UWB wireless sensor networks: UWEN - a practical ... - IEEE Xplore

UWB Wireless Sensor Networks: UWEN — A Practical Example IAN OPPERMANN, LUCIAN STOICA , ALBERTO RABBACHIN, ZACK SHELBY, JUSSI HAAPOLA , UNIVERSITY OF OULU CENTER FOR WIRELESS COMMUNICATIONS Abstract The research topic of sensor networks has been around for some time. With improvements in device size, power consumption, communications, and computing technology, sensor networks are becoming more popular for an ever increasing range of applications. Since 2002 there has been greatly increased popularity of commercial applications based on ultra wideband. This in turn has ignited interest in the use of this technology for sensor networks and fuelled research in the area. Impulse-radio-based UWB technology has a number of inherent properties that are well suited to sensor network applications. In particular, UWB systems have potentially low complexity and low cost; have noise-like signal; are resistant to severe multipath and jamming; and have very good time domain resolution, allowing for location and tracking applications. This article examines one example of a UWB sensor network for outdoor sport and lifestyle applications.

Introduction Sensor networks are typified by devices with low complexity that have limitations on processing power and memory, and severe restrictions on power consumption. By the very nature of the application, traffic in sensor networks is often bursty with long periods of no activity. For event detection operations, a device may remain idle for long periods, sending only “heartbeat” information, then suddenly be required to send significant amounts of information when an event occurs. For devices deployed in the field, this has significant implications for the design of efficient medium access protocols, radio communications technology, and the reliability of information transfer. For devices involved in continuous monitoring, the flow of traffic will be more stable. However, efficient multiple access, reliability, and battery life are still major considerations. Since the U.S. Federal Communications Commission (FCC) released the First Report and Order in 2002 covering commercial use of ultra wideband (UWB) [1], interest in UWB-based applications has increased greatly. This in turn has ignited interest in the use of UWB for sensor networks and fuelled research in the area. Impulse-radio-based UWB technology has a number of inherent properties that are well suited to sensor network applications. In particular, impulseradio-based UWB systems have potentially low complexity and low cost; have noise-like signals; are resistant to severe multipath and jamming; and have very good time domain resolution, allowing for location and tracking applications [2]. The low complexity and low cost of impulse radio UWB systems arise from the essentially baseband nature of the signal transmission. Unlike conventional radio systems, the UWB transmitter produces a very short time domain pulse that is able to propagate without the need for an additional radio frequency (RF) mixing stage. The RF mixing stage takes a baseband signal and “injects” a carrier frequency or translates the signal to a frequency that has desirable propagation characteristics. The very wideband nature of the UWB signal

IEEE Radio Communications • December 2004

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means it spans frequencies commonly used as carrier frequencies. The signal will propagate well without need for additional upconversion and amplification. The UWB receiver also does not require the reverse process of downconversion. Again, this means a local oscillator in the receiver can be omitted, which means the removal of associated complex delay and phase tracking loops. High achievable burst data rates for UWB systems means that sensors can transfer their payload data quickly and spend much of the rest of the time “asleep” or in a low-power state. To realize the benefits of UWB in sensor networks, careful consideration must be given to the design of the medium access control (MAC), conservation of power, and efficient radio technology. The solutions developed depend very much on the application examined. This article presents the architecture of a sensor system based on low-power low-complexity UWB transceivers. The UWB transceiver circuit is designed for lowdata-rate low-cost applications with built-in location and tracking capabilities. The UWB receivers are based on a noncoherent architecture that enables them to be extremely simple and largely insensitive to the transmitted pulse shape. The UWB circuit consists of an oscillator, the transmitter, the receiver, and a baseband digital signal processing (DSP) block. The circuits have been designed in a 0.35 µm Silicon-Germanium (Si-Ge) complementary metal oxide semiconductor (BiCMOS) process.

