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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 55, NO. 2, APRIL 2006 415

Architectural Design of a Sensory Node Controllerfor Optimized Energy Utilization in Sensor Networks

Robert X. Gao, Senior Member, IEEE, and Zhaoyan Fan

Abstract—The increasing complexity of manufacturing ma-chines and the continued demand for high productivity have ledto growing applications of sensor networks to enable more reli-able, timely, and comprehensive information gathering from themachines being monitored. An effective and efficient utilization ofsensor networks requires new sensor designs that enable adaptiveevent-driven information gathering based on the condition of themachines, as well as a coordinated information distribution ad-justed to the available communication bandwidth of the network.This paper investigates several fundamental aspects regardingthe architectural design of a sensory node controller (SNOC).The SNOC is the key element in a large-scale sensor networkthat coordinates the operation of individual sensors and the com-munication among various sensing clusters to realize distributedintelligent sensing. A parametric SNOC design that dynamicallyadjusts the power supply and the data-acquisition procedure toreduce the overall energy consumption of the sensor network ispresented. Considerations on both the hardware and softwareaspects of the design to achieve energy efficiency are described,and analytical formulations are derived. Simulation results for asensor network consisting of 40 SNOCs, each coordinating eightphysical sensors, have shown that the design is able to reduce theenergy consumption by about 43%, as compared to traditionaltechniques. A prototype SNOC was designed and implemented,based on the platform of a commercially available microcontroller,and experimentally tested for its ability to dynamically adjustthe power consumption. The study has provided a concrete inputto the design optimization and experimental realization of anSNOC-based sensor network for machine-system monitoring.

Index Terms—Machine-system monitoring, power efficiency,sensor networks, system-on-a-chip, vibration signal processing.

I. INTRODUCTION

THE INCREASING degree of complexity and automa-tion in manufacturing machines has led to an increased

demand on advanced sensing techniques for effective andefficient monitoring and diagnosis of the working status ofthese machines to achieve high productivity and minimizecostly machine downtime. Recent advancements in informationtechnology, reflected in a rapidly expanding communicationnetwork infrastructure and a new generation of miniaturizedsensors capable of wireless data communication, have openeda new horizon for improved sensing coverage of large-scalemachine systems. The effective utilization of data influx arisingfrom the large number of sensors requires efficient transfer ofdiscrete data points through the network to the decision maker.

Manuscript received August 12, 2004; revised October 4, 2005. This workwas supported by the National Science Foundation under Grant DMI-0330171.

The authors are with the Department of Mechanical and Industrial En-gineering, University of Massachusetts, Amherst, MA 01003 USA (e-mail:[email protected]).

Digital Object Identifier 10.1109/TIM.2006.870321

Fig. 1. Paper web printing machine with various local sections to bemonitored [7].

Sensor networks available at the present time are generallylimited by constraints in the communication bandwidth and thelack of flexibility in the sensor design to adapt their operationconditions (e.g. sampling rate and clock speed) to the state ofthe machine being monitored and the available source of powersupply [1]–[3].

The need for improved sensor design to enable large-scalenetworked sensing and, subsequently, reliable machine condi-tion monitoring is evidenced in a wide range of commercialand industrial applications, such as food processing, newspa-per printing, oil refining, mining facilities, and transportationsystems. In the food-processing industry, automated dispensingmachines have been extensively used to insert fully processedfood into cans or cartons, e.g., coffee, soup, sugar, or spices.Even small errors due to a low positioning accuracy of thedispense mechanism or fluid-flow control can quickly lead tosignificant economic losses. It was reported that a mere 2%loss of the yield can result in the loss of several million dollarsfor a large operation [4]. In web printing machines, whichrepresent a class of complex large-scale continuous materialprocessing machines widely used for newspaper printing, paperwebs run through hundreds of rollers while being color printedat a high speed that often exceeds 400 m/min [5]. Due tothe large amount of materials being processed per time unit,an unexpected machine stoppage due to bearing failures mayresult in losses in production revenue of over $50 000/h [6]. Toeffectively monitor a complex machine system such as a webprinting machine as illustrated in Fig. 1, a large number of sen-sors will be needed to gather critical physical data such as thepaper web tension, web moisture content, dryer temperature,amount of ink dispensation, and roller vibrations. To efficientlyprocess the large amount of data gathered and generate controlresponses to faulty conditions in a timely manner, data fusion(DF), inferencing, and decision making at multiple levels are

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416 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 55, NO. 2, APRIL 2006

Fig. 2. SNOC-based wireless sensor network with distributed sensing for monitoring a printing machine.

needed. Such a scheme for distributed intelligence reducescommunication traffic through the network between physicalsensors at the machine level and the remotely located centralmonitoring station, shortens the response latency, reduces thesignal interference, and improves the overall machine monitor-ing efficiency.

The scenario of distributed sensing and DF is illustrated inFig. 2, where the operating condition of four representativemachine sections (i.e., paper web tension control, web dryingstation, web speed control, and web moisture measurement)is monitored by a dedicated group of sensors, referred to inthis paper as the “sensory node.” As an example, the sensorynode consisting of sensors Sx, Sy , and Sz is responsible formonitoring the web moisture content. Instead of having a cen-tral control station initiate various control responses based onits communications with each individual sensors, measurementdata on the web tension, moisture content, drying time, andweb speed will be initially exchanged among the four sensorynodes, coordinated by the respective sensory node controller(SNOC), which is a dedicated hardware component residentwithin each sensory node. Based on the result of DF at the locallevels, a coordinated control response will be generated. As anexample, assuming elevated humidity in the web is measuredby the humidity sensor node. This indicates that a prolongedweb drying time will be needed, for which the web speedneeds to be decreased to reduce the material flow to the webdryer station to accommodate the extended drying period. Thehumidity information will also be relayed to the web-tension-control section such that the threshold level of the force sensorswill be decreased accordingly to accommodate the reduced webspeed. From a hardware-design perspective, each SNOC canbe viewed as an application-specific system-on-a-chip, withchip-resident firmware for sensor DF and control actuation.Miniaturized by the state-of-the-art microelectromechanical-system (MEMS) technologies [8]–[10], the SNOCs can be

structurally embedded into the machine system to be monitoredfor applications.

