Wireless Sensor Development Platforms

A Wireless Sensor Development Platforms Benny Lo and Guang-Zhong Yang A.1 Introduction The development of BSN has greatly benefited from the rapid advances in Wireless Sensor Networks (WSNs) in recent years. Since the introduction of the concept of WSN and ubiquitous computing, a large number of development platforms have been introduced [1, 2]. During 2004 and 2005, for example, more than twenty different WSN hardware platforms have been proposed. Figure A.1 demonstrates this trend since 1998 with some of the WSN hardware platforms illustrated. A more de- tailed list of some of the major WSN platforms is provided in Table A.2Error! Reference source not found.. It must be pointed out, however, that this table is by no means exhaustive and unintentional omission is likely. As stated in Chapter 1, the general design and requirements for BSNs can be different from typical WSN applications. However, many of the WSN development platforms can be modified to cater for general BSN applications. Thus far, most research in BSN is based on general WSN platforms, particularly for wireless communication, data fusion and inferencing. This appendix outlines the system architecture of common WSN platforms and provides an overview of some of the main hardware components involved. A.2 System Architecture The system architectures of all WSN development platforms are relatively similar, and they mainly consist of six major components as depicted in Figure A.2: • Processor – The brain of the sensor node • Wireless Communication – wireless link between sensor nodes • Memory – External storage for sensor readings or program images • Sensor Interface – Interface with sensors and other devices 404 Body Sensor Networks • Power Supply – Power source of the sensor node • Operating System – Software for managing the network and re- sources Figure A.1 Examples of WSN hardware platforms developed in recent years. (See colour insert.) Wireless Communication Memory Sensor Interface Processor Power Operating Supply System Figure A.2 The main components of common WSN nodes. A.2.1 Processor Most WSN platforms are based on COTS (Commercial Off-The-Shelf) components, and the development of WSN depends extensively on the rapid advancement of mi- croprocessors. For instance, the Mica2 node has about eight times the memory ca- A. Wireless Sensor Development Platforms 405 pacity and communication bandwidth as its predecessor, the Rene node developed in 1999, whilst maintaining the same power consumption and cost [3]. Unlike common Personal Computing (PC) applications, WSN requires much less processing power due to tight constraints on size and power consumption. For this reason, WSN platforms are mainly based on low power Microcontroller Units (MCUs) rather than using conventional PC-type processors. Depending on the amount of processing required, a number of WSN platforms are based on mobile computing MCUs, which are designed for Personal Digital Assistants (PDAs). Recently, significant effort has also been invested by a number of manufacturers in developing processors with integrated radio transceivers. A.2.1.1 Low Power MCU For many WSNs, node size and power consumption are often considered more im- portant than the actual processing capacity because in most applications the amount of processing involved is relatively light. Among currently available MCUs, Atmel ATmega 128L and Texas Instruments (TI) MSP430 are the most popular processors used in WSN platforms due to their integrated low power design, multiple sensor interfaces, and widely available developing tools. The Atmel ATmega 128L processor is an 8-bit microcontroller designed for embedded applications. With a 16MHz clock, the ATmega processor can deliver up to 16MIPS (Million Instructions Per Second) processing power [4]. Equipped with a relatively large programmable flash memory (128KB), 8-channels of 10-bit ADCs (Analogue-to-Digital Converters) and low operating voltage (2.7V), the ATmega processor has been widely used in many WSN platforms. They include the Mica motes [5], BTnode [6], Nymph [7], AquisGrain, DSYS25 [8], Ember [9] and Fleck [10]. Figure A.3 illustrates the Atmel processor used for the Mica2 mote. Figure A.3 Atmel processor on a Mica2 mote. The TI MSP430 processor is an ultra low power 16-bit RISC (Reduced Instruc- tion Set Computer) processor [11]. Compared to the Atmel processor, which con- sumes 8mW in active mode and 75µW in sleep mode, the MSP430 requires much less power in both active (3mW) and sleep modes (15µW). It also has a much lower operating voltage of 1.8V [12]. With its wide range of interconnection functions, 12-bit ADCs and the serial programming interface, the MSP430 has been widely adopted in platforms such as Telos [5], Tmote sky [13], eyesIFXv2 [14], Ant [15], Pluto [16] and BSN node [17]. Figure A.4 illustrates the MSP430 processor used on the Telos node. 406 Body Sensor Networks Figure A.4 MSP430 