UWEN: A Multihop UWB Sensor System The UWEN concept is to develop a system offering low-rate communications with location and tracking for outdoor applications. The system concept is targeted for recreational activities such as cross country skiing, athletics, and running. The concept includes the development of small low-power devices that are worn (carried) by the user. In a low infrastructure environment, the user is able to relay positioning and performance information via peer-to-peer connections to fixed nodes in the network. Figure 1 shows an example usage of the system in the initial target application of outdoor snow sports. Each user carries a UWB sensor. Communication takes places in a peer-to-peer manner. Sensors that are not within range of the fixed nodes send their information using multihop techniques via intermediate nodes that act as relays. The fixed nodes communicate with each other to exchange information about the perceived position of each sensor in the network. The estimated position of the user is stored in the network for later retrieval. The sensors relay information on speed, direction, and possibly biometric data from the mobile user. The UWB signal from the sensor also allows the sensor position to be directly calculated by the fixed nodes. The main system features include the ability to support a large number of low power sensors; low-data-rate communications (up to 10 kb/s per sensor); ad hoc peer-to-peer network topology; limited fixed infrastructure requirement (few fixed nodes); and a multihop architecture to extend coverage of the system. The performance requirements of the system include

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TABLE 1. Target system performance. the ability to support a target maximum link distance of 50 m and mobile users with speeds of 40–60 km/hr. Table 1 summarizes some of the target performance parameters for the system. The system is initially targeted at relatively slow moving users. The tracking accuracy requirement is higher than individual location accuracy as smoothing algorithms may be applied to sequential estimated locations to improve tracking performance. This table also lists the number of location measurements required per second. The UWB sensors themselves must be very low power, very low cost (disposable), rugged, lightweight, and wearable or embeddable. The intention is that the sensor device will be purchased as part of admission to a recreational area (e.g., embedded in a ski lift ticket), then discarded when the user leaves the area or the admission period has expired.

Demonstration Environment The UWEN project includes two separate demonstration phases. The first demonstration is an in-lab proof of concept to be completed in December 2004. This will test the functionality of the system concept and UWB application-specific integrated circuit (ASIC) implementation. In 2005 a large set of UWB sensor devices will be produced, and the system will be deployed to sites located around the Finnish ski resort in Vuokatti. This demonstration will test the concept in a real sport environment with difficult conditions. Vuokatti is a world-class sport, holiday, and professional training center located in Kainuu. In addition, the Snowpolis, Sport College, and other organizations in Kajaani provide high-tech sport and well-being technology to the area. Thus, UWEN will be tested in a very realistic environment.

UWB Technology The FCC defines a radio system to be UWB if the fractional bandwidth B f or –10 dB bandwidth of the signal is greater than 20 percent or 500 MHz, respectively [1]. The UWB concept can be based on time-hopping (TH), direct-sequence

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(DS) spread spectrum approaches, fast frequency sweeping, or multicarrier techniques. The data modulation schemes most often used in UWB systems are pulse position modulation (PPM) and pulse amplitude modulation (PAM). The bandwidth of the UWB signal means that there are a large number of distinguishable multipath components [3]. This characteristic can give a gain in terms of both diversity and positioning accuracy. The low-complexity, low-cost, lowpower UWB transceiver design combined with channel characteristics supports the concept of simplifying the transceiver as much as possible and moving all the computation capability into the fixed network. The choice for UWEN receiver design is the non-coherent “energy collection” approach [4, 5], employing a simple IC that is able to recover much of the energy coming from the rich multipath channel. This non-coherent approach avoids the need for channel estimation. The drawback, however, is noise and interference enhancement. As the modulation does not undergo correlation in the receiver, signals need to be orthogonal in the time domain. The low-complexity UWB sensor in UWEN uses a low-frequency clock and a delay locked loop (DLL) frequency synthesis approach [5] for pulse generation. An alternate design for an UWB transceiver that makes use of an architecture based on sample/hold and A/D converters was presented in [6]. The analog-to-digital conversion resolution used in this case was 1 bit. A UWB transceiver architecture and performance parameters based on a multiband approach are presented in [7]. The ADC resolution used in [7] is 6 bits. A time-modulated UWB receiver block diagram is presented in [8] where the ASIC implementation requirements of an IC correlator are determined. A much more complex digital processor module implemented in field programmable gate array (FPGA) technology that is used in a UWB transceiver is presented in [9].

