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AI, Blockchain, IoT Convergence Use Case System Implementation Insights from Patents


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I. AI, Blockchain, IoT, and Their Convergence Technology Innovation Status
II. AI, Blockchain, IoT Convergence Use Case System Implementation Examples
1. Blockchain-based Privacy-Preserving Federated Learning System
2. Blockchain-based Decentralized IoT & AI Data Marketplace
3. Blockchain-based Trustworthy AI & IoT Systems
4. Blockchain-based Decentralized Parallel Edge Machine Learning
5. Predictive Maintenance Platform for Industrial Machine using Industrial IoT
6. AI+Blockchain System for Car Sharing Service
7. Connected Autonomous Vehicle Communication Management System
8. 5G-based AI+Blockchain+IoT Edge Computing System

Published in: Business
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AI, Blockchain, IoT Convergence Use Case System Implementation Insights from Patents

  1. 1. 1 ©2020 TechIPm,LLC All RightsReserved AI, Blockchain, IoT Convergence Use Case System Implementation Insights from Patents1 Alex G. Lee2 Contents I. AI, Blockchain, IoT, and Their ConvergenceTechnologyInnovation Status II. AI, Blockchain, IoT ConvergenceUse Case SystemImplementation Examples 1. Blockchain-basedPrivacy-Preserving FederatedLearning System 2. Blockchain-basedDecentralizedIoT & AI Data Marketplace 3. Blockchain-basedTrustworthy AI & IoT Systems 4. Blockchain-basedDecentralizedParallelEdge Machine Learning 5. Predictive Maintenance Platform for Industrial Machine using Industrial IoT 6. AI+BlockchainSystemfor Car Sharing Service 7. ConnectedAutonomous Vehicle Communication ManagementSystem 8. 5G-basedAI+Blockchain+IoTEdge Computing System 1 This research was supported partly by the WEF Global Partnership Program for the Fourth Industrial Revolution through KAIST KPC4IR funded by the Ministry of Science and ICT of S. Korea. 2 Alex G. Lee,Ph.D/Patent Attorney,is a principal consultant at TechIPm,LLC.
  2. 2. 2 ©2020 TechIPm,LLC All RightsReserved I. AI, Blockchain, IoT, and Their ConvergenceTechnologyInnovation Status3 Patents are a good information resource for obtaining the state of the art of technology innovation insights. Patents that specifically describe the major technology filed of a specific technology innovation are a good indicator of the technology innovation status in a specific innovation entity. A patent counting for growth in patenting over a period of times can be a good measuring tool for monitoring the evolution of technology innovation. To find AI, blockchain, IoT, and their convergence technology innovation status, patent applications in the USPTO, EPO, IPO during the period of January 1, 2010 - September 30, 2020 in priority date (technology innovation date) that specifically describe the major AI, blockchain, IoT, and their convergence technologies are searched and reviewed. Total of 48,000, 17,000, 32,000, 1,288 published patent applications that are related to the key AI, blockchain, IoT, and their convergence technology innovation respectively are selected for detail analysis. Figure 1 summarizes our patent analysis results. Figure 1 (1) shows the top 100 AI, blockchain, and IoT technology innovation entities respectively selected by the total number of published patent applications. The top 100 AI technology innovation entities represent 23,593 patent applications. The top 10 AI innovation leaders are IBM, Google, Microsoft, Samsung Electronics, Intel, 3 AI, Blockchain, IoT Convergence Insights from Patents:
  3. 3. 3 ©2020 TechIPm,LLC All RightsReserved Siemens, Facebook, Philips, GE, and Accenture. The top 100 blockchain technology innovation entities represent 7,809 patent applications. The top 10 blockchain innovation leaders are Alibaba Group, IBM, nChain, Mastercard, Walmart, Bank of America, Visa, Microsoft, Intel, and Accenture. The top 100 IoT technology innovation entities represent 18,786 patent applications. The top 10 IoT innovation leaders are Qualcomm, Ericsson, LG Electronics, Samsung Electronics, Intel, Ford, IBM, Huawei, GM, and Toyota. Figure 2 (2) shows the AI, blockchain, and IoT patenting activities with respect to the technology innovation date (priority year) respectively. The AI patent application activity chart indicates that the AI technology innovation activity started in a rapid growth stage from 2016. Since there is usually a time lag between the initial application date (priority year) and the publication date by around two years, the AI patent application activity chart indicates that the AI technology innovation activity is still in a growth stage. The blockchain patent application activity chart indicates that the Blockchain technology innovation activity started in a rapid growth stage from 2016. The Blockchain patent application activity chart indicates that the Blockchain technology innovation activity is still in a growth stage. The IoT patent application activity chart indicates that the IoT technology innovation activity started in a growth stage already before 2010. The IoT patent application activity chart indicates that the IoT technology innovation activity became matured in 2018. Figure 1 (3) shows the convergence status among AI, blockchain, and IoT, the top 10 AI, blockchain, and IoT convergence technology innovation entities, and the AI, blockchain, IoT convergence patenting activities with
  4. 4. 4 ©2020 TechIPm,LLC All RightsReserved respect to the technology innovation date (priority year) respectively. The key AI, Blockchain, IoT convergence innovation entities are IBM, Strong ForceIP, Intel, Accenture , Microsoft , Bank of America , Bao Tran, Capital One Services, LG Electronics, Cisco, Ericsson, Samsung Electronics, HP, nChain. Nokia, Inmentis, LLC, Salesforce, Tata Consultancy Services, Siemens, and T-Mobile. AI+IoT convergence is the most innovated AI, Blockchain, IoT convergence technology followed by AI+Blockchain, Blockchain+IoT, and AI+Blockchain+IoT. Patent application activity chart indicates that the AI, Blockchain, IoT convergence technology innovation activity is in a rapid growth stage.
