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Rofouei_Sensors_2011

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Detecting Local Events Using Global Sensing Mahsan Rofouei, Majid Sarrafzadeh, Miodrag Potkonjak Computer Science Department University of California, Los Angeles Los Angeles, USA {mahsan, majid, miodrag}@cs.ucla.edu Abstract—In order to create low power, low latency and reliable sensing systems, we propose a sensing strategy that identifies local events by the means of global measurements. We claim that capturing events globally, although seems against intuition, can save energy by enabling the organization of effective searching queries. To enable this capability, sensor readings can be combined using electronic switches and therefore enable event detections in groups of sensors with single measurements. We demonstrate this sensing mechanism on a prototype keyboard system made from E-Textile pressure sensors. I. INTRODUCTION Maximizing the lifetime of sensing systems by minimizing their energy consumption is considered important in design of sensing systems. Some of these systems are required to collect samples at all times, and events may occur at any moment of time without a known pattern. Therefore, it is crucial to design intelligent sampling techniques that are capable of capturing all events with minimal energy required. In this paper we show that global sensing can help to minimize the number of samples needed to localize the position of events. Our power consumption model suggests that coarse grained measurements that use fewer measurements to find events are beneficial in minimizing the amount of energy. By using electronic switches, sensing locations can be combined to enable group sensing. We also use methods such as interleaved sampling and processing that enables organizing intelligent sampling schedules based on observation from sampling a subset of sensors through global sensing. Smart E-Textiles provide a good platform for design and implementation of pressure sensor arrays for use in a wide spectrum of applications. Due to their flexible and fabric feel nature they can be used in smart clothing items, medical shoe insoles, bed sheets and many more areas [5, 6, 21]. We study the use of E-Textiles in the design of a soft portable keyboard and design global sensing techniques to detect key presses on the keyboard. This keyboard can be used with wireless devices such as smart phones and Personal Digital Assistants (PDAs). This enables light, low cost and personalized keyboards that can be used with multiple devices with adequate keys to perform fast and easy interactions with small wireless systems. The rest of the paper is organized as follows: In Section II we present an overview of related work in this area. Section III provides a description of the platform used for our study in addition to the power consumption model used. Global sensing techniques such as binary sensors and interleaved sampling and processing are described in Section IV. We show the effectiveness of methods presented in design of a soft E-Textile keyboard in Section V and finally draw conclusions in Section VI. II. RELATED WORK Electronic textiles or smart fabrics [3] have enabled a good platform for pervasive computing. The work in [2] provides an overview of efforts and challenges associated with building systems that have sensors and computational elements embedded into E-Textiles with the goal of gathering sensitive information and monitoring. SmartShirt [4] is an example of such systems that measures human heart rhythm and respiration with conductive fiber grid and sensors fully integrated in the shirt. E-Textiles are used in sheets and pillows in order to monitor sleep in [5]. A smart E-Textile based soft keyboard is presented in [6]. In the last two decades energy emerged as one of the most important design metrics [22-25]. Energy efficiency and optimizing sampling techniques in sensor networks and embedded sensing systems has received a great deal of attention [7, 8]. Design of optimal periodic sampling techniques for embedded sensing environments has been studied in [11, 12]. Sampling problems in event-driven sensor sampling techniques have been addressed in [9,10]. Recently, several distributed event-triggered sampling strategies have been proposed [15]. We use the global sensing method presented in the design of an E-Textile keyboard. There has been a great amount of research performed in developing optimized keyboards by techniques such as studying typing characteristics [16, 17, 18]. In addition, different technologies have been used to develop soft keyboards [19, 20].III. PRELIMINARIES In this section we describe characteristics of E-Textiles and how they can be used in building pressure sensor arrays. Next, we describe the power consumption model used in our studies. This model shows why global sensing is beneficial. A. E-Textile Technology E-Textiles [3] are composite yarns made of fibers coated with piezo-electric polymer. They offer a good platform for design of high density and low cost pressure sensor arrays. E-Textile pressure sensors can be built using a three stacked layer structure where E-Textile material is sandwiched between two layers of conductive fabric. In the event of applied force on the surface of this pressure sensor, the intra fibers within the E-Textile material are squeezed together, resulting in a smaller throughout resistor. Figure 1 shows the relation between resistance and force in an E-Textile pressure sensor. 01002003004005006007000.00000 2.00000 4.00000 6.00000 8.00000 10.00000 12.00000Force(N)Resistance ( Kohm) Figure 1. Resistance response based on applied force B. Power Consumption Model Power consumption in sensing systems is mainly due to three tasks: 1) Data transmission to host, 2) Power consumed by the sensing circuit and 3) Sampling power. Table 1 [13, 14] shows the energy consumption of these three tasks in our platform. Our platform uses E-Textiles in the sensing circuit and MSP430f2274 microcontroller with CC2500 wireless chip. TABLE I. ENERGY CONSUMPTION OF SUB-MODULES. Sub-module Energy (J) ADC( per sample) 9×E-9 Data Transmission( per sample) 7.2×E-9 Sensing circuit (average per sensor) 4.8×E-10 Table 1 shows


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