Target tracking is one of the most widely used applications of wireless sensor network (WSN). Efficient usage of energy is a key issue in WSN application such as target tracking. Another important criterion is a tracking accuracy that can be achieved by using appropriate tracking mechanism. Because of the special characteristic of WSN, there is a trade-off between tracking accuracy and power consumption. Our aim is to improve tracking accuracy as well as provide energy-efficient solution by integrating the concept of clustering and prediction techniques. This paper presents Energy-Efficient Constant Gain Kalman Filter based Tracking (EECGKFT) algorithm to optimize the energy usage and to increase the tracking accuracy. There is also a need to collect data from network having a mobile Base Station (BS). Hence, performance of proposed algorithm is analyzed for a static BS and also for mobile BS. The results depict that proposed algorithm performs better compared to the existing algorithms in energy efficiency and prediction accuracy. Analysis of results validates that EECGKFT increases energy efficiency by reducing transmission of unnecessary data in the sensor network environment and also provides good tracking results.
Wireless Sensor Network (WSN) has been extended in various different applications from commercial to industrial and military to medical domains. With thousands of sensor nodes WSN has been deployed to observe the physical environment and to detect the event of interest. In general, tracking includes monitoring and detecting the target location.
One of the basic issues of WSN is the energy constraint. Maximum energy consumed during data communication from sensor nodes to the BS rather than data processing [
This paper presents a hybrid Energy-Efficient Constant Gain Kalman Filter based Tracking (EECGKFT) algorithm to detect and track the moving target in sensor network area. The proposed algorithm relies on clustering and prediction based collaborative approach. The proposed technique provides energy-efficient solution by reducing redundant data transmission among sensor nodes and the BS. The proposed algorithm is analyzed for static BS model when BS location is at fixed position and also for mobile BS model when BS moves dynamically in sensor network area. It finds accurate target path and reduces localization error.
The next section describes an overview of related work in this area. Section
Target tracking algorithms are categorized as tree based, cluster based, prediction based, and hybrid based algorithms [
In [
The cluster and prediction based approach is proposed in [
The boundary problem solutions are presented in [
In existing approach, the target localization is carried out by local sensor nodes. The trilateration approach is used to locate the target in [
An Interactive Multiple Model (IMM) based tracking scheme for WSN has been proposed in [
Kalman in [
The gain values remain constant after the initial transits using KF. It is observed that the gain values have high impact on the filter estimation. Hence good tracking results can be achieved by optimizing the gain value. CGKF [
Comparative study of target tracking algorithms in WSN.
Authors, year | Mechanism | Tracking accuracy | Energy consumption | Boundary node solution | Mobility of BS |
---|---|---|---|---|---|
Olule et al., 2007 [ |
Clustering | Less | Moderate | No | No |
Dayana Pravin and Vijeyakumar, 2012 [ |
Clustering & prediction | Moderate | Low | No | No |
Hosseini et al., 2013 [ |
Clustering & prediction | Moderate | High | No | No |
Deldar and Yaghmaee, 2011 [ |
Clustering & prediction | Moderate | Moderate | No | No |
Misra et al., 2015 [ |
Clustering & prediction | High | Moderate | No | No |
Wang et al., 2010 [ |
Clustering | High | High | Yes | No |
Hajiaghajani et al., 2012 [ |
Clustering | High | High | Yes | No |
Hajiaghajani et al., 2013 [ |
Clustering | High | High | Yes | No |
Akter et al., 2015 [ |
Clustering | High | High | Yes | No |
Imran and Ko, 2017 [ |
Clustering & prediction | High | High | Yes | No |
Vasuhi and Vaidehi, 2016 [ |
Prediction | High | High | No | No |
Vázquez and Míguez, 2017 [ |
Prediction | High | Moderate | No | No |
Jain et al., 2004 [ |
Prediction | Moderate | Moderate | No | No |
Wang et al., 2012 [ |
Prediction | Moderate | Moderate | No | No |
Shantaiya et al., 2015 [ |
Prediction | High | Moderate | No | No |
Mahfouz et al., 2014 [ |
