A Low-Cost VLSI Architecture for Robust (1)

8/2/2019 A Low-Cost VLSI Architecture for Robust (1)

http://slidepdf.com/reader/full/a-low-cost-vlsi-architecture-for-robust-1 1/10

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: REGULAR PAPERS, VOL. 58, NO. 6, JUNE 2011 1277

A Low-Cost VLSI Architecture for Robust

Distributed Estimation in Wireless Sensor NetworksLi-Yuan Chang, Pei-Yin Chen , Member, IEEE , Tsang-Yi Wang , Member, IEEE , and Ching-Sung Chen

Abstract— A robust distributed estimation scheme for fusion

center in the presence of sensor faults via collaborative sensorfault detection (CSFD) was proposed in our previous research.The scheme can identify the faulty nodes ef ficiently and improvethe accuracy of the estimates significantly. It achieves very good

performance at the expense of such extensive computations aslogarithm and division in the detecting process. In many real-time

WSN applications, the fusion center might be implemented withthe ASIC and included in a standalone device. Therefore, a simpleand ef ficient distributed estimation scheme requiring lower hard-ware cost and power consumption is extremely desired for fusion

center. In this paper, we propose the ef ficient collaborative sensorfault detection (ECSFD) scheme and its VLSI architecture. Given

the low circuit complexity, it is suitable for hardware implemen-tation. The circuit of ECSFD contains 22589 gates and requires a

core size of 571 559 m by using TSMC 0.18 m cell library.Simulation results indicate the accuracy of the estimates obtained

from the ECSFD is better than that obtained from a conventionalapproach.

Index Terms— Distributed estimation, fusion, sensor fault detec-tion, VLSI architecture, wireless sensor networks.

I. I NTRODUCTION

A S WIRELESS communications technology and micro-

electromechanical systems (MEMS) techniques have

matured in recent years, wireless sensor networks (WSN) have

emerged as a promising solution for a variety of remote sensingapplications, including battlefield surveillance, environmental

monitoring, intruder detection systems, weather forecasting,

health care, agricultural technology, and so on. Irrespective of

their purpose, all WSN are characterized by the requirement

for energy ef ficiency, scalability, and fault tolerance [1]. These

requirements are particularly crucial in sensor networks de-

signed to perform an estimation function. The fusion center

makes the distributed estimation based upon the information

received from the local nodes. In such networks, the estimation

performance is critically dependent upon the availability and

reliability of the local information, and substantial errors are

Manuscript received August 17, 2010; revised October 29, 2010; accepted November 07, 2010. Date of publication January 06, 2011; date of currentversion May 27, 2011. This work was supported in part by the NationalScience Council, Taiwan, under Grant NSC 99-2220-E-006-026 and Grant NSC 99-2220-E-006-024, and in part by the Applied Information ServicesDevelopment & Integration Project, Phase II, Institute for Information Industry,subsidized by the Ministry of Economy Affairs of the Republic of China. This paper was recommended by Associate Editor V. Gaudet.

L.-Y Chang, P.-Y. Chen, and C.-S Chen are with the Digital IC Design Labo-ratory, Department of Computer Scienceand Information Engineering, NationalCheng Kung University, Tainan 701, Taiwan (e-mail: [email protected]; [email protected]; [email protected]).

T.-Y. Wang is with the Institute of Communications Engineering at NationalSun Yat-sen University, Kaosiung 80424, Taiwan (e-mail: [email protected]).

Digital Object Identifier 10.1109/TCSI.2010.2096117

induced if the nodes become unavailable (e.g., as a result of

consuming all their energy) or unreliable (e.g., as a result

of intermittent malfunctions). Hence, the design of a robust

distributed estimation for fusion center in WSN is essential.

The problem of distributed estimation systems have attracted

significant interest in recent years [2]–[5]. The research focuses

principally on the problem of developing ener gy-ef ficient and

bandwidth-constrained designs. By contrast, the problem of en-

hancing the fault tolerance capability of decentralized estimation

systems has attracted relatively little attention. In practical net-

works, fault tolerance is a critical concern since the sensor nodes

are invariably battery-powered and randomly deployed, and aretherefore not easily recharged or r eplaced. Furthermore, the sen-

sors aregenerally deployed in outdoor or similarly harsh environ-

ments, and thus the occurrence of sensor failures or malfunctions

isalmostinevitable.Tosolvetheproblem,wehaveproposedacol-

laborative fault detection (CSFD) scheme [6] to detect the faulty

nodes within the network such that their quantized messages can

be excluded from the parameter estimation process.

