Dependency-aware adaptive task offloading in edge-AI IoT networks using transformer-DDQN

SEQUENCE-TO-SEQUENCE DEEP LEARNING FOR DEPENDENCY-AWARE TASK OFFLOADING IN MULTI-ACCESS EDGE COMPUTING

The proliferation of Internet of Things (IoT) devices and resource-intensive applications has placed growing pressure on computation, storage, and network resources. Cloud computing helps, but its centralized nature adds latency that delay-sensitive services cannot tolerate, whereas Multi-Access Edge Computing (MEC), b...

Deep reinforcement learning-based di stributed edge caching for smart industrial IoT: a multi-edge-node optimization framework

Guarded deep reinforcement learning for SLA latency and energy aware task scheduling in IoT edge cloud environments

With the fast-growing IoT applications, there is a need for intelligent task-scheduling mechanisms that can meet the latency, energy, and service-level agreement (SLA) constraints in a dynamic edge–cloud environment. This may not be met with traditional heuristic and deep reinforcement learning (DRL)-based schedulers u...

Risk-Aware Hierarchical Meta-Reinforcement Learning with Quantile Regression LSTM Framework for Adaptive Task Prioritization and Congestion Avoidance Offloading in Fog–Cloud Systems

A graph network with a reinforcement learning framework to avoid congestion and schedule tasks with a low makespan in fog–IoT systems and demonstrates the improved ability to ensure reliable and effective fog–cloud allocation in response to dynamically changing workloads.

Optimized deep convolutional pulse-coupled neural network-based dynamic load balancing framework for effective edge-cloud IoT processing in smart cities

The rapid expansion of Internet of Things (IoT) ecosystems in smart cities generates massive heterogeneous data streams that demand energy-efficient, secure and low-latency processing. Traditional cloud-centric systems face communication overhead, workload imbalance and limited adaptability to dynamic traffic. This...

Deep Reinforcement Learning-Based Age Aware Task Scheduling Algorithm for Computing Power Network

Computing power networks (CPNs) have emerged as a promising paradigm for supporting delay-sensitive and freshness-critical IoT services in the 6G era by jointly orchestrating heterogeneous networking and computing resources. However, existing scheduling schemes mainly focus on offloading decisions and computation-node...