Cooperative Streaming Inferences in IoT Networks
Mengyuan Li, George Iosifidis, Ramjee Prasad · 2024
Collaborative execution of inference tasks by extreme-edge nodes can effectively address the challenge of scarce resources in the Next Generation IoT and 6G networks. In this paper, we study how such nodes can coordinate the execution of streaming inferences to jointly optimize their task performance (accuracy and latency) and energy consumption. We formulate this process as an online learning problem, and design an online task assignment algorithm, which is proven to provide optimality guarantees even when the network parameters (node resources and task properties) are unkown and subject to arbitrary variations over time. Further, we validate the performance of the proposed algorithm using data-driven simulations of representative scenarios and compare it with non-cooperative benchmarks.