Some related works about var iants of enhancing the fault tol-

erant capability of decentralized estimation systems have been

considered in the following literature. I. Rapoport et al. [7] ad-

dressed the problem of sensor fault detection and estimation in

dynamic systems using an a priori sensor-fault model. Mean-while, Delouille et al . [8] used an embedded subgraphs algo-

rithm to design a robust distributed estimation scheme for sensor

networks in which the sensors observe different physical phe-

nomena. The scheme considers only temporary communication

faults such as failing links and sleeping nodes, whereas the ro-

bust CSFD estimation scheme proposed considers all manner of

possible sensor failures. Ishwar et al. [9] utilized a packet-era-

sure model to examine various aspects of distributed estimation

in WSN, including its robustness toward sensor unreliability, its

power-cycling characteristics, and the effects of uncertainties in

the wireless transmissions. However, the estimation problem as-

sumes that the fusion center requires the ability to discriminate

between the local messages received from normally operating

nodes and those messages received from faulty nodes.

In [6], CSFD takes the concept of collaborative signal pro-

cessing to perform robust distributed estimation. Specifically,

this work employs the homogeneity test [10] to implement

CSFD scheme to detect the faulty nodes within the network

such that their quantized messages can be excluded from the

parameter estimation process. Utilizing the proposed CSFD

mechanism, the fusion center identifies the faulty nodes with

the WS N and then excludes theirs information when estimating

the parameter of interest. With the aid of CSFD scheme, dif-

ferent sensor faults can be tolerated to improve the performance

of estimating the parameter of interest. As predicted, CSFD

1549-8328/$26.00 © 2011 IEEE



1278 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: REGULAR PAPERS, VOL. 58, NO. 6, JUNE 2011

performs better than the conventional approach in estimating

theta in terms of different sensor faulty types and faulty number.

In the detecting process, CSFD requires such extensive com-

putations as logarithm and division though it achieves very good

performance. In many real-time WSN applications, the fusion

center might be implemented with the ASIC and included in

a standalone device, so a simple and good distributed estima-

tion scheme of lower computational complexity is extremely de-

sired. This motivation makes us modify CSFD and propose an

ef ficient collaborative sensor fault detection (ECSFD) scheme

and its VLSI architecture in this paper. Compared with CSFD,

ECSFD performs slightly better and requires only about 55% of

computations. Therefore, it does qualify as a good candidate for

hardware implementation.

In the recent years, some VLSI circuits for transmitter, re-

ceiver, demodulator, sensor node, and specific detector in WSN

have been presented. In [11], an on-off LC oscillator-based ul-

trawideband (UWB) impulse radio transmitter for long-range

application is presented. Verhelst et al. proposed a quadrature

analog correlation receiver for UWB [12]. In [13], a demodu-lator architecture capable of dealing with most of the previous

limitations in an ASK-utilized medical implant, especially in

want of being powered through wireless delivering, is proposed.

In [14], Alippi et al. proposed a low-power maximum power

point tracker (MPPT) circuit, which conveys solar energy into

rechargeable batteries for wireless sensor nodes. Furthermore,

Aguilar-Ponce et al.. proposed a VLSI architecture for Wron-

skian Change Detector [15] and Goldberg et al. proposed a low-

power VLSI wake-up detector for the use in an acoustic surveil-

lance sensor network [16]. To our knowledge, ECSFD circuit is

the first ASIC implementation for fault-tolerance fusion center

for distributed estimation and no related state-of-the-art ASICdesign exists in the literature.

The remainder of the paper is organized as follows. Section II

presents the system model, sensor fault models and the overview

of CSFD scheme. The details of ECSFD are described in

Section III. Section IV shows the VLSI architecture of ECSFD.

Section V presents the performance and implementation result

of ECSFD. Conclusions are finally drawn in Section VI.

II. OVERVIEW OF CSFD

Fig. 1 illustrates the basic structure of the distributed esti-

mation network considered in the present study. The Bayesian

formulation is considered here. Let be afi

-nite set corresponding to the sensor nodes observing sensor

measurement sequences generated from a common status of

phenomenon , the parameter under estimation. It is as-

sumed thatthe distributionof isknownand isdenoted by .