Modulation UWB utilizes very narro time-domain pulses to produce a very wideband signal of up to several gigahertz [9, 10]. The main candidates for UWB modulation schemes are 1. Time hopping spread spectrum impulse radio (TH-UWB) Data modulation schemes: • M-ary pulse position (BPM) • M-ary pulse amplitude (PAM) • On-off-keying (OOK) • M-ary pulse shape modulation (PSM) 2. Direct sequence spread spectrum impulse radio (DS-UWB) Data is spread over multiple pulses using a pseudo random code. Pulse waveform has a UWB spectrum. The chip rate of the spreading code does not need to be so high because the code is used only for user separation, not to spread the spectrum. Data modulation schemes: • PAM • OOK • PSM The objective of the UWEN project is to develop an architecture that is as simple as possible. For this reason, a noncoherent modulation scheme has been adopted, specifically bit position modulation (BPM) combined with direct sequence (DS). The BPM signal allows very simple noncoherent integrator receiver architectures to be adopted. The DS is used to randomize the spectrum of the transmitted signal so as to avoid strong spectral lines associated with simple pulse repetition as shown in Fig. 2. The UWEN spectrum is substantially wider than that shown in the figure.


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Randomization of the transmitted pulses is required in order to smooth the spectrum. Since the system is time-division multiple access (TDMA)-based, the spectrum can be smoothed using a DS spreading approach compatible with the clock and energy consumption restrictions. All the devices use the same spreading code sequence. BPM is shown in Fig. 3. The decision at the receiver of the sensor is based on a noncoherent approach. The receiver collects the energy in the two possible symbol position windows, and the bit decision is based on comparison between the energies as shown in Fig. 3. The impact of the DS is ignored by the receiver.

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It is possible for any single device to estimate –20 the arrival time of a signal from another –30 device based on its own time reference. This single data point in relative time needs to be –40 combined with other measurements to produce a 3D position estimate relative to some –50 system reference. Exchange of such timing 0.5 1 1.5 2 2.5 3 0 3.5 4 information requires cooperation between Frequency (GHz) devices. b Being able to locate all devices also presents a variation of the “hidden node” probFIGURE 2. Spectrum of an example pulse train a) without and b) with randomizing lem. The problem is complicated for techniques. positioning as multiple receivers need to detect the signal of each node to allow a position in 3D space to be determined. Fur2D positioning with three sensors/two TDOA measurements, thermore, tracking requires that each device be able to be and for 3D positioning with four sensors. Another interesting sensed/measured at a suitable rate to allow a reasonable method is the spherical interpolation method proposed in update rate. This is relatively simple for a small number of [11]. It is suitable for situations where at least five sensors are devices, but becomes very difficult for an arbitrarily large available for 3D positioning. number of devices. Information exchange between devices of timing and Optimization-Based Methods position estimates of neighbors also requires coordination. Calculation of the position in this case is performed centralNon-iterative positioning methods may not yield satisfactory position estimate for situations where a highly accurate posily and the results fed to the information sink (central contion estimate is required. To achieve high accuracy requiretroller). Finally, it is important to have the received signal ments, iterative methods such as optimization-based as unencumbered by multiple access interference as possible algorithms may be applied. in order to allow the best estimation of time of arrival: There are many optimization schemes and techniques. every 3.3 ns error translates to a minimum 1 m extra posiHowever, we are interested in examining several practical tion error. optimization methods and applying them to 3D position estiAll of these issues (information exchange, device sampling mation. These interesting methods include the Gauss-Newton rate, node visibility, signal conditioning) require MAC support method, Levenberg-Marquardt method, and quasi-Newton and are significant obstacles to wireless LAN and other radio algorithms. For these methods, an objective function is norsystems offering reliable positioning/tracking when added to the MAC post-design. Work in this area has focused on the minimum MAC complexity required to support the target positioning/tracking accuracy and availability. ‘0’

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Non-iterative methods for positioning are straightforward as they provide the position estimate in just one step given four simultaneous measurements for a 3D position. They may achieve acceptable performance for some applications. When high accuracy is required, non-iterative algorithms may provide a good initial position estimate for more accurate but more time-consuming iterative algorithms. In UWEN, the focus is on developing non-iterative algorithms to minimize complexity in real-time operations. One such method under consideration is the direct calculation method. This method involves solving a set of simultaneous equations based on time difference of arrival (TDOA) measurements. Therefore, exact solutions can be obtained for

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FIGURE 4. UWEN packet structure. mally required, which may be defined as the sum of the squared range errors produced at all sensors. The optimization purpose is to minimize the objective function to produce the optimal position estimate.

