With the prevalence of commercial WiFi devices and the development of Internet of Things (IoT), researchers have extended the usage of WiFi from communication to sensing. Recently, device-free human activity recognition has been applied to support WiFi based remote control and human-computer interaction. However, prior works usually recognize each individual activity by extracting the feature of corresponding WiFi signals, and hence distinguishing differences between human activities. Once the activity has multiple variations of different body parts (such as head, arm and leg), distinguishing such sub-activities is extremely difficult. In this paper, we propose a Multi-variations Activity Recognition (MAR) system to identify multiple-variations of body parts. Our work is based on an observation that the Channel State Information (CSI) is sensitive to the activities of body parts. Firstly, CANDECOMP/ PARAFAC (CP) decomposition and Dynamic Time Warping (DTW) are applied to recognize multi-variations activities. Secondly, we theoretically analyse the uniqueness of CP decomposition. Then, we design specific experiment to verify the reliability and stability of uniqueness. Finally, we apply MAR in gaits recognition of multiple volunteers to evaluate the accuracy performance. The experiment results demonstrate that MAR achieves average 95% accuracy in gaits recognition.

Multi-Variations Activity Based Gaits Recognition Using Commodity WiFi

In this paper, we propose a deep learning approach for smartphone user identification based on analyzing motion signals recorded by the accelerometer and the gyroscope, during a single tap gesture performed by the user on the screen. We transform the discrete 3-axis signals from the motion sensors into a gray-scale image representation which is provided as input to a convolutional neural network (CNN) that is pre-trained for multi-class user classification. In the pre-training stage, we benefit from different users and multiple samples per user. After pre-training, we use our CNN as feature extractor, generating an embedding associated to each single tap on the screen. The resulting embeddings are used to train a binary Support Vector Machines (SVM) model in a few-shot user identification setting, i.e. requiring only 20 taps on the screen during the registration phase. We compare our identification system based on CNN features with two baseline systems, one that employs handcrafted features and another that employs recurrent neural networks (RNN) features. All systems are based on the same classifier, namely SVM. To pre-train the CNN and the RNN models for multi-class user classification, we use a different set of users than the set used for few-shot user identification, ensuring a realistic scenario. The empirical results demonstrate that our CNN model yields a top accuracy of 90.75% in multi-class user classification and a top accuracy of 96.72% in few-shot user identification. We thus believe that our system is ready for practical use, having a better generalization capacity than both baselines. We also conduct experiments showing that the binary SVM provides better results than the one-class SVM, although the negative samples added during training do not belong to the attackers (known only at test time).

/pdf/convolutional-neural-networks-for-user-identification-based-ee8t1e8a17.pdf

Convolutional Neural Networks for User Identification Based on Motion Sensors Represented as Images

Drowsy driving is one of the biggest threats to driving safety, which has drawn much public attention in recent years. Thus, a simple but robust system that can remind drivers of drowsiness levels with off-the-shelf devices (e.g., smartphones) is very necessary. With this motivation, we explore the feasibility of using acoustic sensors on smartphones to detect drowsy driving. Through analyzing real driving data to study characteristics of drowsy driving, we find some unique patterns of Doppler shift caused by three typical drowsy behaviours (i.e., nodding, yawning and operating steering wheel), among which operating steering wheels is also related to drowsiness levels. Then, a real-time D rowsy D riving D etection system named D $^3$ 3 -Guard is proposed based on the acoustic sensing abilities of smartphones. We adopt several effective feature extraction methods, and carefully design a high-accuracy detector based on LSTM networks for the early detection of drowsy driving. Besides, measures to distinguish drowsiness levels are also introduced in the system by analyzing the data of operating steering wheel. Through extensive experiments with five drivers in real driving environments, D $^3$ 3 -Guard detects drowsy driving actions with an average accuracy of 93.31%, as well as classifies drowsiness levels with an average accuracy of 86%.

Real-Time Detection for Drowsy Driving via Acoustic Sensing on Smartphones

The lifetime of underwater sensor networks (USNs) can be prolonged significantly thanks to wireless power transfer technology. In this paper, we first proposed a Shortest Path Partial Charging based on Charging Curve Scheme (SPBS) to increase the survival rate of nodes in 3D underwater networks, and then we proposed a concept of secondary charging stations for mobile charging ships to reduce the traveling cost and improve charging efficiency. We first use k-means clustering algorithm to divide our network with k clusters, and then we place our secondary stations at k clustering centers, in this way, mobile charging ships can be charged at secondary stations quickly. Based on secondary stations, we proposed Hamilton Charging Scheme (HCS) using the Hamilton ring, and then we proposed a temporal and spatial collaborative charging algorithm (mCS-TS) for USNs with multiple mobile charging ships and secondary charging stations, which also takes the cluster factor and deadline time into consideration. Simulation results show the effectiveness of our proposed algorithms.

/pdf/research-on-hybrid-cooperative-charging-scheduling-schemes-41ubwkr6of.pdf

Research on Hybrid Cooperative Charging Scheduling Schemes in Underwater Sensor Networks

This article debates on notions of context-awareness as a relevant asset of networking and computing architectures for an Internet of Things (IoT), in particular in regards to a smoother support of the the networking operation between Cloud and Edge. Specifically, the paper debates on notions of context-awareness and goes over different types of context-awareness indicators that are being applied to Edge selection algorithms, covering the approaches currently used, the role of the algorithms applied, their scope, and contemplated performance metrics. Lastly, the paper provides guidelines for future research in the context of Cloud-Edge and the application of context-awareness to assist in a higher degree of automation of the network and, as consequence, a better support of the Cloud to Edge continuum.

/pdf/a-discussion-on-context-awareness-to-better-support-the-iot-3esx6ph26h.pdf

A Discussion on Context-Awareness to Better Support the IoT Cloud/Edge Continuum

Implicit authentication is a new research direction to enhance the privacy protection of smartphones. However, implicit authentication has low robustness due to its vulnerability to environment. Motivated by this, this paper proposes a context-aware implicit authentication, which is a scheme to improve the robustness of authentication by introducing context awareness module. In the scheme, multi-sensor data (including accelerometer, gyroscope, magnetometer, timestamp, pressure, touch size) is first captured in a fine-grained manner to characterize one's touch action. Then, based on various sensors data, gesture features and touch features are extracted by employing both statistical method and distance measurement method. Particularly, one's body posture when touch action happens can be used as context. In each context, we present a weighted sum fusion rule to fuse the results of different features obtained by One-Class SVM (OC-SVM). We have collected 10000+ sensor data from 87 participants for experimental evaluation. The results show that the authentication in an unrestricted environment can achieve a best equal error rate (EER) of 0.0071%, which is more than one percent lower than the non-context-aware authentication. The proposed method can effectively improve the reliability and practicability of implicit authentication.

/pdf/context-aware-implicit-authentication-of-smartphone-users-489ew4j8f3.pdf

Context-Aware Implicit Authentication of Smartphone Users Based on Multi-Sensor Behavior

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Context-Aware Implicit Authentication of Smartphone Users Based on Multi-Sensor Behavior