


ARTICLE 

Year : 2012  Volume
: 2
 Issue : 2  Page : 6974 

A Censoring and Quantization Scheme for EnergyBased Target Localization in Wireless Sensor Networks
Zhenxing Luo
Department of Electrical and Computer Engineering, The University of Alabama at Birmingham, Birmingham, AL, USA
Date of Web Publication  4Aug2012 
Correspondence Address: Zhenxing Luo Department of Electrical and Computer Engineering, The University of Alabama at Birmingham, Birmingham, AL USA
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/09768580.99291
Abstract   
This article presents a censoring and quantization scheme for energybased target localization in wireless sensor networks (WSNs). This scheme can save energy and communication bandwidth. Moreover, a multiobjective optimization method is used to balance the energy consumption and target localization performance of the energybased target localization method using this scheme. Results show that the root square mean (RMS) errors provided by the energybased target localization method using this scheme were close to the CramerRao lower bound (CRLB). Moreover, the Paretofronts provided by the multiobjective optimization method can guide practical applications. Keywords: Wireless sensor networks, maximum likelihood estimation, censoring scheme, CramerRao lower bound, quantization
How to cite this article: Luo Z. A Censoring and Quantization Scheme for EnergyBased Target Localization in Wireless Sensor Networks. J Eng Technol 2012;2:6974 
How to cite this URL: Luo Z. A Censoring and Quantization Scheme for EnergyBased Target Localization in Wireless Sensor Networks. J Eng Technol [serial online] 2012 [cited 2019 Sep 22];2:6974. Available from: http://www.onlinejet.net/text.asp?2012/2/2/69/99291 
1. Introduction   
Wireless sensor networks (WSNs) have been successfully applied to solve many problems, such as, environmental monitoring, medical asset tracking, and fire rescue [1 7]. Typically, a WSN is comprised of a large number of widely distributed sensors, which are of lowcost and are resourceconstrained [8]. After gathering environmental information, the sensors will send information back to the fusion center, which has a greater computational capacity to further process the information from the sensors. In the energybased target localization method, based on the information from the sensors, the fusion center estimates the target position [8].
Usually, sensors can send either uncompressed data, such as analog data, or compressed data, such as quantized data, to the fusion center [8]. However, sending compressed data is better for resourceconstrained sensors, for two reasons. One reason is that sensors are powered by irreplaceable batteries, and therefore, energy is a precious resource for sensors. Sending compressed data will save sensor energy [8]. The second reason is that the bandwidths of communication channels between sensors and the fusion center are limited [8]. Sending compressed data uses less of the communication bandwidths.
A target localization method in which sensors send analog data to the fusion center was presented in [3]. This method was improved by a target localization method, using quantized data in [8]. In recent times, another scheme to save sensor resources, which was called the 'censoring scheme', was presented in [9], for the distributed detection problem. The censoring scheme was further discussed in [1012]. A scheme to combine the censoring scheme and the quantization scheme for the distributed detection problem was presented in [13]. Moreover, a scheme to combine the censoring scheme and the quantization scheme was presented for estimation, based on a linear estimation model in [14].
This article presents a censoring and quantization scheme for the energybased target localization method based on a nonlinear estimation model, the energy decay model. Following this, a modern multiobjective optimization method, the nondominated sorting genetic algorithm (NSGAII), will be used to balance the energy consumption and the estimation performance for the energybased target localization method, using the quantization and censoring scheme. The NSGAII is a modern multiobjective optimization algorithm, with low computational complexity [15]. In most applications, it can find a good spread of solutions near the true Paretofront. Although the energy function and performance function for the energybased target localization method, using only the quantization scheme, has been presented in [16], the energy function and the performance function have not been presented for the energybased target localization method, which used the quantization and censoring schemes.
The main contribution of this article is a quantization and censoring scheme for the energybased target localization method, and a multiobjective optimization approach to balance energy consumption and the target localization performance.
Section 2 presents a censoring and quantization scheme for the energybased target localization method, followed by the performance function and energy function in WSNs, with onedimensional sensor array in Section 3. The energy function and performance function in WSNs with twodimensional sensor arrays are presented in Section 4. Section 5 discusses the simulation setup, and Section 6 presents results and analysis. The concluding remarks are presented in Section 7.
