Khaldy Mohammad A Al, Nabot Ahmad, Al-Qerem Ahmad, Jebreen Issam, Darem Abdulbasit A, Alhashmi Asma A, Alauthman Mohammad, Aldweesh Amjad
Business Intelligence & Data Analytics, University of Petra, Amman, Jordan.
Department of Software Engineering, Al Zaytoonah University of Jordan, Amman, Jordan.
Sci Rep. 2025 Jul 15;15(1):25589. doi: 10.1038/s41598-025-09698-1.
This paper introduces a novel Reinforcement Learning-Based Hybrid Validation Protocol (RL-CC) that revolutionizes conflict resolution for time-sensitive IoT transactions through adaptive edge-cloud coordination. Efficient transaction management in sensor-based systems is crucial for maintaining data integrity and ensuring timely execution within the constraints of temporal validity. Our key innovation lies in dynamically learning optimal scheduling policies that minimize transaction aborts while maximizing throughput under varying workload conditions. The protocol consists of two validation phases: an edge validation phase, where transactions undergo preliminary conflict detection and prioritization based on their temporal constraints, and a cloud validation phase, where a final conflict resolution mechanism ensures transactional correctness on a global scale. The RL-based mechanism continuously adapts decision-making by learning from system states, prioritizing transactions, and dynamically resolving conflicts using a reward function that accounts for key performance parameters, including the number of conflicting transactions, cost of aborting transactions, temporal validity constraints, and system resource utilization. Experimental results demonstrate that our RL-CC protocol achieves a 90% reduction in transaction abort rates (5% vs. 45% for 2PL), 3x higher throughput (300 TPS vs. 100 TPS), and 70% lower latency compared to traditional concurrency control methods. The proposed RL-CC protocol significantly reduces transaction abort rates, enhances concurrency management, and improves the efficiency of sensor data processing by ensuring that transactions are executed within their temporal validity window. The results suggest that the RL-based approach offers a scalable and adaptive solution for sensor-based applications requiring high-concurrency transaction processing, such as Internet of Things (IoT) networks, real-time monitoring systems, and cyber-physical infrastructures.
本文介绍了一种基于强化学习的新型混合验证协议（RL-CC），该协议通过自适应边缘-云协调，彻底改变了对时间敏感的物联网交易的冲突解决方式。在基于传感器的系统中，高效的事务管理对于维护数据完整性以及在时间有效性约束内确保及时执行至关重要。我们的关键创新在于动态学习最优调度策略，该策略在不同工作负载条件下，将事务中止降至最低，同时使吞吐量最大化。该协议由两个验证阶段组成：边缘验证阶段，在此阶段，事务根据其时间约束进行初步冲突检测和优先级排序；以及云验证阶段，在此阶段，最终冲突解决机制确保在全球范围内的事务正确性。基于强化学习的机制通过从系统状态学习、对事务进行优先级排序以及使用考虑关键性能参数（包括冲突事务数量、中止事务成本、时间有效性约束和系统资源利用率）的奖励函数动态解决冲突，不断调整决策。实验结果表明，与传统并发控制方法相比，我们的RL-CC协议将事务中止率降低了90%（两阶段锁为5%对45%），吞吐量提高了3倍（300TPS对100TPS），延迟降低了70%。所提出的RL-CC协议通过确保事务在其时间有效性窗口内执行，显著降低了事务中止率，增强了并发管理，并提高了传感器数据处理效率。结果表明，基于强化学习的方法为需要高并发事务处理的基于传感器的应用提供了一种可扩展且自适应的解决方案，如物联网（IoT）网络、实时监测系统和信息物理基础设施。