The effectiveness and efficiency of a sensor network in exe-cuting various monitoring tasks are affected by the architecturaldesign of the SNOC, which is subject to two constraints:

1) flexible hardware platform to realize various functionsneeded for a coordinated sensor network operation;

2) low-power design that ensures a prolonged operationspan of the SNOCs under the given power supply.

Since maintaining network flexibility while satisfying spacerestrictions are typically required for real-world applications,the use of small-sized batteries having limited energy capacitybecomes both a requirement and a constraint for the SNOCdesign. Given that hundreds or even thousands of sensory nodesmay be required for monitoring a complex machine system,frequent replacement of the batteries is not a viable solution.Assuming an SNOC is driven by four AA-size batteries with acombined capacity of 9200 mAh or 49 600 J [11], the averagepower consumption of the SNOC must be kept below 1.6 mW ifthe required operation span is 1 year. Such power consumptionis considerably lower than that of most commercial electronicdevices presently available on the market. This example illus-trates that an energy-efficient design that minimizes the powerconsumption of the SNOC is of paramount concern to thedesign of an effective wireless sensor network.

In recent years, various techniques for low-power sensingin a networked environment have been investigated. In [12], asensory node is presented for temperature, lightness, and soilmoisture monitoring. Power saving is achieved by a power-aware routing protocol to dynamically change the networkstructure and minimize the communication energy consump-tion. The feasibility of employing synchronized and embed-ded sensors for health monitoring of buildings and bridges isdemonstrated in [13], where a part of the data processing tasks

GAO AND FAN: SENSORY NODE CONTROLLER FOR OPTIMIZED ENERGY UTILIZATION IN SENSOR NETWORKS 417

was performed locally on individual sensors, thus reducing datatransmission to a central controller and saving energy. Wirelesssensors based on the microadaptive-multidomain-power-aware(µAMP) design have been applied to tracking vehicle positionsby means of acoustic triangulation [14]. A common featureshared by these efforts is a centrally located controller, at whichfeature extraction, DF, decision making, and control responsesare performed. While such a mode of data management isacceptable for systems with relatively small dimensions, trans-mitting a large amount of data from distributed sensors to acentral controller is energy inefficient, due to the relationshipbetween the transmission energy required and the square of thedistance between a transmitter and a receiver [15].

The concept of dynamic voltage scheduling (DVS) has beeninvestigated on various platforms for improved power effi-ciency [16]–[18]. The goal of DVS is to minimize the energyrequired for running a given task by dynamically adjustingthe supply voltage and, consequently, the clock speed of theprocessor, according to the time constraints prescribed by eachtask. Significant energy benefits have been reported by schedul-ing the computational load and utilizing the CPU idle time[18]. Commercial products that have implemented by DVSinclude low-power microprocessors and high-efficiency dc–dcconverters [19]. For cluster-based network models [20], reduc-tion in the computation energy was achieved by uniformlyslowing down the clock speed for all the sensory nodes. Inan example of real-time vehicle tracking and localization, anenergy savings of 65% was demonstrated in comparison withnon-DVS techniques. However, since data communication andcomputation share the same time span for each data processingcycle, the extension of the data computation time is lowerbounded by the minimum time needed for data communication.The restriction becomes more pronounced, as the number ofsensory nodes in the network increases. This indicates that theconventional voltage scheduling technique for data computa-tion may not be optimal for large-scale sensor networks.

This paper is motivated by the increasing demand for im-proved energy utilization and management in sensor networksfor efficient and sustainable manufacturing system monitor-ing. To achieve this goal, a hardware platform for design-ing SNOCs has been developed, which is aimed at enabling1) distributed sensing through coordination of discrete sensorsinto subsensing networks; 2) local DF and inferencing, based onfeatures extracted from raw data points collected by individualsensors; 3) coordinated responses upon detection of machinedefect, before communicating with the remote central controlstation; and 4) dynamic adjustments of the energy consumptionof the SNOC electronics, based on the sensing requirement.To maximize the life span of SNOCs powered by batteries, anew data processing and communication scheme that enablesSNOC coordination has been developed, which minimizescomputation energy through the DVS technique. In contrast topreviously reported efforts, the developed technique providesa series of voltage levels for each individual sensory nodeand schedules the computation period to fully utilize the CPUidle time, while waiting for data communication to take place.Based on the platform of a commercially available microcon-troller, a prototype SNOC was designed, implemented, and

experimentally tested on its ability to dynamically adjustingthe power supply levels for multiple vibration sensors thatmonitored a spindle testbed. The performance of such SNOCsin a networked environment involving over 300 sensors wassimulated, which demonstrated an energy saving of over 43%.

The remainder of this paper is organized as follows.Section II presents a functional overview of the SNOC archi-tectural design, based on several global requirements for manu-facturing system monitoring. In Section III, a parametric designof the respective hardware functional blocks of the SNOC ispresented. Based on the analysis and simulation of a sensornetwork consisting of 40 SNOCs, an energy-efficient powermanagement algorithm is described in Section IV, which canbe implemented as an SNOC-resident firmware. In Section V,conclusions regarding the design of the SNOCs are drawn.

II. SYSTEM CONFIGURATION

An SNOC-based wireless sensor network consists of a mul-tiple number of sensory nodes that are able to communicateamong themselves as well as with a remotely located centralcontrol station via a public wireless medium. Each sensorynode needs to be configured to perform four functions inorder to realize distributed intelligent sensing in a networkenvironment:

1) serve as a data router that provides network access to agroup of sensors such that physical parameters on themachine being monitored can be collected and distributedthroughout the network;

2) optimize the data collection process by dynamically ad-justing the data sampling rate to enable event-driven(instead of fixed rate) information coverage tuned to thecondition of the machine being monitored (e.g., increasedsampling rate for more accurate identification of defectinitiation and reduced sampling rate when no abnormaltrend is detected, to conserve energy);

3) compress raw data points and extract characteristic fea-tures out of the data, for reduced network traffic and moreefficient utilization of the available bandwidth;

4) perform local DF and inferencing through communica-tion with adjacent sensory nodes and to form sensory“clusters” (subnets) for decision making and system-leveloptimization of the energy utilization.