Prediction | High | Low | No | No |
Karthika and Ramalakshmi, 2013 [ |
Clustering & prediction | Moderate | High | No | No |
There are various applications in WSN where a need arises to collect data for mobile BS model [
The proposed hybrid EECGKFT algorithm runs CGKF [
The proposed Energy-Efficient Constant Gain Kalman Filter based Tracking (EECGKFT) algorithm is based on clustering and prediction techniques. In the proposed algorithm, BS predicts the next location of the target by using Constant Gain Kalman Filter (CGKF) [
We assume that all the nodes are randomly distributed and stationary. Once the network is deployed, nodes can not change their positions. At the time of network deployment all sensor nodes are in sleep state except CHs. The CHs have higher energy compared to other sensor nodes. Initially other sensor nodes have same energy level and BS has large amount of energy. The BS is a resourceful node. It has information about the location of each node and their initial residual energy. Single hop communication model is used in proposed algorithm [
Let IF target is detected within the sensing range of sensor node, that sensor node sends initial location to the BS through CH. BS predicts next location where
BS sends predicted location (PL) to the active CH. Active CH selects three sensor nodes (
CH selects leader node
Selected nodes
Leader node (
The active CH calculates difference between PL and CL. It compares it with a predefined threshold (
If
CH sends CL to the BS. Else
No data transmission from CH. BS stores the PL. End if.
Repeat steps (
This section presents the results of numerical experiments. The performance of above-mentioned pseudocode was evaluated using MATLAB for a static BS model and also for mobile BS model. We have used Radio Hardware Energy Dissipation model [ Tracking accuracy in terms of RMSE (Root Mean Square Error) Network residual energy Network lifetime
Simulation parameters.
Experimental parameters | Values |
---|---|
Field size | 100 × 100 m2 |
# of sensor nodes |
|
Static BS location | (50, 50) |
Initial energy ( |
0.5 J |
Threshold ( |
1 m |
Target’s speed | 0–10 m/s |
Sensing range | 15 m |
Communication range | 30 m |
Speed of BS movement | 2 m/s |
We measure the performance of proposed algorithm based on path estimation accuracy. Figure
Path estimation.
Path estimation (a part of the path is enlarged).
Trilateration algorithm gives best estimate for exact range measurement. But it is not possible in the case of real-world because of environmental noise [
RMSE analysis of trilateration, KF, and EECGKFT.
Estimation algorithm | RMSE |
---|---|
Existing approach using trilateration [ |
19.78% |
Existing approach using KF [ |
12.05% |
Proposed EECGKFT | 1.03% |
During the target movement in a network of 100, 200, 300, and 400 nodes, we have analyzed the behaviour of the network in terms of energy utilization. The Radio Hardware Energy Dissipation model is used in the proposed approach to calculate the transmitting and receiving energy [
Network residual energy (400-node network).
The proposed algorithm is also analyzed by comparing network lifetime. The network lifetime is extended when we apply the proposed algorithm as compared to existing algorithms. Figure
Network lifetime (5% of nodes die).
We also measure the performance of proposed algorithm when BS is moving dynamically on predefined path. Tracking accuracy is not affected because location of BS is not playing any role in the proposed algorithm. Since the proposed algorithm operates with a single hop communication model, routing will also not be affected. Moreover, the energy consumption will not vary drastically due to BS’s movement. The comparative analysis of network residual energy and network lifetime with node size 400 is shown in Figures
Network residual energy (400-node network).
Network lifetime (5% of nodes die).
This paper presents novel approach for target tracking by combining clustering and prediction based techniques to improve lifetime of WSN. In addition, the proposed algorithm also provides accurate trajectory tracking by minimizing the RMS error. The proposed technique becomes computationally light weight and gives more accurate results. Simulation results show that the proposed algorithm improves the path estimation accuracy up to 18.75% and 11.02% compared to trilateration technique [
The authors declare that they have no conflicts of interest.