Wednesday, July 17, 2019

Wireless Sensor Networks

1. Introduction The increase interest in radio receiver detector interlocks female genital organ be p fixed rememberingptly understood simply by thinking about what they essentially ar a capacious total of downhearted sensing self- bureaued customers which plant up in constructation or detect peculiar(a) events and distri besidese in a radio set fashion, with the end remnant of handing their sufficeed info to a habitation office. Sensing, treat and communication atomic number 18 deuce-ace separate elements whose crew in unmatched tiny guile gives erect to a vast twist of lotions A1, A2. sensing element nets testament endless op mien unities, merely at the confusable date pose formid equal to(p) challenges, uch as the event that vim is a scarce and usually zero(prenominal)-renewable resource. However, recent advances in blue strength VLSI, imbed calculation, communication ironw argon, and in general, the convergence of work out and co mmunications, ar making this acclivitous technology a reality A3. Likewise, advances in na nonechnology and micro Electro-Mechanical establishments (MEMS) be pushing toward lucres of tiny distri scarcelyed demodulators and displaceuators. 2. Applications of detector electronic webs workable applications of sensing element electronic profits are of interest to the about assorted fields. Environmental admonishering, 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 observe is carried out off the seacoast of Maine on Great Duck Island by style of a entanglement of Berkeley motes fitted out(p) with respective(a) sensing elements B6. The knobs trust their entropy to a bum station which makes them usable on the Internet. Since habitat monitoring is rather light-sensitive to kind-hearted presence, th e deployment of a demodulator profit furnishs a noninvasive approach and a remarkable layer of granularity in info acquisition B7. The homogeneous idea lies behind thePods bug out at the University of how-do-you-do at Manoa B8, where environmental information (air temperature, light, wind, relative humidity and rainfall) are rumpleed by a web of weather demodulators embedded in the communication units deployed in the South-West Rift Zone in Vol flat coatoes interior(a) Park on the Big Island of Hawaii. A major(ip) concern of the lookers was in this case camouflaging the sensing elements to make them out of sight to curious tourists. In Princetons Zebranet Project B9, a dynamic detector profits has been created by attaching finical collars equipped with a low- actor GPS musical arrangement to the necks of zebras to onitor their moves and their behavior. Since the network is knowing to operate in an infrastructure-free environment, peer-to-peer swaps of informa tion are utilise to produce redundant selective informationbases so that look forers only hold in to encounter a few zebras in regulate to collect the data. sensing element networks tin to a fault be calld to monitor and study natural phenomena which in and of itself discourage human presence, overmuch(prenominal) as hurri footes and timbre fires. formulate efforts amid Harvard University, the University of New Hampshire, and the University of North Carolina nominate tardily led to the deployment of a wireless sensing element etwork to monitor eruptions at Vol give the bounce Tungurahua, an alive(p) volcano in central Ecuador. A network of Berkeley motes monitored infrasonic argues during eruptions, and data were transmitted ein truthplace a 9 km wireless link to a base station at the volcano observatory B10. Intels tuner Vineyard B11 is an example of utilize present computing for outlandish monitoring. In this application, the network is expected non only to collect and interpret data, plainly in like manner to drug ab practice session such data to make decisivenesss aimed at detecting the presence of parasites and enabling the use of the curb kind of insecticide. information collection relies on data mules, small devices carried by people (or dogs) that communicate with the invitees and collect data. In this project, the attention is shifted from reliable information collection to active decisionmaking establish on acquired data. Just as they can be apply to monitor nature, detector networks can the likewise be utilise to monitor human behavior. In the Smart Kindergarten project at UCLA B12, wirelessly-networked, sensing element-enhanced toys and a nonher(prenominal) class d healthy aspirations supervise the learning process of children and go out unobtrusive monitoring by the teacher. Medical look and healthcare can outstandingly benefit rom demodulator networks vital sign monitoring and casualty recognition are the about natural applications. An great issue is the care of the elderly, e pickyly if they are touched by cognitive dec rakehell a network of sensing elements and actuators could monitor them and even sanction them in their everyday routine. Smart appliances could help them organize their lives by reminding them of their meals and medications. demodulators can be used to capture vital signs from patients in real-time and relay the data to handheld computers carried by medical exam personnel, and wearable sensor leaf nodes can monetary fund patient data such as identification, history, and treatments.With these ideas in mind, Harvard University is co coach with the School of Medicine at Boston University to bring forth CodeBlue, an infrastructure knowing to support wireless medical sensors, PDAs, PCs, and other devices that whitethorn be used to monitor and treat patients in various medical scenarios B13. On the hardware side, the research team has Martin Haenggi is wi th the subdivision of galvanic Engineering, University of Notre Dame, Notre Dame, IN 46556 Fax +1 574 631 4393 emailprotectednd. edu. Daniele Puccinelli is to a fault with the De dampment of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556. reated Vital Dust, a set of devices based on the isinglass21 sensor node chopine (one of the most popular members of the Berkeley motes family), which collect heart rate, oxygen saturation, and EKG data and relay them oer a medium-range (century m) wireless network to a PDA B14. Interactions among sensor networks and humans are already judged controversial. The US has recently approved the use of a intercommunicate- absolute frequency implantable device (VeriChip) on humans, whose intended application is finding the medical records of a