Deep learning (DL) based Wi-Fi sensing has witnessed great development in recent years. Although decent results have been achieved in certain scenarios, Wi-Fi based activity recognition is still difficult to deploy in real smart homes due to the limited cross-environment adaptability, i.e. a well-trained Wi-Fi sensing neural network in one environment is hard to adapt to other environments. To address this challenge, we proposeAdaWiFi, a DL-based Wi-Fi sensing framework that allows multiple Internet-of-Things (IoT) devices to collaborate and adapt to various environments effectively. The key innovation ofAdaWiFiincludes a collective sensing model architecture that utilizes complementary information between distinct devices and avoids the biased perception of individual sensors and an accompanying model adaptation technique that can transfer the sensing model to new environments with limited data. We evaluate our system on a public dataset and a custom dataset collected from three complex sensing environments. The results demonstrate thatAdaWiFiis able to achieve significantly better sensing adaptation effectiveness (e.g. 30% higher accuracy with one-shot adaptation) as compared with state-of-the-art baselines.

In order to perform deep learning tasks everywhere, many optimizations have been proposed to address the resource limitations on mobile systems like IoTs. A key approach among others is to dynamically adjust computational resources of the deep learning inference according to the characteristics of incoming inputs. For example, one of popular optimizations is to pick for each input a suitable combination of computations with respect to its inference difficulty. However, we find out that such “dynamic routing” of computations could be exploited to drain/waste precious resources on mobile deep learning systems. In this work, we introduce a new deep learning attack dimension, the computational resources draining, and demonstrate its feasibility in one of possible attack manners, the adversarial examples of input data. We describe how to construct our special adversarial examples aiming to the resource draining, and show that these poisoned inputs are able to increase the computation loads on purpose with two experiment datasets. We hope that our findings can shed light on the path of improving the robustness of mobile deep learning optimizations.