AI Deep Q-Network Boosts Energy Efficiency in Edge Computing

The explosion of edge-cloud infrastructures and latency-sensitive Internet of Things (IoT) applications poses significant challenges for efficient task management. Researchers have introduced an adaptive and intelligent customized deep Q-network (DQN) to enable energy-efficient task offloading in mobile edge computing (MEC) environments. This approach addresses the core tension between computational demands and energy constraints in resource-limited devices.

Mobile edge computing pushes processing power closer to data sources, reducing latency for IoT devices like sensors in power networks or smart cities. Traditional cloud computing requires data transmission over long distances, consuming substantial energy and time. MEC servers, often at base stations or access points, allow devices to offload tasks locally, extending battery life and supporting billions of IoT nodes.

However, optimizing offloading decisionssuch as what portion of a task to send to the edge server, transmission power, and bandwidth allocationremains complex. These decisions must balance latency constraints with minimal energy use, especially in multiuser scenarios like power IoT networks where devices share resources.

The proposed method customizes a deep Q-network, a reinforcement learning technique, to dynamically learn optimal offloading strategies. Unlike static algorithms, this adaptive DQN adjusts to varying network conditions, user loads, and task requirements. It formulates the problem as minimizing total system energyincluding local computation, transmission, and edge processingwhile meeting deadlines.

Key innovations include:

• Joint Optimization: Integrates offloading ratio, transmit power, and bandwidth allocation using alternating optimization and successive convex approximation (SCA) for non-convex problems.
• Intelligence via RL: The DQN agent explores actions like partial offloading, learning from rewards based on energy savings and latency compliance.
• Customization for MEC: Tailored state spaces capture IoT-specific factors, such as device energy states and edge server capacities.

Simulations demonstrate superior performance. In a six-user system, the scheme achieves about 0.1 Joules of energy consumption, cutting usage by over 60% compared to conventional methods. This outperforms benchmarks in energy efficiency across diverse loads.

This work builds on prior advances in MEC energy optimization. For instance, studies on UAV-assisted MEC incorporate trajectory design and dynamic voltage frequency scaling (DVFS) to minimize mechanical and computational energy. NOMA-based MEC networks reduce total energy by enabling uplink sharing, outperforming orthogonal multiple access (OMA) especially with high cloud capacity.

In power IoT networks, MEC empowers multiuser optimization, maintaining low energy even as user counts grow. The DQN approach extends these by adding AI-driven adaptability, crucial for real-world variability like fluctuating bandwidth or task arrivals.

By slashing energy needs, this technology supports sustainable IoT deployments, reducing reliance on frequent battery replacements or grid power for edge devices. In energy-constrained sectors like smart grids, it enables scalable monitoring without excessive consumption. As 5G and beyond-5G networks evolve, such intelligent offloading will be vital for low-latency apps in industrial IoT, autonomous systems, and smart cities.

Challenges remain, including real-time implementation on edge hardware and scalability to massive IoT swarms. Future research may integrate this with emerging tech like federated learning for privacy-preserving optimization. Overall, the adaptive DQN marks a step toward greener, smarter edge computing infrastructures.