The observation sequences taken by sensor are denoted by

, where is the node index and is the time index.

Every sensor node quantizes its own observations to output

and send it to the fusion center. The local messages are

mapped to a binary signal vector where

is the number of bits used to represent the local

message and is the number of partition levels at the local

sensors.

In the distributed estimation network shown in Fig. 1, two

types of errors may affect the received quantized messages at

Fig. 1. System model for distributed estimation fusion scheme.

the fusion center. The first error is caused by the faulty node.

The considered WSN herein is very possible to contain faulty

nodes because of random deployment in a harsh environment.

The second error is the channel transmission error due to inter-

ference or noise. In this situation, the received at the fusioncenter may not be equal to and we denote by

for all and .

Consider the case where the fusion center estimates at

some arbitrary time . Note that in performing this estimation

process, all the messages received from the local nodes up to

time , i.e., are available

at the fusion center. If sensor faults exist within the network,

the estimated value of is liable to deviate significantly from

the true value. To solve the problem, CSFD adopts the con-

cept of collaborative signal processing to identify the faulty

nodes , where is the set of faulty nodes at time

. Then, the fusion center can eliminates the local messageassociated with these nodes , and makes the final

estimate at time , based only on the censored messages,

, where denotes and

.

In CSFD, the following sensor fault models are considered

in order to include different misbehavior. Given partition

levels for the quantizer, then we denote

when node operates in a fault-free manner.

In one fault model, the output of local quantizers is independent

of the parameter . For example, a stuck-at fault may occur in

which the output of the affected node is frozen at a fixed quanti-

zation level. Alternately, a random fault may arise in which thedistribution of the output of a faulty node is different from the

normal situation and equals a particular value regardless of the

true parameter. By contrast, some nodes may exhibit a -depen-

dent error in which a sensor offset bias transforms the sensor

measurement uniformly to a certain value and therefore alters

the value of .

The process of CSFD can be divided into three stages. The

first stage is to measure the faulty weights of all nodes. Then,

the faulty nodes are determined. Thefinal distributed estimate is

generated in the last stage. The detail of each stage is described

as follows.

Measuring Faulty Weight: This stage consists of two steps

and its aim is to decide the faulty weight of each node. The

faulty weight is used to measure the deviation of a node. In the



CHANG et al.: A LOW-COST VLSI ARCHITECTURE FOR ROBUST DISTRIBUTED ESTIMATION IN WIRELESS SENSOR NET WORKS 1279

first step, we compute the number of received from sensor

and denote it as .

(1)

where is the indicator function.

As mentioned in [17], the Kullback–Leibler (K-L) distance between distributions can be used to measure sensor-fault

deviation. In CSFD, we use K-L distance to estimate the

faulty weights of all sensors. According to the local decisions

, the K-L distance for node is em-

ployed to measure the distribution distance from average sensor

weight to faulty sensor weight , and is

defined as

(2)

where

(3)

Determining Faulty Nodes: The aim of this stage is to

decide which sensor nodes are faulty, based on the faulty

weights computed in the previous stage. First, all sensor

nodes are sorted in descending order based on their mag-

nitude of to get the faulty-weight-oriented se-

quence, . After is determined,

we can obtain the candidate set of faulty sensors, denoted as

where is the possible number of

faulty nodes, and let represent the empty set . In order

to determine the value of , the following homogeneity testing problem can be formulated to test for the existence of a set of

sensor nodes at time .

(4)

Then, the following statistics are utilized for homogeneity

testing to determine whether or not a candidate set is :

(5)

where

Utilizing the statistic , the binary hypothesis testing

problem given in (4) can be set as follows:

(6)

where is a threshold indicating the crit-

ical value of the chi-square distribution with

degrees of freedom at a significance level .

Fig. 2. Algorithm of CSFD.

In CSFD, the maximum value of is constrained to the

sensor faults search policy, i.e., , where is the

maximum number of possible faulty nodes. The step of deter-mining faulty nodes of CSFD scheme can be summarized as

follows:

CSFD-1: Set and assign the required significance

level . In addition, choose suitable value of in ac-

cordance with the network requirements and/or any prior

information regarding the failure characteristic of the net-

work.

CSFD-2: Perform (6) for the candidate set . If is

accepted, terminate the CSFD process and determine the

final decision . If is rejected, increase the

value of by 1.

