SENSOR SELECTION SCHEME IN TEMPERATURE WIRELESS SENSOR NETWORK

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 7, No. 3, June 2015
DOI : 10.5121/ijwmn.2015.7304 47
SENSOR SELECTION SCHEME IN TEMPERATURE
WIRELESS SENSOR NETWORK
Mohammad Alwadi1
and Girija Chetty2
1
Department of Information Sciences and Engineering, The University of Canberra,
Canberra, Australia
2
Faculty of ESTM, The University of Canberra, Canberra, Australia
ABSTRACT
In this paper, we propose a novel energy efficient environment monitoring scheme for wireless sensor
networks, based on data mining formulation. The proposed adapting routing scheme for sensors for
achieving energy efficiency from temperature wireless sensor network data set. The experimental
validation of the proposed approach using publicly available Intel Berkeley lab Wireless Sensor Network
dataset shows that it is possible to achieve energy efficient environment monitoring for wireless sensor
networks, with a trade-off between accuracy and life time extension factor of sensors, using the proposed
approach.
KEYWORDS
Wireless sensor Networks, Physical environment Monitoring, machine learning, data mining, feature
selection, adaptive routing.
1. INTRODUCTION
Wireless and wired sensor networks (WSNs/SNs) has become a focus of intensive research today,
especially for monitoring and characterizing of large physical environments, and for tracking
environmental or physical conditions such as temperature, pressure, wind and humidity. A
wireless or a wired sensor network (WSN/SN) consists of a number of sensor nodes (few tens to
thousands) storing, processing and re-laying the sensed data, often to a base station for further
computation [1, 2]. Sensor networks can be used in many applications, such as wildlife
monitoring [3], military target tracking and surveillance [4], hazardous environment exploration
[5], and natural disaster relief [6]. Many of these applications are expected to run unattended for
months or years. Sensor nodes are however constrained by limited resources, particularly in terms
of energy. Since communication is one order of magnitude more energy consuming than
processing, the design of data collection schemes that limit the amount of transmitted data is
therefore recognized as a central issue for wireless sensor networks. An efficient way to address
this challenge is to approximate, by means of mathematical models, the evolution of the
measurements taken by sensors over space and/or time. Indeed, whenever a mathematical model
may be used in place of the true measurements, significant gains in communications may be
obtained by only transmitting the parameters of the model instead of the set of real
measurements. Since in most cases there is little or no a priori information about the variations
taken by sensor measurements, the models must be identified in an automated manner. This calls
for the use of machine learning and data mining techniques, which allow modelling the variations
of future measurements on the basis of past measurements.

48
In this paper, we introduce a novel data mining based formulation for energy efficient WSN
(Wireless Sensor Network) monitoring. The proposed approach involves an adaptive routing
scheme to be used for energy efficiency and is based on selecting most significant sensors for the
accurate modelling of the WSN environment. The experimental validation of the proposed
scheme for publicly available Intel Berkeley lab Wireless Sensor Network dataset shows it is
indeed possible to achieve energy efficiency without degradation in accurate characterization and
understanding of WSN environment. The proposed machine learning and data mining
formulation for achieving energy efficiency, provides better implementation mechanism in terms
of trade-off between accuracy and energy efficiency, due to an optimal combination of feature
selection and classifier techniques used in machine learning chain. By approaching the
complexity of WSN/SN with a data mining formulation, where each sensor node is equivalent to
an attribute or a feature of a data set, and all the sensor nodes together forming the WSN/SN set
up - equivalent to a multiple features or attributes of the data set, it is possible to use powerful
feature selection, dimensionality reduction and learning classifier algorithms from machine
learning/data mining field, and come up with an energy efficient environment monitoring system
[7]. In other words, by employing a good feature selection algorithm along with a good
classification algorithm, for example, it is possible to obtain an energy efficient solution with
acceptable characterization or classification accuracy (where the WSN/SN set up is emulated
with a data set acquired from the physical environment). Here, minimizing the number of sensors
for energy efficiency is very similar to minimizing the number of features with an optimal feature
selection scheme for the data mining problem. Further, the number of sensors chosen by the
feature selection scheme leads to a routing scheme for collecting the data from sensors,
transmitting them until it reaches the base station node. As accuracy of data mining schemes rely
on amount of previous data available for predicting the future state of the environment, it is
possible to obtain an adaptive routing scheme, through the life of WSN, as more and more
historical data becomes available, allowing trade-off between energy efficiency and prediction
accuracy. We have shown that it is possible to do this, with an experimental validation of our
proposed scheme with a publicly available WSN dataset acquired from real physical
environment, the Intel Berkeley Lab [8]. Rest of the paper is organized as follows. Next Section
describes the details of the dataset used, and Section 3 de-scribes the proposed machine learning -
data mining approach. The details of experimental results obtained are presented in Section 4, and
the paper concludes in Section 5, with conclusions and plan for further research.
2. DATA SET DESCRIPTION
The publicly available data set used for experimental validation consists of temperature and
humidity measurements, which come from a deployment of 54 sensors in the Intel research
laboratory at Berkeley [8]. The deployment took place between February 28th and April 5th,
2004. A picture of the deployment is provided in Figure 1 below, where sensor nodes are
identified by numbers ranging from 1 to 54.

