A Survey on Data Fusion Techniques and WSN simulators

Volume 2, Issue 1, 2015

96 Available online at www.ijiere.com

International Journal of Innovative and Emerging

Research in Engineering

e-ISSN: 2394 - 3343 p-ISSN: 2394 - 5494

A Survey on Data Fusion Techniques and WSN Simulators

Nilay Patel

_{, Hemant Vasava}

_{and Udesang Jaliya}

a_{Master of Computer Engineering, Birla Vishwakarma Mahavidyalaya, Vallabh Vidyanagar, Gujarat, India} b_{Department of Computer Engineering, Birla Vishwakarma Mahavidyalaya, Vallabh Vidyanagar, Gujarat, India}

ABSTRACT:

A research on data fusion techniques is increasing day to day due to wide application of sensor network in various different fields. To get more accurate result in these methods certain filters are used in addition to the above mentioned techniques. To test all these new techniques one needs to implement it on some simulators before using it in real world. There is no combine survey on all the techniques available for data fusion, filters used for fusion process and the simulators available for the deployment and checking the validity of the algorithm. So this paper presents the review of recent most widely used data fusion techniques, filters and simulators with its merits and demerits. In addition to this future direction of research are highlighted.

Keywords: Data Fusion Techniques, Information Fusion, Filters, Wireless Sensor Network(WSN) Simulators

I. INTRODUCTION

Data Fusion is a technique for fusing or merging information data from one or more source to generate a single view. Many definitions of Data fusion is given by various researcher based on the application and field of implementation. Pohl and Van Genderen suggested one definition for data fusion: “Data fusion is the combination of two or more different data sets to form a new data set by using a certain algorithm for decision making” [20]. While Mangolini other scientist proposed a different definition: “set of methods, tools and means using data coming from various sources of different nature in order to increase the quality (in broad sense) of the requested information” [21]. Finally, Wald suggests a definition combining three essential features of data fusion: i) the emphasis on the framework, ii) the diversity of the data sources and iii) the increased quality of the information that is obtained from those data sets i.e. “data fusion is a formal framework in which expressed means and tools for the alliance of data originating from different sources” [22]. Data Fusion is now a very common process in various fields like Image processing, wireless sensor networking, data mining, robotics, Military and many more areas. It is a combination of many discipline capturing ideas from many different fields such as statistical estimation, information theory, artificial intelligence and signal processing.

Researchers have spent good amount of time on different techniques of data fusion and have advanced with all the recent tools and terminology. But still a lot more can be done in terms of optimizing the methods and algorithms. Also to test the correctness of different algorithms used in sensor network one need to use particular simulator. Numbers of wireless sensor network simulator are available for specific job. So to select appropriate simulator and deploy the algorithms or technique one needs to survey on all the available simulators. Thus this paper describes in general about different data fusion techniques, optimizing those fusion results by using different filters like variants of kalman filter and particle filter and some commonly used WSN simulators with its merits and demerits. Also they are shown in tabular format for brief understanding.

This paper is organized as follows. Section II describes different data fusion techniques. Section III categorize and describe various filters that are available and can be used for generating better final results. Section IV describes the simulators that are most widely used for wireless sensor network. Section V states the future work that can be done based on this review. And Section VI concludes the paper.

II. DATA FUSION TECHNIQUES

97 possibilities method and Evidential Belief Reasoning as reported in [1]. The selection of the data fusion technique depends on the type of data generated by the sensors and the kind of uncertainty in the data sensed by sensor network.

A. Probability Fusion Technique [1],[2]

As the name suggests this method mainly depends on the probability distribution/density functions to show the data uncertainty in the problem. The most important term of this method lies in the Bayes estimator, which enables fusion of pieces of data, hence the name “Bayesian fusion”. This theorem was named after the mathematician Reverend Thomas Bayes (1701–61). His friend Richard Price edited and presented this work in 1763, after Bayes’ death [27].This method is based on the decision approach based on the previous or apriori data. This method provides the mean to estimates the object ‘x’ in an environment of interest, given by an observation ‘z’.

