Deep Beacon: Image Storage and Broadcast over BLE Using Variational Autoencoder Generative Adversarial Network

Chong Shao, Shahriar Nirjon

Abstract: This paper describes Deep Beacon which uses a set of cheap, low-power, storage-constrained Bluetooth Low Energy (BLE) devices to beacon (i.e. broadcast) a color image over a very long period (months, as opposed to days or weeks). The system employs deep neural network image encoder to encode a given input image and generates an extremely compact representation (as small as 10 bytes) of the image. At the receiver end, another deep neural network decoder runs on a mobile device which decodes (i.e. generates) the original image from the BLE broadcast messages. We evaluate Deep Beacon's performance using hand-written digit images and different types of RGB images that contain objects such as birds, flowers, and traffic signs. We empirically determine the tradeoffs between the system lifetime and the quality of broadcast images, and determine an optimal set of parameters for our system, under user-specified constraints such as the number of available beacon devices, maximum latency, and life expectancy. We develop a smartphone application that takes an image and user-requirements as inputs, shows previews of different quality output images, writes the encoded image into a set of beacons, and reads the broadcasted image back. Our evaluation shows that one beacon device is capable of broadcasting high-quality images (90% structurally similar to original images) for a year-long continuous broadcasting, and both the lifetime and the image quality improve when two beacons are used.