Wireless Sensor Networks

1. Introduction The increasing interest in radiocommunication demodulator webs can be promptly understood simply by thinking about what they essentially ar a whacking number of small sensing self-powered knobs which gather information or detect limited events and promulgate in a wireless fashion, with the end goal of handing their processed data to a grip office. Sensing, affect and communication are three blusher elements whose combining in one tiny whatsis gives rise to a vast number of exertions A1, A2. sensing element engagements leave alone endless opportwholeies, only if at the same duration pose formidable challenges, uch as the fact that zero is a scarce and usually non-renewable resource. However, recent advances in outset power VLSI, engraft computing, communication computer computer hardware, and in general, the convergence of computing and communications, are making this emerging technology a reality A3. Likewise, advances in na nonechnology and Micro Electro-Mechanical strategys (MEMS) are pushing toward networks of tiny distri justed sensors and actuators. 2. Applications of detector profitss executable applications of sensor networks are of interest to the most diverse fields. Environmental monitor, warfare, child education, surveillance, micro-surgery, and griculture are only a few examples A4. Through joint efforts of the University of California at Berkeley and the College of the Atlantic, environmental monitoring is carried out off the coast of Maine on Great Duck Island by means of a network of Berkeley motes equipped with various sensors B6. The pommels accuse their data to a baseborn station which makes them available on the Internet. Since habitat monitoring is rather sensitive to merciful presence, the deployment of a sensor network provides a noninvasive approach and a remarkable degree of granularity in data acquisition B7. The same idea lies behind thePods project at the University of how-do-you-do at Manoa B8, where environmental data (air temperature, light, wind, relative humidity and rainfall) are gathered by a network of weather sensors embedded in the communication units deployed in the South-West Rift Zone in Volcanoes National Park on the Big Island of Hawaii. A major concern of the questioners was in this case camouflaging the sensors to make them c everyplacet to curious tourists. In Princetons Zebranet Project B9, a dynamic sensor network has been created by attaching surplus collars equipped with a low-power GPS frame to the necks of zebras to onitor their moves and their behavior. Since the network is intentional to operate in an infrastructure-free environment, peer-to-peer swaps of information are used to produce redundant databases so that seekers only take away to encounter a few zebras in rove to collect the data. Sensor networks can also be used to monitor and study natural phenomena which intrinsically discourage human presence, much(prenominal) as h urricanes and woodland fires. common efforts in the midst of Harvard University, the University of New Hampshire, and the University of North Carolina have recently led to the deployment of a wireless sensor etwork to monitor eruptions at Volcan Tungurahua, an lively volcano in central Ecuador. A network of Berkeley motes monitored infrasonic symptoms during eruptions, and data were transmitted over a 9 km wireless link to a base station at the volcano observatory B10. Intels radio set Vineyard B11 is an example of apply ubiquitous computing for clownish monitoring. In this application, the network is expected not only to collect and interpret data, further also to use such data to make decisions aimed at detecting the presence of parasites and enabling the use of the earmark kind of insecticide. info collection relies on data mules, small devices carried by people (or dogs) that communicate with the nodes and collect data. In this project, the attention is shifted from rel iable information collection to active decisionmaking based on acquired data. Just as they can be used to monitor nature, sensor networks can handlewise be used to monitor human behavior. In the Smart Kindergarten project at UCLA B12, wirelessly-networked, sensor-enhanced toys and other classroom tendencys supervise the learning process of children and allow unobtrusive monitoring by the teacher. Medical research and healthcare can greatly benefit rom sensor networks vital sign monitoring and accident recognition are the most natural applications. An eventful issue is the care of the elderly, especially if they are affected by cognitive dec source a network of sensors and actuators could monitor them and even assist them in their periodical routine. Smart appliances could help them organize their lives by reminding them of their meals and medications. Sensors can be used to capture vital signs from patients in real-time and relay the data to handheld computers carried by medical personnel, and wearable sensor nodes can store patient data such as identification, history, and treatments.With these ideas in mind, Harvard University is cooperating(a) with the School of Medicine at Boston University to damp CodeBlue, an infrastructure public figureed to support wireless medical sensors, PDAs, PCs, and