  5. 5. 5 ©2020 TechIPm,LLC All RightsReserved Figure 1. AI, blockchain, IoT, and their convergence patent analysis results
  6. 6. 6 ©2020 TechIPm,LLC All RightsReserved II. AI, Blockchain, IoT Convergence Use Case SystemImplementation Examples Patent information can provide many valuable insights that can be exploited for developing and implementing new technologies/ products/services. Patents also can be exploited to identify new use case development opportunities. Our patent analysis indicates that the AI, blockchain, and IoT convergence technology innovation covers the AI, blockchain, and IoT convergence systems that can used for diverse use cases4:AI+Blockchain+IoT convergence for privacy-preserving IoT/AI systems, AI+Blockchain+IoT convergence for IoT/AI data/software marketplace systems; Blockchain-based crowdsourced AI storage/computing systems; AI systems for IoT/blockchain network performance improvements (e.g., secure IoT networks, smarter smart contracts, improved consensus mechanism, highly scalable networks), AI+Blockchain+IoT convergence for regulation compliance systems (e.g., automatic GDPR compliance5), and Trustworthy IoT/AI systems. Our patent analysis also indicates that the AI, blockchain, and IoT convergence technology innovation covers very broad industry wise business applications6: advertisement services, agriculture management, automotive 4 AI+Blockchain+IoT Convergence AT A Glance: 5 AI, Blockchain, IoT GDPR Compliance AT A Glance: 6 AI Blockchain IoT Convergence System Technology & Business Development: convergence-system-technology-business-development-239302652
  7. 7. 7 ©2020 TechIPm,LLC All RightsReserved manufacturing and transportation, consumer electronics products, cybersecurity systems, data marketplace, electric grids, financial services including insurance, healthcare services, public services, retail, real estate, sharing services, smart home, industrial machinery, smart factory, supply chain management, hospitality, social networking service, sports applications, telecommunications, and tourism.
  8. 8. 8 ©2020 TechIPm,LLC All RightsReserved 1. Blockchain-basedPrivacy-Preserving FederatedLearning System A federated learning system trains a machine learning model based on data generated from large numbers of users interacting with their devices while the users maintain their data locally in their devices. Each data owner (e.g. federated learning participant) only provides locally trained machine learning model to a trusted third party (aggregator) for a single consolidated and improved global model. The global machine learning model is then sent back to user devices iteratively for updating the local machine learning model. Such collaboration among the federated learning participants lead to more accurate machine learning model than any party could learn in isolation. The federated learning system is a collaborative machine learning method which alleviates privacy issue by performing the machine learning training process in a distributed manner, without the need of centralizing private data. Federated learning systems, however, provide insufficient data privacy. To protect the privacy of the datasets, federated learning systems need to also consider inferences derived from the machine learning process and information that can be traced back to its source in the resulting trained model. The conventional attempts to ensure adequate data privacy in federated learning systems have resulted in poorpredictive performance of the resulting model. For example, federated learning systems using local differential privacy7 can result in the generation of an 7 Typical privacy-preserving techniques for machine learning include homomorphic encryption, multiparty computation, and differential privacy.
  9. 9. 9 ©2020 TechIPm,LLC All RightsReserved abundant amount of noise, which can deteriorate machine model performance. To provide additional measures for privacy protection, blockchain can be integrated to form decentralized federated learning systems. IBM’s patent application US20200394552 illustrates a blockchain-based federated learning system. In the blockchain-based federated learning system, client nodes of the blockchain network act as federated learning participants. Each training participant client trains a machine learning model using a stochastic gradient descent and its variants. Stochastic gradient descentis a standard optimization technique used in training machine learning models. This technique involves computation of the so called "gradients" of a particular loss function defined on the training dataset and the type of machine learning model involved. In a typical machine learning training process, one starts from an initial machine learning model and updates the model iteratively based on a stochastic gradient descent calculated in each step of iteration. The role of the blockchain-based federated learning system is to ensure that the model is being properly trained by verifying gradients calculated by each federated learning participant at each step of iteration. As an implementation example of the blockchain-based federated learning system, the Hyperledger Fabric blockchain network is used for fast verification of machine learning training procedures. In conventional Hyperledger Fabric blockchain networks, the consensus mechanism to verify gradient descentupdates involves multiple endorsing peer nodes (endorsers) performing the same gradients computation comparing the results with the gradients calculated by each federated learning participant at for each iteration as part of a smart contract on
  10. 10. 10 ©2020 TechIPm,LLC All RightsReserved blockchain (chaincode). The endorsers arrive at the consensus if and only if a specified number of them agree on the results. This is a wasteful and highly inefficient approach. Thus, a significantly more efficient and computationally light approach is needed to be developed. The example implementation of the blockchain-based federated learning system is based on approximate and provable correct verification protocols instead of re-compute processesfor providing efficiently verifiable consensus mechanisms. The example implementation also can enable real-time auditability by the real-time computation guarantees. The example implementation is based on a new type of smart contract that accompanies the machine learning training procedure. To this end, smart contracttransactions have relevant details about parts of training data and machine learning model updates. Instead of performing the computationally expensive model training step again, an endorser verifies the training computation using the metadata of the smart contract transactions. Figure 2 illustrates a block diagram of the example implementation of the blockchain-based federated learning system. The training participant network 112 includes M training participant clients 104, each including M training datatsets 108. The training aggregator 132 collects gradient calculations 136 from the training participants 104 and combines them to create an aggregate model. Model parameters 136 specify a machine learning model in mathematical terms. The training aggregator 132 is a trusted blockchain node. The training aggregator generates verify gradient transaction proposals 124 in responseto the received gradient calculations 136.
  11. 11. 11 ©2020 TechIPm,LLC All RightsReserved Figure 2. Block diagram of the example implementation of the blockchain-based federated learning system.
  12. 12. 12 ©2020 TechIPm,LLC All RightsReserved Figure 3 illustrates a block diagram of a gradient calculation verification subsystem 150 of the blockchain-based federated learning system. The gradient calculation verification subsystem 150 includes endorsers 116. The endorsers 116 can access the independent auditor 154 that includes the verify gradient smart contract120. The independent auditor 154 is an auditing entity responsible in verifying the progress of a machine learning training process. Endorsers 116 refer to the independent auditor 154 to execute the verify gradient smart contract 120 and verify gradient computation steps. In responseto receiving the gradient computation transaction proposals 124 from the training participant network 112, the endorsers forward the gradient computation transaction proposals 162 to the independent auditor 154. The independent auditor 154 executes the verify gradient smart contract 120 on the received forwarded gradient computation transaction proposals 162. The verification results 166 include endorsements or rejected transaction proposals, and are returned to the endorsers 116. The endorsed verification results are eventually included in the shared ledger 158 of the gradient calculation verification subsystem150.
  13. 13. 13 ©2020 TechIPm,LLC All RightsReserved Figure 3. Block diagram of a gradient calculationverification subsystemof the blockchain-basedfederated learning system.
  14. 14. 14 ©2020 TechIPm,LLC All RightsReserved 2. Blockchain-basedDecentralizedIoT & AI Data Marketplace Interconnected IoT devices can generate a huge amount of date that can be used for IoT applications. IoT data marketplace can connect IoT data sellers and buyers so that IoT data can be collected, processedand finally consumed by different parties for IoT applications. Blockchain enables a trusted IoT data marketplace that allows secure and anonymous trading of IoT data. The decentralized nature of the data marketplace based on blockchain means that any participant who qualifies can enter the marketplace as a data seller or a data buyer. Decentralization also means that there is no central authority to regulate the participants of the market. There is no central data repository. The data sellers are the owners of, and remain in full control over, their data. Grandata Inc's patent application US20200058023 illustrates a blockchain-based decentralized data marketplace that can trade IoT data and other types of data. An example of a data buyer is a company who would like to train its own machine learning models using data purchased via the decentralized data marketplace. Figure 4 illustrates a block diagram of the blockchain-based decentralized data marketplace and protocolfor exchanging data within the marketplace. Example participants of the data marketplace 100 include a data buyer 102, a data seller 104, and a notary 106. Each participant is associated with a correspondingcomputing device 110/112/114 that can be used for access the data marketplace 100 by virtue of a client application 108.