To satisfy these requirements, each SNOC will contain fourbasic elements, as illustrated in Fig. 3: 1) an analog front for in-terfacing with physical sensors (e.g., vibration, humidity, force,or temperature sensors); 2) a wireless data transceiver capableof transmitting and receiving information; 3) a firmware con-taining embedded algorithms for the coordination and controlof data sampling, processing, and network communication; and4) an energy source, such as a set of batteries. As an applicationspecific system-on-a-chip, the parametric design of an SNOCis dependent on the hardware peripherals to be connected to(e.g., type of sensors) and the characteristics of the signals tobe measured (e.g., frequency and time scales). In this paper, theSNOC is intended for coordinating vibration sensors for rotary-machine (such as rolling bearings) monitoring, with a requireddata sampling rate of up to 30 kHz [21]. Given that the energy


Fig. 3. System configuration of a sensory node with SNOC as the nodecontroller.

consumption in an electronic circuitry is directly proportionalto the square of the operating frequency, the overall energyconsumption of the SNOC hardware needs to be minimizedwhile satisfying the high clock frequency requirement of theSNOC hardware to ensure an energy-efficient operation withinthe specified time frame.

Suppose a structural defect is initiated in a ball bearing, asillustrated in Fig. 4. Every time the rolling balls roll over thedefect, an impulsive response of the bearing structure will begenerated due to the defect–ball interactions. Depending on thesize (i.e., severity) of the defect [as shown in Fig. 4(a)] and itslocation within the bearing [on the inner race, outer race, or theball, shown in Fig. 4(b)], the vibration pattern and intensity willchange, providing an input to a diagnostic algorithm [22].

For defect diagnosis, feature extraction from the vibrationsignals needs to be performed by the SNOC. Hence, the com-putational efficiency of the algorithm is a major attribute inthe SNOC software design. Of the various feature-extractiontechniques developed in the time and frequency domains, thediscrete harmonic wavelet packet transform (DHWPT) [23] hasshown to be both effective and efficient in revealing the time–frequency composition of defect-induced vibrations, particu-larly in the high frequency region. In Fig. 5(a), the waveformof a vibration signal is illustrated, whereas its time–frequencydistribution obtained from the DHWPT algorithm is shownin Fig. 5(b).

To realize the DHWPT algorithm on the SNOC platform,its calculation procedure was first analyzed. As illustrated inFig. 6. after prefiltering by a finite-impulse-response (FIR)filter, vibration data measured from individual sensors aredecomposed into a series of frequency subbands. Then, themost significant indicator for the data in each subband, e.g.,the energy content of the data, is chosen as the representative“feature” for the corresponding sensory node. Energy featuresfrom various sensory nodes are then combined as inputs to a DFalgorithm, performed by one of the SNOCs that serves as the“cluster head” for the corresponding cluster of sensory nodes.From the result of DF, quantitative measures for the defect, suchas its location and severity, are determined. Such measures pro-vide input to the machine control mechanism (e.g., the central

control station) for subsequent control responses. Detailedaspects of the hardware and software designs of the SNOChave been developed, as described in the following sections.

III. HARDWARE DESIGN

Each SNOC has been designed to coordinate up to eightphysical sensors in a sensory node. Analog signals sampledfrom the sensors are first A/D converted and temporarily storedin a data buffer. Subsequently, extracted features are transmittedto other SNOCs and/or to the central control station. To ensureoptimized energy utilization and prolonged battery life, anenergy control algorithm is incorporated for each componentin the SNOC.

A. Data Storage

A prior study [23] has established that major frequencycomponents related to bearing defects can be identified in therange of 5–15 kHz. Since eight vibration sensors need to becoordinated by an SNOC in each sensory node, a minimumdata sampling rate of 240 kHz is needed for the sensor interfacein an SNOC. Assuming each of the eight interface channelswill need to operate continually for at least 1 s to satisfy theminimum frequency resolution requirement of 1 Hz [24], atotal of 240 000 data points will be generated for every secondof data sampling. While a large memory provides flexibilityin data buffering and processing, total on-chip memory inan SNOC needs to be kept low to minimize chip size andreduce energy consumption. To meet the requirements, a databuffering mechanism capable of reading sampled data whilebeing accessible by the processing unit for real-time processingwas developed. Fig. 7 illustrates a dual-buffer structure forthe SNOC design. The structure utilizes two memory units(MEM A and MEM B) to allow for data reading through thesensor data bus and writing through the DSP bus without acollision between the inputs and outputs of the buffer.

Each of the dual buffers provides 256 000 × 16 bit (4 Mb)storage space for caching the 240 000 data points acquiredby the SNOC. The two buffers will be addressed alternately:while one receives a new set of sensor data at a speed of240 000 samples/s, the other outputs the data set buffered duringthe previous time unit to the digital signal processor (DSP),which is a core element of the data processor module of theSNOC. Swapping of the roles is controlled by the DSP at aperiod of 1 s. Through such an alternating mode of operation,the two functions of data acquisition and data processing canproceed concurrently.

B. Computation Speed

A core functionality of the SNOC is to conduct data analysisand DF for decision making in a sensor network. As illustratedin Fig. 8, during each sampling period T , a data set containingNDS sample points will be collected, and a series of subsequentoperations need to be executed. The overall computational-speed requirement for the SNOC is determined based on thespecific time needed for each operation step on the cluster head,where local processing, communication, and DF are performed.


Fig. 4. Vibration signal generation in a ball bearing due to interactions between a ball and a defect. (a) Different defect size. (b) Different defect location.

Fig. 5. Time–frequency decomposition of a bearing vibration signal based on the DHWPT. (a) Realistic bearing vibration signal. (b) Time–frequency decom-position of the signal.

Fig. 6. Computation steps for processing vibration signals for machine con-dition monitoring.

As shown in Fig. 8, the time for executing data filtering(TFILT) is determined by the time associated with each CPUclock cycle (Tm) of the data processor, the order of the digitalfilter (Nf), the length of the data set (NDS), and the numberof sensors in a given sensory node (NS). Similarly, the timeneeded for the time–frequency decomposition (TWT) of thesignal through DHWPT and for the feature-extraction operation(TFE) are primarily dependent on the number of decompositionlevels (b), in addition to the data length and CPU cycle. TheDHWPT decomposition is based on an FFT algorithm [22], forwhich the time required for calculating a data series of x pointis given by FFT(x) = 4 · x · log2 x. The time for transmitting adata sample from an SNOC to the cluster head using the time-division multiple-access method, with the data length being Band the transmission rate being K, depends on the number ofsensory nodes (M) within the sensor cluster. A transmissioninterval (TTX) is assumed for each of the M − 1 SNOCs

Fig. 7. Dual buffer for concurrent data acquisition and processing.