Bayesian proposed a method of posterior probability based on the prior knowledge and the amount of like hood function. If the variables taken into consideration are continuous in nature then it calculates using total probability theorem.

The Bayesian probability for a two continuous random variable can be given by following equation:

𝑓𝑥(𝑥|𝑌 = 𝑦) =

𝑓𝑌(𝑦) --- (1)

Where term fx(x|Y=y) is called as posterior probability term fY(y|X=x) is likely hood function term fX(x) is called the priory probability with the denominator term called as normalization term.

B. Random set fusionTechnique [1],[2]

This method was first developed for solving the problems related to integral geometry in late 90s. This can be used in combination with the Bayesian approach to deal with the complex data unlike other method to deal with incorrect data and this method provides the best results compared to others. RFS is a very attractive solution to fusion of complex soft/hard data that is supplied in disparate forms and may have several imperfection aspects. As in [1] this approach relies more on random subsets of data/measurements to represents many aspects of imperfect data. This theory was generally proposed to deal with the tracking of multiple objects. As this concept is new and not very well appreciated it is not widely used in fusion technology [1].

C. Rough Set-based FusionTechnique [1],[2]

Rough set is a theory for incorrect data to deal with the incorrectness of the data set, by neglecting the incorrectness up to some acceptable level. Rough set theory enables dealing with data granularity. This method deals with the in appropriates of data by approximating upper bound and lower bound. Limitation of this approach is that it requires appropriate level of data granulations [1].

D. Hybrid Fusion Technique [1], [2]

This method as the name says it’s the combination of all the above mentioned approach to data fusion to best deal with inaccurate, incomplete and inconsistent data obtained from the sensors. As all the above method is used for different purpose hybrid can be used to deal with very complex problems where all the parameters needs to take care of. Generally it is mixture of Bayesian fusion, evidence reasoning and fuzzy logic.

E. Evidential Belief Reasoning Technique [1],[2]

Dempster-Shafer theory comes in the category of Evidential Belief Reasoning which introduces the notion of assigning beliefs and plausibility to possible measurement hypotheses along with the required combination rule to fuse them. This method is also termed as the identity fusion method. And is the big competitor of Bayesian fusion as it deals with data uncertainty in an efficient way. It can be considered as a generalization to the Bayesian theory that deals with probability mass functions. Unlike the Bayesian Inference, the Dempster-Shafer theory allows each source to contribute information in different levels of detail. It allows one to combine evidence from different sources and arrive at a degree of belief (represented by a belief function) that takes into account all the available evidence.

F. possibilities fusion[1],[2]

This method unlike fuzzy set theory is mainly used for the incomplete data but not the incorrect data. This approach is similar to those of probability and D-S evidence theory but with different equations and approach in solving the problems.

G. Fuzzy reasoning Technique[1],[2]

This is another set of theoretical reasoning to deal with incorrect/inaccurate data. Generally this type of method is used to deal with the inaccurate data or the result generated by the human error. In this approach the basic “if-then” condition generates the result by following the predefined task or function for the given condition. Artificial intelligence is the core of this method. All the decision is taken by the artificial intelligence i.e. logic dumped in the algorithm.

Volume 2, Issue 1, 2015

98 Table 1: Summary of data fusion methods into category [1]

No. Category Methods

1

Estimation methods

Rough Set Based Method Random Set Theory Kalman filtering

2 Inference methods (Naive) Bayesian or Probability inference Dempster-Shafer evidential reasoning 3 Artificial Intelligence methods Fuzzy logic

Neural Networks

4 Advanced Combined methods Hybrid fusion

Table 2: Summary of data fusion methods with merits and demerits [1]

No. Methods Merits Demerits

1 Probability or Bayesian Technique Most widely used Technique for data uncertainty problems using

Bayesian concept.

Incapable to deal with other spurious or

conflicted data 2 Random Set Technique Deal with imperfect data obtained

from sensors to provide a fused near accurate results.