  15. 15. 15 ©2020 TechIPm,LLC All RightsReserved Figure 4. Block diagram of the blockchain-baseddecentralizeddata marketplace and protocolfor exchanging data within the marketplace.
  16. 16. 16 ©2020 TechIPm,LLC All RightsReserved The notary 106 validates the data quality and trustworthiness (e.g., data is not falsified or fabricated) that is being offered in the marketplace before the data is purchased by a data buyer. The notary can access to “ground truth” data that is usable to validate the offered data for accuracy. For example, in case of a data seller who wants to sell his/her financial transaction data, the seller's bank is an ideal notary becausethe bank already has the financial transaction data and can validate the offered data. The data marketplace can provide: (i) the infrastructure for a exchange; (ii) mechanisms for trading data to be evaluated and valued; (iii) incentives for participants to ensure that data is trustworthy and to provide quality data. The data marketplace also can be a privacy-preserving marketplace by allowing users to sell private data while providing them with privacy guarantees including (i) participants' anonymity; (ii) transparency over data usage; and (iii) control over data usage. Participants can register with the smart contracts so that the smart contracts 118 can be utilized by the participants. In responseto registering with the smart contract, a participant receives an identifier associated with that smart contract. The data exchange smart contract 118(1) is used by data buyers to create data orders for requested data that the buyers are interested in buying. Thus, it provides a querying system for buyers to communicate their data requirements by placing data orders on the blockchain. The use of on-chain operations provides a secure mechanism for exchanging data. The data exchange smart contract maintains references to the data orders created by data buyers so that the data orders are accessible to other market participants.
  17. 17. 17 ©2020 TechIPm,LLC All RightsReserved The batch payments smart contract 118(2) provides a payment protocolthat enables the direct transfer of payments between participants. The batch payments smart contract manages the costs ofsmart contract transactions by batching multiple payments associated with the smart contract transaction together into a single batch of payments, and performing a single smart contract transaction to pay potentially multiple participants, which helps to reduce transaction costs associated with operations in the blockchain. The marketplace protocolsetups the data exchange procedure and payments for that data within the marketplace. It is executed partly “on-chain” and partly “off-chain”. On-chain operations (denoted by the dashed lines as indicated in the Key 116) are performed with respect to smart contracts 118 of the blockchain network. Off-chain operations (denoted by the solid lines as indicated in the Key 116) are performed independent of the smart contract. Figure 4 illustrates an example marketplace protocolfor the exchange of data between a data buyer and a data seller, which is partitioned into separate phases: a setup phase, and a transaction phase. At step 1, during a setup phase, a buyer device receives user input from a data buyer to create a new data order 122 for requested data. The data buyer can indicate, in the data order, the intended audience and the requested data, which can both be specified using a data ontology 120. The data ontology is a publicly available document that formalizes naming, definition, structure and relationships for the marketplace's data and can be used as a reference to generate
  18. 18. 18 ©2020 TechIPm,LLC All RightsReserved audiences and data requests. The creation of the data order causes an event to be emitted via the blockchain so that participants of the marketplace 1are aware of this new data order. With the data order created in the data exchange smart contract, the notary device 114 provides a notification of this data order, and the notary indicates its agreement to validate requested data for the newly-created data order by providing user input to the notary device. In response to this user input, the notary is listed as one of the available notaries for auditing the exchange of data associated with the newly-created data order. The notary device, using its client application 108, generates a master key that is usable to encrypt cryptographic keys generated by seller devices of sellers who indicate that they are interested in selling their data to the data buyer in fulfillment of the data order. The notary device also generates a lock that can be used by the buyer with the batchpayments smart contract during a transaction phase. The lock is sent to the public address of the buyer, which is specified in the data order. At step 2, sellers can monitor data orders that have been created using the data exchange smart contract in order to look for opportunities where they match the audience, agree on the requested data, acceptthe price offered by the data buyer, acceptthe terms and conditions of data use, and acceptone of the suggested notaries in the set of notaries.
  19. 19. 19 ©2020 TechIPm,LLC All RightsReserved At step 3a, during a transaction phase, when the seller 104 selects one of the data orders, the seller device 112 encrypts relevant data 124 owned by the seller using a cryptographic key. The seller device sends the encrypted data and the cryptographic key which was used to encrypt the data to a public address of the notary. At step 3b, the seller device sends the encrypted data to the public address of the data buyer 102. The public address of the data buyer is determined from the selected data order. The seller device sends the encrypted data without sending the cryptographic key so that the data remains inaccessible to the data buyer until the notary reveals a master key. The steps 3a and 3b are examples of “off-chain” operations. At step 4, the buyer device sends, to a public address of the notary, an identifier of a data seller that the buyer has selected, as well as a hash of the encrypted data provided by that seller who selected the data order. At step 5, the notary can validate the seller's data, encrypt the cryptographic key of the seller using a master key. The notary also can verify that it has the same seller data as the buyer has in his/her possessionbased on the hash value the buyer device sent to the public address of the notary. After the notary performs the notarization and approves the data, at step 5, the notary device sends, to the public address ofthe data buyer, the master key and the notarization result. At step 6, the buyer device executes instructions to call a method of the batch payments smart contract. The method specifying an identifier(s) of a seller(s) and an identifier of the notary to authorize respective payments to be made to respective accounts of the data seller(s) and the notary. At step 7, the notary executes instructions to call a method of the batch payments smart contract 118 to reveal the master key.
  20. 20. 20 ©2020 TechIPm,LLC All RightsReserved At step 8a, the seller device executes instructions to call a method of the batch payments smart contract to receive a batch of payments that includes the payment for the data sold to, and purchased by, the data buyer. Similarly, at step 8b, the notary device executes instructions to call the method of the batch payments smart contractto receive a batch of payments that includes the payment for notarizing the data purchased by the data buyer. This method can be called at any time by the devices of the seller and the notary. IBM’s patent application US20190287027 illustrates a blockchain based AI data/model marketplace that can keep the data hidden and secure while still enabling a model to be built. AI data/model consumers can interact with the AI data/model marketplace to obtain additional data/models to improve accuracy, efficiency, etc., of their AI model without having to generate the data on their own. However, proprietary data is sensitive and often protected data of clients, trade secrets, financial, and other types of data that should not be exposed to outside parties. Figure 5 illustrates a block diagram of a blockchain based AI data/model marketplace. The AI marketplace 100 includes a consumer 11, a consumer node 110, an aggregator node120, a plurality of producernodes 130, and a blockchain node 140. The blockchain node 140 can store a blockchain that can be accessed byany of the other nodes in the AI marketplace that are connected through the network 150.
