Data Augmentation and Dense-LSTM for Human Activity Recognition using WiFi Signal

in IEEE Internet of Things Journal (2020)

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere wireless ... [more ▼]

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere wireless signal propagation, that manifested as unique patterns for activities recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carry substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in certain speed and scale, recent works commonly have moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi based human activity recognition system that synthesize variant activities data through 8 CSI transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep learning model that cater to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data. [less ▲]

Sparse Gaussian Process Based On Hat Basis Functions

in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (2020, August 28)

Gaussian process is a popular non-parametric Bayesian methodology for modeling the regression problem, which is completely determined by its mean and covariance function. Nevertheless, this method still ... [more ▼]

Gaussian process is a popular non-parametric Bayesian methodology for modeling the regression problem, which is completely determined by its mean and covariance function. Nevertheless, this method still has two major disadvantages: it is difficult to handle large datasets and may not meet inequality constraints in specific problems. These two issues have been addressed by the so-called sparse Gaussian process and constrained Gaussian process in recent years. In this paper, to reduce the overall computational complexity in the exact Gaussian process, we propose a new sparse Gaussian process method to solve the unconstrained regression problem. The idea is inspired by the constrained Gaussian process method. The critical point of our method is that we introduce the hat basis function, which is mentioned in the constrained Gaussian process, and modify its definition according to the range of training or test data. It turns out that this method belongs to the spectral approximation methods. Similar to the exact Gaussian process and Gaussian process with Fully Independent Training Conditional approximation, our method obtains satisfactory approximate results on analytical functions or open-source datasets. [less ▲]

Performance Modelling of V2V based Collective Perceptions in Connected and Autonomous Vehicles

in 2019 IEEE 44th Conference on Local Computer Networks (LCN) (2019, October 14)

With the introduction of Connected and Autonomous Vehicles (CAVs), it is possible to extend the limited horizon of vehicles on the road by collective perceptions, where vehicles periodically share their ... [more ▼]

With the introduction of Connected and Autonomous Vehicles (CAVs), it is possible to extend the limited horizon of vehicles on the road by collective perceptions, where vehicles periodically share their sensory information with others using Vehide-2-Vehicle (V2V) communications. This technique relies on a certain number of participants to have a measurable advantage. Nevertheless, the spread of CAVs will take a considerable period of time, it is critical to understand the benefits and limits of V2V based collective perceptions in different market stages. In this work, we characterise the effective Field of View (eFoV) of a vehicle as the perception range using local sensors only, and the collective Field of View (cFoV) as the region learn from the network. Applying analytic and simulation studies in highway scenarios, we show that the eFoV drops quickly with the increase in traffic density due to blockage effects of surrounding vehicles, and it is insufficient to overcome this problem by increasing the sensing range of local sensors. On the other hand, vehicles can gain around 16 folds more information about the road environment by leveraging collective perceptions with only 10\% CAV penetration rate. When the penetration rate reaches to around 30\%, collective perceptions can provide 95\% coverage over the road environments. Our analyses also show that apart from the benefits, employing collective perceptions could result in heavy broadcast redundancy, hence wasting the already scarce network resources. This observation suggests that the sharing of sensory information should be controlled appropriately to avoid overloading the communication networks. [less ▲]

CarAgent: Collecting and disseminating floating car data in vehicular networks

in Vehicular Communications (2018), 13

Floating car data (FCD) refers to the motion and sensor data produced by moving vehicles on the road. Given that traffic management authorities and drivers can benefit from FCD enormously, there is an ... [more ▼]

Floating car data (FCD) refers to the motion and sensor data produced by moving vehicles on the road. Given that traffic management authorities and drivers can benefit from FCD enormously, there is an urgency to develop efficient FCD collection and dissemination techniques that scale with peak road traffic. In this paper, we present CarAgent, a message exchange protocol that periodically collects and uploads FCD to data centres with the minimal utilisation of existing mobile network resources. We also show how CarAgent can be easily extended to efficiently disseminate FCD to all vehicles in a target area using Dedicated Short Range Communication (DSRC), a recently released wireless communication standard for vehicles. We analytically derive the key performance metrics of CarAgent and validate them with simulations. We evaluate CarAgent using microscopic simulation of road traffic in real street maps while incorporating wireless protocol details of Long Term Evolution (LTE) and DSRC. Simulation results confirm that, compared to the state-of-the-art, CarAgent consumes 50% less LTE resources for FCD collection. For FCD dissemination, CarAgent consumes 45% less DSRC resources while improving the speed of dissemination significantly. [less ▲]