CSFD-3: If , terminate the CSFD process anddetermine . If , accept hypothesis

and decide . Otherwise, rerun to Step

CSFD-2 and repeat Steps CSFD-2 to CSFD-3 iteratively

until is determined

Making Distributed Estimation: Once the set of faulty nodes

is determined, the fusion center removes the quantized

messages of the faulty nodes and performs the parameter esti-

mation. Then, the estimate obtained by minimum mean square

error (MSE) criterion is adopted and is given by

(7)

Fig. 2 shows the CSFD algorithm in the C language style. Moredetails of CSFD can be found in [6].

III. EFFICIENT CSFD

CSFD performs better than the conventional approach with

regard to fault tolerance. However, there are three dif ficulties

to be overcome for implementing CSFD with a VLSI circuit.

The first one is that it requires some extensive and complex

computations, such as logarithm and division in the detecting

process (see (2)–(6)). The second dif ficulty is that the integra-

tion required for the estimate of in (7) is quite complex. The

last dif ficulty is that the calculation of numerical integration

needs many bits. In order to overcome these dif ficulties, we

modify CSFD and propose an ef ficient collaborative sensor




fault detection (ECSFD) scheme in this paper. ECSFD is

simple and requires lower computational complexity, thus

lower hardware cost and power consumption can be achieved.

Furthermore, ECSFD achieves almost the same performance

as CSFD. The details of ECSFD are described in the following.

A. Avoid the Logarithm and Division Operations

To avoid the logarithm and division operations required

in (2), a simple and ef ficient sensor faulty weight estimate

method is provided. We take advantage of collaborative signal

processing to estimate the sensor faulty weight. More con-

cretely, without knowing the true distribution of , most nodes

in the networks can be reasonably assumed to normally report

their decisions inferring the true distribution of to the fusion

center. If the sensor behavior deviates from the average

sensor behavior more obviously, the sensor

has larger faulty sensor weight. Hence, the faulty weight of

the sensor nodes can be estimated by the sum of the absolute

differences between and

(8)

Besides, the final purpose of this stage is to calculate the

for obtaining the faulty-weight-oriented sequence . By multi-

plying all with a constant simultaneously, we can

further reduce the computational complexity of without

affecting the decided . Finally, the can be estimated

with less computational complexity and is given as

(9)

In the stage of determining the faulty nodes, we must calculate

first. The computation of suffers from the

problem of massive division which needs large computational

complexity. In order to overcome the problem, the hypothesis

testing can be rewritten in the following formation by multi-

plying (6) with a constant:

(10)

Substituting (5) to (10) gives

TABLE IMSE OF CSFD AND ECSFD FOR DIFFERENT TYPES OF FAULTY NODES

TABLE IICOMPUTING TIME OF ECSFD FOR TWO PROCESSORS

(11)

where and .

The required division operation in (5) is replaced with multi-

plication and the corresponding computational complexity cost

can be reduced. Using (9) and (11), we can choose the ac-

cording to the step of determining faulty nodes of CSFD scheme

listed in Section II.

After deciding , ECSFD using the same operation in (7)

to obtain the . By implementing (9), (11), and (7), ECSFD re-

quires less computation than CSFD, and can achieve quite good

performance with regard to fault tolerance. We show the per-

formance comparison of CSFD and ECSFD for different types

of faulty nodes in Table I. Obviously, the distributed estimation

performance of ECSFD is a little better than CSFD.

To verify the computational complexity, both CSFD and

ECSFD ((9), (11), and (17)) are implemented in C language on

the 2.8 GHz Pentium 4 processor with 512 MB memory and

the 520 MHz INTEL XScale PXA270 with 64 MB memory,

respectively. Table II shows the computing time for the two

processors. ECSFD requires about 55% of CSFD’s computingtime on Pentium 4 processor.

B. Simplify the Integration

However, minimum MSE in (7) needs integral operation

which is dif ficult for hardware implementation. Therefore, the

numerical integration is used in the stage of making distributed

estimation. (7) can be written in the following form:

(12)




where

(13)

In the issue of wireless communication, the additional noise

model can be reasonable assumed as a Gaussian function

. Therefore, can be given as

(14)

where denotes the quantized range of .