2. A Censoring and Quantization Scheme for the EnergyBased Target Localization Method   
The signal emitting from a target received at the ith sensor can be determined by the decay model originally presented in [8]
in which G _{i} is the gain of the ith sensor and P _{0} is the power of the target, measured at a reference distance d_{0} . The distance between the ith sensor at (x_{i} ,y _{i}0) and a target at (x_{t} ,y _{t}) can be defined as
A typical area of the sensor field used in the simulations is shown in [Figure 1], and our simulations for twodimensional sensor arrays were based on this sensor field with sensors in the range of (90 90), (90, 90), (90, 90), and (90 90).
To avoid numerical problems, the target is assumed to be at least d_{0} meters away from any sensor. To simplify the model (1), it is further assumed that G_{i = 1} and d_{0} = 1. Then, model (1) can be expressed as
On account of the presence of environmental noises, the signal received at sensor i can be expressed as
where w_{i}is a Gaussian noise with zero mean and variance σ ^{2}.
In the censoring and quantization scheme, two thresholds are used: A high threshold s_{i}0 and a low threshold γ _{2}. If the received signal s_{i} is greater than the low threshold, sensors will quantize s_{i}0 and send the decisions to the fusion center. Otherwise, the sensors will not send any decision. This is called the censoring process. In the quantization process, if s_{i} is higher than the high threshold γ _{1}, the decision is 1. If s_{i}0 is less than the high threshold γ _{1}, the decision is 1. The censoring and quantization scheme can be denoted by:
The probability that m_{i} takes value m is
Based on decision vector M = [m _{1} m _{2} ... m _{N} ] ^{T} received at the fusion center, the fusion center finds
θ = [P _{0} x _{t} y _{t} ] ^{T} that can maximize
The maximum likelihood estimator tries to find the θ value to maximize
If an unbiased estimate of θ exists, the CramerRao lower bound (CRLB) can be derived by
The derivation of the CRLB matrix is similar to the process in [8]. Therefore, the detailed procedures to derive the CRLB will not be presented here.
3. The Performance Function and Energy Function in WSNS with One Dimensional Sensor Array   
For onedimensional sensor arrays, as shown in [Figure 2], the energy and performance functions are defined here.
3.1 The energy function
The energy used by sensors can be divided into three parts: Energy to keep sensors awake, energy sensors used to measure the environmental phenomena, and energy sensors used to send decisions to the fusion center.
Energy to keep sensors awake and energy sensors used to measure the environmental phenomena are assumed to be constant. Then, only the energy sensors used to send decisions to the fusion center are defined. According to [16,17], the energy consumption (in Joules) for sensor i to send m bits of information to the fusion center can be determined by
In (12), E _{elec} = 50 nJ/bit, ε_{amp} = 100 pJ/bit/m ^{2} , and d _{f,i} 0is the distance between sensor i and the fusion center. The overall energy required by the sensors to transmit the decision vector M to the fusion center is the sum of the energy used by each sensor to transmit m_{i}
where m_{i}0 is the ith element of decision vector M.
For a onedimensional sensor array with a specific target location and parallel decision transmission scheme, if it is assumed that the target is always present, it is easy to have [16,17].
where M is the decision vector made by the sensors and u_{i} is the decision made by sensor i. If a target is located at (x _{t} ,y _{t}), the probability that sensor i has decision m_{i} 0is p(m _{i}/x _{t},y _{t}). In (14), E _{T} is the average energy needed for sensors to send the decision vector M to the fusion center. In determining the p(m _{i}/x _{t},y _{t}) value, high threshold γ _{1} and the low threshold γ _{2} are used. A detailed method to calculate p(m _{i}/x _{t},y _{t}) can be found in [17].
Throughout this article, it is assumed that the sensing cycle time is one microsecond. If A is the energy required to keep a sensor awake plus the energy needed for a sensor to sense the environment in one microsecond, the overall energy consumption by all sensors in one microsecond is
where N is the total number of sensors in the WSN. The energy consumption function is ƒ _{1} = E _{a} (γ _{2} ) and the only input is the low threshold γ _{2}. However, the computation of E _{T} using (14) increases exponentially with N [16,17]. Therefore, this approach can only be used for a sensor network with a small number of sensors.
3.2 The performance function
Similar to the setup in [16], if the target power is known, then for a onedimensional sensor array, only one variable θ = x _{t} is estimated. As the maximum likelihood estimation is unbiased, the variance of estimation errors is an important indicator of estimation performance. The CRLB is a lower boundary of the variance of the estimation errors, and therefore, can be used
