Proteus II: design and evaluation of an integrated power-efficient underwater sensor node

We describe the design and evaluation of an integrated low-cost underwater sensor node designed for reconfigurability, allowing continuous operation on a relatively small rechargeable battery for one month. The node uses a host CPU for the network protocols and processing sensor data and a separate CPU performs signal processing for the ultrasonic acoustic software-defined Modulator/Demodulator (MODEM). A Frequency Shift Keying- (FSK-) based modulation scheme with configurable symbol rates, Hamming error correction, and Time-of-Arrival (ToA) estimation for underwater positioning is implemented. The onboard sensors, an accelerometer and a temperature sensor, can be used to measure basic environmental parameters; additional internal and external sensors are supported through industry-standard interfaces (I2C, SPI, and RS232) and an Analog to Digital Converter (ADC) for analog peripherals. A 433 MHz radio can be used when the node is deployed at the surface. Tests were performed to validate the low-power operation. Moreover the acoustic communication range and performance and ToA capabilities were evaluated. Results show that the node achieves the one-month lifetime, is able to perform communication in highly reflective environments, and performs ToA estimation with an accuracy of about 1-2 meters

SomBe:Self-Organizing Map for Unstructured and Non-Coordinated iBeacon Constellations

Bluetooth Low Energy (BLE) devices such as iBeacons have been popularly deployed for Location Based Services (LBS), including indoor infrastructure monitoring, positioning, and navigation. In these applications, the positions of iBeacons are assumed to be known. However, the location information is often unavailable or inaccurate as most iBeacons were deployed by different external parties. In addition, manual localizing the already-deployed iBeacons is costly and even impractical, especially in large-scale and complex indoor environments. Therefore, we propose a novel method, namely SomeBe, which can localize deployed iBeacons with a minimal effort and invasiveness to existing infrastructures. Specifically, our approach uses cooperative multilateration based on Received Signal Strength (RSS) of available smartphones and WiFi access points (APs) in the environment. Both Bluetooth signal strengths (between smartphones and iBeacons) and WiFi signal strengths (between smartphones and APs) are jointly employed in a single optimization cost function to surpass the local minima. Requiring that the positions of the APs are known only, the proposed cost function can also localize the iBeacons without knowing the positions of smartphones. To improve the localization accuracy, we employ a clustering method based on the RSS values for the coarse estimation of iBeacons' positions. SomBe also can be used to simplify iBeacon deployment as it can localize the iBeacons with a minimal effort. The performance evaluation results of our testbed experiments as well as realistic simulations show that SomBe outperforms non-cooperative approaches with 85% better in terms of accuracy

Underwater Localization by combining Time-of-Flight and Direction-of-Arrival

In this paper we present a combined ToF and DoA localization approach suitable for shallow underwater monitoring applications such as harbor monitoring. Our localization approach combines one-way ranging and DoA estimation to calculate both position and time-synchronization of the blind-node. We will show that using this localization approach, we are able to reduce the number of reference nodes required to perform localization. By combining ToF and DoA, our approach is also capable of tracking and positioning of sound sources under water.\ud \ud We evaluate our approach through both simulation and underwater experiments in a ten meter deep dive-center (which has many similarities with our target application in terms of depth and reflection). Measurements taken at the dive-center show that this environment is highly reflective and resembles a shallow water harbor environment. \ud \ud Positioning results using the measured ToA and DoA indicate that the DoA approach outperforms the ToF approach in our setup. Investigation of the DoA and ToF measurement error distributions, however, indicate the ToF-based localization approach has a higher precision. Shown is that both ToF and DoA and the combined approach achieve sub-meter positional accuracy in the test environment.\ud \ud Using the error distributions derived from the measurement in the dive-center, we run simulations of the same setup. Results from the simulation indicate ToF is more accurate than DoA positioning. Also in simulation all approaches achieve sub-meter accuracy