IoTeX
$0.019811
-2.14%
  • Rank #188
  • Market Cap -1.80% $187,042,202
  • 24H Spot Volume -24.81% $3,130,967.01
  • 24H Volume / Market Cap +2.00%
  • Circulating Supply 9,441,369,057.00
  • Total Supply 9,496,266,827
  • Max Supply 9,496,266,827.32
  • Fully Diluted Valuation $188,129,778
  • API ID iotex copy duigou
  • Project Start Date Jun, 2019
  • Contracts & Explorer(s) coin-img 0xBCB...e38C1
    copy duigou
  • Websites Website
  • Links
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About IOTX Roadmap & Milestones
IoTeX ($IOTX) is a decentralized platform that combines blockchain, secure hardware, and confidential computing to empower privacy-focused devices, apps, and services.

Starting as an open-source project in 2017, IoTeX has built a decentralized platform whose aim is to empower the open economics for machines — an open ecosystem where people and machines can interact with guaranteed trust, free will, and under properly designed economic incentives.

With a global team of over 40 research scientists and engineers, IoTeX has built their EVM-compatible blockchain from scratch using the innovative Roll-DPoS consensus and launched in 2019 April, which has been running by 100+ delegates worldwide and has processed more than 10 million transactions already. On top of the IoTeX blockchain, the team has built the essential blocks of infrastructures to connect with Ethereum, BSC, and Heco blockchains such as ioPay wallet (https://iopay-wallet.iotex.io/) and ioTube bridge (https://tube.iotex.io/), which serve ten thousands of users. 

Besides, middleware such as Decentralized Identity, Confidential Computing, and Secure Hardware has been built on top of IoTeX blockchain to enable self-sovereign devices such as Ucam real-world oracle such as Pebble. The former (https://ucam.iotex.io/) has been deployed to 3000+ households (http://iott.network/) and still proliferates. In contrast, the latter has been launched to 300+ developers initially and enables innovative DApps that connect the physical world with the crypto world, such as real-world NFTs, weather derivatives, and machine learning-as-mining.

 

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