(excluding the cluster head itself) to transmit data to the clusterhead. For DF, a total of Ns · 2b data features from each sensorynode will be processed by the sensor cluster head, resulting in2b · Ns · M2 computation steps and a DF time interval TDF.The fused data feature will be compared against a thresholdvalue established from experimental evaluations of test data.Thus, the total computational time Tcomp needed for eachSNOC to process data points sampled from multiple sensorsin each sensory node is given by

Tcomp =TFILT + TWT + TFE + TDF

=TmNs · [NfNDS + 8NDS log2 NDS − 4NDSb

+ (2 · 2b + 1)NDS + 2bM2]. (1)

Accordingly, the total data sampling period is calculated as

T = Tcomp + (M − 1) · TTX. (2)


Fig. 8. Time requirements for data processing on a cluster head.

Fig. 9. Required system clock frequency for different numbers of sensory nodes and data length.

To satisfy the above time requirement, the presented SNOCdesign was based on a four-level DHWPT (b = 4), a 32-orderFIR filtering (Nf = 32), and a data transmission time ofTTX = 0.018 s. A data transmission rate of 115.2 kb/s wasassumed to transmit 16 features [25], with each feature con-taining 14 bits. A start bit and an end bit were added to the rest12 data bits for wireless transmission. For each data samplingperiod (T = 1 s), the clock frequency required for the SNOC is

expressed as a function of the nodes number M and data lengthNDS, as illustrated in Fig. 9.

As the number of sensory nodes within a sensor clusterincreases, a higher clock frequency will be needed to completethe required data processing operations (for a given data-setlength NDS) to accommodate the increasing data transmissiontime. As an example, the curve in bold in Fig. 9 indicatesthe required clock frequencies (55–180 MHz) for processing


Fig. 10. Comparison of energy consumption: Fixed voltage versus DVS.

a set of 30 000 vibration data points (each 12-bit long) in amachine-condition-monitoring application. If a sensor clustercontaining five sensory nodes (each coordinating eight sen-sors) needs to be coordinated, a minimum clock frequency of180 MHz will be needed to satisfy the SNOC computationalrequirement.

C. Energy Dissipation

To process a data set, the energy dissipated by the SNOC iscalculated as [20]

Ecomp = N · C · V 2dd (3)

where N is the number of clock cycles needed for processinga data set, C is the effective switching capacitance, and Vdd

is the supply voltage to the SNOC. Since the supply voltagedirectly affects the speed of the semiconductor componentsin an electronic chip, the clock speed of the processor f isexpressed as a function of Vdd

f ≤ K(Vdd − ε) (4)

where ε and K are constants depending on the processorhardware [20]. From (3) and (4), it is evident that decreasing thesupply voltage causes a linear reduction of the chip operatingspeed but a quadratic decrease in the energy consumption.Thus, the tradeoff between energy consumption and processingtime presents a key issue in the SNOC design, linked togetherby the supply voltage of the chip.

The advantage of DVS from an energy-saving point of viewis illustrated in Fig. 10, where two processors with and withoutDVS are assigned the same task. For the processor powered bya fixed supply voltage Vdd, the task is completed within theperiod of Tcomp = Ta, under an energy dissipation of Efixed =N · C · V 2

dd. The processor idles afterwards, until the time 2Ta

is reached. In comparison, the DVS-enabled system reduces thesupply voltage and, consequently, the operating speed by halfand consumes only one fourth of the energy as required by thefixed supply voltage scheme, within the allocated period of 2Ta.

Several state-of-the-art microprocessors have incorporatedthe DVS design, where both the frequency and the proces-sor core voltage are variably controlled by internal registers,based on the computational load requirement. A comparativestudy has identified three such microprocessors: BF532 fromAnalog Device [26], PXA255 from Intel [27], and TM5600

from Transmeta Crusoe [28]. All three processors satisfy thecomputational-speed and memory-space requirements for thispresented study. In Table I, several major specifications forthe processors are comparatively listed. Given that low energyconsumption is of paramount concern to achieving long batterylife in sensor network applications, the BF532 processor wasselected as the hardware platform for the SNOC design.

D. Circuit Design

A complete circuit diagram of the SNOC, consisting of theBF532 CPU and various peripheral components, is shown inFig. 11. Controlled by a firmware embedded on the CPU chip,the SNOC coordinates data acquisition from eight vibrationsensors and performs feature extraction, DF, and decision mak-ing. Wireless communication between the SNOC and otherSNOCs as well as with the central control station is performedby the data transceiver. The sensor interface contains a 12-bitA/D converter for digitizing vibration signals. A serial pe-ripheral interface on the processor controls A/D channel se-lection and accesses the sensor data. A dual-buffer structureis realized by two banks of low-power RAM, bridged to theA/D converter and the CPU via an integrated direct-memory-access (DMA) channel. Each data buffer has a memory capacityof 4 Mb to store digitized data sets from the A/D converter.Alternation of the buffers’ addressing modes is controlled bythe embedded firmware, whereby data processing is performedin parallel with data sampling. To realize the DVS algorithm,voltage levels of the processor are controlled by a feedbackloop consisting of a pulsewidth-modulation (PWM) regulator,a switch regulator, and a voltage detector integrated withinthe processor.

The SNOC hardware design was simulated and subsequentlyprototyped on a 125 × 90 mm printed circuit board using com-mercial off-the-shelf components, as shown in Fig. 12. Eachof the functional modules was powered independently suchthat the corresponding energy consumption can be controlledby the processor and measured through the programming/testport. The SNOC is connected with an RFC TR1000 wirelesstransceiver [25] through a six-pin port, which includes a serialbus and a control port, for data communication and energymanagement. Whenever monitoring is started by the software,continuous data sets were sampled through the eight-channelsensor interface and stored in the memory chips using a 16-bit format. To effectively debug the program resident on theSNOC hardware, an additional interface circuit consisting ofthree key pads and four LEDs was designed and realized. Theinterface circuit can be completely shutdown when the SNOC isin operation, such that extra energy consumed by the LEDs andinput/output (I/O) ports will not affect the energy measurementof the SNOC hardware.

