Convolutional neural network (CNN) is a powerful tool for many data applications. However, its high dimension nature, large network size and computational complexity, and the need of large amount of training data make it challenging to be used in edge computing applications, which are becoming increasingly popular, relevant and important. In this paper, we propose a descriptor based approach to accelerate convolutional neural network training, reduce input dimension and network size, which greatly facilitates the use of CNN for edge computating and even cloud computing. By using image descriptors to extract features from original images, we report a simpler convolutional neural network with fast training speed, low memory usage and outstanding accuracy without the need for a pre-trained network as opposed to the state of art. In indoor localization, our SURF descriptors accelerated CNN (SurfCNN) can reach an average position error of 0.28 m and orientation error of 9.2 •. Compared to the conventional CNN that uses original images as input, our algorithm reduces the dimension of the input features by a factor of 48 without impairing the accuracy. Further, at an extreme feature reduction of 14,440 times, our model still retains an average position error retained at 0.41 m and orientation error at 14 • .

This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of ∼0.8 m when the phone is actively transmitting data frames and ∼1.5 m when it is not transmitting data frames.

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