  21. 21. 21 ©2020 TechIPm,LLC All RightsReserved Figure 5. Block diagram of a blockchain based AI data/model marketplace. Figure 6 illustrates a communication sequence between nodes of the AI marketplace. The consumer node 281 initiates a new AI project by transmitting a request to the aggregator 282 node. The request includes an initial data
  22. 22. 22 ©2020 TechIPm,LLC All RightsReserved set 290 of the consumer node (a validation set) which is hashed using a locality preserving hash function and unreadable to the aggregator node 120, metadata of the initial data set, and desired AI model. The request is stored on blockchain 220. Figure 6. Communication sequence between nodes of the AI marketplace.
  23. 23. 23 ©2020 TechIPm,LLC All RightsReserved In 291, the aggregator node retrieves the hashed summary either directly from the consumer node or from the blockchain, and generates a broadcasthash summary transaction on the blockchain. The hash summary includes the hashed initial data set, the metadata, and any other information about the AI model. The hash function used by the aggregator node is different from the hash function used by the consumer and any of the producernodes. In 292, the first producernode 283 and the second producernode284 detect the new AI project based on the broadcasthash summary transaction and execute an improvement value function on the hashed initial data to determine how much value data of a respective producernode can provide to the initial data set. Here, the improvement value can be determined based on how similar the producerdata is to the consumer data. In 293 and 294, the first and second producernodes submit their respective bids including value to be provided. The bids can be submitted directly to the aggregator node or they can be stored on the blockchain and detected by the aggregator node. The aggregator node then selects a winning bid based on the improvement value of the different bids, and notifies the winning producernode in 296. The winning producernode then generate a hash data set of its data using the locality-preserving hash which encodes the data while enabling an improvement value to be calculated/verified the aggregator node, and transmits the hashed data to either the aggregator node or the blockchain, in 297.
  24. 24. 24 ©2020 TechIPm,LLC All RightsReserved Upon successfulverification of the improvement value provided from the producernode within the bid 293 based on the hashed data received in 297, the aggregator node updates the hashed initial data set and metadata stored on the blockchain (an initial summary) with the hashed data of the winning producer node to generate an updated summary, in 298. The aggregator node iteratively solicit additional bids from the producernodes until no more winning bids are left or some other condition such as the AI model is completed, the expiration of period of time, etc. The blockchain-based AI marketplace enables distributed composition of models and data with fair and trustable value attribution. Consumers can pick and combine AI models and data for their given need. In addition, producers can monetize AI models and data where more useful models can receive higher value. A combination of locality preserving hash-exchanges and blockchain enables composition with value attribution. The system enables, simultaneously, consumers to determine how best to composeexisting data/models, producers to be compensated for their AI moels/data used, and privacy of the consumer's and the producer's assets.
  25. 25. 25 ©2020 TechIPm,LLC All RightsReserved 3. Blockchain-basedTrustworthyAI & IoT Systems A lack of track and trace data used for a AI model can lead to vulnerabilities that can be exploited in different types of attacks on the AI model and that can produceinferences that are susceptible to forms of discrimination and bias. Another known issue with current AI models is that it is impossible to know how a deep learning model arrives at a prediction or decision with respect to performing the specific task. In other words, a deep learning process canbe thought of as a “black box,”to which input is provided to achieve a desired output. While the output may be independently verified, to within a measurable degree of statistical probability, as being accurate or inaccurate, understanding how the deep learning is used the input to arrive at the output is more complicated (e.g., Explainable AI (XAI)). Acronis International GmbH's patent application US20190228006 illustrates a blockchain-based machine learning anti-discrimination verification system. Figure 7 is a block diagram depicting operations for verifying discrimination or bias of a machine learning operation. The machine learning (ML) model has been trained by the training data set 112. The machine ML module 102 applies the ML model to the input data set 106 to generate an initial prediction 201. Then, the ML module determines whether the initial prediction violates a nondiscrimination policy based on an evaluation data set (e.g., test set). If so, the ML module modifies the initial prediction, using a corrective model 202, to generate a corrected prediction 205 based on a corrective algorithm. The corrective model provides an offset or coefficient factor to be applied to the initial prediction to calculate the corrected prediction.
  26. 26. 26 ©2020 TechIPm,LLC All RightsReserved In another implementation aspect, the ML module has been trained the ML model using a corrective input data set 204 to achieve a requested level of non-discrimination by the ML model. The corrective input data set includes multiple inputs selected to modify the internal rules of the ML model such that a level of discrimination in relation to a protected attribute (e.g., race) is removed or reduced. Figure 7 Block diagramdepicting operations for verifying discrimination or bias of a machine learning operation.
  27. 27. 27 ©2020 TechIPm,LLC All RightsReserved The ML module generates a blockchain transaction data structure 114 and transmit the transaction to the blockchain network 103. The blockchain transaction data structure includes a field 210 that references a prior transaction data structure using a cryptographic hash, an identifier specifying the prediction 205 associated with the transaction data structure. The blockchain transaction data structure also includes a field 212 that is a state of the ML model at the time of generating the prediction, a copyof the input data set 106, and an indication 214 of a correction by the ML model. If the correction to the ML model was the use of the corrective model, the indication 214 of the correction includes a copyof the initial output by the ML model prior to correction (e.g., prediction 201), a state of the corrective algorithm 202, and a result (prediction 205) of the corrective algorithm 202. If the correction to the ML model involved a dedicated training using a corrective training data set 204, the indication of the correction includes an indication that the ML model has been verified using an evaluation data set subsequent to training using the corrective input data set. At a later time, blockchain transaction data structures can be retrieved from the blockchain network 103, and used to validate compliance of a nondiscrimination policy based on the received second transaction. For example, the state of the ML model 110 and corrective model 202 can be used to re-create the prediction and ML output under review.
  28. 28. 28 ©2020 TechIPm,LLC All RightsReserved IBM's patent application US20190165949 illustrates a blockchain-based trustworthy IoT system. Blockchain assigns a unique transaction key to each respective IoT device so that the blockchain can verify whether the IoT data sent to the blockchain are from a trusted source. The IoT data also can be anonymized by the blockchain system. Thus, the blockchain maintains a record of where and who provided the data based on the unique transaction key signature, while also shielding the user/device that captured the data. In addition, the blockchain can remove details from the data captured that is not relevant to a particular event. For example, the anonymization process extracts specific details of an event while preventing other details from being revealed thereby maintain privacy of individuals out in public environments as well as maintaining a privacy of the user or the entity that captured the IoT data.