Based on the required computational speed for implementingthe DHWPT algorithm, a total of ten voltage–frequency pairs(from 0.85 V/67.5 MHz to 1.30 V/189 MHz) were realizedto perform vibration signal decompositions on the SNOCs, asshown in Table II. Given a specific monitoring task, e.g., vi-bration sensing on a bearing rotating under a specific speed, theCPU will calculate the working frequency required based on (2)


TABLE ICOMPARISON OF CPUS FOR THE SNOC HARDWARE PLATFORM DESIGN

Fig. 11. Circuit diagram of the SNOC.

Fig. 12. SNOC hardware and the corresponding sensory node.

TABLE IIVOLTAGE–FREQUENCY PAIRS FOR IMPLEMENTING THE DVS DESIGN

and (4) and select the lowest possible voltage–frequency pair toensure minimal energy consumption for completing the task.

To further reduce energy consumption during the CPU idletime, low-power ICs that support one or more sleep modeswere considered for the SNOC design. Table III lists the powerconsumption of several major peripheral ICs employed forthe SNOC circuitry. A comparison between the active and

sleep modes indicates that significant energy savings can beachieved by mode control of the ICs. Corresponding to specificmonitoring conditions, the ICs can be switched into the sleepmode, through control signals generated by the CPU andinterfaced via one of the general-purpose I/O (GPIO) ports.Mode switching of the BF532 CPU itself is achieved throughprogramming of its internal registers.


TABLE IIIPOWER CONSUMPTION OF MAJOR ICS CONTROLLED BY THE SNOC

IV. SOFTWARE DESIGN

The operation of each sensory node is controlled by the soft-ware codes residing in the corresponding SNOC. In designingthe SNOC software, modularity and flexibility were the mainattributes considered to allow for easy customization and re-configuration of each SNOC for different applications withminimal overhead. The system-level software architecture of anSNOC is shown in Fig. 13, which is divided into three distinctpartitions. The event-based energy control partition activatesand controls the DVS algorithm for the SNOC operation. Con-trol responses to machine defects reported by the monitoringsensors are initiated at the knowledge-based data processingpartition based on the knowledge of the relationship betweenphysical signals and machine conditions, which are obtainedfrom experimental studies. Depending on the data processingresults and the corresponding features extracted from the sensordata, the operation mode of a sensory node can be modifiedin situ. For example, when no defect features are identified,SNOC electronics will be forced into the sleep mode by theworking state control module. Consequently, the energy controlmodule will reduce the supply voltage to the system, thuspreserving battery capacity. The protocol-based internode com-munication partition is in charge of the coordination of networkaccess and protocols, which is needed for data inferencingamong the various SNOCs in a sensor cluster, to realize localdecision making.

The design of the software for the energy control partition isillustrated in the presented study. Design considerations for thepartitions for data processing and communication protocols arebased on [22] and [23], respectively.

As illustrated in Table III, the CPU is the major energyconsumer (200 mW when active) in each sensory node. To opti-mally utilize the limited battery capacity for sustained machinemonitoring, adaptive energy resource allocation according tothe instantaneous system requirements has been realized in theSNOC software design, as discussed below.

A. Computation Scheduling

To realize distributed machine condition monitoring, neigh-boring sensory nodes are grouped into a “cluster” where local-ized DF is performed. Each cluster will first randomly select anSNOC as the cluster head. The cluster head first synchronizesdata sampling processes at each sensory node and subsequently

performs DF based on the feature-extraction operation by eachof the SNOCs for their corresponding sensory nodes. Thecluster head is randomly chosen by a random number generator(RNG), which assigns an initial waiting time to each SNOC.The RNG is itself an SNOC-resident component. The SNOCthat “wakes up” first from the assigned waiting time willbecome the cluster head for the corresponding sensor cluster.Based on the number of sensory nodes available in the cluster,the cluster head creates a communication schedule that specifiesthe time assigned to each sensory node, when it is allowed totransmit data and receive a feedback from the cluster head.

To avoid buffer overflow, all the data sets collected by eachsensory node during the data-set sampling period must becompletely processed. This includes local-level processing (LP)by each of the SNOCs within a cluster, such as data filtering,DHWPT-based signal decomposition in the time–frequencydomain, and feature extraction. It also includes DF operationson the cluster head. Traditionally, all sensory nodes withina network are uniformly controlled by a global strategy, andthe processors operate under the same clock speed [21]. As aresult, data processing tasks at the local level are completedsimultaneously during each period of data acquisition, by allthe sensory nodes. This technique is referred to in this paperas unscheduled data acquisition (UDA) in which the DVStechnique is employed to uniformly change the voltage on allthe sensory nodes for data processing, while the communicationtime on different sensory nodes is not utilized and scheduled.The timing diagram for UDA in a cluster consisting of Msensory nodes is illustrated in Fig. 14(a). Upon completionof the local-data-processing tasks, the sensory nodes withinthe cluster will join the schedule and transmit their respectiveDF results to the cluster head. Required by the sequentialtransmission scheme, the CPU of a waiting SNOC will remainin the idle state until its turn for data transmission. As a result,all the sensory nodes will have the same computation timeand consume the same amount of energy in processing thedata sets.

In order to complete the data acquisition in each data-set sampling period T , the computation time TUDA is con-strained by

TUDA =NLP

fUDA≤ T − (M − 1) · TTX − TDF (5)

where NLP is the number of machine cycles for local dataprocessing, fUDA is the clock speed or operating frequencyof the processor, M is the number of sensory nodes in thecluster, and TTX and TDF are the fixed time for communicationand data inferencing, respectively. Since the sensory nodes aretriggered by events, the number of sensory nodes involved ina cluster may be different as the nature of the event changes.It can be seen from (5) that the upper bound of TUDA is afunction of M : As M increases in a given sensor cluster, thetime for communication (M − 1)TTX increases. Consequently,the time allocated for local computation TUDA will be reduced.Hence, a large M implies that a high operating frequency fis required to ensure completion of computation within TUDA,and vice versa. If Mmax refers to the maximum number of


Fig. 13. Software architecture of the SNOC as a local controller for sensory node.

