Dynamic Scheduling for IoT Analytics at the Edge

We propose an online policy that schedules the transmission and processing of data analytic tasks in an Internet of Things (IoT) network. The tasks are executed with different precision at the (possibly heterogeneous) nodes; the network is subject to link bandwidth and node processing capacity changes; and the task requests vary following unknown statistics. For this general IoT scenario, we formulate a resource allocation problem towards maximizing the aggregate tasks precision, and design a dynamic solution policy by combining the FrankWolfe and dual subgradient algorithms. Our policy (FWDS) is guaranteed to converge within bounded distance from the optimal solution, while ensuring interference-free transmissions, and being oblivious to network and/or traffic load changes. We use a wireless testbed and a state-of-the-art face recognition application to implement FWDS and compare it with static or dynamic (maxweight-type) competitor policies. Our findings verify that FWDS pushes the envelope of network control algorithms by handling time-varying objectives and possibly non-i.i.d. network/load statistics, with smaller complexity than its competitors.