and the CRLB is the inverse of the J matrix. Then the performance function is defined as
The details of the method used to calculate the J matrix can be found in [8].
Now that the energy function (15) and the performance function (17) have been defined, the multiobjective optimization method can be used to balance (15) and (17).
4. Energy Function and Performance Function in WSNS with TwoDimensional Sensor Arrays   
As the calculation of (14) involves the summation of E _{c} (M) for all possible M, this method is not suitable for sensor networks with a large number of sensors [16,17]. In this section, an approximation method is used to calculate the energy used by all sensors. This method is similar to the method presented in [16].
In a noisefree case, which means w = 0 in (4), then it is easy to have s_{i} = a_{i}, and the following steps can be used as an approximate approach to calculate the energy consumption.
(1) It is assumed that all sensors employ the same low threshold γ _{2}. Then, under the quantization and censoring scheme, only sensors which receive signals higher than the low threshold γ _{2} will send decisions to the fusion center. In the noisefree case, these sensors are located within the circular area centered at the target location. The radius R of this circular area can be determined by
The total number of sensors within this circular area is N _{range}.
(2) For each of the sensors within the circular area, calculate the energy E _{TX} (m _{i} ,d _{f,i} ) required for each sensor to transmit one bit of information to the fusion center using (14)
(3) The total energy consumption consumed by all sensors in the circular area to send one bit of information to the fusion center can be calculated by using
(4) The overall energy consumption of all sensors in one microsecond is
The first part of (20) is the energy required to keep sensors awake and energy used by sensors to measure the environmental phenomena. The second part of (20) is the energy required for sensors in the circular area to send onebit information to the fusion center.
The overall energy consumption function in (20) can be used as the energy function for a multiobjective optimization purpose. Similar to the setup in [16], for twodimensional sensor arrays, the CRLB is used as the performance function. However, for twodimensional sensor arrays, parameters θ = [P _{0} x _{t} y _{t} ] ^{T} are estimated, and therefore, the CRLB matrix is a 3 × 3 matrix. As the estimation errors of x _{t} and y _{t} are the most important, the performance function can be set to the variance of the estimation errors for x _{t} and y _{t}
Now, a multiobjective optimization algorithm can be used to jointly optimize the energy function (20) and the performance function (21).
5. Simulation Setup   
To verify the quantization and censoring scheme, the root square mean (RMS) errors given by the quantization and censoring scheme will be compared with the CRLB for twodimensional sensor arrays. The sensor layout in [Figure 1] is used, and set x _{t} ,y _{t} = (12,13), P _{0} = 10,000, and γ _{1} = 6 is used for all sensors. The low threshold γ _{2} varied from 1 to 5. For each γ _{2} value, the RMS error is based on 1000 Monte Carlo simulations.
To balance the energy consumption and localization performance, the NSGAII algorithm is used. For onedimensional sensor array in [Figure 2], the fusion center is located at (100,0), A = 60_{e}  9 joule, x _{t} ,y _{t} = (0,0),
P0 = 100, and γ _{1} = 2 for all sensors. The value of γ _{2} was varied from 1 to 1.5. To generate the Paretofront corresponding to a onedimensional sensor array, the generation number was set to 30 and the population size of the NSGAII algorithm was set to 100. For twodimensional sensor arrays, the fusion center was located at (100,100), A = 60_{e}  9 joule, γ _{1} = 6 for all sensors, x _{t} ,y _{t} = (12,13), and P _{0} = 10,000. The value of γ _{2} was varied from 1 to 5. To generate the Paretofront corresponding to twodimensional sensor arrays, the generation number was set to 30 and the population size of the NSGAII algorithm was set to 100.
6. Results and Analysis   
The RMS errors given by the energybased target localization method using the censoring and quantization scheme were compared with the CRLB [Figure 3]. The low threshold γ _{2} was varied from 1 to 5. The RMS errors were close to the CRLB [Figure 3]. It was also clear that the RMS errors varied for different values of the low threshold γ _{2} [Figure 3]. It could be that a low threshold could give the lowest RMS errors.  Figure 3: RMS estimation errors and the CRLB given by the energybased target localization method in WSNs with twodimensional sensor arrays (varying low threshold)
Click here to view 