Let and denote the number of

received from in the fusion center. Then can be

calculated by the following equation:

(15)

Using the numerical integration, can be approximated byintegrating from to with an interval

(16)

In addition, the value of , and are decided according to the

prior distribution of .

C. Transform the Numerical Integration

However, the bit width required for the numerical repre-

sentations of the numerator and the denominator in (16) are

quite large when is large enough. With the aid of loga-

rithm property, we transform and

to

and , which need smaller bit

width, respectively. Hence, (16) can be rewritten as

(17)

(18)

Then, all the items of the numerators and denominators are

sorted to find the one with the maximum exponent denoted as

. According to the found value, all the other items which

satisfy or are selected to

calculate the value of approximated . (The selected items

are times larger than the ignored ones.) With the aid of the

logarithm and the sorting process, the can be calculated

ef ficiently.

By implementing (9), (11), and (17), ECSFD is suitable for

hardware implementation and can achieve quite acceptable

performance with regard to fault tolerance as demonstrated in

Section V.

IV. CHIP ARCHITECTURE FOR ECSFD

Observing the required operations in ECSFD, we develop a

low-cost VLSIarchitecture for ECSFD where and isset

as 8 and 3, respectively, in the current implementation. This set-

ting, as mentioned in [6], is suitable for general applications in

WSN. Furthermore, the word length of signals is decided based

on the following two considerations:

a) The performance of ECSFD circuit must be comparable

to that of CSFD.

b) The hardware cost of ECSFD circuit must be minimized.

After careful analysis and software simulation, we have

chosen the 11-bit widths for representing different signals

in the ECSFD circuit to meet the precision requirement and

maintain the acceptable performance.

TheVLSI architecture of ECSFDconsists of a logarithm unit,

antilogarithm unit, sort unit, register file, 11 11multiplier unit,

comparator unit, and adder/subtractor unit connected to a shared

bus. A top-level FSM coordinates the operations among these

functional units. In the following subsections, the implementa-

tions of four important operations, multiplication, logarithm/an-

tilogarithm, and sorting, are described in detail.

A. Multiplication

Since the largest width of the signals in ECSFD is 11-bit, a

basic 11 11 multiplier is developed where the multiplier is de-

noted as , the multiplicand is denoted as , and the product

is denoted as . Many multiplication operations are required

in ECSFD. Since the width of most signals is 11-bit, we need

the 11 11 multiplier. These multiplication operations are per-

formed sequentially at different time instant, so we can apply the

concept of hardware resource sharing and design special-pur-

pose multipliers (11 22, 22 22, and 22 33) to implement

them. Hence, we utilized the 11 11 multiplier to realize the

four different multiplications where the multiplier, multiplicand

and product are all realized with different bit widths of integer and fractional parts for respective precisions.

Let mean that the bit widths of the integer and frac-

tional parts of multiplier are bits and bits respectively.

The input/output precisions of four modes based on our 11 22,

22 22, and 22 33 multipliers are defined in Table III respec-

tively. For most WSN applications, the cost issue is more im-

portant than timing performance in the design of fusion center.

Hence, the 11 22, 22 22, and 22 33 multipliers are real-

ized with a normal 11 11 multiplier circuit (multiplying two

11-bit operands to produce a 22-bit product) and a dedicated

control circuit under multicycle implementation to reduce the

hardware cost. With the help of the control circuit, the 11 11multiplier can implement all the required multiplication opera-

tions for different modes with multiple clock cycles.

The full-precision 11 22 multiplier is realized with a

11 11 multiplier under multicycle implementation. Let ,

and represent the 11-bit data, and is multiplied

by through the 11 22 multiplier to get the product

result . The strategy of synthesizing the 11 22

multiplier with a 11 11 multiplier is shown in Fig. 3.

The full-precision 22 22 and 22 33 multipliers are de-

signed in the same way as the 11 22 multiplier. The strategies

of synthesizing the 22 22 multiplier with a 11 11 multiplier

for mode 2 and 3 are shown in Fig. 4(a) and (b) respectively.

Furthermore, the strategy of synthesizing the 22 33 multiplier

with a 11 11 multiplier is shown in Fig. 5. Specially, some bits




TABLE IIII NPUT/OUTPUT PRECISIONS OF THE 11 22, 22 22, AND 22 33

MULTIPLIERS

Fig. 3. The strategy forsynthesizing the 11 22 multiplierwith a 11 11mul-tiplier.