other devices that may be used to monitor and treat patients in various medical scenarios B13. On the hardware side, the research team has Martin Haenggi is with the department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556 Fax +1 574 631 4393 emailprotectednd. edu. Daniele Puccinelli is also with the Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556. reated Vital Dust, a inflexible of devices based on the MICA21 sensor node weapons platform (one of the most popular members of the Berkeley motes family), which collect heart rate, oxygen saturation, and EKG data and relay them over a medium-range ( 100 m) wireless network to a PDA B14. Interactions between sensor networks and humans are already judged controversial. The US has recently approved the use of a radio receiver-frequency implantable device (VeriChip) on humans, whose intended application is gatewaying the medical records of a patient in an emergency. Potential future tense repercussions of this decision have been discussed in the media.An elicit application to complaisant engineering is the idea of Smart Buildings wireless sensor and actuator networks integrated within buildings could allow distributed monitoring and control, improving living conditions and reducing the zip fastener consumption, for instance by controlling temperature and air flow. Military applications are plentiful. An intriguing example is DARPAs self-healing minefield B15, a selforganizing sensor network where peer-to-peer communication between anti-tank mines is used to respond to attacks and distribute the mines in order to heal breaches , complicating the progress of enemy troops.Urban warfare is another application that distributed sensing lends itself to. An ensemble of nodes could be deployed in a urban landscape painting to detect chemical attacks, or track enemy movements. PinPtr is an ad hoc acoustic sensor network for sniper localization highly-developed at Vanderbilt University B16. The network detects the muzzle blast and the acoustic shock wave that originate from the sound of gunfire. The arrival times of the acoustic events at different sensor nodes are used to estimate the speckle of the sniper and send it to the base station with a special data aggregation and routing service.Going back to peaceful applications, efforts are underway at Carnegie Mellon University and Intel for the design of IrisNet (Internet- scale leaf Resource-Intensive Sensor net profit Services) B17, an architecture for a mankindwide sensor web based on common computing hardware such as Internet-connected PCs and low-cost sensi ng hardware such as webcams. The network interface of a PC thusly senses the virtual environment of a LAN or the Internet rather than a physical environment with an architecture based on the concept of a distributed database B18, this hardware can be orchestrated into a global sensor system hat responds to queries from users. 3. feature of speech Features of Sensor Networks In ad hoc networks, wireless nodes self-organize into an infrastructureless network with a dynamic topology. Sensor networks (such as the one in throw 1) voice these traits, but also have several distinguishing features. The number of nodes in a characteristic sensor network is much higher than in a typical ad hoc network, and dense deployments are often coveted to ensure coverage and connectivity for these reasons, sensor network hardware must be cheap. Nodes typically have mean energy limitations, which make them more misadventure-prone. They are enerally assumed to be stationary, but their relatively f requent breakdowns and the volatile nature of the wireless channel nonetheless impart in a variable network topology. Ideally, sensor network hardware should be power- high-octane, small, inexpensive, and reliable in order to maximize network lifetime, add flexibility, allay data collection and minimize the take aim for primary(prenominal)tenance. Lifetime Lifetime is extremely critical for most applications, and its primary limiting factor is the energy consumption of the nodes, which need to be self-powering. Although it is often assumed that the transmit power associated with acket transmitting accounts for the lions consider of power consumption, sensing, augury processing and even hardware doing in standby mode possess a consistent amount of power as intumesce C19, C20. In some applications, extra power is necessitate for macro-scale actuation. M some(prenominal) researchers suggest that energy consumption could be reduced by considering the existing interdependencies between individual layers in the network protocol stack. Routing and channel portal protocols, for instance, could greatly benefit from an information exchange with the physical layer. At the physical layer, benefits can be obtained with ower radio duty cycles and dynamic modulation scaling (varying the constellation size to minimize energy expenditure third QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 21 External Infrastructure Gateway Base Station Sensing Nodes Figure 1. A generic sensor network with a two-tiered archi1 tecture. See Section 5 for a hardware overview. D35). Using low-power modi for the processor or disabling the radio is generally advantageous, even