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Related Experiment Video

Updated: Sep 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

664

Adaptive conflict resolution for IoT transactions: A reinforcement learning-based hybrid validation protocol.

Mohammad A Al Khaldy1, Ahmad Nabot2, Ahmad Al-Qerem3

  • 1Business Intelligence & Data Analytics, University of Petra, Amman, Jordan.

Scientific Reports
|July 15, 2025
PubMed
Summary

This study introduces a Reinforcement Learning-Based Hybrid Validation Protocol (RL-CC) for Internet of Things (IoT) transactions. RL-CC significantly reduces transaction aborts and boosts throughput for time-sensitive sensor data processing.

Keywords:
Concurrency ControlConflict ResolutionEdge ComputingHigh-Concurrency SystemsHybrid Edge–Cloud ValidationInternet of ThingsIoT TransactionsReal-Time DatabasesReinforcement LearningTemporal Validity

Related Experiment Videos

Last Updated: Sep 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

664

Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Efficient transaction management is critical for sensor-based systems, especially in time-sensitive Internet of Things (IoT) applications.
  • Maintaining data integrity and timely execution within temporal validity constraints poses a significant challenge.

Purpose of the Study:

  • To introduce a novel Reinforcement Learning-Based Hybrid Validation Protocol (RL-CC) for adaptive edge-cloud coordination.
  • To minimize transaction aborts and maximize throughput in time-sensitive IoT transactions.

Main Methods:

  • The RL-CC protocol employs a two-phase validation: edge validation for preliminary conflict detection and prioritization, and cloud validation for global conflict resolution.
  • A Reinforcement Learning (RL) mechanism dynamically adapts decision-making, prioritizing transactions and resolving conflicts based on a reward function considering performance parameters.

Main Results:

  • RL-CC achieved a 90% reduction in transaction abort rates (5% vs. 45% for 2PL).
  • The protocol demonstrated 3x higher throughput (300 TPS vs. 100 TPS) and 70% lower latency compared to traditional methods.
  • Significant improvements in concurrency management and sensor data processing efficiency were observed.

Conclusions:

  • The RL-CC protocol offers a scalable and adaptive solution for high-concurrency transaction processing in sensor-based applications.
  • It effectively ensures transactions are executed within their temporal validity window, crucial for IoT networks and real-time systems.