match, di node dem consider to don act honestly. If dem no match, di node dem consider to don act dishonestly.
For real user tasks, verification dey more complex. Di verifier dey use several techniques, including:
1. Cross-validation: When di same task dem assign to multiple nodes, di verifier dey compare di results. If consensus dey among di nodes, di result dem consider accurate. If discrepancy dey, di verifier fit request additional nodes to process di task to resolve di conflict.
2. Cryptographic verification: Some tasks include cryptographic proofs wey dey allow di verifier to check di accuracy of di result without to reprocess di entire task. Dis one particularly useful for complex tasks wey go expensive to reprocess.
3. Spot checking: Di verifier dey randomly select one subset of real tasks to reprocess imsef. Dis one dey help to ensure say nodes no fit consistently provide incorrect results for real tasks without dem detect.
Di verification process na dem design to be efficient, so say e no go introduce significant overhead to di network. Di goal na to provide high level of security while maintaining di performance and scalability of di network.
B5. Judgement
Di judgment system na responsible for evaluating di behavior of nodes based on di results of di verification process. E dey assign each node one reputation score, wey dey reflect di node im history of honesty and reliability.
Nodes wey consistently provide correct results dey see dia reputation scores increase. Nodes wey provide incorrect results dey see dia reputation scores decrease. Di magnitude of di change dey depend on di severity of di infraction.
For minor infractions, such as occasional incorrect result, di reputation score fit decrease slightly. For more serious infractions, such as consistently providing incorrect results or attempting to game di system, di reputation score fit decrease significantly.
In addition to adjusting reputation scores, di judgment system fit also impose other penalties. For example, nodes with very low reputation scores fit dem temporarily or permanently exclude from di network. Dem fit also confiscate dia staked CCN tokens.
Di judgment system na dem design to be transparent and fair. Di rules for evaluating node behavior dey publicly available, and di system im decisions dey based on objective criteria.
B6. Incentive protocol
Di incentive protocol na dem design to reward nodes wey dey act honestly and dey contribute to di network. E dey use one combination of block rewards, transaction fees, and task completion rewards to incentivize desirable behavior.
Block rewards dey issued to nodes wey successfully validate transactions and create new blocks for di MCP blockchain. Di amount of di reward na dem determine by di network im inflation schedule.
Transaction fees na di ones wey users dey pay to make dia transactions dey included for di blockchain. Dese fees dey distributed to di nodes wey dey validate di transactions.
Task completion rewards na di ones wey dem dey pay to nodes wey successfully complete computational tasks. Di amount of di reward dey depend on di complexity of di task, di node im reputation, and di current demand for computing resources.
Nodes with higher reputation scores dey receive higher rewards for completing tasks. Dis one dey create one positive feedback loop, where honest behavior dey rewarded, and nodes dey incentivized to maintain good reputation.
In addition to dese rewards, di incentive protocol also include mechanisms to prevent malicious behavior. For example, nodes dey required to stake CCN tokens to participate for di network. If one node dem find to dey act maliciously, im stake fit dem confiscate.
Di combination of rewards and penalties dey create strong incentive for nodes to act honestly and contribute to di network im success.
C. System optimization
To ensure say di Computecoin network efficient, scalable, and responsive, we don implement several system optimization techniques:
1. Sharding: Di MCP blockchain na dem divide into multiple shards, each wey fit process transactions independently. Dis one dey significantly increase di throughput of di network.
2. Parallel processing: Both PEKKA and MCP na dem design to take advantage of parallel processing. Dis one dey allow di network to handle multiple tasks simultaneously, increasing im overall capacity.
3. Caching: Frequently accessed data and results dey cached to reduce di need for redundant computations. Dis one dey improve di performance of di network and dey reduce di cost of using am.
4. Dynamic resource allocation: Di network dey continuously monitor di demand for computing resources and dey adjust di allocation of resources accordingly. Dis one dey ensure say resources dey used efficiently and say di network fit scale to meet changing demands.
5. Compression: Data dey compressed before dem transmit am over di network, reducing bandwidth requirements and improving performance.
6. Optimized algorithms: Di algorithms wey dem dey use for task scheduling, result verification, and consensus dey continuously optimized to improve efficiency and reduce computational overhead.
Dese optimizations dey ensure say di Computecoin network fit handle di high demands of metaverse applications while maintaining high level of performance and security.
IV. AI POWERED SELF-EVOLUTION
Di Computecoin network na dem design to continuously improve and adapt to changing conditions through AI-powered self-evolution. Dis capability dey allow di network to optimize im performance, enhance im security, and expand im functionality over time.
For di core of dis self-evolution capability na one network of AI agents wey dey monitor various aspects of di network im operation. Dese agents dey collect data on network performance, node behavior, user demand, and other relevant factors.
Using machine learning algorithms, dese agents dey analyze di collected data to identify patterns, detect anomalies, and make predictions about future network behavior. Based on dis analysis, di agents fit suggest improvements to di network im algorithms, protocols, and resource allocation strategies.