49
Figure 1. Intel Berkeley Wireless Sensor Network Data set: location of 54 sensors in an area of 1200m2
Many sensor readings from WSN test bed were missing, due to this being a simple prototype.
We selected from this data set few subsets of measurements. The readings were originally
sampled every thirty-one seconds. A pre-processing stage where data was partitioned was applied
to the data set. After pre-processing, we prepared several subsets of data. The approach we used
for this WSN test bed, involves, an assumption that data collection and transmission is done by
some of the sensors (source nodes), that purely sense the environment and transmit their
measurement to collector nodes (sink nodes/base station), and based on the relative distance
between source nodes and sink nodes, the route or the path taken for sensor data to be transmitted
from one node to other is predetermined at sink/base station node. Those nodes who actively
participate in sensing the environment, and transmit the data, consume the power and those who
do not participate in this activity do not consume any power. This is how the WSN can be made
energy efficient; by involving optimum number of sensors to participate in environment sensing
and transmission task, and leaving non-participating sensors in sleep mode (no energy
consumption). This can however, impact on the accuracy of sensing the environment, if number
of sensors participating in routing scheme is not properly chosen. To ensure a trade-off between
accuracy and energy efficiency is achieved, it is essential that a dynamic or adaptive routing
scheme is used, where, the machine learning/data mining technique can use larger training data
from previous/historical data sets to predict the future environment accurately, and continuously
adapt the routing scheme for nodes based on a threshold error measure for prediction accuracy
and energy efficiency. Next Section describes the proposed machine learning data mining scheme
used for sensor selection and routing in this approach.
3. SENSOR SELECTION AND ROUTING APPROACH
The sensor selection and routing approach is based on a feature selection technique that selects
the attributes (sensors), by evaluating the worth of a subset of attributes by considering the
individual predictive ability of each feature/sensor along with the degree of redundancy between
them. Subsets of features that are highly correlated with the class while having low inter
correlation are preferred [9, 10]. Further, this feature/sensor selection algorithm identifies locally
predictive attributes, and iteratively adds the attributes with the highest correlation with the class
as long as there is not already an attribute in the subset that has a higher correlation with the
attribute in question. Once the appropriate group of sensors are selected, the prediction of sensor
output at sink node or base station is done by linear regression algorithm, using the Akaike
criterion [10, 11], which involves stepping through the attributes, removing the one with smallest
standardised coefficient until no improvement is observed in the estimate of the error given by

50
Akaike information metric. Figure 2 and the table below shows how the sensor selection evolves
as the training data (historic data) used for predicting the sink sensor output is increased, and
ensures the prediction accuracy/error is maintained at a particular threshold value.
Figure 2. Sensor selection map for Temperature Experiment 1,2 and 3
4. EXPERIMENTAL RESULTS AND DISCUSSION
Different sets of experiments were performed to examine the relative performance of sensor
selection and routing approach proposed here. We used k-fold stratified cross validation
technique for performing temperature data set experiments, with k=2, 5 and 10, based on the
training data available (using larger folds for larger training data). Further, to estimate the relative
energy efficiency achieved, we performed experiments with all sensors (without feature
selection/sensor selection) algorithm, and with sensors selected by feature selection algorithm. As
mentioned before, the feature selection algorithm allows selection of an optimal number of
features or sensor nodes needed to characterize or to classify the environment (which in turn leads
to an energy efficient scheme). Further, time taken to build the model is also an important
parameter, particularly for adaptive sensor routine scheme to be used for real time environment
monitoring.
Table 1. Results for temperature Three experiments scenarios
Exp
number
Number of
Sensors
Number of
Training
samples
Features/sensors selected
1 54 35 17,50
2 53 2700 3,14,16,19,39
3 53 5400 3,13,14,16,19,24,34,53
Exp
number
Time (No F
selection)
Time(F
Selection)
RMSE No F
selection
RMSE with
F selection
1 0.02 sec 0.01 sec 20.26% 0.04%
2 0.43 sec 0.02 sec 5.02% 2.23%
3 0.57 sec 0.03 sec 3.93% 2.93%