though periodically turning a subsystem on and off may be more costly than always keeping it on. Techniques aimed at reducing the idle mode leakage current in CMOS-based rocessors are also noteworthy D36. Medium Access Control (MAC) solutions have a hold impact on energy consumption, as some of the primary causes of energy waste are found at the MAC layer collisions, control computer software overhead and idle listening. Powersaving forward error control techniques are not easy to implement due to the high amount of computing power that they exact and the fact that unyielding packets are normally not practical. Energy-efficient routing should avoid the going away of a node due to battery depletion. Many proposed protocols tend to minimize energy consumption on forwarding aths, but if some nodes happen to be located on most forwarding paths (e. g. , close to the base station), their lifetime pass on be reduced. Flexibility Sensor networks should be scalable, and they should be able to dynamically adapt to changes in node density and topology, like in the case of the self-healing minefields. In surveillance applications, most nodes may remain quiescent as long as nothing interesting happens. However, they must be able to respond to special events that the network intends to study with so me degree of granularity. In a self-healing minefield, a number of sensing mines ay rest period as long as none of their peers explodes, but need to quickly be enumerate operational in the case of an enemy attack. Response time is also very critical in control applications (sensor/actuator networks) in which the network is to provide a delay-guaranteed service. Untethered systems need to self-configure and adapt to different conditions. Sensor networks should also be robust to changes in their topology, for instance due to the failure of individual nodes. In particular, connectivity and coverage should always be guaranteed. Connectivity is achieved if the base station can be reached from any node.Coverage can be seen as a stride of quality of service in a sensor network C23, as it defines how well a particular bailiwick can be observed by a network and characterizes the probability of detection of geographicly constrained phenomena or events. Complete coverage is particularly imp ortant for surveillance applications. Maintenance The only desired form of maintenance in a sensor network is the complete or partial update of the program code in the sensor nodes over the wireless channel. All sensor nodes should be updated, and the restrictions on the size of the new code should be the same as in the case of fit out programming.Packet loss must be accounted for and should not impede correct reprogramming. The portion of code always running in the node to guarantee reprogramming support should have a small footprint, and updating procedures should only cause a brief interruption of the normal operation of the node C24. The functioning of the network as a only should not be endangered by unavoidable failures of single nodes, which may occur for a number of reasons, from battery depletion to unpredictable external events, and may all be independent or spatially correlated C25. Faulttolerance is particularly crucial as ongoing maintenance s rarely an option in sen sor network applications. Self-configuring nodes are necessary to allow the deployment process to run smoothly without human interaction, which should in principle be limited to placing nodes into a given geographical area. It is not desirable to have humans configure nodes for habitat monitoring and destructively interfere with wildlife in the process, or configure nodes for urban warfare monitoring in a hostile environment. The nodes should be able to assess the quality of the network deployment and indicate any problems that may arise, as well as coif to hanging environmental conditions by automatic reconfiguration. Location awareness is important for selfconfiguration and has definite advantages in terms of routing C26 and security. Time synchronization C27 is advantageous in promoting cooperation among nodes, such as data fusion, channel find, coordination of sleep modi, or security-related interaction. Data Collection Data collection is related to network connectivity and co verage. An interesting solution is the use of ubiquitous mobile agents that randomly move around to gather data bridging sensor nodes and access points, whimsically named dataMULEs ( bustling Ubiquitous LAN Extensions) in C28. The predictable mobility of the data lower can be used to save power C29, as nodes can learn its schedule. A similar concept has been implemented in Intels radiocommunication Vineyard. It is often the case that all data are relayed to a base station, but this form of centralized data collection may shorten network lifetime. Relaying data to a data sink causes non-uniform power consumption patterns that may overburden forwarding nodes C21. This is particularly harsh on nodes providing end links to base stations, which may end up relaying traffic coming from all ther nodes, thus forming a critical bottleneck for network throughput A4, C22, as shown in Figure 2. An interesting technique is clustering C30 nodes team up to form clusters and transmit