Very recent technique and not very widely known

and accepted in fusion area

3 Rough set theory Technique No prior information is required to deal with erroneous data.

These methods require the data set approximation. 4 Hybrid Technique Combination of fusion techniques to

deal with imperfect data more accurately.

Difficult to implement and the cost of implementation is more

compared to other techniques. 5 Evidential belief Reasoning Technique This method deals with uncertain

and incorrect data using Bayesian approach with Belief and plausibility rule like Dempster

Shafer Rule.

Incapable to deal with other data imprecision and conflicted data

6 Possibilities Technique Deals with incomplete data similar to probabilistic and Evidential belief

techniques.

Not very well defined and used rarely in fusion

technology. 7 Fuzzy Reasoning Technique Deals with vague data generated

mainly by human using fuzzy logic.

Limited application in due to prior knowledge of

vague data.

III. FILTERS

Many good filters are available for filtration in data fusion process. Each specific application requires different kind of filters. Some of them are linear and other is nonlinear in nature. Based on the application some of the common filters used are described below. From the below list kalman filter is the oldest filter available and many modification have been done on it to adjust it according to type of data and the application. Some of those filters are also discuss below

A. Kalman filter [3], [6], [7], [8], [14]

This is an algorithm that uses a series of measurement sensed over different time interval having noise, inaccuracy and imperfect data to produce the good accurate final decision. This algorithm is also called as linear quadratic estimation (LQE). This method produced more accurate result than compared to the single observed data. This is a recursive algorithm that operates on noisy data. Uses of kalman filter are at many places like for navigation, guidance, in signal processing, econometrics and controlling vehicle or mobile robot in a nuclear plant.

99 based on this estimation on the observed values. Because of the algorithm's recursive nature, it can run in real time using only the present input measurements and the previously calculated state and its uncertainty matrix; no additional past information is required. The kalman filter using Bayesian method can be written as:

𝑝(formation | data) =𝑝(data | formation) 𝑝(formation)

𝑝(data) --- (2)

B. Particle filter [4], [6], [8]

These types of filters are also called as Sequential Monte Carlo approach that estimates the posterior density of the state by directly implementing the recursive Bayesian equations. This filters use a sampling approach which use particle to represent the posterior density. They are also called as nonlinear filters. They work by first creating samples from data without any prior assumption on the distribution or the model. However, when applied to the high dimensional methods they do not perform well as expected. This method implements the Bayesian recursion equations directly by using an ensemble based approach. The samples created above are now represented by a set of particles that has its own weight to represent the probability of that particle being sampled from the probability density function. Each iteration of this filter require a large amount of sampling. The recursive nature of this algorithm resample all the samples and neglects the samples having negligible weights and the other are resample further to get more accurate results. Weight unbalancing problem in this approach can be solved by this resampling technique.

Steps in executing particle filter:

1. Generate samples to represent the initial probability 2. Using the prior equation, predict the next state

3. Using the observation, get the weights for the states computed. Predicted states (from step 2) along with the weights collectively represent the state distribution

4. Resample it so as to have the uniformly distributed current state omitting the least-significant representation 5. Continue steps 2 through 4, till all the observations are exhausted

C. Extended Kalman filter [4], [19]

Extended kalman filter unlike it’s precede this one is a nonlinear version. Many researches have been done on kalman filter and many modified version of simple kalman filter have been proposed. Extended kalman filter is one of them that linearize an estimate of the current mean and covariance. It is mostly used in GPS and navigation system to fusion nonlinear data. In the extended Kalman filter, the state transition and observation models need not be linear functions of the state but may instead be differentiable functions.

𝑥𝑘 = 𝑓(𝑥𝑘−1, 𝑢𝑘−1) + 𝑤𝑘−1 --- (3)

𝑧𝑘= ℎ(𝑥𝑘) + 𝑣𝑘--- (4)

Equation 3 and 4 above describe the estimation equation and the updating equation respectively. Where wk= process noise, vk= observation noises, uk is the control vector.