  29. 29. 29 ©2020 TechIPm,LLC All RightsReserved 4. Blockchain-basedDecentralizedParallelEdge Machine Learning Efficient AI model building requires large volumes of data. While distributed computing has been developed to coordinate large AI computing tasks using multiple computers, applications to large scale machine learning (ML) problems is difficult: For example, in distributed AI computing environments (e.g., Interconnected IoT sensors in a smart factory, Interconnected IoT devices for smart mobility services, connected vehicles), the accessibility of large and sometimes private training datasets across the distributed devices can be prohibitive and changes in topology and scale of the network over time makes coordination and real-time scaling difficult. HPE’s patent application US20190332955 illustrates a blockchain based decentralized machine learning that is performed at blockcahin nodes where local training datasets are generated to build AI models. The blockchain is used to coordinate decentralized machine learning over a series of iterations. Foreach iteration, a distributed ledger is used to coordinate the nodes. Each blockchain node can participate in a consensus decision to enroll another physical computing node to participate in the first iteration. The consensus decision applies only to the first iteration and cannot register the second physical computing node to participate in subsequentiterations. Upon registration of a specified number of blockchain nodes required for an iteration of training, each node obtains a local training dataset accessible locally but not accessible at other ones of the physical computing nodes in the blockchain network. Each participant node trains the first local AI model based on the local training dataset during the first iteration, and obtains at least the first training parameter based on the first local model. In this manner,
  30. 30. 30 ©2020 TechIPm,LLC All RightsReserved each participant node trains on data that is locally accessible but cannot be shared with other nodes. Each participant nodegenerates a blockchain transaction comprising an indication that the participant nodeis ready to share the first training parameter, and transmit the first training parameter to a master node. The master node generates a new transaction to be added as a ledger block to each copyof the distributed ledger based on the indication that the first physical computing node is ready to share the first training parameter. The master node can be elected from among the participant nodes by consensus decision. The master node obtains the training parameters from each of the participant nodes, and creates final training parameters based on the obtained training parameters. Upon generation of the final training parameters, the master node broadcasts an indication to the blockchain network that the final training parameters are available and relinquishes status as a master node for the current iteration. Each participant node obtains, from the blockchain network, the final training parameters that were generated by the master node based on the first training parameter and at least the second training parameter generated at, and shared from, the second physical computing node. Each participant node then applies the final training parameters to the first local model. Smart contracts can enforce node participation in the iteration of AI model building and parameter sharing, as well as provide logic for electing a node that serves as a master node for the iteration. The master node obtains the AI
  31. 31. 31 ©2020 TechIPm,LLC All RightsReserved model parameters from the nodes and generates final parameters based on the obtained parameters. The master node writes its state to the distributed ledger indicating that the final parameters are available. Each node can discover the master node's state and obtain and apply the final parameters to its local model via its copyof the distributed ledger, thereby learning from other nodes. Computing power at the IoT edge devices can serve as computing nodes to perform model training. These IoT edge nodes can be placed at or near the boundary of an IT infrastructure where the real world interacts with large information technology infrastructure. For example, autonomous vehicles can include more than one computing device that can communicate with fixed server assets. Another example includes real-time traffic management in smart cities, which divert their data to a data center. Because decentralized machine learning occurs over multiple iterations and different sets of nodes can enroll to participate in any iteration, the decentralized model building activity can dynamically scaled as the availability of nodes changes. Furthermore, dynamic scaling does not cause degradation of model accuracy. By using a distributed ledger to coordinate activity and smart contracts to enforce synchronization by not permitting stale or otherwise uninitialized nodes from participating in an iteration, the stale gradients problem can be avoided. Use of the decentralized ledger and smart contracts also can make the system fault-tolerant. Node restarts and other downtimes can be handled seamlessly without loss of model accuracy by dynamically scaling participant nodes and synchronizing learned parameters.
  32. 32. 32 ©2020 TechIPm,LLC All RightsReserved Figure 8 illustrates an example process 300 of an iteration of model building using blockchain according, where operations 302-312 and 318 are applicable to participant nodes and operations 314, 316, and 320 are applicable to master node. In the operation 302, each participant node enrolls to participate in an iteration of model building. In an implementation, the smart contracts encoderules for enrolling a node for participation in an iteration of model building. In the operation 304, each of the participant nodes executes local model training on its local training dataset. Figure 8. Example process of an iteration of model building using blockchain.
  33. 33. 33 ©2020 TechIPm,LLC All RightsReserved In the operation 306, each of the participant nodes generates local parameters based on the local training and keeps them ready for sharing with the blockchain network. In the operation 308, each participant node checks in with the blockchain network for co-ordination. In the operation 310, participant nodes collectively elect a master node for the iteration. For example, the smart contracts can encoderules for electing the master node. In the operation 312, participant nodes that are not a master node periodically check the state of the master node to monitor whether the master node has completed generation of parameters based on local parameters shared by participant nodes. In the operation 314, the master node enters a sharing phase in which some or all participant nodes are ready to share their local parameters. In an implementation, the master node writse the transactions as a block on the distributed ledger 42 using a blockchain API. In the operation 316, the master node signals completion of the combination. Forexample, the master node transmits a blockchain transaction indicating its state (that it combined the local parameters into the final parameters). In the operation 318, each participant node obtains and applies the final parameters on their local models. In the operation 320, the master node signals completion of an iteration and relinquishes controlas master nodefor the iteration. Figure 9 illustrates an example process 400 at a node that participates in an iteration of model building using blockchain. In the operation 402, a participant nod enrolls with the blockchain network to participate in an iteration
  34. 34. 34 ©2020 TechIPm,LLC All RightsReserved of model training. In the operation 404, the participant node participates in a consensus decision to enroll a second node that requests to participate in the iteration. The consensus decision can be based on factors such as, for example, the requesting node's credentials/permission, current state, whether it has stale data, and/or other factors. In the operation 406, the participant node obtains local training data. The local training data can be accessible at the participant node, but not accessible to the other participant nodes. In the operation 408, the participant node trains a local model based on the local training dataset. Such model training is based on the machine learning that is executed on the local training dataset. In the operation 410, the participant node obtains the local training parameter. For example, the local training parameter can be an output of model training at the participant node. In the operation 412, the participant node generates a blockchain transaction that indicates it is ready to share its local training parameters. In the operation 414, the participant node provides its training parameters to a master node, which is elected by the participant node along with other participant nodes in the blockchain network. In the operation 416, the participant node obtains final training parameters that were generated at the master node, which generated the final training parameters based on the training parameters provided by the participant node and other participant nodes for the iteration as well. In the operation 418, the participant node applies the final training parameters to the local model and updates its state (indicating that the local model has been updated with the current iteration's final training parameters).
