Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g. industrial control systems) that require Time-Critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things (IoT) devices, those devices lack computing capabilities to guarantee reasonable performance for Time-Critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytics tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and QoS-based multi-level queue traffic scheduling system that can undertake semi-automatic bandwidth slicing to process Time-Critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.

Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their lifetime. This paper proposes reinforcement learning (RL) based dynamic data streams for time-critical IoT systems in energy-aware IoT devices. The designed solution employs the Q-Learning algorithm. The proposed mechanism has the potential to adjust the data transport rate based on the amount of renewable energy resources that are available, to ensure collecting reliable data while also taking into account the sensor battery lifetime. The solution was evaluated using historical data for solar radiation levels, which shows that the proposed solution can increase the amount of transmitted data up to 23%, ensuring the continuous operation of the device.