Both wk and vk are assumed to be zero mean multivariate Gaussian noises with covariance Qk and Rk respectively. Here two functions f and h are used to find the estimation state from previous state and to compute the predicted measurement from the predicted state respectively. However, f and h cannot be applied to the covariance directly. A matrix of partial derivatives (the Jacobian) is computed to use it.

There are some demerits or demerits like it’s not the best or optimal estimator. As the process first converts the nonlinear function to linear one it may diverge more if the first estimate is stated wrong or the process is modeled incorrectly. Also the covariance matrix underestimates the true covariance matrix and thus can be inconsistent in the statistical sense.

D. Unsent kalman filter [4], [19]

This filter is an improved version of extended kalman filter having a nonlinear functionality. Here probability is approximated by a deterministic sampling of points which is as Gaussian. Here too the nonlinear are transformed to linear to estimate the posterior distribution. This transformation is called as unscented transform. This gives more robust and accurate estimation compared to other kalman variants considering errors in all directions. Also the extended kalman filter is more difficult to implement and handle it is not widely accepted. This is due to its linearization problems.

IV.WIRELESSSENSOR NETWORK SIMULATORS

Volume 2, Issue 1, 2015

100 A. OMNET++ [3], [5], [16], [17]

Omnet means “Objective Modular Network Test-bed” which is in C++ a public-source, open architecture simulation in combination with strong GUI support and a kernel. It was developed mainly to deal with the simulation of communication network but due to its open nature or flexibility many other frameworks or so called project are built to coup with other kind of problems. This model is based on modules in nested format. To run any topology or algorithm on networks one need to generate three files 1) .ned file, which saves the network topology 2) .ini file, which describe the predefined values for certain parameters used in the project and 3) a simple c++ file, that contains the core of algorithm to be implement.

One needs to spend enough time on this tool to get through the basic functionality provided. The implementation of a simple project or a network also takes a good amount of time for a good programmer. This is the only demerits of this tool in comparison to the other simulator available in the market

B. NS 2 [3], [5], [16]

The Network Simulator (NS) is one of the widely used tool for networking research which is object oriented and event based simulator. The operating language for this tool is c++ and OTcl. OTcl is another version of TCL which is object oriented. There is huge support from the nsnam website that provides codes to support some common protocol in wireless technology. This website also contains source code to implement Bluetooth and some 801.11 protocols. TO deal with other layer protocol special other tools are created on this tool such as Mannasim and SensorSim. One more tool called NS2-MIUN is also available as an extension to NS2. This tool provide additional facility such as battery power consumption, radio model communication, channel models and lightweight protocols stack which are specially designed for tiny sensors.

C. OPNET [3], [5], [16], [17]

OPNET Modeler Wireless Suite is a commercial modeling and simulation tool for various types of wireless networks. It is developed by devel¬¬oped by OPNET Technologies, Inc. and based on the well-known product OPNET Modeler. The simulation environment uses a fast discrete event simulation engine operating with a 32-bit/ 64-bitfully parallel simulation kernel, which is available for Windows and Linux. The OPNET Modeler provides an object-oriented modeling approach and a hierarchical modeling environment. Although there are no special routing protocols for wireless sensor network available, at least different propagation and modulation techniques as well as a ZigBee (802.15.4) MAC layer are provided. Additional modules have to be customized or developed from the scratch. The simulations of wireless networks can be run as discrete event, hybrid or analytical, encompassing terrain, mobility and path-loss models. Due to the open interface external object files, libraries as well as other simulators can be integrated to the OPNET Modeler. Optional a System-in-the-Loop is available to interface simulations with live systems. Furthermore, the OPNET Modeler Wireless Suite provides grid computing support so that simulations can be executed in a distributed manner.

