Thomas Lentsch

A Bulk Acoustic Wave (BAW) Based Transceiver for an In-Tire-Pressure Monitoring Sensor Node

Attaching a tire pressure monitoring system (TPMS) on the inner liner of a tire allows sensing of important additional technical parameters, such as vehicle load or tire wearout. The maximum weight of the sensor is limited to 5 grams including package, power supply, and antenna. Robustness is required against extreme levels of acceleration. The node size is limited to about 1 cm3 to avoid high force-gradients due to device-deformation and finally, a long power supply lifetime must be achieved. In this paper a low-power FSK transceiver is presented. Exploiting BAW resonators the use of a bulky and shock-sensitive crystal and a PLL can be avoided. This makes the system more robust and radically reduces the start-up time to 2 ¿s from few ms as in state-of-the-art crystal oscillator based systems. The current consumption of the transceiver is 6 mA in transmit mode with a transmit output power of 1 dBm and 8 mA in receive mode with a sensitivity of -90 dBm at a data rate of 50 kBit/s and a bit error rate of 10-2. The transceiver ASIC and a microcontroller ASIC, a MEMS sensor, and a BAW die are arranged in a 3-D chip stack for best compactness, lowest volume, and highest robustness. The sensor node allows sensing of pressure, acceleration, supply voltage and temperature.

A robust wireless sensor node for in-tire-pressure monitoring

State-of-the-art tire pressure monitoring systems (TPMS) are wireless sensor nodes mounted on the rim. Attaching the node on the inner liner of a tire allows sensing of additional technical parameters, such as road condition, tire wearout, temperature, tire friction, side slip, wheel speed, and vehicle load. They may be used for improved tracking and engine control, feedback to the power train and car-to-car communication purposes.

EYES – Energy Efficient Sensor Networks

The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It will address the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This document gives an overview of the EYES project.

A bulk acoustic wave(BAW)-based sensor node for automotive wireless sensor networks

SummaryThe following paper presents a 2.1 GHz transceiver, which makes use of BAW resonators to replace the external quartz crystal and the external band select filter. It has been fabricated in a 130 nm CMOS process and has a power consumption of 5 mA. To derive the specifications for the transceiver the requirements of a TPMS have been taken into consideration.ZusammenfassungDer folgende Artikel stellt einen 2,1-GHz-Transceiver vor, der anstelle einer externen Quarzreferenz und eines externen Hochfrequenzfilters BAW-Resonatoren einsetzt. Der Chip wurde in einem 130-nm-CMOS-Prozess gefertigt und hat einen Stromverbrauch von 5 mA. Um die Spezifikationen abzuleiten, wurden die Anforderungen an ein TPMS (Reifendrucküberwachungssystem) betrachtet.

EYES - Energy efficient sensor networks

Sensor networks are being used for implementation of a large number of applications involving distributed and collaborative computation. Extensive research has focused upon design of time optimal parallel and distributed algorithms for two dimensional mesh connected computers (MCC). In this paper, we discuss a simple scheme for emulating the above algorithms for mesh-like sensor arrays. We show that a large set of parallel algorithms (see Property 1), that take time T (n) on MCC of size n, can be implemented on a wireless sensor mesh of size n in time O(r + T (n/r).r). Here r represents the transmission range of the sensor nodes. We discuss implementation of algorithms for ranking and sorting using our techniques and analyze them for time and energy efficiency.