their informa tion to their cluster heads, which fuse the data and forward it to a 22 IEEE CIRCUITS AND SYSTEMS MAGAZINE 3rd QUARTER 2005 sink. Fewer packets are transmitted, and a uniform energy consumption pattern may be achieved by periodic re-clustering. Data redundancy is minimized, as the aggregation process fuses strongly correlated measurements. Many applications require that queries be sent to sensing nodes.This is true, for example, whenever the goal is gathering data regarding a particular area where various sensors have been deployed. This is the rationale behind looking at a sensor network as a database C31. A sensor network should be able to protect itself and its data from external attacks, but the severe limitations of lower-end sensor node hardware make security a true challenge. Typical encoding schemes, for instance, require large amounts of memory that are unavailable in sensor nodes. Data confidentiality should be preserved by encrypting data with a secret key shared with t he intended receiver. Data integrity should be ensured to revent unauthorized data alteration. An authenticated broadcast must allow the verification of the legitimacy of data and their sender. In a number of commercial applications, a serious disservice to the user of a sensor network is compromising data availability (denial of service), which can be achieved by sleep-deprivation torture C33 batteries may be drained by continuous service requests or demands for legitimate but intensive tasks C34, preventing the node from entering sleep modi. 4. Hardware shape Issues In a generic sensor node (Figure 3), we can identify a power module, a communication block, a processing unit ith internal and/or external memory, and a module for sensing and actuation. Power Using stored energy or harvesting energy from the outside world are the two options for the power module. Energy storage may be achieved with the use of batteries or alternative devices such as fuel cells or miniaturized pepper iness engines, whereas energy-scavenging opportunities D37 are provided by solar power, vibrations, acoustic encumbrance, and piezoelectric effects D38. The vast majority of the existing commercial and research platforms relies on batteries, which dominate the node size. firsthand (nonrechargeable) batteries are often chosen, predominantlyAA, AAA and coin-type. Alkaline batteries offer a high energy density at a cheap price, offset by a non-flat discharge, a large physical size with respect to a typical sensor node, and a shelf life of only 5 years. Voltage regulation could in principle be employed, but its high inefficiency and large quiescent current consumption call for the use of components that can deal with large variations in the supply emf A5. Lithium cells are very compact and boast a flat discharge curve. Secondary (rechargeable) batteries are typically not desirable, as they offer a lower energy density and a higher cost, not to mention the fact that in most pplicatio ns recharging is simply not practical. open fire cells D39 are rechargeable electrochemical energy- con meter reading devices where electricity and heat are produced as long as hydrogen is supplied to react with oxygen. Pollution is minimal, as irrigate is the main byproduct of the reaction. The potential of fuel cells for energy storage and power delivery is much higher than the one of traditional battery technologies, but the fact that they require hydrogen complicates their application. Using renewable energy and scavenging techniques is an interesting alternative. Communication Most sensor networks use radio communication, even if lternative solutions are offered by optical maser and infrared emission. Nearly all radio-based platforms use COTS (Commercial Off-The-Shelf) components. Popular choices include the TR1000 from RFM (used in the MICA motes) and the CC1000 from Chipcon (chosen for the MICA2 platform). More recent solutions use industry regulations like IEEE 802. 15. 4 (MICAz and Telos motes with CC2420 from Chipcon) or pseudo-standards like Bluetooth. Typically, the transmit power ranges between ? 25 dBm and 10 dBm, while the receiver sensitivity can be as good as ? 110 dBm. deuce-ace QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 23 Base Station Critical Nodes Figure 2.A uniform energy consumption pattern should avoid the depletion of the resources of nodes located in the vicinities of the base station. Communication Hardware Power Sensors (? Actuators) ADC Memory Processor Figure 3. Anatomy of a generic sensor node. Spread spectrum techniques increase the channel reliability and the noise tolerance by spreading the signal over a wide range of frequencies. Frequency hopping (FH) is a spread spectrum technique used by Bluetooth the bearer frequency changes 1600 times per second on the basis of a pseudo-random algorithm. However, channel synchronization, hopping sequence search, and the high data rate ncrease power consumption this is one of the strongest caveats when using Bluetooth in sensor network nodes. In Direct Sequence Spread Spectrum (DSSS), communication is carried out on a single carrier frequency. The signal is calculate by a higher rate pseudo-random sequence and thus spread over a wide frequency range (typical DSSS radios have spreading factors between 15 and 100). Ultra Wide Band (UWB) is of great interest for sensor networks since it meets some of their main requirements. UWB is a particular carrier-free spread spectrum technique where the RF signal is spread over a spectrum as large as several GHz.This implies that UWB signals look like noise to conventional radios. Such signals are produced using baseband pulses (for instance, Gaussian monopulses) whose length ranges from 100 ps to 1 ns, and baseband transmission is generally carried out by means of pulse position modulation (PPM). Modulation and demodulation are indeed extremely cheap. UWB provides built-in ranging capabilities (a wideband signal al lows a good time resolution and therefore a good location accuracy) D40, allows a very low power consumption, and performs well in the presence of multipath fading. Radios with relatively low bit-rates (up to 100 kbps) re advantageous in terms of power consumption. In most sensor networks, high data rates are not needed, even though they allow shorter transmission times thus permitting lower duty cycles and alleviating channel access contention. It is also desirable for a radio to quickly switch from a sleep mode to an operational mode. Optical transceivers such as lasers offer a strong power advantage, mainly due to their high directionality and the fact that only baseband processing is required. Also, security is intrinsically guaranteed (intercepted signals are altered). However, the need for a line of sight and recise localization makes this option impractical for most applications. Processing and Computing Although low-power FPGAs might become a viable option in the near future D41, microcontrollers (MCUs) are now the primary choice for processing in sensor nodes. The key metric in the selection of an MCU is power consumption. Sleep modi deserve special attention, as in many applications low duty cycles are essential for lifetime extension. Just as in the case of the radio module, a extravagant wake-up time is important. Most central processing units used in lower-end sensor nodes have clock speeds of a few MHz. The memory requirements depend on the pplication and the network topology data storage is not critical if data are often relayed to a base station. Berkeley motes, UCLAs Medusa MK-2 and ETHZs BTnodes use low-cost Atmel AVR 8-bit RISC microcontrollers which consume about 1500 pJ/instruction. More sophisticated platforms, such as the Intel iMote and Rockwell WINS nodes, use Intel StrongArm/XScale 32-bit processors. Sensing The high consume rates of modern digital sensors are usually not needed in sensor networks. The power efficiency of sensors an d their turn-on and turn-off time are much more important. Additional issues are the physical ize of the sensing hardware, fabrication, and assembly compatibility with other components of the system. Packaging requirements come into play, for instance, with chemical sensors which require contact with the environment D42. Using a microcontroller with an onchip analog comparator is another energy-saving technique which allows the node to avoid sampling values falling outside a certain range D43. The ADC which complements analog sensors is particularly critical, as its resolution has a direct impact on energy consumption. Fortunately, typical sensor network applications do not have stringent resolution requirements.Micromachining techniques have allowed the miniaturization of many types of sensors. Performance does decrease with sensor size, but for many sensor network applications size matters much more than accuracy. Standard integrated circuits may also be used as temperature sens ors (e. g. , using the temperaturedependence of subthreshold MOSFETs and pn junctions) or light intensity transducers (e. g. , using photodiodes or phototransistors) D44. Nanosensors can offer promising solutions for biological and chemical sensors while concurrently meeting the most ambitious miniaturization needs. 5. Existing Hardware PlatformsBerkeley motes, made commercially available by Crossbow, are by all means the best known sensor node hardware implementation, used by more than 100 research organizations. They consist of an embedded microcontroller, low-power radio, and a small memory, and they are powered by two AA batteries. MICA and MICA2 are the most successful families of Berkeley motes. The MICA2 platform, whose layout is shown in Figure 4, is equipped with an Atmel ATmega128L and has a CC1000 transceiver. A 51-pin expansion connector is available to interface sensors (commercial sensor boards designed for this specific platform are available).Since the MCU is to hand le 24 IEEE CIRCUITS AND SYSTEMS MAGAZINE THIRD QUARTER 2005 medium access and baseband processing, a fine-grained event-driven real-time operating system (TinyOS) has been implemented to specifically address the concurrency and resource management needs of sensor nodes. For applications that require a better form factor, the circular MICA2Dot can be used it has most of the resources of MICA2, but is only 2. 5 cm in diameter. Berkeley motes up to the MICA2 generation cannot interface with other wireless- enabled devices E47. However, the newer generations MICAz and Telos support IEEE 802. 