Fig. 4. The strategy for synthesizing the 22 22 multiplier for different modeswith a 11 11 multiplier. (a) Mode 2. (b) Mode 3.

of the output product is ignored to save the required registers

since they have very little influence on the calculated results.

This multimode multiplier realized with multicycle implemen-

tation can meet the required precision of ECSFD and achievethe goal of low cost design.

B. Logarithm and Antilogarithm

As shown in (17), some logarithm and antilogarithm conver-

sion operations are required in ECSFD. Let and represent

the input and output, thus the logarithm conversion can be de-

noted as where is the 22-bit input and is the

converted 11-bit output. The reason of using is to match

the binary representation. Using a proper lookup table, we can

implement the logarithm conversion with a dedicated control

circuit. In our implementation, the prior distribution of is a

Gaussian function (0, 0.5), the range of the integration is from

to 3, the integral interval, is set as 0.05, and .

Fig. 5. The strategy forsynthesizing the 22 33 multiplier with a 11 11mul-tiplier.

Fig. 6. Flow chart of the antilogarithm conversion.

Hence, the lookup ROM table is constructed with 121 6 en-

tries ( , and ).

The antilogarithm conversion operations are also performed

based on a lookup table. Let and represent the input and

output, thus the antilogarithm conversion can be denoted as

, where is the 10-bit input and is the converted 22-bit

output. The exponents of the selected items in (18)

are normalized (subtracted by a common constant) to the range

from 0.00 to 10.00 without affecting the approximated .

Fig. 6 shows flow chart of antilogarithm computation in our

design. We use 10-bit to represent . The

lookup table is constructed with 10 entries and each stores the

17-bit value of , where is an integer to represent the

position number and . At every clock cycle, if , we find by looking u pthe ROM with the current

, multiply it by . Otherwise, remains the same value. The

22 22 multiplier (mode 3) is accessed 0 to 9 times to get the

result of 22-bit . After getting the values of numerators and

denominators in (18) through the antilogarithm module,

can be calculated by a divider. Finally, the division required in

(18) is replaced by repeated subtractions to reduce the hardware

cost.

C. Sorting

In ECSFD, the faulty weights of sensors are represented as

and is 3 in the current implementa-

tion, so we need to find the three biggest values from these eight






Fig. 10. Performance comparison of CSFD, ECSFD circuit, and the conven-tional approach in a fault-free WSN.

Fig. 11. Performance comparison of CSFD, ECSFD circuit, and the conven-tional approach in a WSN with two sensors with stuck-at faults, i.e.,

.

For verification, the architecture was also implemented on the

Altera Stratix II EP2S60F1020C5 FPGA platform. The imple-

mentation result shows that our circuit generates correct output

under the operating clock frequency of 87.31 MHz with 2013

logic elements and 1435 registers.

Furthermore, we investigate the performance of ECSFD cir-

cuit in the presence of different sensor faults. The evaluation

is performed in various faulty scenarios where the fault typesand the number of faulty nodes are not known in advance. This

simulation results obtained for the MSEs of both estimation

schemes are also compared with those obtained using a con-

ventional distributed Bayesian estimation system in which the

unreliable local messages are included within the parameter es-

timation. The sensor measurements are processed using an ad-

ditive noise model, and the sensor observations are given by

(19)

where is drawn from a Gaussian distribution with zero-mean

and a variance , and , i.e., the additive noise at node

at time , also has a Gaussian distribution with zero-mean and

a variance . It is assumed that all the local nodes apply

Fig. 12. Performance comparison of CSFD, ECSFD circuit, and the conven-tional approach in a WSN with two sensors with random faults, i.e.,

.

Fig. 13. Performance comparison of CSFD, ECSFD circuit, and the conven-tional approach in a WSN with two faulty sensors characterized by

and .

the Lloyd–Max quantizer [18] with . The corresponding

partitions are given by

(20)

The communication channels between the local sensors and the

fusion center are assumed to be binary symmetric channels with

a crossover probability of . A value of is

specified in all the simulations.

Fig. 10 compares the estimation performance of CSFD,

ECSFD, and the conventional scheme for the case in which all

of the sensors within the network are fault-free. It is evident

that the MSE values of CSFD and ECSFD are virtually iden-

tical to those of the conventional scheme, implying that CSFD

and ECSFD have exceedingly small possibility to remove the

normally operating nodes.