Fig. 14. Timing diagrams for the UDA and SDA techniques. (a) Unscheduled data acquisition. (b) Scheduled data acquisition (LP = local processing, Tx =data transmission, and DF = data fusion).

sensory nodes to be included in a cluster, the worst-case sce-nario of the system can be defined as M = Mmax, where theminimum time constraint (TUDA)min and maximum systemfrequency fmax are required for the operation completion. Fora given Mmax, the associated (TUDA)min and fmax can becalculated as

(TUDA)min = T − TDF − (Mmax − 1) · TTX (6)

(fUDA)max =NLP

(TUDA)min

=NLP

T − TDF − (Mmax − 1) · TTX. (7)

Since (6) and (7) are derived from the time-sharing scenarioin real-time sensing, both conditions must be satisfied so thateach data set can be processed in time to avoid buffer overflow.


When the DVS technique is applied, the CPU frequency willbe dynamically adjusted according to the current number ofnodes M , as per (5), and reduction of the core supply voltagewill be activated to reduce the computation energy. While itis energy efficient to minimize fUDA to allow a low operatingvoltage, (5) denotes that fUDA is constrained by M for a giventask, which means that the computation time for local dataprocessing decreases when the number of nodes increases inthe network. By substituting NLFE and TUDA into (3) and (4),respectively, the computation energy in the ith sensor EUDA(i)and the total computation energy dissipation in the cluster areexpressed as

EUDA(i) =NLP · C · V 2UDA

≥NLP · C ·(

NLP

K · TUDA+ ε

)2

(8)

EUDA =∑

EUDA(i)

≥M · NLP · C ·(

NLP

K · TUDA+ ε

)2

. (9)

Equation (8) indicates that the energy dissipated by computa-tion is the same for every sensory node, with a homogeneousVUDA and fUDA. However, improved energy savings can beachieved by using separately controlled voltage for each sen-sory node and by scheduling the computation time individually.Fig. 14(b) shows the timing diagram for the scheduled dataacquisition (SDA), in which the computation time is scheduledand extended according to the length of the processor idle time.Instead of adopting a unified control scheme for all the sensorynodes, the computation time TSDA(i) is separately controlledfor each sensory node. Since all the sensory nodes must followthe given transmission schedule for communication with eachother, it is possible to estimate their idle time and use it toprolong the computation time TSDA(i), which is expressed as

TSDA(i) =NLP

fSDA(i)≤ TUDA + i · TTX

=TUDA

(1 + i · TTX

TUDA

)

=TUDA(1 + i · q) (i = 0, 1, . . . , M − 2) (10)

where q = TTX/TUDA denotes the ratio of transmission timeto computation time in the UDA technique, and fSDA(i) isthe operating frequency of the ith sensor. Equation (10) indi-cates that if the computation speeds can be adjusted separatelyfor various sensory nodes, a prolonged computation time isavailable within the range of TUDA−TUDA(1 + M · q − q).Hence, except for the first SNOC in the transmission schedule(i = 0), all the other SNOCs can operate at a reduced fUDA

without violating their assigned computation deadline. Undersuch circumstances, the supply voltage and energy can befurther reduced in DVS-capable processors.

The computation energy dissipated by the ith sensor ESDA

can be derived as a function of i

ESDA(i) =NLP · C · V 2SDA(i)

≥NLP · C ·[

NLP

K · TUDA(1 + i · q) + ε

]2

,

(i = 0, 1, . . . ,M − 2) (11)

where VSDA(i) is the supply voltage for the ith sensor. Conse-quently, the total computation energy consumption in the sensorcluster can be calculated as

ESDA =M−2∑i=0

ESDA(i)

≥M−2∑i=0

NLP · C ·[

NLP

K · TUDA(1 + i · q) +ε

]2

. (12)

A comparison between (12) and (9) indicates that the SDAtechnique is more energy efficient than the UDA, because itbetter utilizes the individual waiting time in view of the overallcommunication schedule. When applying the SDA technique,each SNOC will calculate the required voltage level and willselect the minimum voltage value as required by the CPUspecification.

In order to evaluate the computational efficiency of the UDAand SDA algorithms on the developed SNOC platform, anexperiment was designed to measure the timing characteristicsof BF532 CPU core when different voltage–frequency pairs areapplied. In this experiment, a homogeneous model involvingvibration data from eight vibration sensors for monitoring adefective ball bearing was employed. Each of the sensors wasresponsible for a specified frequency range of the bearingstructure identified from an initial modal analysis. The overallfrequency range was 5–15 kHz. Defect-induced-vibration fea-tures, e.g., energy on each subfrequency band, were extractedusing the DHWPT algorithm resident on each of the localSNOCs. Features from each identical subfrequency band werethen fused by a cluster head using statistical analysis with theoutput being the root-mean-square and kurtosis [19] values ofthe measurement. The CPU time and the corresponding powerconsumption were measured on the programming/test port onthe SNOC platform and listed in Table IV. Given that thefunction of “feature extraction” was shared by multiple SNOCsin a cluster, a low core voltage and frequency level contributeto energy savings on all the SNOCs.

To investigate the effect of the number of sensory nodeson the network operation, the computational energy consumedby the UDA and SDA techniques to operate a sensor networkconsisting of 40 sensory nodes, each containing eight sensors,were simulated using the experimental parameters obtainedfrom the SNOC platform. In the simulation, it was assumedthat all the nodes would have the same performance for datacomputation as that shown in Table III, and all the nodes wouldbe grouped into one sensor cluster for local feature extractionand DF. By referring to a look-up table where ten designed


TABLE IVMEASURED TIMING SEQUENCE OF BF532

Fig. 15. Comparison of energy consumption in a sensor cluster containing320 sensors.

voltage–frequency pairs were arranged in an increasing order,the appropriate voltage levels could be selected based on thedata transmission schedule and the related CPU parameters.Voltage selection started with the lowest value from the pro-grammed look-up table, and a decision was made to switchthe voltage level when it was higher than the required value.The voltage-selection procedure was updated by the respectiveSNOCs every time the transmission schedule was changed.

Fig. 15 illustrates the result of the simulation, where theenergy consumption of the two DVS-based sensing schemes(UDA and SDA) and the traditional non-DVS sensing schemefor the 320-sensor network are contrasted. It is seen that boththe UDA and SDA techniques have outputs of lower energyconsumption than that of the non-DVS technique. The differ-ence between the three curves along the vertical axis representsthe respective energy savings.