The results of a multiobjective optimization algorithm are a Paretofront consisting of Pareto points. Pareto points are solutions to the multiobjective optimization problem. The NSGAII is a modern multiobjective optimization method, and, in most cases, it can generate uniformly spread Paretofront points [15]. The Paretofront corresponding to a onedimensional sensor array is shown in [Figure 4], and the Paretofront corresponding to twodimensional sensor arrays is shown in [Figure 5]. The points in [Figure 5] are sparser than the points in [Figure 4]. The reason is that many points in [Figure 5] overlap each other. Therefore, one point in [Figure 5] may actually be several identical points. In contrast, in [Figure 4], the points were spread out more uniformly. Therefore, it appears that [Figure 4] has more points, and the points in [Figure 4] are denser than the points in [Figure 5]. The Paretofront is useful because one can pinpoint a point on the Paretofront to choose the desired estimation performance and acceptable energy consumption [16].
7. Conclusion   
In this article, a censoring and quantization scheme for the energybased target localization method was presented. The results showed that the RMS errors given by the energybased target localization method using this scheme were close to the CRLB. Moreover, the energy functions and performance functions corresponding to the onedimensional sensor array and twodimensional sensor arrays were provided, respectively. The NSGAII algorithm was used to balance the energy consumption and target localization performance. The Paretofronts generated are useful in practice.
References   
1.  D. Li, K. D. Wong, Y. H. Hu, and A. N. Sayeed, "Detection, Classification, and Tracking of Targets," IEEE Signal Processing Magazine, vol. 19, no. 3, pp. 1729, Mar. 2002. 
2.  Z. X. Luo, and T. C. Jannett, "Optimal Threshold for Locating Targets within a Surveillance Region Using a Binary Sensor Network," in Proceedings of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec. 2009. 
3.  D. Li, and Y. H. Hu, "Energy Based Collaborative Source Localization Using Acoustic Microsensor Array," EURASIP Journal on Applied Signal Processing, no. 4, pp. 321337, 2003. 
4.  Z. X. Luo and T. C. Jannett, "Modeling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks", in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012. 
5.  Z. X. Luo, and T. C. Jannett, "Performance Comparison between Maximum Likelihood and Heuristic Weighted Average Estimation Methods for EnergyBased Target Localization in Wireless Sensor Networks", In Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012. 
6.  K. Kim, J, Jun, S. Kim, and B. Y. Sung, "Medical Asset Tracking Application with Wireless Sensor Networks," in Proceedings of Second International Conference on Sensor Technologies and Applications (SENSORCOMM 2008), Cap Esterel, France, August 2008, pp. 531536. [PUBMED] 
7.  K. Sha, W. Shi, and O. Watkins, "Using Wireless Sensor Networks for Fire Rescue Applications: Requirements and Challenges," in the Proc. of the 2006 IEEE International Conference on Electo/information Technology, East Lansing, MI, USA, May 710, 2006, pp. 239244. 
8.  R. X. Niu, and P. K. Varshney, "Target Location Estimation in Sensor Networks with Quantized Data," IEEE Transactions on Signal Processing, vol. 54, pp. 45194528, Dec. 2006. 
9.  C. Rago, P. Willett, and Y. BarShalom, "Censoring Sensors: A Lowcommunicationrate Scheme for Distributed Detection," IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 2, pp. 554568, April 1996. 
10.  S. Appadwedula, V. V. Veeravalli, and D. L. Jones, "Decentralized Detection with Censoring Sensors," IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 13621373, April 2008. 
11.  S. Marano, V. Matta, and P. K. Willett, "Distributed Detection with Censoring Sensors Under Physical Layer Secrecy," IEEE Transactions on Signal Processing, vol. 57, no. 5, pp. 19761986, May 2009. 
12.  R. Jiang, and B. Chen, "Fusion of Censored Decisions in Wireless Sensor Networks," IEEE Transactions on Wireless Communications, vol. 4, no. 6, pp. 26682673, Nov. 2005. 
13.  V. W. Cheng, and T. Wang, "Performance Analysis of Distributed Decision Fusion Using a Censoring Scheme in Wireless Sensor Networks," IEEE Transactions on Vehicular Technology, vol. 59, no. 6, pp. 28452851, July 2010. 
14.  E. J. Msechu, and G. B. Giannakis, "SensorCentric Data Reduction for Estimation with WSNs via Censoring and Quantization," IEEE Transactions on Signal Processing, vol. 60, no. 1, pp. 400414, Jan. 2012. 
15.  K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGAII," IEEE Transactions on Evolutionary Computing, vol. 6, no. 2, pp. 182197, Aug. 2002. 
16.  Z. X. Luo, and T. C. Jannett, "A MultiObjective Method to Balance Energy Consumption and Performance for EnergyBased Target Localization in Wireless Sensor Networks," in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012, in press. 
17.  E. Masazade, P. K. Varshney, C. K. Mohan, and M. Keskinoz, "A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor network," IEEE Transactions on System, Man, and Cybernetics, part B, vol. 40, no. 2, pp. 444457, Apr. 2010. 
Authors   
Mr. Zhenxing Luo received his B.S. degree in Telecommunications from Xidian University, China and his M.S. degree in Electrical Engineering from the University of Alabama at Birmingham, USA. His research focuses on statistical signal processing including detection, estimation, and tracking.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
This article has been cited by  1 
Data censoring with network lifetime constraint in wireless sensor networks 

