In this paper we present a combined Time-of-Flight (ToF) and Direction-of-Arrival (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 timesynchronization 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.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. Positioning results using the measured Time-of-Arrival (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.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.

In this paper we describe a smart way to apply dynamic wireless sensor networks (WSN) in logistics. Especially in the temperature controlled supply chain (cold chain), perishable goods like fruits and pharmaceuticals greatly benefit from real-time quality monitoring during storage and transport in order to avoid quality degradation and spoilage. In our system, wireless sensor nodes called SmartPoints monitor the environmental conditions and generate alarms when specific events are detected. Additionally, they calculate the remaining shelf life of the perishable goods they travel with. When there is an Internet-connected WSN available during travel, the shelf-life prediction and associated alarms are directly sent to a back-end server. Alternatively they are logged on the SmartPoints and flushed upon arrival, such that the remaining shelf-life and alarms are immediately clear and a full history will be available later. Our dynamic WSN supports a number of protocols that enable support for the dynamic processes in logistic processes. The Ambient middleware supports real-time monitoring and remote maintenance across the Internet via wired and mobile wireless network access technologies. Additionally, the middleware offers easy integration with third-party applications. Ambient Studio utilizes the middleware for remote WSN configuration and monitoring.

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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.

Localization, a process of determining the position of a blind node, can be used in various applications. Signal-strength localization provides a low-cost and lowpower solution to positioning. Signal-strength positioning approaches using fingerprinting or calibrated approaches require a time-consuming calibration phase. Existing self-calibrating approaches, which do not require a priori calibration, use a least-squares fitting model to determine both the position of the blind node as well as the optimal environmental parameters. In this paper, we propose an approach using the Product-Moment correlation between the measured signal strength and the estimated signal strengths. Such approach does not require estimation of the environmental parameters or prior calibration and outperforms existing self-calibrating least-squares approaches. We compare our approach to existing least-squares calibration-free positioning approaches. Moreover, we look at the Cramer-Rao Bound (CRB) of signal-strength localization and using simulations we show that the product-moment correlation outperforms least-squares approaches and follows the CRB closely. Simulation and evaluation using a real-world experiment dataset show the product-moment approach significantly outperforms least-squares approaches. The product-moment approach follows the CRB much more closely and achieves up to twice more accurate positions in certain scenarios. When the error ratio increases and the number of reference positions stays fixed at 6, the product-moment approach scores 20% more accurate positions. In the cooperative localization scenario, the product-moment correlation performs 40% better.