IoT Smart Contract Generation via Deep Learning for Secure Transactions
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Solution Overview
Problem
Existing technologies face challenges in securing automated transactions between IoT devices, as these devices can be hacked, leading to unauthorized events and transactions.
Innovation Solution
A computing platform that monitors communication between IoT devices, extracts event features, and uses a deep learning engine to generate smart contracts, which are then verified on a distributed ledger to authorize secure transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated transactions between IoT devices are enabled, then productivity and convenience are improved, but security and reliability deteriorate due to potential hacking and unauthorized events
Solution Approach 1:
The patent introduces multiple intermediary components between IoT devices and the transaction processing system, including event processing requests that route through verification systems, smart contracts that mediate transaction rules, and distributed ledger nodes that act as trusted intermediaries. These intermediaries verify and validate transactions without preventing automation, thus maintaining productivity while improving security.
Solution Approach 2:
The system performs preliminary actions by pre-defining smart contracts that encode transaction rules and requirements before actual transactions occur. The distributed ledger maintains pre-established rules and authorization mechanisms, so when automated transactions happen, they inherently follow pre-validated security protocols, resolving the security concern while preserving automation benefits.
2Measurement precision
If deep learning engines and smart contract generation are implemented, then security and measurement precision are improved, but device complexity increases
Solution Approach 1:
The patent segments the complex verification system into distinct functional modules: event processing requests that extract features from IoT events, separate deep learning engines that analyze patterns, smart contract generation components that create verification rules, and distributed ledger systems that store and enforce contracts. This segmentation allows each component to specialize in one task, improving measurement precision while managing complexity through modular architecture.
Solution Approach 2:
The system employs universal components that perform multiple functions: the distributed ledger serves as both a transaction record and a smart contract repository, the deep learning engine both analyzes event features and generates compliance rules, and smart contracts simultaneously encode transaction terms and verification logic. This multi-functionality reduces overall system complexity while maintaining high verification precision.
3Reliability
If distributed ledger verification is performed for each transaction, then reliability and security are improved, but processing time and loss of time increase
Solution Approach 1:
The system performs preliminary action by pre-establishing smart contracts on the distributed ledger that contain all verification rules and authorization logic before transactions occur. When a transaction is initiated, the system doesn't create verification rules from scratch but rather references and executes pre-defined smart contracts, dramatically reducing verification time while maintaining full security and reliability checks.
Solution Approach 2:
The distributed ledger creates and stores copies of smart contracts and verification rules that can be rapidly replicated and executed across multiple nodes. Instead of centralized sequential verification, the system uses copied verification logic distributed across the network, enabling parallel processing that reduces overall verification time while maintaining the reliability of consensus-based validation.
Data Source
AI summary
Aspects of the disclosure relate to computing hardware and software for IoT event processing. A computing platform may monitor communication between an initiating IoT device and a service provider IoT device to detect an event processing request. The computing platform may extract, from the event processing request, event features. The computing platform may feed the event features into a deep learning engine, which may produce a smart contract corresponding to the event processing request. The computing platform may identify, using a distributed ledger, whether the event features comply with the smart contract. Based on identifying that the event features do comply with the smart contract, the computing platform may send, to an event processing system, authorization to process the event processing request, which may cause the event processing system to transfer funds from an initiating user to the service provider.