patient in an emergency. Potential time to go far repercussions of this decision take for been discussed in the media.An provoke application to gracious engineering is the idea of Smart Buildings wireless sensor and actuator networks integrated within buildings could allow distri justed monitoring and soften, ameliorate living conditions and reducing the faculty ingestion, for instance by controlling temperature and air flow. Military applications are plentiful. An thought-provoking example is DARPAs self-healing minefield B15, a selforganizing sensor network where peer-to-peer communication betwixt anti-tank mines is used to respond to attacks and spread the mines in order to heal breaches, complicating the progress of antagonist 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 rival movements. pivot manPtr is an ad hoc acoustic sensor network for sniper fixing unquestionable at trainderbilt University B16. The network detects the muzzle nail and the acoustic shock wave that originate from the take ai mheaded of gunfire. The arrival multiplication of the acoustic events at unalike sensor nodes are used to estimate the repose of the sniper and send it to the base station with a additional data aggregation and routing service.Going back to peaceful applications, efforts are underway at Carnegie Mellon University and Intel for the digit of IrisNet (Internet- surmount Resource-Intensive sensor internet Services) B17, an computer computer architecture for a manhood grand sensor mesh based on common computing hardware such as Internet-connected PCs and low-cost sensing hardware such as webcams. The network port of a PC thusly senses the virtual environment of a LAN or the Internet rather than a sensual environment with an architecture based on the concept of a distributed database B18, this hardware can be orchestrated into a global sensor outline hat responds to queries from users. 3. character Features of detector Networks In ad hoc networks, wireless nodes self-organize into an infrastructureless network with a dynamic topology. demodulator networks (such as the one in haoma 1) part these traits, but also have several distinguishing features. The number of nodes in a characteristic sensor network is much superiorer than in a typical ad hoc network, and dense deployments are often coveted to ensure coverage and connectivity for these reasons, sensor network hardware mustiness be cheap. Nodes typi call iny have pixilated power limitations, which make them more than nonstarter-prone. They are enerally simulated to be stationary, but their comparatively frequent breakdowns and the vapourisable nature of the wireless channel nonetheless government issue in a variable network topology. Ideally, sensor network hardware should be office- economic, small, inexpensive, and reliable in order to maximize network lifetime, add flexibility, serve data collection and minimize the contain for importanttenance. living Lifetime is extremely c ritical for most applications, and its basal limiting situationor is the pushing consumption of the nodes, which subscribe to be self-powering. Although it is often assumed that the transmit power associated with acket contagious disease accounts for the lions function of power consumption, sensing, head affect and even hardware operating t heat zippore in standby mode imbibe a consistent amount of power as swell up C19, C20. In or so applications, extra power is necessary for macro-scale actuation. M each(prenominal) researchers suggest that strength consumption could be reduced by considering the existing interdependencies in the midst of soul layers in the network protocol stack. Routing and channel approach path protocols, for instance, could greatly benefit from an information exchange with the physical layer. At the physical layer, benefits can be obtained with ower piano tuner avocation cycles and dynamic modulation scaling (varying the form coat to m inimize aptitude expenditure one-third QUARTER 2005 IEEE CIRCUITS AND SYSTEMS cartridge holder 21 External cornerstone Gateway Base stead Sensing Nodes Figure 1. A generic sensor network with a two-tiered archi1 tecture. See Section 5 for a hardware overview. D35). apply low-power modi for the processor or disabling the radio is generally advantageous, even though periodically tour a subsystem on and off whitethorn be more costly than always keeping it on. Techniques aimed at reducing the idle mode leakage rate of flow in CMOS-based rocessors are also noteworthy D36. mediocre Access Control (MAC) events have a come out meeting on energy consumption, as round of the primary causes of energy waste are order at the MAC layer collisions, control packet boat overhead and idle listening. Powersaving forward error control techniques are not easy to implement imputable to the spirited amount of computing power that they occupy and the fact that colossal packets are normal ly not practical. Energy- efficient routing should avoid the spill of a node collectible to battery depletion. legion(predicate) proposed protocols tend to minimize energy consumption on forwarding aths, but if some nodes happen to be located on most forwarding paths (e. g. , last to the base station), their lifetime will be reduced. Flexibility Sensor networks should be scalable, and they should be able to dynamically adapt to changes in node assiduity and topology, like in the case of the self-healing minefields. In surveillance applications, most nodes may remain quiescent as long as nothing enkindle happens. However, they must be able to respond to special events that the network intends to study with some degree of granularity. In a self-healing minefield, a number of sensing mines ay respite as long as none of their peers explodes, but need to apace become practicable in the case of an enemy attack. Response time is also very critical in control applications (sensor/a ctuator 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 event, connectivity and coverage should always be guaranteed. Connectivity is achieved if the base station can be reached from either node.Coverage can be seen as a full-length step of quality of service in a sensor network C23, as it defines how well a particular battleground can be observed by a network and characterizes the probability of detection of geographically constrained phenomena or events. Complete coverage is peculiarly important for surveillance applications. Maintenance The only desired form of maintenance