Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges (1907.09059v3)

Abstract: The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without external intervention over an extended period of time. The integration of IoT and ACS results in a new concept - autonomous IoT (AIoT). The sensors collect information on the system status, based on which the intelligent agents in the IoT devices as well as the Edge/Fog/Cloud servers make control decisions for the actuators to react. In order to achieve autonomy, a promising method is for the intelligent agents to leverage the techniques in the field of artificial intelligence, especially reinforcement learning (RL) and deep reinforcement learning (DRL) for decision making. In this paper, we first provide a tutorial of DRL, and then propose a general model for the applications of RL/DRL in AIoT. Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model. Finally, the challenges and open issues for future research are identified.

Deep Reinforcement Learning for Autonomous IoT: A Comprehensive Survey

The paper "Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges" offers an extensive exploration of the integration of Deep Reinforcement Learning (DRL) and the Autonomous Internet of Things (AIoT). At the core of this discussion is the evolving synergy between intelligent agents utilizing DRL models to enhance the decision-making processes within AIoT systems. This convergence results in autonomous systems capable of operating with minimal human intervention, achieving optimal outcomes in dynamic environments.

Overview and Proposed Model

The fusion of IoT, Autonomous Control Systems (ACS), and DRL creates a robust framework for realizing AIoT. The authors propose a comprehensive DRL model tailored to AIoT environments, encapsulating the diverse architectures typically found in these systems. This model includes:

• Perception Layer: Sensors collect environmental data to facilitate informed decision-making.
• Network Layer: Ensures connectivity through robust communication protocols.
• Application Layer: Involves edge, fog, and cloud computing resources responsible for processing and storing the vast amounts of data generated by IoT devices.

The proposed model leverages DRL's potential to address the multi-faceted challenges that AIoT systems encounter, such as resource allocation, network optimization, and autonomous decision-making.

Applications in AIoT

The paper conducts a meticulous survey of DRL applications across various domains within AIoT systems:

• IoT Communication Networks: DRL enhances network resource allocation, facilitating improved throughput and energy efficiency in Wireless Sensor Networks (WSNs) and Wireless Sensor and Actuator Networks (WSANs). Novel methods such as Deep Q-Network (DQN) and its extensions are employed to optimize transmission scheduling.
• IoT Edge/Fog/Cloud Computing Systems: DRL plays a critical role in task offloading and resource allocation, ensuring efficient data processing and reduced latency in edge computing environments. Algorithms like DDPG (Deep Deterministic Policy Gradient) are applied for optimizing computation offloading policies.
• Autonomous Robots: Leveraging DRL for mobile behavior control and robotic manipulation leads to improved coordination in multi-robot systems. DRL algorithms such as Actor-Critic methods enable efficient path planning and manipulation tasks.
• Smart Vehicles: In vehicular networks and autonomous driving, DRL facilitates real-time decision-making to enhance safety and efficiency. Actors such as edge servers utilize DRL to optimize vehicular communication and task handling.
• Smart Grid: DRL supports energy storage management and demand response in smart grids, addressing the stochastic nature of renewable resources. Q-Learning and its variants are utilized to optimize energy trading and consumption.

Challenges and Future Directions

The paper underscores several unresolved challenges in deploying DRL within AIoT systems:

• Incomplete Perception: The limited sensing capabilities of IoT devices pose challenges for DRL in achieving informed decision-making.
• Delayed Control: Latency in sensor data processing and action execution impacts the efficacy of DRL models in real-time applications.
• Multi-Agent Coordination: Coordinating multiple DRL agents in a decentralized AIoT environment requires sophisticated strategies to handle cooperation and competition among agents.

Addressing these challenges entails advanced research into POMDP-based DRL methodologies, efficient hierarchical learning techniques, and more effective integration of DRL frameworks with underlying IoT infrastructures.

Conclusion

The paper provides a pivotal foundation for further exploration into DRL's role in advancing AIoT systems. As challenges in perception, latency, and coordination are addressed, the potential for DRL to revolutionize autonomous operations in IoT environments becomes increasingly attainable. This work thus serves as both a comprehensive guide and a call to action for researchers seeking innovations in this interdisciplinary field.