As the number of clustered sensory nodes increases, theenergy-saving potential of the UDA technique becomes lesssignificant than the non-DVS technique. This is due to the fact

TABLE VENERGY CONSUMPTION OF A SENSORY NODE FOR

REAL-TIME MONITORING

that the computational time TUDA will be compressed by the in-crease of communication time (M − 1) · TTX, as illustrated in(5). Hence, the utility of DVS is limited in the UDA technique.In comparison, since SDA schedules the operation speed foreach sensory node separately, more voltage reduction for eachsensor node can be enabled by utilizing the transmission waittime for data processing. As shown in Fig. 15, when the numberof sensory nodes increases in a sensor cluster from 25 to 40, theSDA technique has enabled a computational energy reductionbetween 18%–43% as compared to the UDA technique.

B. Low-Power Sensing

Although the computational energy consumption can bereduced by introducing scheduling techniques on the SNOC,energy consumption at the sensor level still presents a majorchallenge to long-term machine monitoring where small bat-teries are used. In a sensor cluster consisting of 40 sensorynodes, the average computation energy consumption Ecomp byusing the UDA technique is 21.4 mJ. Table V lists the totalenergy consumption attributed by major electronic componentson an SNOC during each data-set sampling period (1 s). Thetotal energy consumption amounts to approximately 154 mW. Iffour AA-size batteries with a total of 49 600-J energy capacityare used as the energy source for the SNOC, the sensory


Fig. 16. Time diagram of the periodic monitoring mode of operation.

TABLE VIENERGY CONSUMPTION OF A SENSORY NODE UNDER PERIODIC MONITORING

node would be able to operate for only 265 h or 11 days.Such an operational span is not acceptable for most realisticapplications, as a weekly battery replacement is not a viablesolution.

The energy consumption can be reduced by periodicallyputting a sensory node into a low-power operation state wheremost of the ICs on the SNOC are forced into the sleep mode.This is based on the consideration that the main objective ofmachine condition monitoring is to identify potential machinefailures, e.g., characteristic frequency lines that are associatedwith the structural defects in a bearing. Instead of continuallysampling data from the machine when the trending analysis ofprior samples has shown no evidence of abnormal behavior ofthe machine, it is more energy efficient to sample data onlyperiodically based on the result of a trending analysis wherethe rate of the data change is evaluated. Compared to the con-tinuous monitoring mode, such a periodic event-driven modeof sensing can significantly reduce the energy consumptionin a sensor network. To enable event-driven monitoring, thesensory nodes within a cluster will take turns in collectingdata and conduct signal analysis, assuming that an abnormalcondition occurring within the cluster will be detected by theactive node. As an example, Fig. 16 illustrates the scenarioof event-driven monitoring by an M -node cluster, where datasampling is performed successively from sensory node 1 tonode M . Since each node only samples once during the Mperiods, the respective speed of data processing can be reducedby lowering the supply voltage. Moreover, upon completion ofthe data processing, the SNOC can set most of its electronics,including the data buffer, CPU, and transceiver to the sleepmode while waiting for the next round of data sampling andprocessing. The energy consumption corresponding to such amode of operation for each 40 s in a 40-node cluster is listed inTable VI.

Compared with the results from Table V, the average powerconsumption has been reduced by 53 times to 2.9 mW. As aresult, the same batteries would be able to sustain the operationof the SNOC-based sensor network for 4751 h or 198 days.It should be noted that this value is calculated using general-purpose ICs of which the energy consumption is not optimizedfor applications in a sensor network. Custom-designed SNOCsusing state-of-the-art very-large-scale-integration (VLSI) tech-niques will further reduce the energy consumption of the elec-tronics and result in longer battery life for reliable and sustainedlong-term machine-monitoring operations.

V. CONCLUSION

Several fundamental aspects were presented concerning thearchitectural design of an SNOC, which is a key element inlarge-scale sensor networks. To enable the four core func-tionalities of the SNOC that are essential for implementing adistributed energy-efficient sensing scheme (data routing forindividual sensors, optimized data acquisition through event-driven sampling, feature extraction from raw data for betterutilization of bandwidth, and DF at the local level for reducednetwork traffic), detailed analysis and simulation were per-formed for the parametric design of the SNOC hardware andsoftware. A new approach to experimental data acquisition,referred to as the scheduled data acquisition, was developedbased on the concept of DVS. A simulation on a 40-node sensornetwork that incorporates 320 vibration sensors has shown that,compared to the traditional data-acquisition method, the SDAtechnique can improve the energy efficiency of the network byas much as 43%, which is significant for real-world machine-condition-monitoring applications. Using commercially avail-able ICs and four AA-size batteries, the estimated service lifeof the 40-node sensor network that can be achieved through the


SNOC design is 198 days, as compared to only 11 days usingthe traditional technique. The presented study has providedconcrete input for the design optimization and experimentalrealization of the SNOC-based sensor network that can bedeployed for the condition monitoring and health diagnosisof manufacturing machines. Future research will investigateSNOC-design enhancement by incorporating a built-in self-testfunction as an integrated part of the SNOC-resident software. Inaddition, other methods for energy-efficient chip-based designwill be comparatively investigated for sustainable machine-monitoring applications.

ACKNOWLEDGMENT

The authors would like to thank Dr. S. Das for his helpand encouragement in the preparation of this paper and thereviewers for the constructive comments.

REFERENCES

[1] National Science Foundation, Sensors and Sensor Networks, Mar. 2003.Program Solicitation, No. 03-512.

[2] G. Pottie and W. Kaiser, “Wireless integrated network sensors,” Commun.ACM, vol. 43, no. 5, pp. 551–558, May 2000.

[3] S. Simic and S. Sastry, “Distributed environmental monitoring usingrandom sensor networks,” in Proc. 2nd Int. Workshop InformationProcessing Sensor Networks, Palo Alto, CA, 2003, pp. 582–592.

[4] Crossbow Technology Inc., Case Study Overlay Sensor Network in a FoodProcessing Plant, 2002. Company Report.

[5] G. Halmschlager, “Paper Machinery Divisions: Latest Generation ofCylinder Mould Formers,” Voith Paper Company Newsletter, Feb. 1997.

[6] Mitsubishi Heavy Industries, Ltd., “Mitsubishi announces successful test-ing of new high-speed news paper offset press,” News Release, Oct. 2001

[7] KDO Co. (2005). KDO 265 Plus 3 Series Specification. [Online].Available: http://www.kdo.co.uk

[8] N. Yazdi and K. Najafi, “An all-silicon single-wafer micro-g accel-erometer with a combined surface and bulk micromachining process,”J. Microelectromech. Syst., vol. 9, no. 4, pp. 544–550, Dec. 2000.