Fig. 11 compares the estimation performance of the three

schemes when two of the nodes within the WSN experience




stuck-at-zero faults, i.e., . Note

that the two faulty nodes are drawn uniformly from the eight

nodes within the network. The results confirm that both ro-

bust estimation schemes result in a significantly lower MSE

than that obtained using the conventional approach. Moreover,

ECSFD performs slight better than CSFD. Fig. 12 illustrates

the performance of the three estimation schemes for the case

in which two of the eight sensors in the WSN experience a

random fault, i.e., . Again, the

results confirm that both CSFD and ECSFD schemes yield a far

better estimation performance than the conventional approach.

The scenario with two different sensor-fault types is simulated

in Fig. 13. This figure compares the performance of the three

schemes for the case in which the network has two faulty sen-

sors characterized by and

, respectively. The objective

of this evaluation is to investigate the ability of the proposed es-

timation schemes to cope with most sensor-fault types without

any a priori knowledge of the sensor-fault models. Once again,

the results confirm that both CSFD and ECSFD schemes rapidlyconverge to far lower values of the MSE than that obtained

using the conventional method, even when the network is in the

presence of the combined sensor-faults. Furthermore, it is ob-

served that the MSE results obtained using the ECSFD scheme

are slightly lower than those obtained from the original CSFD

scheme. Through the above performance evaluation, we con-

clude that ECSFD is more fault-tolerant than the conventional

approach with a very close level performance as that of CSFD.

VI. CONCLUSION

The ECSFD is designed in order to reduce the computationalcomplexity required for CSFD in this paper. Based on ECSFD,

a low cost VLSI architecture is proposed for fault-tolerant fu-

sion center in WSN. With the multicycle structure, the proposed

VLSI architecture can work fast enough to provide the real-time

operation but only needs a low hardware cost. According to the

performance evaluation, the VLSI architecture for ECSFD can

work better than the conventional approach and its performance

is close to that of CSFD.

R EFERENCES

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A

survey on sensor networks,” IEEE Commun. Mag., vol. 40, no. 8, pp.102–114, Aug. 2002.

[2] Y. Zhu, E. Song, J. Zhou, and Z. You, “Optimal dimensionality re-duction of sensor data in multisensor estimation fusion,” IEEE Trans.Signal Process., vol. 53, no. 5, pp. 1631–1639, May 2005.

[3] R. Niu and P. K. Varshney, “Target location estimation in sensor net-works with quantized data,” IEEE Trans. Signal Process., vol. 54, no.12, pp. 4519–4528, Dec. 2006.

[4] A. Ribeiro and G. B. Giannakis, “Bandwidth-constrained distributedestimation for wireless sensor networks—Part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 7, pp. 2784–2796, Jul. 2006.

[5] A. Ribeiro and G. B. Giannakis, “Bandwidth-constrained distributedestimation for wireless sensor networks—Part II: Unknown probability density function,” IEEE Trans. Signal Process., vol. 54, no. 7, pp. 2784–2796, Jul. 2006.

[6] T.-Y. Wang, L.-Y. Chang, and P.-Y. Chen, “A collaborative sensor-

fault detection scheme for robust distributed estimation in sensor net-works,” IEEE Trans. Commun.., vol. 57, no. 10, pp. 3045–3058, Oct.2009.

[7] I. Rapoport and Y. Oshman, “A new estimation error lower bound for interruption indicators in systems with uncertain measurements,” IEEE Trans. Inf. Theory, vol. 50, no. 12, pp. 3375–3384, Dec. 2004.

[8] V. Delouille, R. N. Neelamani, and R. G. Baraniuk, “Robust dis-tributed estimation using the embedded subgraphs algorithm,” IEEE Trans. Signal Process., vol. 54, no. 8, pp. 2998–3010, Aug. 2006.

[9] P. Ishwar, R. Puri, K. Ramchandran, and S. S. Pradhan, “On ratecon-strained distributed estimation in unreliable sensor networks,” IEEE J.Sel. Areas Commun., vol. 23, no. 4, pp. 765–775, Apr. 2005.

[10] R. C. Elandt-Johnson , Probability Models and Statistical Methods inGenetics. New York: Wiley, 1971.