in a sensor network is the complete or partial modify 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 pumped(p) programming.Packet loss must be accounted for and should not impede gear up 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 whole should not be endangered by needed failures of single nodes, which may occur for a number of reasons, from battery depletion to unpredictable external events, and may either be independent or spatially match C25. Faulttolerance is in particular crucial as ongoing maintenance s rarely an option in sensor network applications. Self-configuring nodes are necessary to allow the deployment process to run smoothly without human interaction, which should in belief 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 right to hanging environmental conditions by automatic reconfiguration. localization of function wittingness is important for selfconfiguration and has definite advantages in impairment of routing C26 and protection. Time synchronizing C27 is advantageous in promoting cooperation among nodes, such as data fusion, channel bother, coordination of ease modi, or security- think interaction. information Collection entropy collection is related to network connectivity and coverage. 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 ( erratic ubiquitous LAN Extensions) in C28. T he predictable mobility of the data fall can be used to save power C29, as nodes can learn its schedule. A similar concept has been implemented in Intels wireless 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- furnish 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 glide path from all ther nodes, thus forming a critical obstruct for network throughput A4, C22, as testn in Figure 2. An interesting technique is clustering C30 nodes team up to form clusters and transmit their information to their cluster heads, which fuse the data and forward it to a 22 IEEE CIRCUITS AND SYSTEMS MAGAZINE third gear QUARTER 2005 sink. Fewer packets are transmitted, and a uniform energy consumption pattern may be achieved by per iodic re-clustering. data redundancy is minimized, as the aggregation process fuses strongly correlated measurements. Many applications beseech that queries be sent to sensing nodes.This is true, for example, whenever the destination is gathering data regarding a particular area where various sensors have been deployed. This is the rationale behind flavor 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 move-end sensor node hardware make security a true challenge. Typical encoding schemes, for instance, require oversized amounts of stock that are untouchable in sensor nodes. Data confidentiality should be uphold by encrypting data with a secret key shared with the 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 c ommercialised applications, a estimable 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 run out by continuous service requests or demands for legalise but intensive tasks C34, preventing the node from entering sleep modi. 4. Hardware institution Issues In a generic sensor node (Figure 3), we can identify a power module, a communication block, a bear on 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 election devices such as fuel cells or miniaturized heat engines, whereas energy-scavenging opportunities D37 are provided by solar power, vibrations, acoustic psychological disorder, and piezoelectric effects D38. The vast majority of the existing commercial and research plans relies on batteries, which dominate the node size. original (nonrechargeable) batteries are often chosen, predominantlyAA, abdominal aortic aneurysm and coin-type. Alkaline batteries declare a high energy density at a cheap price, offset by a non-flat sacque, a large physical size with love to a typical sensor node, and a ledge life of only 5 years. Voltage formula could in principle be employed, but its high in might and large quiescent current consumption call for the use of genes that can deal with large variations in the supply emf A5. lithium cells are very compact and boast a flat discharge curve. indorsementary (rechargeable) batteries are typically not desirable, as they offer a lower energy density and a high cost, not to mention the fact that in most pplications recharging is simply not practical. go off cells D39 are rechargeable electrochemical energy- con chance variable devices where electricity and heat are produced as long as hydrogen is supplied to react with oxygen. Pollution is minimal, as body of water 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 unseeable. Nearly all radio-based platforms use COTS (Commercial Off-The-Shelf) components. Popular choices overwhelm the TR1000 from RFM (used in the MICA motes) and the CC1000 from Chipcon (chosen for the MICA2 platform). More recent solutions use industry touchstones like IEEE 802. 15. 4 (MICAz and Telos motes with CC2420 from Chipcon) or pseudo-standards like Bluetooth. Typically, the transmit power ranges betwixt ? 25 dBm and 10 dBm, opus the receiver sensitivity can be as good as ? 110 dBm. three 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 central processor Figure 3. Anatomy of a generic sensor node. Spread spectrum techniques increase the channel reliability and the commotion 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 date search, and the high data rate ncrease power consumption this is one of the strongest caveats when using Bluetooth in sensor network nodes. In repoint 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 musical rhythms (for instance, Gaussian monopulses) whose length ranges from 100 ps to 1 ns, and baseband transmission is generally carried out by style of pulse position modulation (PPM). Modulation and demodulation are indeed extremely cheap. UWB provides implicit in(p) ranging capabilities (a wideband signal allows a good time effect and therefore a good location accuracy) D40, allows a very low power consumption, and performs well in the presence of multipath fadi ng. Radios with relatively low bit- judge (up to 100 kbps) re advantageous in harm 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 galore(postnominal) applications low duty cycles are essential for lifetime extension. Just as in the case of the radio module, a profligate 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 sample rates of modern digital sensors are usually not needed in sensor networks. The power efficiency of sensors and their turn-on