51
For the first set of experiments, we used first 54 sensors and a small set of training samples (35
temperature measurements). As can be seen from the sensor locations shown in Figure 2, sensor
17 and 50 are the sink node (emulating base station node), and sensors 1 to 54 participate in
measuring and transmitting the environment around them to the sink node, where the machine
learning prediction task is to estimate the measurement at sink node (sensor 50). The RMS error
(root mean squared error) at the sink node (node 50) provides a measure of prediction For all
source sensor nodes (1-54) in WSN participating in measuring the temperature in the
environment and sending it to sink node, the RMS error is 20.26%, and with sensor selection
scheme used with only 2 sensors participating in routing scheme, the RMS error is 0.04%. As can
be seen in Table 1, with a moderate degradation in accuracy (20.26% to 0.04%), energy
efficiency achieved is of the order of 54 (54/2). We used a new measure for energy efficiency, the
life time extension factor (LTEF), which can be defined as:
LTEF =
Total number of sensors
Sensors participating in the routing scheme
With 2 sensors out of 54 sensor nodes in active mode, the LTEF achieved is around 27 times, and
52 sensor nodes are in sleep mode. The trade off is a slight reduction in accuracy. This could be
due to less training data used. We used only 35 temperature samples for prediction scheme. With
more data samples used in the prediction scheme, performance could be better. To test this
hypothesis, we performed next set of experiments.
For second set of experiments, we used 2700 training samples collected on different days. As can
be seen in Table 1, with larger training data size, we found that the participating sensors in the
routing scheme are different, as the proposed feature selection algorithm chooses different set of
sensors (3,14,16,19,39). We used 53 sensors for this set of experiments, as one of the sensors
(sensor 5 did not have more than 35 measurements). With all 53 sensors in the routing scheme,
the RMS errors is 0.96%, and with 5 sensor nodes (3,14,16,19,39), the error is 5.02%. This was
not a significant improvement in prediction accuracy (from 5.02% to 2.23%), with life time
extension of 10.6 (53/5). As is evident here, by using larger training data (2700 temperature
measurements), it was possible to achieve an improvement in prediction accuracy and energy
efficiency as well.
To examine the influence of increasing training data size, we performed third set of experiments
with 5400 samples. The performance achieved for this set of experiments is shown in Table 1.
Here the adaptive routing scheme based on proposed feature selection technique selects 8 sensors
(3,13,14,16,19,24,34,53). For this set of experiments, the RMS error varies from 3.93% for all
sensors participating in the scheme to 2.93 % with LTEF of 6.6 (53/8). Though there is no
degradation in prediction accuracy, there is not much improvement in energy efficiency, with
doubling of training data size for the building the model this could be due to overtraining that has
happened, with the network losing its generalization ability. So by increasing training data size, it
may not be just possible to achieve performance improvement, for pre-diction accuracy (RMS
error) and energy efficiency (LTEF), and a trade off may be needed. An optimal combination of
training data size, and number of sensors actively participating in routing scheme can result in
energy efficient WSN, without compromising the prediction accuracy.
Further, another important parameter is model building time, as for adaptive sensor routing
scheme to be implemented in real time WSN environment, routing scheme has to dynamically
compute the sensors that are in active mode and in sleep mode. Out of 3 experimental scenarios
considered here, as can be seen from Table 1, the model building time from 0.02 seconds to 0.01