D. GloMoSim [3], [5], [16]

Tool GloMoSim was initially developed at UCLA Computing Laboratory which is a scalable simulation environment for wireless and wired networks systems developed. This tool uses base programming language that is c to implement a parallel based event simulation. This language is also called as Parsec. Currently due to lack of support from the developers it only supports wireless protocols. In this tool different standards API are used to work on different layer and to integrate each other functionality. The difficulty with GloMoSim was to describe a simple application that bypasses most OSI layers. The bypass of the protocol stack is not obvious to achieve as most applications usually lie on top of it. This simulation tool is not too flexible to support all the layer of OSI hence is not used widely. An extension to this one other tool is generated called QualNet which provides the functionality of power and battery consumption of wireless sensor. This extension is not an open source but a commercial product hence used mainly by firms and research laboratory where fund is provided in huge amount. The language of implementation is java.

Comparison of different simulators is shown in table 3 below.

Table 3: Summary of different simulators available for wireless sensor network [3], [5], [16], [17]

N o

Simulator Language Merits Demerits

1 Castalia C++

-A sensor biased tool.

-Algorithm for physical, mac, routing and application layer is supported

-Not a sensor specific tool

101 2 Omnet++

C++ / Ned/ Module structure

-Powerful graphical User Interface (making tracing and bugging easier) which Simulate power Consumption problem

-Several frameworks that add WSN functionality to OMNet++ such as MiXiM, Castalia, etc.

-active project with a huge user base; Eclipse-based IDE -Open Source

-Compatibility problem (not portable).

3 OPNET

C++/ Module structure

-Different propagation models; 802.11ZigBee; some MANET protocols, powerful tool with a nice GUI

-expensive

- but no special WSN support

4 MATLAB SIMULINK

C/ Java

-Detailed simulation of the end nodes and their architecture, Physical layer parameters, different modulation & encoding techniques, communication channel modeling and various methods to monitor and record results, making use of the rich library of Matlab/Simulink.

-expensive

5

GloMoSim/Q ualNet

Parsec / C

-Parallel simulation capability. -It is tailored specifically for wireless networks.

-Availability of a visualization tool with basic module implemented. -QualNet: additionally battery and energy model;

-Effectively limited to IP networks because of low level design assumptions.

-Unavailability of new protocols. -QualNet seems to be more up-to-date, but Commercial

6

Ns-2 Otcl/ C++

-Easy to add new protocols.

-A large number of protocols available publicly.

-Availability of a visualization tool.

-Supports only two wireless MAC protocols, 802.11, and a single-hop TDMA protocol.

-complex configuration; unclear situation due to large number of different user contribution.

V. FUTURE WORK

Based on the survey it is clear that the use of data fusion technology is becoming very common. Future work is possible by developing a new process combining appropriate data fusion methods and the filters to optimize the data fusion process. Future work can be done by using particle filter for non-linear data types of sensor data with probabilistic technique of data fusion. Since kalman filter cannot be used for nonlinear data one need to use particle filter and other its variants to deal with such kind of data. Further to more accurately fuse the data one can use the filters before the fusion process, after the fusion process and at both start and end phase of fusion process. By using filters at both end of fusion one can get better results at the cost of computation and power consumed to deploy the algorithm.

VI.CONCLUSION

Data fusion as mention is one of the most important tasks in sensor network due to inaccuracy and uncertainty of data generated by the sensor network. Many approaches are listed in section 2 for data fusion. Merits and demerits of all the available approach is given in [4]. To get more accurate results for the data fusion many approaches are seen using filters that are described in section 3. As implementing this approaches in real world might be not feasible due to operational cost and other limitation numbers of simulators are developed for sensor network as explained in section 4. Demerits and merits of different simulators are given in [3] and in table 2 above. From the survey done different researcher uses different fusion techniques and simulators based on the given data and type of application. But most researcher prefer Bayesian or probabilistic Approach for data fusion and omnet++ or Matlab for simulation purpose based on the financial aid.

