Balancing Message Criticality and Timeliness in IoT Networks

Abstract

We study the problem of balancing timeliness and criticality when gathering data from multiple sources using a two-level hierarchical approach. The devices that generate the data transmit them to a local hub. A central decision maker then has to decide which local hubs to allocate bandwidth to and the local hubs have to prioritize the messages they transmit when given the opportunity to do so. Whereas an optimal policy does exist for this problem such a policy would require global knowledge of messages at each local hub, rendering such a scheme impractical. We propose a distributed reinforcement-learning-based approach that accounts for both the timeliness requirements and criticality of messages. We evaluate our solution using a criticality-weighted deadline miss ratio as the performance metric. The performance analysis is done by simulating the behavior of the proposed policy as well as that of several natural policies under a wide range of system conditions. The results show that the proposed policy outperforms all the other policies - except for the optimal but impractical policy - under the range of system conditions studied and that in many cases it performs close (3% to 12% lower performance depending on the condition) to the optimal policy.

Keywords

• Soft real-time systems
• mixed-criticality streams
• hierarchical scheduling
• reinforcement learning
• Q-learning