15. , which is part of the 802. 15 Wireless Personal Area Network (WPAN) standard being developed by IEEE. At this point, these devices represent a very good solution for generic sensing nodes, even though their unit cost is still relatively high (about $100$200). The proliferation of different lowerend hardware platforms within the Berkeley mote family has recently led to the development of a new v ersion of TinyOS which introduces a flexible hardware abstraction architecture to simplify multi-platform support E48. Tables 1 and 2 show an overview of the radio transceivers and the microcontrollers most commonly used in xisting hardware platforms an overview of the key features of the platforms is provided in Table 3. Intel has designed its own iMote E49 to implement various improvements over available mote designs, such as increased CPU processing power, increased main memory size for on-board computing and improved radio reliability. In the iMote, a powerful ARM7TDMI core is complemented by a large main memory and non-volatile storage area on the radio side, Bluetooth has been chosen. Various platforms have been developed for the use of Berkeley motes in mobile sensor networks to enable investigations into controlled mobility, which facilitates eployment and network repair and provides possibilities for the implementation of energy-harvesting. UCLAs RoboMote E50, Notre Dames M icaBot E51 and UC Berkeleys CotsBots E52 are examples of efforts in this direction. UCLAs Medusa MK-2 sensor nodes E53, developed for the Smart Kindergarten project, expand Berkeley motes with a second microcontroller. An on-board power management and track unit monitors power consumption within the different subsystems and selectively powers down unused parts of the node. UCLA has also developed iBadge E54, a wearable sensor node with sufficient computational power to process the sensed data.Built around an ATMega128L and a DSP, it features a Localization Unit designed to estimate the position of iBadge in a room based on the presence of special nodes of known location attached to the ceilings. In the context of the look project (a joint effort among several European institutions) custom nodes E55, C24 have been developed to test and demonstrate energy-efficient networking algorithms. On the software side, a trademarked operating system, PEEROS (Preemptive EYES veridical Time O perating System), has been implemented. The Smart-Its project has investigated the possibility of embedding computational power into objects, leading o the creation of three hardware platforms DIY Smartits, tinge computers and BTnodes. The DIY Smart-its E56 have been developed in the UK at Lancaster University their modular design is based on a core board that provides processing and communication and can be elongated with add-on boards. A typical setup of Smart-its consists of one or more sensing nodes that broadcast their data to a base station which consists of a standard core board connected to the serial port of a PC. Simplicity and extensibility are the key features of this platform, which has been developed for the creation of Smart Objects.An interesting application is the pack Table four load cells placed underneath a coffee table form a Wheatstone twain and are connected to a DIY node that observes load changes, determines event types like placement and removal of obj ects or a person moving a finger across the surface, and also retrieves the position of an object by correlating the values of the individual load cells after the event type (removed or placed) has been recognized E57. Particle Computers have been developed at the University of Karlsruhe, Germany. Similarly to the DIY platform, the Particle Smart-its are based on a core board quipped with a Microchip PIC they are optimized for energy efficiency, scalable communication and small scale (17 mm ? 30 mm). Particles communicate in an ad hoc fashion as two Particles come close to one another, THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 25 Oscillator 7. 3728-MHz DS2401P Silicon Serial No. Antenna Connector Connector LEDs Battery Connection 32. 768-kHz Oscillator 14. 7456-MHz Oscillator ATMEL ATMega 128L CPU CC1000 Transceiver ATMEL AT45DB041 Data ostentatiousness Figure 4. Layout of the MICA2 platform. they are able to talk. Additionally, if Particles come near a gateway device, they can be connected to Internet-enabled evices and access services and information on the Internet as well as provide information E58. The BTnode hardware from ETHZ E47 is based on an Atmel ATmega128L microcontroller and a Bluetooth module. Although advertised as a low-power technology, Bluetooth has a relatively high power consumption, as discussed before. It also has long connection setup times and a lower degree of freedom with respect to possible network topologies. On the other hand, it ensures interoperability between different devices, enables application development through a standardized interface, and offers a significantly higher bandwidth (about 1 Mbps) ompared to many low-power radios (about 50 Kbps). Moreover, Bluetooth support means that COTS hardware can be used to create a gateway between a sensor network and an external network (e. g. , the