[11] S. Diao, Y. Zheng, and C.-H. Heng, “A CMOS ultra low-power andhighly ef ficient UWB-IR transmitter for WPAN applications,” IEEE.Trans. Circuits Syst. II, Exp. Briefs, vol. 56, no. 3, pp. 200–204, 2009.

[12] M. Verhelst and W. Dehaene, “Analysis of the QAC IR-UWB receiver for low energy, low data-rate communications,” IEEE. Trans. CircuitsSyst. I, Reg. Papers, vol. 55, no. 8, pp. 2423–2432, 2008.

[13] C.-S. A. Gong, M.-T. Shiue, K.-W. Yao, T.-Y. Chen, Y. Chang, andC.-H. Su,“A truly low-cost high-ef ficiencyASK demodulator based onself-sampling scheme for bioimplantable applications,” IEEE. Trans.Circuits Syst. I, Reg. Papers, vol. 55, no. 6, pp. 1464–1477, 2008.

[14] C.Alippi andC. Galperti,“An adaptive systemfor optimal solar energyharvesting in wireless sensor network nodes,” IEEE. Trans. CircuitsSyst. I, Reg. Papers, vol. 55, no. 6, pp. 1742–1750, 2008.

[15] R. Aguilar-Ponce, J. Tessier, A. Baker, C. Emmela, J. Das, J. L. Tec-

panecatl-Xihuitl, A. Kumar, and M. Bayoumi, “VLSI architecture for an object change detector for visual sensors,” in IEEE Workshop Signal Process. Syst. Design Implementation, 2005, pp. 290–295.

[16] D. H. Goldberg, A. G. Andreou, P. Julian, P. O. Pouliquen, L. Riddle,andR. Rosasco, “A wakeup detectorfor an acoustic surveillance sensor network: Algorithm and VLSI implementation,” in Proc. IPSN’04, pp.134–141.

[17] T.M. Coverand J.A. Thomas , Elements of Information Theory. NewYork: Wiley, 1991.

[18] S. P. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Inf.Theory, vol. IT-28, pp. 129–136, Mar. 1982.

Li-Yuan Chang received the B.S. and M.S. degreesin computer science and information engineeringfrom National Chi Nan University, Nantou Hsien,Taiwan, in 2004 and 2006, respectively. He is cur-

rently working toward the Ph.D. degree in computer science and information engineering at NationalCheng Kung University, Tainan, Taiwan.

His research interests include wireless sensor net-works, distributed detection, and embedded systems.

Pei-Yin Chen (M’08) received the B.S. and Ph.D.degrees in electrical engineering from NationalCheng Kung University, Tainan, Taiwan, in 1986and 1999, respectively, and the M.S. degree inelectrical engineering from Pennsylvania StateUniversity, State College, in 1990.

He is currently a Professor in the Department of

Computer Science and Information Engineering, Na-tional Cheng Kung University. His research interestsinclude VLSI chip design, video compression, fuzzylogic control, and gray prediction.

Tsang-Yi Wang (S’01-M’04) received the B.S. andM.S. degrees from the National Sun Yat-sen Univer-sity, Kaohsiung, Taiwan, in 1994 and 1996, respec-tively, and the Ph.D. degree in electrical engineeringfromthe Syracuse University, Syracuse, NY, in 2003.

From 2004 to 2006, he was an Assistant Pro-fessor in the Graduate Institute of CommunicationEngineering, National Chi Nan University, Nantou,Taiwan. In February 2006, he joined the faculty of

the Institute of Communications Engineering, Na-tional Sun Yatsen University, Kaohsiung, Taiwan, as

an Assistant Professor, and in August 2008 he became an Associate Professor.




He is currently a Reviewer Editor of the Journal of Wireless Communicationsand Mobile Computing . His research mainly focuses on distributed detectionand estimation with applications in wireless communications and wirelesssensor networks.

Dr. Wang received the 2008 Best Paper Award for Young Scholars awardedfrom IEEE Information Society Taipei Chapter and IEEE Communications So-ciety Taipei/Tainan Chapter.

Ching-Sung Chen received the B.S. degree incomputer science and engineering from Yuan ZeUniversity, Taoyuan County, Taiwan, in 2008, andthe M.S. degree in computer science and informationengineering from National Cheng Kung University,Tainan County, Taiwan in 2010.

His research interests include VLSI chip designand embedded systems.

Documents

A Low-Cost VLSI Architecture for Robust (1)