Some examples of how AI dey use to enhance di network include:
1. Predictive resource allocation: AI algorithms dey predict future demand for computing resources and dey adjust di allocation of resources accordingly. Dis one dey ensure say di network get sufficient capacity to meet demand during peak periods.
2. Anomaly detection: AI agents dey detect unusual patterns of behavior wey fit indicate malicious activity. Dis one dey allow di network to respond quickly to potential security threats.
3. Performance optimization: AI algorithms dey analyze network performance data to identify bottlenecks and suggest optimizations. Dis one dey help to continuously improve di speed and efficiency of di network.
4. Adaptive security: AI agents dey learn from past security incidents to develop new strategies for protecting di network. Dis one dey allow di network to adapt to new types of threats as dem dey emerge.
5. Personalized service: AI algorithms dey analyze user behavior to provide personalized recommendations and optimize di user experience.
Di self-evolution process na dem design to be decentralized and transparent. AI agents dey operate within one set of guidelines wey dey ensure say dia recommendations dey aligned with di overall goals of di network. Proposed changes to di network dey evaluated by one decentralized community of validators before dem implement am.
Dis AI-powered self-evolution capability dey ensure say di Computecoin network remain at di cutting edge of technology, continuously adapting to meet di evolving needs of di metaverse.
V. TOKENOMICS
A. CCN token allocation
Di total supply of CCN tokens na fixed at 21 billion. Di tokens dey allocated as follows:
1. Mining rewards: 50% (10.5 billion tokens) dey allocated for mining rewards. Dese tokens dey distributed to nodes wey dey contribute computing resources to di network and dey help secure di MCP blockchain.
2. Team and advisors: 15% (3.15 billion tokens) dey allocated to di founding team and advisors. Dese tokens dey subject to one vesting schedule to ensure long-term commitment to di project.
3. Foundation: 15% (3.15 billion tokens) dey allocated to di Computecoin Network Foundation. Dese tokens dem dey use to fund research and development, marketing, and community initiatives.
4. Strategic partners: 10% (2.1 billion tokens) dey allocated to strategic partners wey dey provide essential resources and support to di network.
5. Public sale: 10% (2.1 billion tokens) dey allocated for public sale to raise funds for di project and distribute tokens to di broader community.
Di token allocation na dem design to ensure say balanced distribution of tokens dey among all stakeholders, with strong emphasis on rewarding those wey dey contribute to di network im growth and security.
B. CCN stakeholders and dia rights
Several types of stakeholders dey for di Computecoin network, each with dia own rights and responsibilities:
1. Miners: Miners dey contribute computing resources to di network and dey help secure di MCP blockchain. In return, dem dey receive mining rewards and transaction fees. Miners also get di right to participate for di consensus process and vote on network proposals.
2. Users: Users dey pay CCN tokens to access computing resources on di network. Dem get di right to use di network im resources and to receive accurate and reliable results for dia computational tasks.
3. Developers: Developers dey build applications and services on top of di Computecoin network. Dem get di right to access di network im API and to use im resources to power dia applications.
4. Token holders: Token holders get di right to vote on network proposals and to participate for di governance of di network. Dem also get di right to stake dia tokens to earn additional rewards.
5. Foundation: Di Computecoin Network Foundation na responsible for di long-term development and governance of di network. E get di right to allocate funds for research and development, marketing, and community initiatives.
Di rights and responsibilities of each stakeholder group na dem design to ensure say di network remain decentralized, secure, and beneficial to all participants.
C. Mint CCN tokens
CCN tokens dey minted through one process wey dem dey call mining. Mining involve to contribute computing resources to di network and to help secure di MCP blockchain.
Miners dey compete to solve complex mathematical problems, wey dey help to validate transactions and create new blocks for di blockchain. Di first miner wey solve one problem dey rewarded with certain number of CCN tokens.
Di mining reward dey decrease over time according to one predefined schedule. Dis one na dem design to control di inflation rate of CCN tokens and ensure say di total supply reach 21 billion over period of 100 years.
In addition to block rewards, miners also dey receive transaction fees. Dese fees na di ones wey users dey pay to make dia transactions dey included for di blockchain.
Mining na dem design to be accessible to anybody with computer and internet connection. But, di difficulty of di mining problems dey adjust dynamically to ensure say new blocks dey created at one consistent rate, regardless of di total computing power for di network.
D. Token release plan
Di release of CCN tokens na dem govern by one predefined schedule wey dem design to ensure one steady and predictable supply of tokens into di market.
1. Mining rewards: Mining rewards dey start at 10,000 CCN per block and dey decrease by 50% every 4 years. Dis one similar to di Bitcoin halving mechanism.
2. Team and advisors: Tokens wey dem allocate to di team and advisors dey released gradually over period of 4 years, with 25% vesting after 1 year and di remaining 75% vesting monthly over di next 3 years.