ACKNOWLEDGMENT

Volume 2, Issue 1, 2015

102 I would like to express my deep sense of gratitude to Hemant D. Vasava and Udesang K. Jaliya for their valuable guidance, motivation and for giving me such an opportunity to explore new ideas. I appreciate all my friends whose direct and indirect contribution helped me a lot to accomplish this survey work and who made the period of my work more pleasant and fruitful. I would also like to thank all the teaching and non-teaching staff for cooperating with me and providing valuable advice and resources which helped me in the completion of this work. Last but not the least I would like to thank my family members, who taught me the value of hard work by their own example. They provided me enormous support during this work directly and indirectly.

REFERENCES

[1] B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, “Multisensor data fusion: A review of the state-of-the-art,” in Information Fusion, 2011.

[2] Alaa Khamis, Waleed A. Abdulhafiz, “Bayesian approach to multisensor data fusion with pre and post filtering” IEEE 2013, 978-4673-5200-0/13/$31.0

[3] Prof. Sachin Deshpande, Dr. J. W. Bakal, Prof. Mritunjaykumar Ojha, “Simulation of Target Tracking in Wireless Sensor Network” Volume 4, Issue 2, February 2014 ISSN: 2277 128X, International Journal of Advanced Research in Computer Science and Software Engineering

[4] Multi sensor data fusion techniques (9783540239574-c2): chapter 25.

[5] Sushruta Mishra, HirenThakkar, “Features of WSN and Data Aggregation techniques in WSN”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 4, ISSN: 2277-3754, April 2012 [6] M. Marrón, J.C. García, M.A. Sotelo, M. Cabello, D. Pizarro, F. Huerta, J. Cerro, “Comparing a Kalman Filter

and a Particle Filter in a Objects Tracking Application”, IEEE Trans. on tracking filters, December 2003. [7] Jiahong Li, Jie Chen, Chen Chen, Fang Deng, “Federated Kalman Consensus Filter in Distributed Track Fusion”

Proceedings of the 2013 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems May 26-29, 2013, Nanjing, China

[8] S. Haykin and E. Moulines, "From Kalman to Particle Filters," IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, Pennsylvania, USA, March 2005.

[9] WOLFGANG KOCH, “On Bayesian Tracking and Data Fusion: A Tutorial Introduction with Examples”, IEEE A&E SYSTEMS MAGAZINE VOL. 25, NO. 7 JULY 2010.

[10]Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Norwell, MA, 1992.

[11]D. Kendall, Foundations of a theory of random sets, in: Stochastic Geometry. J. Wiley, 1974.

[12]B. Khaleghi, A. Khamis, and F. Karray, “Random finite set theoretic based data fusion with application for target tracking,” in IEEE 2010International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2010.

[13]P. Manjunatha, A.K. Verma and A. Srividya, “Multi-Sensor Data Fusion in Cluster based Wireless Sensor Networks Using Fuzzy Logic Method” 2008 IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, INDIA December 8-10.

[14]G. Welch and G. Bishop, An Introduction to the Kalman Filter. Departmentof Computer Science, University of North Carolina at Chapel Hill,July 2006.

[15]A Review of Data Fusion Techniques, www.iit.it/en/pavis-project/Data-fusion-Techniques.

[16]Comparison of Simulators, September 2014: www.ti.tuwien.ac.at/cps/research/projects/comparision-of-network-simulatuion.

[17]Omnet++Guide, official Website, September 2014. www.omnet.org.

[18]Multi-Sensor Data Fusion on springer, http://www.springer.com/978-3-540-23957-4.

[19] Introduction to Extended Kalman Filter: www.en.wikipedia.org/wiki/ExtKalmanFilter.

[20]C. Pohl, J. L. Van Genderen, “Review article on Multisensor Image Fusion in Remote Sensing: Concept,Methods & Application,” in International Journal of Remote Sensing, 1998, Vol. 19, No. 5, pp. 823-854.

[21]M. Mangolini, ”Apport de la Fusion D’images Satelliatinos multicaptures au niveau pixel in photo-interpretation,” in france 1994, pp. 174.