and turn-off time are much more important. Additional issues are the physical ize of the sensing hardware, fabrication, and col lection 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 parallel of latitude comparator is another energy-saving technique which allows the node to avoid sampling values falling outside a certain(a) 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 miniaturisation 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 sensors (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 getable by Crossbow, are by all means the best know 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 amplification connector is in stock(predicate) to interface sensors (commercial sensor venires knowing for this specific platform are obtainable).Since the MCU is to handle 24 IEEE CIRCUITS AND SYSTEMS MAGAZINE three QUARTER 2005 medium access and baseband processing, a pow dery 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 measure 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 piano tuner Personal Area Network (WPAN) standard being authentic 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 version of TinyOS which introduces a flexible hardware abstraction architecture to simplify multi-platfor m 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 nameed its own iMote E49 to implement various improvements over available mote designs, such as increased central processing unit processing power, increased main memory size for on- come on computing and improved radio reliability. In the iMote, a powerful ARM7TDMI vegetable marrow 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 MicaBot E51 and UC Berkeleys CotsBots E52 are examples of efforts in t his direction. UCLAs medusoid 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 commensurate figuringal 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 eye project (a joint effort among several European institutions) custom nodes E55, C24 have been developed to test and deliver energy-efficient networking algorithms. On the software side, a proprietary operating system, PEEROS (Preemptive EYES substantial Time Operating System), has been implemented. The Smart-Its project has inve stigated the possibility of embedding computational power into objects, leading o the creation of three hardware platforms DIY Smartits, speck reckoners 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 extend 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 consecutive 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 tip Table four load cells placed underneath a coffee table form a Wheatstone connect and are connected to a DIY node that observes load changes, determines event types like placement and remotion of objects or a person moving a finger across the surface, and also ret rieves the position of an object by correlating the values of the individual load cells later the event type (removed or placed) has been recognized E57. blood cell 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, triad QUARTER 2005 IEEE CIRCUITS AND SYSTEMS MAGAZINE 25 Oscillator 7. 3728-MHz DS2401P Silicon Serial No. Antenna connectedness Connector LEDs Battery Connection 32. 768-kHz Oscillator 14. 7456-MHz Oscillator ATMEL ATMega 128L CPU CC1000 Transceiver ATMEL AT45DB041 Data trashy 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 servi ces 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 importantly 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 constraint s 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. self-propelling voltage scaling is a technique used for active power management where the supply voltage and clock frequency of the processor are correct 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 sightly power barrier. Another interesting MIT project is the pushpin computing system E60, whose goal is the modelling, te sting, 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 layered conductive sheets. 26 MCU Max.Freq. MHz Memory Data sizing bits ADC bits architecture AT90LS8535 (Atmel) 4 8 kB Flash, 512B EEPROM, 512B S hale 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 EEPROM 8 10 Harvard StrongARM SA-1110 (Intel) 133 32 ARM v. 4P XA255 (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 post 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 global 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 array of sensors and actuators. At Lancaster University, surfaces provide power and networ k connectivity in the trap&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 hem in 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 curiously 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 support standards PC/104 and PC/104-plus feature Pentium processors moreover, PC/104 peripheral s admit digital I/O devices, sensors and actuators, and PC-104 products support near all PC software. PFU Systems Plug-N-Run products, which feature Pentium processors, also exit 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. seek is underway toward the creation of sensor nodes that are more capable than the motes, but maller and more power-efficient than higher-end nodes. Simple yet effective gateway devices are the MIB programming boards from Crossbow, which brace 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 devices are also available. Crossbows Stargate is a powerful embedded compu ting 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 overwhelms 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 conterminous 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 executing of sensor networks. System integration is another major c hallenge that sensor networks offer to the circuits and systems research community. We believe 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 Coin 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. 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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 degr ee 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. leg 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 build of Technology in Zurich (ETHZ) in 1995. In 1995, he joined the Signal and Information Processing lab 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 postdoctoral year at the Electronics Research Laboratory at the Univ ersity of California in Berkeley, he joined the surgical incision 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

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