The Scientific World Journal

Volume 2015 (2015), Article ID 149767, 9 pages

http://dx.doi.org/10.1155/2015/149767

## Novel Strategy to Improve the Performance of Localization in WSN

^{1}Department of Electronics and Communication Engineering, Latha Mathavan Engineering College, Madurai, Tamil Nadu 625301, India^{2}Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Sivagangai District, Pottapalayam, Tamil Nadu 630612, India

Received 9 April 2015; Revised 13 August 2015; Accepted 18 August 2015

Academic Editor: Long Cheng

Copyright © 2015 M. Vasim Babu and A. V. Ramprasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

A novel strategy of discrete energy consumption model for WSN based on quasi Monte Carlo and crude Monte Carlo method is developed. In our model the discrete hidden Markov process plays a major role in analyzing the node location in heterogeneous media. In this energy consumption model we use both static and dynamic sensor nodes to monitor the optimized energy of all sensor nodes in which every sensor state can be considered as the dynamic Bayesian network. By using this method the power is assigned in terms of dynamic manner to each sensor over discrete time steps to control the graphical structure of our network. The simulation and experiment result shows that our proposed methods are better in terms of localization accuracy and possess minimum computational time over existing localization method.

#### 1. Introduction and Related Work

In many real time applications of wireless senor network, localization [1–3] plays an important parameter to identify the location of an object or moving stimuli in geographical area. But still there are some research challenges available in order to improve the localization accuracy [4], better energy consumption model, and reduce the localization error while finding the moving stimuli. Various localization algorithms and analytical models have been proposed [5–7] for past decades based on centralized, distributed [8, 9], bacon based, diffusion based, and bounding box localization algorithms. But these algorithms may either suffer from low energy consumption or poor sampling efficiency. Particularly in GPS based localization method [10] the line of sight problem is a major issue and the power consumption in GPS will reduce the entire battery life of the wireless sensor network.

In order to minimize the cost of energy in GPS model, few nodes which are considered as beacon nodes represent the GPS modules. As the geographical area increases, the number of beacon nodes also increases which leads to high cost. Another popular localization method which has wide range of possible applications is called source localization method. In this method author has analyzed both indoor and outdoor applications [11] including movement of vehicle and also tracked the human voice.

There are so many ways to implement the source localization in real time environment based on energy, AOA, and TDOA which are the high level parameter of WSNT. The further classification of source localization [12] is single and multiple target localization in WSN and WBSN. However, very few papers are investigated for the purpose of multiple target localization scheme. All these papers are based on the maximum likelihood estimation. The RSS of this method could be calculated in the following manner:where is the distance between the th sensor and the th source and is the gain of the th sensor.

Range based [13] and range-free localization technique [14, 15] are under the self-localization method. The classical method of range based localization is used to estimate TOA [16], TDOA, RSSI [17], and AOA and the range-free method is used in pattern matching and hop count based applications.

In this paper we proposed a new scheme of localization method based on quasi and crude Monte Carlo technique. In our energy consumption model we consider novel discrete generalization of hidden Markov model to balance the node energy within the particular samples of dynamic Bayesian networks. In our sensor model we assume that each sensor power acts independently and cost of each sensor consists of weighted discrete power storage from supervisor node. The power update is based on the number of hop counts from sender to receiver at a particular state.

The rest of the paper is organized as follows. Section 2 briefly discusses the network model of Bayesian network in discrete manner. Section 3 elaborates the crude Monte Carlo method analysis in three-dimensional manner. The details of discrete power monitoring strategy are discussed in Section 4. We evaluate the performance of proposed method discussed in Section 5.

#### 2. Proposed Method

##### 2.1. Network Model

Our network model is based on Bayesian network. In this model we assume that each sensor power acts independently and cost of each sensor consists of a weighted amount of discrete power storage. The power update is based on the number of hop counts from sender to receiver at a particular state.

The behavior of discrete time dynamic system is described byLet us assume that the sensor observation time is by the state variable with probability function with hidden Markov model. It can be denoted by state equation and by the following observation:where is the observations of the time of the packets arriving sequentially and is the state variables of interest which is the current posterior distribution of :where is the integer factor to count the total number of states in entire sensor network from one hop to another.

Choose an integer and let

is the posterior probability that was observed as the function of the unknown model parameter like RSS of each sensor hop in the network. The current state is independent of all the states prior to . In this communication model we take three centralized discrete nodes. Each centralized node has three subordinate nodes to communicate with each other based on Bayesian network model. Let , , and be three centralized nodes which are going to share the power to their neighboring nodes with the help of Bayesian network.

Figure 1 shows that the three independent nodes , , and (three states) are connected with the subnodes to . Each centralized node can potentially depend on other nodes, that is,The conditional probabilities of above Bayesian network are described by the following factorization:This factorization shows a set of conditional independence relations of each node. So dynamically all the nodes of Bayesian network are interconnected in time series modeling. By using this time series method we can predict the time index “” to each independent node like for a sequence of data . Each state is directly influenced by the previous state; that is,Based on the above condition we can draw the conditional independence relations between states which is shown in Figure 2. This Markov relation model extends the static modeling to dynamic Bayesian model in discrete manner.