processor used on a Telos node. A.2.1.2 Mobile Computing MCUs For certain WSN applications, such as those for video based monitoring, relatively high processing power is required and typical MCUs will not be able to process the acquired sensor data in real-time [18]. To balance power consumption and processing performance, a few of the WSN platforms have used ARM processors designed for handheld devices such as the PDAs. For example, two of the early WSN platforms, AWAIRS 1 [19] and µAMPS [20], both used the Intel StrongARM SA-100 processor. This is a 32-bit RISC processor with operating frequency up to 206Mhz. The newly announced Sun Spot system also uses a 32-bit ARM processor, which is a new processor with lower power consumption and smaller size [62]. The recently proposed iMote2 [21] uses the new Intel PXA 271 processor operated at a frequency up to 416MHz. With its adjustable operating frequency function, the PXA processor can be configured for low power application as well as computationally demanding tasks. In addition, the PXA processor provides a wide range of connec- tivity including SD (Secured Digital), which allows iMote2 to use SD as an ex- tended memory storage or existing SD-based wireless connections such as Blue- tooth and Wireless LAN. A.2.1.3 Integrated Processor with Radio Transceiver Recently, System-on-Chip (SoC) Processors or integrated processors with radio transceivers are becoming popular due to their miniaturised size and simplicity in board design. One exemplar is the Berkeley Spec, which is a custom-made processor with an 8-bit RISC processor combined with a FSK (Frequency-Shift Keying) transceiver [3]. By integrating the radio transceiver, the size of the Spec is only 5mm2. Since then, several commercial integrated MCUs have been introduced, and WSN platforms such as iMote1 [22], MITes [23], RFRAIN [24], RISE [25] and uPart0140ilmt [26] are designed with this type of MCU to facilitate size reduction. In addition to the radio transceiver, recent research has taken a further step to miniaturise the WSN node by integrating sensors and power supply onto the MCUs. One example of this is the SAND (Small Autonomous Network Devices) platform proposed by Philips research, which is a multiple stacked die SoC. It consists of sensors, signal processing, data storage, power management, low bit-rate wireless communication and a power source [27]. Similar 3D stacked sensor system has A. Wireless Sensor Development Platforms 407 been proposed in IMEC’s Human++ project and Match-X’s VDMA system [28, 29]. A.2.2 Wireless Communication Amongst all of the elements required, wireless communication is the most power- demanding component of WSNs. This often accounts for more than half the overall power consumption of a sensor node [30]. Parallel to the development of micro- power radio transceivers, such as the Pico radio [31], existing research in WSNs has been focused on developing energy-efficient protocols and routing strategies. The three main components of wireless communication are the radio transceiver, an- tenna, and communication protocols. A.2.2.1 Radio Transceiver Most of the early WSN platforms, such as WeC [32], Rene [33], Dot [5], SpotOn [34, 35] and CENS Medusa MK-2 [36], used RFM TR1000 as the radio transceiver due to its low power, small size, and hybrid design. Subsequent platforms, such as Mica2 [5] and Mica2Dot [5], are based on the Chipcon CC1000 chipset because it provides a more reliable FSK modulation, selectable modulating frequency, and low power architecture. Figure A.5 shows a Mica2Dot with a CC1000 transceiver. Other platforms, such as BTnode, iBadge [37] and iMote1, are based on Bluetooth radio transceivers in order to achieve high bandwidth data communication and ease of integration with other Bluetooth based mobile devices. Figure A.5 Chipcon CC1000 on a Mica2Dot. Since the introduction of the IEEE 802.15.4 standard for WSNs, most of the new platforms are using the Chipcon CC2420 wireless transceiver, which is one of the first IEEE 802.15.4 compatible chipsets. Although the power consumption of the CC2420 (19.7mA) is higher than the CC1000 (7.4mA), the CC2420 can deliver 250kbps, which is 6.5 times higher than its predecessor (38.4kpbs) [38, 39]. In addition, it also incorporates an AES-128 (Advanced Encryption Standard) hardware encryption engine and the IEEE 802.15.4 MAC (Medium Access Control) function, which enables it to act as a co-processor to handle all packet communications. Compared to the CC1000 and RFM TR1000, where the MCU has to handle all of the MAC layer communications, the CC2420 significantly reduces the computa- 408 Body Sensor Networks tional demands on the MCU, thus leading to significant overall performance im- provement on the sensor node. As such, most of the recent WSN platforms developed are based on the CC2420, such as Telos, Tmote sky, MicaZ, Pluto, iMote2, Sun Spot, and BSN node.

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