  35. 35. 35 ©2020 TechIPm,LLC All RightsReserved Figure 9. Example process at a node that participates in an iteration of model building using blockchain.
  36. 36. 36 ©2020 TechIPm,LLC All RightsReserved Figure 10 illustrates an example process 500 at a master node elected to generate final training parameters based on training parameters from participant nodes in an iteration of model building using blockchain. In the operation 502, the master node generates a distributed ledger block that indicates a sharing phase is in progress. In the operation 504, the master node obtains blockchain transactions from participant nodes. These transactions include indications that a participant node is ready to share its local training parameters. In the operation 506, the master node writes the transactions to a distributed ledger block, and adds the block to the distributed ledger. In the operation 508, the master node identifies a location of training parameters generated by the participant nodes that submitted the transactions. In the operation 510, the master node generates final training parameters based on the obtained training parameters. Forexample, the master node merges the obtained training parameters to generate the final training parameters. In the operation 512, the master node makes the final training parameters available to the participant nodes. Each participant nodeobtains the final training parameters to update its local model using the final training parameters. In the operation 514, the master node updates its state to indicate that the final training parameters are available.
  37. 37. 37 ©2020 TechIPm,LLC All RightsReserved Figure 10. Example process at a master node elected to generate final training parameters.
  38. 38. 38 ©2020 TechIPm,LLC All RightsReserved 5. Predictive Maintenance Platform for Industrial Machine using Industrial IoT Heavy industrial environments, such as environments for large scale manufacturing (e.g., aircraft, ship, automobile manufacturing), energy production environments (e.g., oil and gas plants), energy extraction environments (e.g., mining), construction environments (e.g., construction of large buildings) involve highly complex machines and workflows, in which operators must account for a host of parameters and metrics in order to optimize design, development, deployment, and operation of different technologies in order to improve overall results. Many of the large industrial machines that require ongoing maintenance, service and repairs are involved in high stakes production processesand other processes, suchas energy production, manufacturing, mining, drilling, and transportation, that preferably involve minimal or no interruption. An unanticipated problem, or an extended delay in a service operation that requires a shutdown of a machine that is critical to such a process cancostthousands, or even millions of dollars per day. IoT enables automatic data collection in industrial environments and AI enables improved methods and systems using collected data to provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments. IoT sensors can collect, and AI can process data from industrial machines for predicting faults, anticipating needs for maintenance, and facilitating repairs
  39. 39. 39 ©2020 TechIPm,LLC All RightsReserved (predictive maintenance). Blockchain based distributed ledger can record and track maintenance transactions related to the industrial machine. Strong Force IoT’s patent application US20200133257 illustrates a system for detecting operating characteristics of an industrial machine. Figure 11 is a diagrammatic view that depicts portions of an overall view of an industrial IoT data collection, monitoring and controlsystem 100. The system 100 has a server based portion 10 of the industrial IoT system that can be deployed in the cloud or on an enterprise owner's or operator's premises. The server portion 10 includes network coding including self-organizing network coding and/or automated configuration. The network coding model is based on feedback measures, network conditions, or the like, for highly efficient transport of large amounts of data across the network to and from data collection systems and the cloud. Network coding provides a wide range of capabilities for intelligence, analytics, remote control, remote operation, remote optimization, various storage configurations and the like. The various storages include blockchain storage for supporting transactional data of the system. A mobile ad hoc network 20 forms a secure, temporal network connection 22 with a cloud 30 or other remote networking system so that network functions may occurover the mobile ad hoc network within the environment, without the need for external networks, but at other times information can be sent to and from the server portion 10. This allows the industrial environment to use the benefits of networking and controltechnologies, while also providing security, such as preventing cyber-attacks.
  40. 40. 40 ©2020 TechIPm,LLC All RightsReserved Figure 11. Diagrammatic view of an industrial IoT data collection, monitoring and control system.
  41. 41. 41 ©2020 TechIPm,LLC All RightsReserved The intelligent data collection system is deployed locally, at the edge of an IoT deployment, where heavy industrial machines are located. This includes various sensors 52, IoT devices 54, data storage capabilities (e.g., data pools 60, or distributed ledger 62), sensor fusion, and the like. The data pools 60 collect data published by machines or sensors that detect conditions of machines, such as for later consumption by local or remote intelligence. The distributed ledger system 62 distributes storage throughout the system. The on-device sensorfusion 80 analyzes data from multiple analog sensors 82 locally or in the cloud using a machine learning 84. The data marketplace 70 provides the available data that is collected in industrial environments, such as from data collectors, data pools, and distributed ledgers.
  42. 42. 42 ©2020 TechIPm,LLC All RightsReserved 6. AI+BlockchainSystemfor Car Sharing Service Ford’s patent application US20200134592 illustrates a blockchain based car sharing service. A matchmaking system uses AI to provide the best car sharing service based on clients’ and providers’ records and transaction history along with proximity and availability of the provider with respectto the client who made the car sharing request. Figure 12 shows a block diagram of an example system for the car sharing service. The system architecture 100 includes users (e.g., client 102) and service providers (e.g., provider 104). The client 102 and the provider 104 are linked via the system architecture to perform a car sharing service 116. Devices associated with the users are connected on a peer-to-peer network 112, where the user devices represent blockchain nodes on that peer-to-peer network.
