A Computational Modeling Framework for Verifiable Computation Offloading in Resource-Constrained IoT Smart Contract Systems Using Zero-Knowledge and Fuzzy Logic

This study presents a computational modeling framework for efficient and secure computation offloading in Internet of Things (IoT)-enabled smart contract systems. The integration of IoT, edge computing, and blockchain introduces significant challenges, including limited device capacity, high verification cost, and scalability constraints. Existing blockchain verification approaches depend on computationally intensive cryptographic operations that are inefficient for resource-constrained IoT devices, resulting in increased latency, energy consumption, and transaction costs. To address these issues, this study proposes the Zero-Knowledge Fuzzy Logic Offloading and Rollup (Z-FLOR) framework, an adaptive and energy-efficient model designed to enable secure and verifiable computation in IoT-based smart contract systems. The proposed framework integrates three key components. First, a zero-knowledge proof-based verification model using the Groth16 zkSNARK module generates compact and privacy-preserving proofs that enable fast and reliable verification. Second, a Fuzzy Logic–Driven Energy-Aware Offloading module dynamically allocates computational tasks between IoT devices, edge servers, and cloud platforms based on energy availability, network delay, and device reliability. Third, an Optimistic Rollup Verification module aggregates proofs off-chain and submits them in batches to reduce gas costs and enhance scalability. Extensive simulation and experimental evaluation across diverse IoT scenarios demonstrate the effectiveness of the proposed computational framework. Results indicate that Z-FLOR achieves 99.7% verification accuracy and 98.9% proof compression efficiency, while gas cost analysis indicates gas cost reductions in the range of 80%–98%. Z-FLOR additionally achieves a 44.0% reduction in latency, 51.0% savings in gas costs, and 38.0% energy consumption compared to baseline approaches. These findings highlight the capability of the proposed approach to serve as a scalable and energy-efficient modeling solution for secure IoT smart contract execution in decentralized environments.