Abstract-The omnipresence of indoor lighting makes it an ideal vehicle for pervasive communication with mobile devices. In this paper, we present a communication scheme that enables interior ambient LED lighting systems to send data to mobile devices using either cameras or light sensors. By exploiting rolling shutter camera sensors that are common on tablets, laptops and smartphones, it is possible to detect high-frequency changes in light intensity reflected off of surfaces and in direct line-of-sight of the camera. We present a demodulation approach that allows smartphones to accurately detect frequencies as high as 8kHz with 0.2kHz channel separation. In order to avoid humanly perceivable flicker in the lighting, our system operates at frequencies above 2kHz and compensates for the non-ideal frequency response of standard LED drivers by adjusting the light's duty-cycle. By modulating the PWM signal commonly used to drive LED lighting systems, we are able to encode data that can be used as localization landmarks. We show through experiments how a binary frequency shift keying modulation scheme can be used to transmit data at 1.25 bytes per second (fast enough to send an ID code) from up to 29 unique light sources simultaneously in a single collision domain. We also show how tags can demodulate the same signals using a light sensor instead of a camera for low-power applications.
The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
Abstract-In this paper, we present the design and evaluation of a platform that can be used for time synchronization and indoor positioning of mobile devices. The platform uses the Time-Difference-Of-Arrival (TDOA) of multiple ultrasonic chirps broadcast from a network of beacons placed throughout the environment to find an initial location as well as synchronize a receiver's clock with the infrastructure. These chirps encode identification data and ranging information that can be used to compute the receiver's location. Once the clocks have been synchronized, the system can continue performing localization directly using Time-of-Flight (TOF) ranging as opposed to TDOA. This provides similar position accuracy with fewer beacons (for tens of minutes) until the mobile device clock dirfts enough that a TDOA signal is once again required.Our hardware platform uses RF-based time synchronization to distribute clock synchronization from a subset of infrastructure beacons connected to a GPS source. Mobile devices use a novel time synchronization technique leverages the continuously freerunning audio sampling subsystem of a smartphone to synchronize with global time. Once synchronized, each device can determine an accurate proximity from as little as one beacon using Time-Of-Flight (TOF) measurements. This significantly decreases the number of beacons required to cover an indoor space and improves performance in the face of obstructions. We show through experiments that this approach outperforms the Network Time Protocol (NTP) on smartphones by an order of magnitude, providing an average 720µs synchronization accuracy with clock drift rates as low as 2ppm.
Abstract-Time synchronization in wireless sensor networks is important for event ordering and efficient communication scheduling. In this paper, we introduce an external hardwarebased clock tuning circuit that can be used to improve synchronization and significantly reduce clock drift over long periods of time without waking up the host MCU. This is accomplished through two main hardware sub-systems. First, we improve upon the circuit presented in [1] that synchronizes clocks using the ambient magnetic fields emitted from power lines. The new circuit uses an electric field front-end as opposed to the original magnetic-field sensor, which makes the design more compact, lower-power, lower-cost, exhibit less jitter and improves robustness to noise generated by nearby appliances. Second, we present a low-cost hardware tuning circuit that can be used to continuously trim a micro-controller's low-power clock at runtime. Most time synchronization approaches require a CPU to periodically adjust internal counters to accommodate for clock drift. Periodic discrete updates can introduce interpolation errors as compared to continuous update approaches and they require the CPU to expend energy during these wake up periods. Our hardware-based external clock tuning circuit allows the main CPU to remain in a deep-sleep mode for extended periods while an external circuit compensates for clock drift. We show that our new synchronization circuit consumes 60% less power than the original design and is able to correct clock drift rates to within 0.01 ppm without power hungry and expensive precision clocks.
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