  43. 43. 43 ©2020 TechIPm,LLC All RightsReserved Figure 12. Block diagram of an example system for the car sharing service. When a client 102 makes a request 103, a decentralized matchmaking algorithm 114 matchs the client 102 that made the request 103 to a service provider 104 on the peer-to-peer network 112. The decentralized matchmaking algorithm uses AI algorithms and techniques. The decentralized matchmaking algorithm use the client's or provider's records and transaction history along with proximity and availability of the provider with respectto the
  44. 44. 44 ©2020 TechIPm,LLC All RightsReserved client who made the request. The records and transaction history, proximity, availability, and other relevant information are stored on the peer-to-peer network in accordancewith a blockchain protocol113. Additionally, the transactions and records are stored and validated on the peer-to-peer network by using the blockchain protocol. The peer-to-peer network comprises multiple nodes on a public infrastructure or a private infrastructure. Further, the request is delivered to the provider who accepts the request 109. Accordingly, a payment 106 is facilitated between the client's digital wallet 105 and the provider's digital wallet 107. A validation 110 comprises automated validators that serve to validate the request, the service, and the payment for the service. The blockchain protocolincludes the smart contracts regarding details of the underlying business logic and transactional details (e.g., details related to the parties involved, terms of service, terms of payment, and the like in a digital contract format). The smart contracts serve to ensure that the provider will be paid by the client upon successfulcompletion of a service. Data stored on the peer-to-peer network under a blockchain protocolare distributed and replicated across the nodes of the peer-to-peer network, which enables transparency and additional tamper-resistance. A payment processing server can be used to process a payment between users (e.g., clients and providers). Figure 13 shows a diagram of an example use case application and process flow 200. A given user 205 requests a service 212 from a provider 207 on a local peer-to-peer network comprising multiple nodes 203. The provider
  45. 45. 45 ©2020 TechIPm,LLC All RightsReserved requests payment 214 to the user. The payments can be made using tokens (e.g., cryptocurrencies). The user of the local peer-to-peer network 210 can convert the tokes to currency 204 through a currency conversion service 211 provided by a third-party service-provider. The user rewards 213 can be determined based on the user's records (e.g., transaction history, reputation, and the like), which may be captured as data stored on the files written to the local peer-to-peer network in accordancewith the blockchain protocol. The analytics data 215 can be generated from an analysis of the local peer-to-peer network using AI-based algorithm (e.g., using machine learning). The analytics data can include usage trends, demographic analysis, environmental context data, and related trends, and the like. The analytics data provides insights to a given service provider such that it improves its service offerings.
  46. 46. 46 ©2020 TechIPm,LLC All RightsReserved Figure 13. Diagram of an example use case application and process flow.
  47. 47. 47 ©2020 TechIPm,LLC All RightsReserved 7. ConnectedAutonomous Vehicle Communication ManagementSystem Autonomous vehicles can determine surrounding environmental conditions using technology such as radar, laser, odometry, global positioning system, and computer vision. Control systems in an autonomous vehicle utilize the environmental information to controlthe autonomous vehicle as it autonomously navigates paths and obstacles, while abiding by relevant traffic signals and signs. Autonomous vehicles can be applied to public or ride-sharing transportation (e.g., self-driving taxis or other types of ride-sharing vehicles). A public or ride-sharing autonomous vehicle can provide transportation services for a large number of passengers per day. Thus, as autonomous vehicle s become more popular, autonomous vehicle operating safety with regard to other autonomous vehicles and non- autonomous vehicles is a critical matter that should be considered. IBM’s patent application US20200010080 illustrates a vehicle to vehicle communication management system to facilitate operational safety via risk assessment. Vehicle to vehicle communication can be used to facilitate operational safety of autonomous vehicles via risk assessment and context analysis. Forexample, operational safety focuses on safe lane passing in which one vehicle overtakes another vehicle on a given roadway can be considered. Autonomous vehicle’s IoT sensors (e.g., cameras, light sensors, audio sensors, LIDAR, radar, ultrasonic sensor, etc.) and vehicle to vehicle communications (e.g., 5G based V2X) can be used to aid in facilitating safe lane passing by detecting signals of a nearby autonomous vehicle or non- autonomous vehicle (e.g.,
  48. 48. 48 ©2020 TechIPm,LLC All RightsReserved alerting a danger ahead, requesting to overtake, allowing a vehicle to merge, driver signals, etc.) and contextual interpreting of signals for situational risk assessment. The assessmentof situational risks and contextual situations comprises analysis of road conditions, vehicle types, vehicle payload (e.g., number of passenger, weight of transported goods), road types, weather, need to pass (e.g., emergency), daytime versus nighttime driving, and road topology (e.g., curves, curve radii, accident history, hills, valleys, etc.). The risk assessmentcan employ real-time visual analytics and deep learning algorithms by scanning the road ahead or back of the given vehicle to attempt to ensure operational safety. The data exchanged between the given vehicle and the other vehicles can be managed via a blockchain-based system for data security measure. The autonomous vehicle can receive rating, points, or rewards based on how well the autonomous vehicle helped to reduce risks, gives appropriate signals for alerting accident location ahead, and/or nature/value of human interactions. The system can facilitate a mechanism for requesting an autonomous vehicle to pay to another autonomous vehicle for facilitating a pass, yielding to another vehicle via a blockchain- based cryptocurrency payment. Blockchain-based DID (decentralized identifier) for an autonomous vehicle can be used to form a decentralized IoT network, where items (or things) in the network are “smart devices” that are connected to the blockchain through their corresponding DID. This allows for institutional wide tracking of vehicles.
  49. 49. 49 ©2020 TechIPm,LLC All RightsReserved Figure 14 illustrates a methodology 800 for providing vehicle to vehicle communication to facilitate operational safety via risk assessment. In step 802, in a given computing node in a given vehicle, the method obtains at the given computing node data from at one of: (i) sensors on the given vehicle; (ii) computing nodes associated with other vehicles via at vehicle to vehicle communication protocol;and (iii) data sources along the path of the given vehicle. In step 804, the method utilizes at the given computing node a portion of the obtained data to compute a risk assessment for the given vehicle with respect to an operational safety level of a proposed vehicle action. In step 806, the method initiates or prevents at the given computing node the proposed vehicle action for the given vehicle based on the computed risk assessment.
  50. 50. 50 ©2020 TechIPm,LLC All RightsReserved Figure 14. Methodology for providing vehicle to vehicle communication.
  51. 51. 51 ©2020 TechIPm,LLC All RightsReserved 8. 5G-basedAI+Blockchain+IoTEdge Computing System In the coming years, it is expected that there will be a greater need for wirelessly accessible, relatively low-latency, relatively high-powered computing placed near the edge of networks. It is expected that various AI algorithms will need to process relatively high-bandwidth streams of data to output results in real-time after that data is acquired. Examples include processingvideos and other sensordata gathered by self-driving cars, autonomous drones, wearable computing devices, and other IoT sensors. In many cases, uploading this data to a traditional public cloud data center to process the data and to generate actionable commands or results is too slow, in part due to the amount of time taken to convey the data over relatively large geographic distances. This is due, in part, to the time consumed transmitting the data and results from the speed of light imposing limits on how fast information can be conveyed over large geographic distances. Additional delays arise from switching and routing equipment along the path and potential congestion. Accordingly, it is expected that there will be a need to distribute relatively high- performance computing facilities, such as data centers, over distributed geographic areas. Forexample, distributing the data centers every few miles in a metropolitan area, county, state, or country, rather than relying exclusively upon data centers that are geographically concentrated and serve, for example, a continent from a single geographic location. Edge computing refers to the transition of compute and storage resources closer to endpoint devices in order to reduce application latency, improve service capabilities, and improve compliance with security or data privacy
  52. 52. 52 ©2020 TechIPm,LLC All RightsReserved requirements. Edge computing provides a cloud-like distributed service that offers orchestration and management for applications among many types of storage and compute resources. As a result, some implementations of edge computing have been referred to as the edge cloud. Edge computing use cases in 5G mobile network settings have been developed for integration with multi-access edge computing (MEC) approaches, also known as mobile edge computing. MEC approaches are designed to allow application developers and content providers to access computing capabilities and an IT service environment in dynamic mobile network settings at the edge of the network. MEC technology has some advantages when compared to traditional centralized could computing environments. For example, MEC technology provides a service by service providers to user agent or a user equipment with a lower latency, a lower cost, a higher bandwidth, a closer proximity, and/or an exposure to real-time radio network and context information. Intel’s patent application US20200195495 illtstrates technological solutions for implementing a MEC-based system to realize 5G network slicing with blockchain traceability. The technological solutions integrates MEC with various types of IoT or Fog networking implementations as well as dynamic network slicing and resource utilization management. The technological solutions benefit a variety of use cases, such as 5G network communications for automotive devices, smart factory sensors, and smart healthcare devices.