Internet), as opposed to more costly proprietary solutions E59. MIT is working on the ? AMPS (? -Adaptive Multidomain Power- aware Sensors) project, which explores energy-efficiency constraints and key issues such as selfconfiguration, reconfigurability, and flexibility. A first prototype has been designed with COTS components three stackable boards (processing, radio and power) and an ptional extension module. The energy dissipation of this microsensor node is reduced through a variety of poweraware design techniques D45 including fine-grain shutdown of inactive components, dynamic voltage and frequency scaling of the processor core, and adjustable radio transmission power based on the required range. Dynamic voltage scaling is a technique used for active power management where the supply voltage and clock frequency of the processor are regulated depending on the computational load, which can vary significantly based on the operational mode D36, C20. The main oal of second generation ? AMPS is clearly stated in D46 as breaking the 100 ? W average power barrier. Another interesting MIT project is the draw ing pin computing system E60, whose goal is the modelling, testing, and deployment of distributed peer-to-peer sensor networks consisting of many identical nodes. The pushpins are 18 mm ? 18 mm modular devices with a power substrate, an infrared communication module, a processing module (Cygnal C8051F016) and an expansion module (e. g. , for sensors) they are powered by direct contact between the power substrate and superimposed conductive sheets. 26 MCU Max.Freq. MHz Memory Data Size bits ADC bits Architecture AT90LS8535 (Atmel) 4 8 kB Flash, 512B EEPROM, 512B SRAM 8 10 AVR ATMega128L (Atmel) 8 128 kB Flash, 4 kB EEPROM, 4 kB SRAM 8 10 AVR AT91FR4081 (Atmel) 33 136 kB On-Chip SRAM, 8 Mb Flash 32 Based on ARM core (ARM7TDMI) MSP430F149 (TI) 8 60 kB + 256B Flash, 2 kB RAM 16 12 Von Neumann C8051F016 (Cygnal) 25 2304B RAM, 32 kB Flash 8 10 Harvard 8051 PIC18F6720 (Microchip) 25 128 kB Flash, 3840B SRAM, 1 kB EEPROM 8 10 Harvard PIC18F252 (Microchip) 40 32 K Flash, 1536B RAM, 256B EE PROM 8 10 Harvard StrongARM SA-1110 (Intel) 133 32 ARM v. 4PXA255 (Intel) 400 32 kB Instruction Cache, 32 kB Data 32 ARM v. 5TE Cache, 2 kB Mini Data Cache Table 2. Microcontrollers used in sensor node platforms. Radio (Manufacturer) Band MHz Max. Data Rate kbps Sensit. dBm Notes TR1000 (RFM) 916. 5 115. 2 ? 106 OOK/ASK TR1001 (RFM) 868. 35 115. 2 ? 106 OOK/ASK CC1000 (Chipcon) 3001,000 76. 8 ? 110 FSK, ? 20 to 10 dBm CC2420 (Chipcon) 2,400 250 ? 94 OQPSK, ? 24 to 0 dBm, IEEE 802. 15. 4, DSSS BiM2 (Radiometrix) 433. 92 64 ? 93 9XStream (MaxStream) 902928 20 ? 114 FHSS Table 1. Radios used in sensor node platforms. IEEE CIRCUITS AND SYSTEMS MAGAZINE THIRD QUARTER 2005MIT has also built Tribble (Tactile reactive interface built by linked elements), a spherical robot wrapped by a wired skinlike sensor network designed to emulate the functionalities of biological skin E61. Tribbles surface is divided into 32 patches with a Pushpin processing module and an depart of sensors and actua tors. At Lancaster University, surfaces provide power and network connectivity in the Pin&Play project. Network nodes come in different form factors, but all share the Pin&Play connector, a custom component that allows physical connection and networking through conductive sheets which re embedded in surfaces such as a argue or a bulletin board E62. Pin&Play falls in between wired and wireless technologies as it provides network access and power across 2D surfaces. Wall-mounted objects are especially suited to be augmented to become Pin&Play objects. In a demonstration, a wall switch was augmented and freely placed anywhere on a wall with a Pin&Play surface as wallpaper. For applications which do not call for the minimization of power consumption, high-end nodes are available. Rockwellis WINS nodes and Sensorias WINS 3. 0 Wireless Sensing Platform are equipped with more powerful rocessors and radio systems. The embedded PC modules based on widely supported standards PC/104 and PC/10 4-plus feature Pentium processors moreover, PC/104 peripherals include digital I/O devices, sensors and actuators, and PC-104 products support closely all PC software. PFU Systems Plug-N-Run products, which feature Pentium processors, also belong to this category. They offer the capabilities of PCs and the size of a sensor node, but lack built-in communication hardware. COTS components or lower-end nodes may be used in this sense C32. Research is underway toward the creation of sensor nodes that are more capable than the motes, moreover maller and more power-efficient than higher-end nodes. Simple yet effective gateway devices are the MIB programming boards from Crossbow, which bridge networks of Berkeley motes with a PC (to which they interface using the serial port or Ethernet). In the case of Telos motes, any generic node (i. e. , any Telos mote) can act as a gateway, as it may be connected to the USB port of a PC and bridge it to the network. Of course, more powerful gateway d evices are also available. Crossbows Stargate is a powerful embedded computing platform (running Linux) with enhanced communication and sensor signal processing capabilities based n Intel PXA255, the same X-Scale processor that forms the core of Sensoria WINS 3. 