Positioning Architecture In the UWEN network, a set of fixed nodes are connected via a cable. The positions of the fixed nodes are known a priori. The sensors transmit data in packets during preassigned time slots based on the TDMA scheme as shown in Fig. 4. Once the signal (transmitted at the sensor) is detected at the fixed node, time of arrival (TOA) estimation commences. The estimated TOA at each sensor is then forwarded to the central controller. After collecting the TOAs estimated at all fixed nodes, one or more positioning estimation algorithms are run to produce the position information of the sensor. The position estimate is updated at a pre-specified rate, such as once per second, to track the moving objects. A central controller is connected to each of the fixed nodes in the network. Its task is to collect the delay information about each of the sensor devices in the network, select the most reliable delay information for each sensor, perform the positioning calculations for each sensor, and then store the estimated position for each sensor in an indexed database. The position information may then be accessed through an online browser or similar.

Data Communications Medium Access Control The MAC protocol for UWEN is TDMA-based. The TDMA solution has been chosen because the sensors must have guaranteed access to a fixed node on a periodic basis. In this article we assume there are at maximum ~1000 sensors in the network at any given time. The fixed nodes are divided a priori into fixed node clusters that consist of an arbitrary number of fixed nodes (e.g., 10 units).

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The purpose of the MAC protocol is to keep the underlying sensor device as simple as possible and move the burden of calculations to the central controller of the network, which communicates and synchronizes the fixed nodes via an Ethernet backbone. All of the communication occurs with slots that are of fixed size consisting of integer k multiple uplink-downlink combinations (k*uplink slots immediately followed by k*downlink slots). The MAC protocol has a hierarchical structure (i.e., the MAC functions with cluster frames that consist of 10 superframes). Each superframe has a predefined number of random access registration uplink/downlink pair slots, and a variable number of k*uplink/k*downlink pair slots. In a full utilization case, each sensor in a cluster has two uplink and two downlink slots per superframe. Figure 4 shows the packet structure.

Hardware Devices The intended application is a device that is capable of communicating and participating in a positioning network. The transceiver is to be sealed with its own power supply. The device is to be disposable, waterproof, and have an operational life of several months. The device has several kilobits of data that is accessed through the UWB interface. The antenna is to be integrated into the final packaging. The fixed nodes are based on the same hardware used in the sensor. However, the fixed nodes have additional functionality: an additional processing unit is included to support the more sophisticated MAC operations performed by the fixed nodes, as well as additional signal processing needed to perform delay estimation. A network interface is also required to support transfer of information to the central positioning calculation point in the network. The fixed nodes are assumed to have no significant battery life limitations, high clock frequency and high clock accuracy, and reliable communication links with the other fixed nodes.


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Energy-Efficient Multihop Energy efficiency is an important topic in wireless sensor networks. It is also an important metric that sets them apart from typical ad hoc networks. The goal in sensor networks is to maximize the lifetime of the network, that is, to maintain a reasonable level of connectivity for as long as possible. The topological nature of these networks often makes multihop communications a necessity, bringing added challenges for the research community. The effects of multihop must be carefully considered as its inefficiency grows quickly with the number of hops, and the window for efficient operation is very narrow. In this section we look at efficiency modeling applied to traditional RF and UWB technologies and multihop communications. Power consumption models of the radio in embedded devices must take both transceiver and startup power consumption into account. The latter actually becomes dominant with small packet sizes and long transition times to receive mode because of frequency synthesizer settling time. In [12] a model for radio power consumption is given for energy per bit eb as eb = etx + erx + Edec/η where e tx and e rx are the transmitter and receiver power consumption per bit, respectively, E dec is the energy required for decoding a packet, and η is the payload length in bits. This model can be used to compare the efficiencies of different radio technologies. In the case of UWB, a possibility exists for extremely efficient sensor networking based on the simplicity of the technique and the possibilities for high channel rates. Figure 5 compares different low-power radio technologies based on energy per bit. Due to the faster transition times and a very high data rate, UWB has the potential for very efficient operation. This also takes into account the long preambles needed for UWB synchronization. The efficiency of multihop communications can be analyzed for these technologies by extending the model above for the multihop case as shown in [13]. Results of the research show that in most realistic cases, it is worthwhile to maximize transmission power of a radio and minimize forwarding in sensor networks for the best energy efficiency. This is because


the amount of energy consumed while listening, receiving, and transitioning to receive mode is similar to that of transmitting, and cannot be ignored.