  53. 53. 53 ©2020 TechIPm,LLC All RightsReserved The technological solutions include providing blockchain quality traceability of 5G network slice instances and virtual network services necessary for Service Provider Service Level Agreements (SLAs). The technological solutions techniques can be used for 5G network slicing to enable multiple owners, users, and applications to utilize a communication network in slice instances through the transfer of computing and communication resources. Blockchain traceability can be used to provide traceability and tracking (e.g., of resource usage and dynamic resource allocation during dynamic slice usage) to meet SLAs associated with services delivered in the 5G communication environment. The technological solutions include on-demand 5G network slice instance deployments with blockchain traceability for an informed 5G service supply chain that includes service providers, regulators, and certain 5G fixed and mobile subscribers. In this regard, the 5G network slice and virtual network service traceability requirements can be met while leveraging public key encryption hardware acceleration to perform and allow for on-demand 5G network slice instance creation, deployment, and re-configuration. Additionally, The technological solutions use AI-based network inferencing functions to perform resourcemanagement in connection with the 5G network slice instance configuration, deployment, and re-configuration. For example, AI-based network inferencing functions can be used to dynamically monitor and predict network resource utilization as well as detect changes in SLAs used in connection with 5G network slice instance management to trigger initial resource allocation as well as re-allocation of the network resources within a specific network slice instance or among a group of network slice instances.
  54. 54. 54 ©2020 TechIPm,LLC All RightsReserved Figure 15 illustrates a block diagram showing an overview of a configuration for edge computing, which includes a layer of processing referenced in many of examples as an edge cloud. This network topology can be extended through the use of 5G network slice instance management using blockchain traceability and AI-based resource management techniques. Figure 15. Block diagram showing an overview of a configuration for edge computing.
  55. 55. 55 ©2020 TechIPm,LLC All RightsReserved The edge cloud 110B is co-located at an edge location, such as the base station 140B, a local processinghub 150B, and a central office 120B. The edge cloud is located much closer to the endpoint data sources 160B (e.g., autonomous vehicles 161B, user equipment 162B, business, and industrial equipment 163B, video capture devices 164B, drones 165B, smart cities and building devices 166B, sensors and IoT devices 167B, etc.) than the cloud data center 130B. Compute, memory, and storage resources which are offered at the edges in the edge cloud are critical to providing ultra-low latency responsetimes for services and functions used by the endpoint data sources as well as reduce network backhaul traffic from the edge cloud toward cloud data center 130. Edge computing is performed at or closer to the edge of a network, typically through the use of a computing platform implemented at base stations, gateways, network routers, or other devices which are much closer to end point devices producing and consuming the data. Depending on the real-time requirements in a communications context, a hierarchical structure of data processing and storage nodes can be defined in an edge computing deployment. Figure 16 illustrates deployment and orchestration for virtual edge configurations across an edge-computing system operated among multiple edge nodes and multiple tenants. Specifically, Figure 16 depicts coordination of the first edge node 122C and the second edge node 124C in an edge-computing system 100C, to fulfill requests and responses for various client endpoints 110C from various virtual edge instances. The virtual edge instances provide
  56. 56. 56 ©2020 TechIPm,LLC All RightsReserved edge compute capabilities and processing in an edge cloud, with access to a cloud/data center 140C for higher- latency requests for websites, applications, database servers, etc. Thus, the edge cloud enables coordination of processing among multiple edge nodes for multiple tenants or entities using 5G network slice instance management using blockchain traceability and AI-based resource management. Figure 16. Deployment and orchestrationfor virtual edge configurations across anedge-computing system.
  57. 57. 57 ©2020 TechIPm,LLC All RightsReserved In the figure, these virtual edge instances include the first virtual edge 132C, offered to a first tenant (Tenant 1), which offers the first combination of edge storage, computing, and services; and the second virtual edge 134C, offering a second combination of edge storage, computing, and services, to a second tenant (Tenant 2). The virtual edge instances 132C, 134C are distributed among the edge nodes 122C, 124C, and include scenarios in which a request and responseare fulfilled from the same or different edge nodes. The configuration of each edge node 122C, 124C to operate in a distributed yet coordinated fashion with shared memory access occursbased on edge provisioning functions 150C and resource, blockchain, and slice management (RBSM) functions 170C. The functionality of the edge nodes 122C, 124C to provide coordinated operation for applications and services, among multiple tenants, occurs based on orchestration functions 160C. The RBSM functions performs the functionalities of the AI-based resource management module 160, the blockchain traceability management module 162, and the slice management module 164. The RBSM functions are used to determine (or predict) network resource utilization using AI-based network inferencing functions, and configure the network slice instances based on the network resource utilization. The edge computing system can be extended to provide orchestration of multiple applications through the use of (Docker) containers, in a multi-owner, multi-tenant environment. A multi-tenant orchestrator can be used to perform key management, trust anchor management, and other security functions related to the provisioning and
  58. 58. 58 ©2020 TechIPm,LLC All RightsReserved lifecycle of the trusted slice. Accordingly, the edge computing system can fulfill requests and responses for various client endpoints from multiple virtual edge instances from a cloud or remote data center. The use of these virtual edge instances supports multiple tenants and multiple applications (e.g., AR/VR, enterprise applications, content delivery, gaming, compute offload) simultaneously. Further, there can be multiple types of applications within the virtual edge instances (e.g., normal applications, latency-sensitive applications, latency-critical applications, user plane applications, networking applications, etc.). The virtual edge instances also can be spanned across systems of multiple owners at different geographic locations.
  59. 59. 59 ©2020 TechIPm,LLC All RightsReserved