Using an unmanned aerial vehicle (UAV) to collect data from wireless sensor networks deployed in the field, one of the key tasks is to plan the path for the collection so as to minimize the energy consumption of the UAV. At present, most of the existing methods generally take the shortest flight distance as the optimal objective to plan the optimal path. They simply believe that the shortest path means the least energy consumption of the UAV and ignore the fact that changing direction (heading) can also consume the UAV’s energy in its flight. If the path can be planned based on the UAV’s energy consumption closer to the real situation, the energy consumption of the UAV can be really reduced and its working energy efficiency can be improved. Therefore, this paper proposes a path planning method for UAV-assisted data collection, which can plan an energy-efficient flight path. Firstly, by analyzing the experiment data, we, respectively, model the relationship between the angle of heading change and the energy consumption of the UAV and the relationship between the distance of straight flight and the energy consumption of the UAV. Then, an energy consumption estimation model based on distance and the angle of heading change (ECEMBDA) is put up. By using this model, we can estimate or predict the energy consumption of a UAV to fly from one point (or node) to another (including the start point). Finally, the greedy algorithm is used to plan the path for UAV-assisted data collection according to the above estimated energy consumption. Through simulation and experiments, we compare our proposed method with the conventional method based on pure distance index and greedy algorithm. The results show that this method can obtain data collection path with lower energy consumption and smoother path trajectory, which is more suitable for actual flight.

In recent years, remote depression detection is becoming a new favorite of the Internet of Things because of its promise. However, deployable and sensitive information security has not been properly addressed. Thus, a novel depression detection framework is proposed to successfully solve the above problems. This article concentrates solely on the development and execution of our proposed core algorithm, LKCT. Further details concerning the system’s construction will be disclosed separately as future work. The contribution of this article can be summarized as follows: 1) proposing a depression detection framework that is easy to promote and can effectively solve the security of sensitive information; 2) proposing a novel two-stream deep network LKCT based on the global–local concept; 3) designing a novel network called purified vision transformer (PIT) to enhance the model’s ability to capture local details in images; and 4) introducing decoupled knowledge distillation to distill LKCT into the lightweight network MobileNet for deployability. To evaluate the true performance of our proposed method, we conducted sufficient experiments on the Chinese Academy of Sciences Institute of Automation (CASIA), facial expression recognition challenge (FERC), AVEC2014, and private depression data sets. LKCT achieved an accuracy of 93.4% on CASIA, demonstrating its strong ability to distinguish facial features. The accuracy on FERC reached 99%, proving its ability to accurately distinguish simple emotions. On AVEC2014, the model achieved state-of-the-art results with an RMSE of 7.41 and MAE of 5.48, validating its ability to identify depression. The distilled model obtained an average accuracy of 83% on the private data set, indicating good generalization ability.

In-air gesture recognition using wireless signals acts as a key enabler for various applications including smart homes, remote healthcare, shared autopilot, etc. Although researchers have conducted extensive research on WiFi-based gesture recognition, it remains an open question of providing accurate, robust, and in-time recognition system with the commodity WiFi infrastructure. We present FaSee, a just-in-time WiFi-based gesture recognition system by identifying the fine-grained Channel state information (CSI) features upon off-the-shelf WiFi devices. The core of FaSee is essentially a novel hybrid recognition algorithm, which combines the classical K-Means algorithm with Dynamic time warping (DTW) together, to transform the feature matching in traditional gesture recognition schemes into a hierarchical manner, thereby significantly improving the recognition efficiency. Experimental results show that FaSee recognizes 9 representative gestures with an average accuracy of 94.75% without tedious per-person training, while achieving 30% signal processing delay saving when compared with the state-of-the-arts gesture recognition schemes.

Deep learning techniques have been widely used in natural language processing (NLP) tasks and have made remarkable progress. However, training the deep learning model relies on a large amount of data which may involve sensitive information like electronic medical records. The attacker can infer sensitive information from the model, which leads to privacy leakage. To solve this problem, we propose a Differentially Private Recurrent Variational AutoEncoder (DP-RVAE) that can generate simulated data in place of the sensitive dataset to preserve privacy. To generate high utility synthetic text, a part of sensitive text data is employed as the conditional input of the model and uses a dropout and noise perturbing mechanism to preserve differential privacy. In addition, we expand the proposed DP-RVAE to a federated learning setting and design a novel training paradigm for NLP tasks. Specifically, DP-RVAE is deployed to the client-side to train and generate personalized text. These DP-RVAE models would be aggregated and updated through the Federated Optimisation (FedOPT) algorithm so that personal information can be well preserved. We evaluate our proposed DP-RVAE through a text classification task on the Tweets depression sentiment and IMDB reviews datasets. Our DP-RVAE achieves a higher average test accuracy by 5.90% and 3.94% compared to the typical centralized training and federated learning approach, respectively. We also perform the keywords inference attack experiment on the medical description dataset collected from the real world. Compared to the typical differentially private preserving approach, the DP-RVAE decreases by 15.2% in average attack accuracy. The experimental results demonstrate that DP-RVAE can be applied to the NLP models to leverage accuracy while preserving sensitive privacy.

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of $82.13\%$ and $86.76\%$ for 1 and 3 data samples.