Medium Access Control Research To utilize UWB technology in sensor networks, specialized MAC algorithms are needed to deal with the properties of the technology and properly minimize energy consumption. Another consideration is detection of the UWB signal for use in calculating TOA and positioning algorithms. Minimization of other device interference and detection of the first signal component are critical factors required to allow UWB positioning. A MAC with low complexity is also required to support devices with limited processing power. These issues must be considered and incorporated in any UWB MAC protocol. In this section we discuss current research for UWB MAC specifically aimed at sensor networks. A MAC technique, NanoMAC [14, 15], has been developed to support both positioning algorithms and efficient data communications. The protocol uses cluster-based fixed nodes with known locations to send TDMA beacon signals. Sensor devices can be extremely simple, implemented on a standalone ASIC. Processing and communications are kept to a minimum on sensors. Most of the MAC and TOA calculations are implemented on the fixed nodes. Sensors can roam through the network with minimal signaling, and TOA values are automatically collected by fixed nodes. The technique allows extremely low power consumption for sensors, while providing data communications and constant TOA values for a positioning algorithm. NanoMAC was originally developed as a carrier sense multiple access with collision avoidance (CSMA/CA) protocol for narrowband radios, and is discussed in detail in [14]. This sensor MAC can also be applied to UWB using energy sense multiple access with collision avoidance (ESMA/CA), as carrier sensing as such is not possible. NanoMAC is p-non-persistent protocol. With probability p, the protocol will act as nonpersistent and with probability 1 – p the protocol will refrain from sending even before CS and schedule a new time for CS. With UWB, an energy sensing method is used instead, and at the MAC layer this differs from CS only by its longer

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[3] J. Foerster and Q. Li, “UWB Channel Modeling Contribution from Intel,” IEEE P.802.15-02/279-SG3a. [4] A. Rabbachin et al., “A Low Cost, Low Power UWB 1.5 Based Sensor Network,” IWUWBS/UWB-ST, Kyoto, P0.01 no sleep Japan, May 2004. P0.1 no sleep [5] A. Rabbachin, R. Tesi, and I. Oppermann, “Bit Error P1 no sleep Rate Analysis for UWB Systems with a Low ComP0.01 SG 01 plexity, Non-Coherent Energy Collection Receiver,” P0.1 SG 01 IST, Lyon, France, 2004. P1 SG 01 [6] I. O’Donnell et al., “Ultra-Wideband Hardware P0.01 SG 10 1 Design,” UWB Tech. Wksp.: From Research to RealiP0.1 SG 10 P1 SG 10 ty, Los Angeles, CA, Oct. 3–4, 2002. P0.01 SG 11 [7] G. R Aiello, “Challenges for Ultra-wideband (UWB) P0.1 SG 11 CMOS Integration,” Microwave Symp. Dig., 2003 IEEE P1 SG 11 MTT-S Int’l., vol. I, June 2003, pp. 8–13. np-CSMA [8] D. Dickson and P. Jett, “An Application Specific Integrated Circuit Implementation of a Multiple Correlator for UWB Radio Applications,” Proc. MILCOM 0.5 1999, vol. II, Oct. 1999, pp. 1207–10. [9] C. Baum, L. Carin, and A. Stone, Ed., UWB, ShortPulse Electromagnetics. 3 Conf. Proc., Plenum, 1997, p. 518. [10] L. Carin and L. Felsen, Ed., UWB, Short-Pulse Electromagnetics 2 Conf. Proc., Kluwer/Plenum, 1995, p. 605. [11] O. Smith and J. S. Abel, “Closed-form least-squares 0 Source Location Estimation from Range Difference 102 103 10-3 10-2 10-1 100 101 104 Measurements,” IEEE Trans. Acous., Speech, Sig. Proc., vol. 35, Dec. 1987, pp. 1661–69. Normalized traffic G (Erlang) [12] Y. Sankarasubramaniam, I. F. Akyildiz, and S. W. McLaughlin, “Energy Efficiency Based Packet Size FIGURE 6. Sleep mode power for nanoMAC. Optimization in Wireless Sensor Networks,” Proc. SNPA, vol. 38, 2003, pp. 393–422. [13] Z. Shelby, C. Pomalaza-Raez, and J. Haapola, “Enerduration. In the request-to-send/clear-to-send (RTS/CTS) gy Optimization in Multihop Wireless Embedded and Sensor Networks,” PIMRC 2004. frames, nanoMAC performs virtual carrier sensing in addition [14] J. Haapola, “NanoMAC: A Distributed MAC Protocol for Wireless Sento informing overhearing nodes of the time they are required sor Networks,” Proc. XXVIII Convention on Radio Science and IV Finnish to refrain from transmission. Virtual carrier sensing enables Wireless Commun. Wksp., 2003, pp. 17–20. overhearing nodes to sleep during that period. The RTS and [15] J. Haapola, “MAC Energy Performance in Duty Cycle Constrained Sensor Networks,” Proc. Int’l. Wksp. Wireless Ad Hoc Networks, June 2004. CTS frames include an IEEE MAC address, as well as sleep Absolute energy consumption E(J) per useful bit transmitted by device (i) with sleep groups