[9] V. Srikar and S. Senturia, “The reliability of microelectromechanicalsystems (MEMS) in shock environments,” J. Microelectromech. Syst.,vol. 11, no. 3, pp. 206–214, Jun. 2002.

[10] N. Loh, M. Schmidt, and S. Manalis, “Sub-10 cm3 interferometricaccelerometer with Nano-g resolution,” J. Microelectromech. Syst.,vol. 11, no. 3, pp. 182–187, Jun. 2002.

[11] Sanyo Energy Co. (2003). GES-NH2300-2 Specifications. [Online].Available: http://www.sanyo.com

[12] J. L. Hill and D. E. Culler, “Mica: A wireless platform for deeply embed-ded networks,” IEEE Micro., vol. 22, no. 6, pp. 12–24, Nov./Dec. 2002.

[13] N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad,R. Govindan, and D. Estrin, “A wireless sensor network for structuralmonitoring,” in Proc. ACM Conf. Embedded Networked Sensor Systems,Baltimore, MD, Nov. 2004, pp. 13–24.

[14] R. Min, M. Bhardwaj, S. Cho, A. Sinha, E. Shih, A. Wang, andA. Chandrakasan, “An architecture for a power-aware distributedmicrosensor node,” in Proc. IEEE Workshop Signal Processing Systems,Lafayette, LA, 2000, pp. 581–590.

[15] R. Yan, D. Ball, A. Deshmukh, and R. Gao, “A Bayesian network ap-proach to energy-aware distributed sensing,” in Proc. IEEE Sensors Conf.,Vienna, Austria, Oct. 2004, pp. 44–47, Paper 1770.

[16] C. L. Liu and J. W. Layland, “Scheduling algorithm for multiprogrammingin a hard real-time environment,” J. Assoc. Comput. Mach., vol. 20, no. 1,pp. 46–61, Jan. 1973.

[17] C. Lm, H. Kim, and S. Ha, “Dynamic voltage scheduling techniquefor low-power multimedia applications using buffers,” in Proc. Int.Symp. Low Power Electronics and Design, Huntington Beach, CA, 2001,pp. 34–39.

[18] L. Shang, L. Peh, and N. Jha, “Dynamic voltage scaling with linksfor power optimization of interconnection networks,” in Proc. 9th Int.Symp. High-Performance Computer Architecture, Anaheim, CA, 2003,pp. 91–102.

[19] V. Gutnik and A. Chandrakasan, “Embedded power supply for low-powerDSP,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 5, no. 4,pp. 425–435, Dec. 1997.

[20] A. Wang and A. Chandrakasan, “Energy-efficient DSPs for wirelesssensor networks,” IEEE Signal Process. Mag., vol. 19, no. 4, pp. 68–78,Jul. 2002.

[21] R. Gao and C. Wang, “Wireless sensor for bearing condition monitoring:Design considerations,” in Proc. 6th World Multiconf. Systematics, Cyber-netics, and Informatics, Orlando, FL, Jul. 2002, vol. 15, pp. 367–372.

[22] R. Yan and R. Gao, “An efficient approach to machine health evaluationbased on harmonic wavelet packet transform,” Robot. Comput.-Integr.Manuf., vol. 21, no. 4/5, pp. 291–301, Aug.–Oct. 2005.

[23] C. Wang and R. Gao, “A virtual instrumentation system for integratedbearing condition monitoring,” IEEE Trans. Instrum. Meas., vol. 49, no. 2,pp. 325–332, Apr. 2000.

[24] ——, “Design of a sensor module for integrated bearing conditionmonitoring,” in Proc. ASME Int. Mechanical Engineering Congr. andExpo., Dynamic Systems and Control Division, Nashville, TN, Nov. 1999,vol. DSC-67, pp. 721–728.

[25] RF Monolithics, Inc. (2003). TR1000: 916.50 MHz Hybrid Transceiver.[Online]. Available: http://www.rfm.com

[26] Analog Devices Inc. (2004). ADSP-BF533 Blackfin Processor HardwareReference, Rev. 3. [Online]. Available: http://www.analog.com

[27] Intel Corporation. (2004). Intel PXA255 Processor Datasheet. [Online].Available: http://www.intel.com

[28] Transmeta Corporation. (2000). Crusoe Processor Model TM5600/TM5400 Processor Product Brief. [Online]. Available: http://www.transmeta.com/

Robert X. Gao (M’91–SM’00) received the B.S.degree in mechanical engineering from the (for-mer) Central Academy of Arts and Design, Beijing,China, in 1982 and the M.S. and Ph.D. degrees inmechanical engineering from the Technical Univer-sity of Berlin (TU Berlin), Berlin, Germany, in 1985,and 1991, respectively.

He is currently a Professor at the Department ofMechanical and Industrial Engineering, Universityof Massachusetts, Amherst. His research and teach-ing interests include physics-based sensing method-

ology, self-diagnostic and energy-efficient sensors and sensor networks,mechatronic systems design, medical instrumentation, and wavelet transformsfor machine health monitoring, diagnosis, and prognosis.

Dr. Gao received the National Science Foundation Career Award in 1996and the University of Massachusetts Outstanding Engineering Junior FacultyAward in 1999. He was the Guest Editor for a Special Section on Built-In-Testfor the IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,published in June 2005, and the Guest Editor for a Special Section on Sensorsfor the Journal of Dynamic Systems, Measurement, and Control, published bythe American Society of Mechanical Engineers in June 2004. He is currently anAssociate Editor for the IEEE TRANSACTIONS ON INSTRUMENTATION AND

MEASUREMENT and co-chairs the Technical Committee on Built-in-Test andSelf-Test of the IEEE Instrumentation and Measurement Society.

Zhaoyan Fan received the B.S. degree in mecha-nical engineering from Department of MechanicalEngineering, Tsinghua University, Beijing, China, in2000 and the M.S. degree in physical electronicsfrom the Institute of Electronics, Chinese Academyof Sciences, Beijing, in 2003, respectively. He iscurrently pursuing his Ph.D. degree in mechanicalengineering at the Department of Mechanical andIndustrial Engineering, University of Massachusetts,Amherst.

His research interests include electromechanical-system design, microelectromechanical-system sensors, machine conditionmonitoring, and signal processing for mechanical systems.

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