0 nodes. Stargate has a connector for Berkeley motes, may be bridged to a PC via Ethernet or 802. 11, and includes built-in Bluetooth support. 6. Closing Remarks Sensor networks offer countless challenges, but their versatility and their broad range of applications are eliciting more and more interest from the research community as well as from industry. Sensor networks have the potential of triggering the next revolution in information technology. The challenges in terms of circuits and systems re numerous the development of low-power communication hardware, low-power microcontrollers, MEMSbased sensors and actuators, efficient AD conversion, and energy-scavenging devices is necessary to enhance the potential and the perfor mance of sensor networks. System integration is another major challenge that sensor networks offer to the circuits and systems research community. We suppose that CAS can and should have a significant impact in this emerging, exciting area. 27 Platform CPU Comm. External Memory Power Supply WesC (UCB) AT90LS8535 TR1000 32 kB Flash Lithium Battery MICA (UCB, Xbow) ATMega128L TR1000 512 kB Flash AAMICA2 (UCB, Xbow) ATMega128L CC1000 512 kB Flash AA MICA2Dot (UCB, Xbow) ATMega128L CC1000 512 kB Flash Lithium Battery MICAz (UCB, Xbow) ATMega128L CC2420 512 kB Flash AA Telos (Moteiv) MSP430F149 CC2420 512 kB Flash AA iMote (Intel) ARM7TDMI Core Bluetooth 64 kB SRAM, 512 kB Flash AA Medusa MK-2 (UCLA) ATMega103L TR1000 4 Mb Flash Rechargeable Lithium Ion AT91FR4081 iBadge (UCLA) ATMega128L Bluetooth, TR1000 4 Mb Flash Rechargeable Lithium Ion DIY (Lancaster University) PIC18F252 BiM2 64 Kb FRAM AAA, Lithium, Rechargeable Particle (TH) PIC18F6720 RFM TR1001 32 kB EEPROM AAA or Lithium Coi n Battery or RechargeableBT Nodes (ETHZ) ATMega128L Bluetooth, CC1000 244 kB SRAM AA ZebraNet (Princeton) MSP430F149 9XStream 4 Mb Flash Lithium Ion Pushpin (MIT) C8051F016 Infrared Power Substrate WINS 3. 0 (Sensoria) PXA255 802. 11b 64 MB SDRAM, 32 MB + 1 GB Flash Batteries Table 3. Hardware features of various platforms. THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE Acknowledgments The support of NSF (grants ECS 03-29766 and CAREER CNS 04-47869) is gratefully acknowledged. References General References A1 I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, A bailiwick on sensor networks, in IEEE Communications Magazine, pp. 02114, Aug. 2002. A2 L. B. Ruiz, L. H. A. Correia, L. F. M. Vieira, D. F. Macedo, E. F. Nakamura, C. M. S. Figueiredo, M. A. M. Vieira, E. H. B. Maia, D. Camara, A. A. F. Loureiro, J. M. S. Nogueira, D. C. da Silva Jr. , and A. O. Fernandes, Architectures for wireless sensor networks (In Portuguese), in Proceedings of the 22nd Brazilian Sym posium on Computer Networks (SBRC04), Gramado, Brazil, pp. 167218, May 2004. Tutorial. ISBN 85-88442-82-5. A3 C. Y. Chong and S. P. Kumar, Sensor networks Evolution, opportunities, and challenges, in IEEE Proceedings, pp. 12471254, Aug. 003. A4 M. 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Paradiso, Pushpin computing system overview A platform for distributed, embedded, ubiquitous sensor networks, in Proceedings of the Pervasive Computing Conference, Zurich, Switzerland, Aug. 2002. E61 J. A. Paradiso, J. Lifton, and M. Broxton, Sensate mediamultimodal electronic skins as dense sensor networks, BT Technology Journal, vol. 2, pp. 3244, Oct. 2004. E62 K. V. Laerhoven, N. Villar, and H. -W. Gellersen, Pin&Mix When Pins Become Interaction Components. . . , in Physical Interaction (PI03) Workshop on Real World User InterfacesMobile HCI Conference, Udine, Italy, Sept. 2003 . Daniele Puccinelli received a Laurea degree in Electrical Engineering from the University of Pisa, Italy, in 2001. After spending two years in industry, he joined the graduate program in Electrical Engineering at the University of Notre Dame, and received an M. S. Degree in 2005. He is currently working toward his Ph. D. degree.His research has focused on cross-layer approaches to wireless sensor network protocol design, with an emphasis on the interaction between the physical and the network layer. Martin Haenggi received the Dipl. Ing. (M. Sc. ) degree in electrical engineering from the Swiss Federal Institute of Technology in Zurich (ETHZ) in 1995. In 1995, he joined the Signal and Information Processing testing ground at ETHZ as a Teaching and Research Assistant. In 1996 he earned the Dipl. NDS ETH (post-diploma) degree in information technology, and in 1999, he completed his Ph. D. thesis on the analysis, design, and optimization of ellular neural networks. After a postdocto ral year at the Electronics Research Laboratory at the University of California in Berkeley, he joined the Department of Electrical Engineering at the University of Notre Dame as an assistant professor in January 2001. For both his M. Sc. and his Ph. D. theses, he was awarded the ETH medal, and he received an NSF CAREER award in 2005. For 2005/06, he is a CAS Distinguished Lecturer. His scientific interests include networking and wireless communications, with an emphasis on ad hoc and sensor networks. THIRD QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 29