3. Foundation: Tokens wey dem allocate to di foundation dey released gradually over period of 10 years, with 10% released each year.
4. Strategic partners: Tokens wey dem allocate to strategic partners dey subject to vesting schedules wey dey vary depending on di partner im agreement, but typically dey range from 1 to 3 years.
5. Public sale: Tokens wey dem sell for di public sale dey released immediately, with no vesting period.
Dis release plan na dem design to prevent large amounts of tokens from to enter di market suddenly, wey fit cause price volatility. E also dey ensure say all stakeholders get long-term incentive to contribute to di network im success.
E. Mining Pass and staking
Mining Pass na one mechanism wey dey allow users to participate for di mining process without to invest for expensive hardware. Users fit purchase one Mining Pass using CCN tokens, wey dey give dem di right to receive one portion of di mining rewards.
Mining Passes dey available for different tiers, with higher-tier passes dey provide larger share of di mining rewards. Di price of Mining Passes na dem determine by di market and dey adjust dynamically based on demand.
Staking na another way for users to earn rewards. Users fit stake dia CCN tokens by to lock dem up for one smart contract for certain period of time. In return, dem dey receive one portion of di transaction fees and block rewards.
Di amount of rewards wey one user dey receive from staking dey depend on di number of tokens wey e stake and di length of time wey e stake dem for. Users wey stake more tokens for longer periods dey receive higher rewards.
Staking dey help to secure di network by reducing di number of tokens wey dey available for trading, wey dey make di network more resistant to attacks. E also dey provide one way for users to earn passive income from dia CCN tokens.
F. Development stage
Di development of di Computecoin network na dem divide into several stages:
1. Stage 1 (Foundation): Dis stage dey focus on developing di core infrastructure of di network, including di PEKKA layer and di MCP blockchain. E also involve to build one small test network with one limited number of nodes.
2. Stage 2 (Expansion): For dis stage, di network dey expanded to include more nodes and support more types of computing tasks. Di AI-powered self-evolution capabilities dem also introduce during dis stage.
3. Stage 3 (Maturity): Dis stage dey focus on optimizing di network and scaling am to handle di high demands of metaverse applications. E also involve to integrate di network with other blockchain networks and metaverse platforms.
4. Stage 4 (Autonomy): For di final stage, di network dey become fully autonomous, with di AI agents dey make most of di decisions about network operations and development. Di foundation im role dey reduced to providing oversight and ensuring say di network remain aligned with im original vision.
Each stage dem expect to take approximately 2-3 years to complete, with regular updates and improvements wey dem dey release throughout di development process.
VI. PUBLICATIONS
Di following publications dey provide additional details about di Computecoin network and im underlying technologies:
1. "Computecoin Network: A Decentralized Infrastructure for di Metaverse" - Dis paper dey provide one overview of di Computecoin network, including im architecture, consensus algorithm, and tokenomics.
2. "Proof of Honesty: A Novel Consensus Algorithm for Decentralized Computing" - Dis paper dey describe di Proof of Honesty consensus algorithm for detail, including im design, implementation, and security properties.
3. "PEKKA: A Parallel Edge Computing and Knowledge Aggregator for di Metaverse" - Dis paper dey focus on di PEKKA layer of di Computecoin network, including im resource aggregation capabilities and computation offloading mechanisms.
4. "AI-Powered Self-Evolution in Decentralized Networks" - Dis paper dey discuss di role of AI for enabling di Computecoin network to continuously improve and adapt to changing conditions.
5. "Tokenomics of Computecoin: Incentivizing a Decentralized Computing Ecosystem" - Dis paper dey provide one detailed analysis of di CCN token economy, including token allocation, mining, staking, and governance.
Dese publications dey available on di Computecoin network website and for various academic journals and conferences.
VII. CONCLUSION
Di metaverse dey represent di next evolution of di internet, dey promise to revolutionize how we dey interact, work, and play online. But, di development of di metaverse currently dey limited by di centralized infrastructure wey dey power di internet today.
Di Computecoin network na dem design to address dis limitation by providing one decentralized, high-performance infrastructure for di metaverse. Our solution dey leverage di power of decentralized clouds and blockchain technology to create one more accessible, scalable, and cost-effective platform for metaverse applications.
Di two-layer architecture of di Computecoin network — PEKKA and MCP — dey provide one comprehensive solution for di metaverse. PEKKA dey handle di aggregation and scheduling of computing resources, while MCP dey ensure di security and authenticity of computations through im innovative Proof of Honesty consensus algorithm.
Di AI-powered self-evolution capability of di network dey ensure say e fit continuously improve and adapt to changing conditions, remaining at di cutting edge of technology.
Di tokenomics of CCN na dem design to create one balanced and sustainable ecosystem, with incentives for all stakeholders to contribute to di network im success.
We dey believe say di Computecoin network get di potential to become di foundational infrastructure for di metaverse, enabling one new generation of decentralized applications and experiences. With di support of our community, we dey committed to make dis vision become reality.
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