Web 4.0 platforms introduce intelligent, decentralized agents and real-time interactions that increase both utility and attack surface. This paper presents a comprehensive, reproducible AI blockchain hybrid designed to (1) detect SQL injection attacks at scale using a textual TFIDF + machine-learning pipeline, (2) incorporate reputation signals from a real-world Bitcoin OTC trust dataset to compute a TrustAlert Score (TAS) that prioritizes alerts and guides logging policy, and (3) record privacy-preserving audit digests on blockchain, optionally attested via a zero-knowledge proof (ZKP) pipeline. We evaluate the system on a 148 k SQL corpus and Soc-SignBitcoinOTC reputation data. The detection module achieves high accuracy (0.9797), F1 (0.9807), and ROCAUC (0.9972). TAS effectively separates malicious from benign events (TAS AUC = 0.96) and enables selective on-chain logging to reduce cost. Blockchain benchmarks indicate that local (Ganache) throughput is adequate for batched logging, while public testnet (Goerli) exhibits significantly higher latency and gas usage; ZKP attachments further increase on-chain cost. We discuss practical deployment patterns (digest-only on-chain, Layer2 batching), propose evaluation extensions (transfer learning, adversarial red-teaming), and release reproducible scripts for the community.

The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1) a DRL-based dynamic priority assignment mechanism that learns from system behavior, (2) a hybrid concurrency control protocol combining local edge validation with global cloud coordination, and (3) an integrated mathematical model that formalizes sensor-driven transactions across edge-cloud architectures. Extensive simulations across diverse workload scenarios demonstrate significant quantitative improvements: 40% latency reduction, 25% throughput increase, 85% resource utilization (compared to 60% for heuristic methods), 40% reduction in energy consumption (300 vs. 500 J per task), and 50% improvement in scalability factor (1.8 vs. 1.2 for EDF) compared to state-of-the-art heuristic and meta-heuristic approaches. These results establish the framework as a robust solution for large-scale IoT and autonomous applications requiring real-time processing with consistency guarantees.