 Liu Yang,Hongbin Zhu,Haifeng Wang,Kai Kang,Hua Qian   Digital Signal Processing. 2019;   [Pubmed]  [DOI]   2 
Distributed State Estimation With Dimension Reduction Preprocessing 

 Ma, H.; Yang, Y.H.; Chen, Y.; Liu, K.J.R.; Wang, Q.   IEEE TRANSACTIONS ON SIGNAL PROCESSING. 2014; 62(12): 30983110   [VIEW]  [PDF]  [DOI]   3 
An Overview of Estimation Methods within Wireless Sensor Networks 

 Everette Adams, Shaoliang Jia   International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2013; 1(8): 2123   [VIEW]  [PDF]  [DOI]   4 
Survey of Networking Techniques for Wireless Multimedia Sensor Networks 

 Zhenxing Luo   International Journal of Recent Technology and Engineering (IJRTE). 2013; 2(2): 182183   [VIEW]  [PDF]  [DOI]   5 
Survey of Corner Detection Techniques in Image Processing 

 Zhenxing Luo   International Journal of Recent Technology and Engineering (IJRTE). 2013; 2(2): 184185   [VIEW]  [PDF]  [DOI]   6 
Parameter Estimation in Wireless Sensor Networks Based on Decisions Transmitted over Rayleigh Fading Channels 

 Zhenxing Luo   International Journal of Soft Computing and Enginee ring (IJSCE). 2013; 2(6): 4245   [VIEW]  [PDF]  [DOI]   7 
Distributed Estimation and Detection in Wireless Sensor Networks 

 Zhenxing Luo   International Journal of Inventive Engineering and Sciences (IJIES). 2013; 1(3): 1316   [VIEW]  [PDF]  [DOI]   8 
Survey of Applications of Pupil Detection Techniques in Image and Video Processing 

 Zhenxing Luo   International Journal of Recent Technology and Engineering (IJRTE). 2013; 2(2): 180181   [VIEW]  [PDF]  [DOI]   9 
The Design and Test of a Private Cloud Storage System, Part I 

 Zhaorui Wang   International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2013; 3(2): 122124   [VIEW]  [PDF]  [DOI]   10 
Parameter Estimation in Wireless Sensor Networks with Normally Distributed Sensor Gains 

 Zhenxing Luo   Page 1 International Journal of Soft Computing and Engineering (IJSCE). 2013; 2(6): 4648   [VIEW]  [PDF]  [DOI]   11 
New ZCW Complete Complementary Code Set and its Analysis 

 Yang Xing , T ony Liu , Xiao Chen   International Journal of Inventive Engineering and Sciences (IJIES). 2013; 1(3): 2325   [VIEW]  [PDF]  [DOI]   12 
Distributed Estimation in Wireless Sensor Networks with Heterogeneous Sensors 

 Zhenxing Luo   International Journal of Innovative Technology and Exploring Engineering (IJITEE. 2012; 1(4): 7982   [VIEW]  [PDF]  [DOI]   13 
Robust Energy  based Target Localization in Wireless Sensor Networks in the Presence of Byzantine Attacks 

 Zhenxing Luo   International Journal of Innovative Technology and Exploring Engineering (IJITEE ). 2012; 1(3): 1215   [Pubmed]  [PDF]  [DOI]   14 
A Coding and Decoding Scheme for Energy based Target Localization in Wireless Sensor Networks 

 Zhenxing Luo   International Journal of Soft Computing and Enginee ring (IJSCE). 2012; 2(4): 5659   [VIEW]  [PDF]  [DOI]   15 
A New Direct Search Method for Distributed Estimation in Wireless Sensor Networks 

 Zhenxing Luo   International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2012; 1(4): 13   [VIEW]  [PDF]  [DOI]   16 


   . ;   [Pubmed]  [PDF]  