52
seconds for experiment 1, from 0.43 seconds to 0.02 seconds for experiment 2, and from 0.57
seconds to 0.03 seconds for experiment 3. So, the proposed adaptive routing scheme for sensor
selection provides an added benefit of reduced model building times, suitable for real time
deployment. Figure below shows the time taken to build the model.
Figure 3. Time taken to build the model (Temperature Exp1,2 and 3)
5. CONCLUSIONS
Energy sources are very limited in sensor networks, in particular wireless sensor net-works. For
monitoring large physical environments using SNs and WSNs, it is important that appropriate
intelligent monitoring protocols and adaptive routing schemes are used to achieve energy
efficiency and increase in lifetime of sensor nodes, with-out compromising the accuracy of
characterizing the WSN environment. In this paper, we proposed an adaptive routing scheme for
sensor nodes in WSN, based on machine learning data mining formulation with a feature
selection algorithm that selects few most significant sensors to be active at a time, and adapts
them continuously as time evolves. The experimental validation for a real world publicly
available temperature WSN dataset, proves our hypothesis, and allows energy efficiency to be
achieved without compromising the prediction accuracy, with an added benefit in terms of
reduced model building times. Further work involves, developing new algorithms for sensor
selection and environment characterization with WSNs and their experimental validation with
other similar datasets, that can lead to better energy efficiency. Also, our further re-search
involves extending this work with adapting these classifiers for big data stream data mining
schemes, for real time dynamic monitoring of complex and large physical environments in an
energy efficient manner.
REFERENCES
[1] Ping, S., Delay measurement time synchronization for wireless sensor networks. Intel Research
Berkeley Lab, 2003.

53
[2] Hall, M., et al., The WEKA data mining software: an update. ACM SIGKDD Explorations
Newsletter, 2009. 11(1): p. 10-18.
[3] Csirik, J., P. Bertholet, and H. Bunke. Pattern recognition in wireless sensor networks in presence of
sensor failures. 2011.
[4] Nakamura, E.F. and A.A.F. Loureiro, Information fusion in wireless sensor networks, in Proceedings
of the 2008 ACM SIGMOD international conference on Management of data2008, ACM: Vancouver,
Canada. p. 1365-1372.
[5] Bashyal, S. and G.K. Venayagamoorthy. Collaborative routing algorithm for wireless sensor network
longevity. 2007. IEEE.
[6] Richter, R., Distributed Pattern Recognition in Wireless Sensor Networks.
[7] Alwadi, M. and G. Chetty, Energy Efficient Data Mining Scheme for Big Data Biodiversity
Environment. 2014.
[8] Bodik, P., et al., Intel lab data. Online dataset, 2004.
[9] Hall, M.A., Correlation-based feature selection for machine learning, 1999, The University of
Waikato.
[10] Akaike, H., Information theory and an extension of the maximum likelihood principle, in Selected
Papers of Hirotugu Akaike1998, Springer. p. 199-213.
[11] Ashraf, M., et al., A New Approach for Constructing Missing Features Values. International Journal
of Intelligent Information Processing, 2012. 3(1).
Authors
Mohammad Alwadi has bachelors degree in Computer Science from Jordan, Masters
degree from the University of Canberra, Australia and Currently a PhD candidate in the
Faculty of Information Science and Engineering at The University of Canberra, Australia.
Mohammad research interests are in the area of computer networking, Data Mining and
wireless sensor networks This author has 8 publications and working towards the
submission of his PhD Thesis at the University of Canberra. Australia. He has more than 6 years of
experience in the research field of Data mining , sensors networks and wireless sensor networks.
Girija has a Bachelors and Masters degree in Electrical Engineering and Computer
Science from India, and PhD in Information Sciences and Engineering from Australia. She
has more than 25 years of experience in Industry, Research and Teaching from
Universities and Research and Development Organisations from India and Australia, and
has held several leadership positions including Head of Software Engineering and
Computer Science, and Course Director for Master of Computing (Mainframe) Course.
Currently, she is an Associate Professor, and Head of the Multimodal Systems and Information Fusion
Group in University of Canberra, Australia, and leads a research group with several PhD students, Post
Docs, research assistants and regular International and National visiting researchers. She is a Senior
Member of IEEE, USA, and senior member of Australian Computer Society, and her research interests are
in the area of multimodal systems, computer vision, pattern recognition, data mining, and medical image
computing. She has published extensively with more than 120 fully refereed publications in several invited
book chapters, edited books, high quality conference and journals, and she is in the editorial boards,
technical review committees and regular reviewer for several IEEE, Elsevier and IET journals in the area
related to her research interests. She is highly interested in seeking wide and interdisciplinary
collaborations, research scholars and visitors in her research group.

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