x 10-4

information and the number of data frames to be transmitted. Figure 6 shows the effect of different sleep modes on the power consumption of nanoMAC compared to nonpersistent CSMA. As the traffic load increases, nanoMAC greatly outperforms nonpersistent CSMA.

Conclusions UWB shows significant promise for low-power, low-cost, widedeployment sensor networks. The high burst data rates, robust signal structure, and potentially high positioning accuracy mean that sensor networks can offer additional location services as well as extended battery life since devices are able to sleep for much of the time. A critical part of designing a UWB sensor network that takes advantages of these features is to develop a suitable MAC that supports positioning, minimizes interference, and maximizes sleep periods. This article presents an example of a UWB sensor network, UWEN, based on a low-complexity UWB transceiver. The UWB circuits have been designed in 0.35 Si-Ge BiCMOS process technology and developed for minimum power consumption. The illustrative application of this article is the sport and lifestyle market, but such UWB networks are rapidly emerging with broader implications.

References [1] FCC, First Report and Order, FCC 02-48, Apr. 22, 2002. [2] I. Oppermann, M. Hämäläinen, and J. Iinatti, UWB Theory and Applications, Wiley, 2004.

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Biographies IAN OPPERMANN ([email protected]) completed his B.Sc., B.E., and Ph.D. at the University of Sydney, Australia, in 1990, 1992 and 1997, respectively. His Ph.D. was related to physical layer aspects of novel spread spectrum/CDMA systems. He became an adjunct professor at the University of Oulu, Finland, in 2001 and subsequently joined the Center for Wireless Communications (CWC) in 2002 as assistant director, becoming director in 2003. His main research responsibilities are UWB and wireless networking. He heads several large research and development projects that cover fundamental research, MAC development, channel measurement/modeling, system design, positioning, antenna, and ASIC development. LUCIAN STOICA graduated with an M.Sc. in electrical engineering from the University of Bucharest, Romania. Currently he is a Ph.D. student at CWC working on UWB transceiver design in CMOS. Other research interests include analog RF design and mixed signal design. ALBERTO RABBACHIN graduated with a Dr. Ing. in electrical engineering from the University of Florence, Italy. Currently he is a Ph.D. student at CWC working on low-complexity UWB transceiver design. Other research interests include synchronization issues and receiver architecture design. ZACH SHELBY graduated with a B.Sc. in electrical engineering from Michigan Technological University in 1999 with honors. He holds an M.Sc. in embedded systems from the University of Oulu. Currently he is a Ph.D. student at CWC working in the field of wireless embedded networking. Other research interests include forwarding algorithms in heterogeneous wireless networks, embedded systems, and cross-layer energy optimization. J USSI H AAPOLA graduated with an M.Sc. in physics from the University of Oulu in 2003. Currently he is a Ph.D. student at CWC working in the field of low-power wireless networking with an emphasis on medium access control. Other research interests include energy optimization in heterogeneous multihop wireless networks.
