Aerospace and Electronic Systems - March 2019 - 60

Energy-Efﬁcient Routing Algorithm Based on Localization and Clustering Techniques for Agricultural Applications
another. In fact, several existing clustering methods are
based on GPS techniques for nodes position estimation.
However, GPS is considered as an expensive, energy
consuming, and hard to maintain distance measurement
technology. For this, energy efficient, flexible, and cost
effective solutions are required to extract real node
positions in the network. For this, alternative localization techniques are investigated. A brief overview about
existing localization techniques in WSNs is described
in this section to highlight our choice.

OVERVIEW OF LOCALIZATION ALGORITHMS IN WSNs
Localization techniques in WSNs for agriculture, allow to
fully control the field, animals, and crops growth. Based on
the gathered data from deployed sensor nodes, farmers can
react to sudden changes in the network. In this context, having a previous knowledge of nodes position is important to
accurately apply urgent interventions. For example, it is
crucial for farmer to control the movement of animals to
prevent any losses and to follow related processes. Tracking
of machinery is important for monitoring, security, and
automation. Nevertheless, localization is a tough, complicated, and an important problem for wireless sensors networks because of higher challenges related to energy
consumption, coverage, and connectivity due to obstacles.
In fact, localization is the process of accrediting a geographical position for each unknown node in the network
based on the information gathered from reference nodes.
Reference nodes get the location data based on results
obtained from the GPS or from the predefined deployment
position. Localization techniques are classified under two
main categories: Range-free and range-based techniques
[12]. Range-free techniques measure the distance between
two nodes based on connectivity information [13].
The radio connectivity is used to estimate nodes location
with no need of any coordinates system. These techniques
are based mainly on the number of surrounding neighbours
known as distance vector-hop [14], approximate point-intriangulation test or centroid system [15], [16]. Range-based
techniques use distance or angle information in order to estimate the position of nodes, such as GPS, angle of arrival
[17], time of arrival [18], time difference of arrival [19],
and received signal strength indicator [20], [21].
By comparing and evaluating the performances of
these techniques, based on the defined metrics and agricultural applications requirements, received signal strength
techniques are selected. RSSI technique enables us to
have an energy efficient, easy to use, and cost-effective
solution for the WSN localization problem designed for
large-scale agricultural applications.

PROPOSED ENERGY-EFFICIENT LOCALIZATION BASED ON RSSI
Localization algorithms are mainly based on two main
phases. First reference nodes, which have a predefined
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position, broadcast a beacon message to all nodes available
in their communication range in the network. Then, each
new unknown node selects nearest reference nodes, accessible in the node's communication range, to measure the distances to each reference and therefore, to estimate its
position in the network. Localization algorithm based on
RSSI values uses the path loss model of the radio frequency
propagation, which makes it vulnerable to fading and shadowing problem, multipath, and environmental effects. In
this context, further improvement is essential to reduce the
energy consumption and the measurement error. Additional
corrections could be suggested like clustering solution and
reference node selection strategies [22]-[24]. In fact, during
the movement of a node in the network, each step should be
done again. The node should find nearest reference nodes in
its communication range, collect RSSI values, measures
distances to the reference nodes and then apply triangulation method to claim its position. Matrix calculation will
request additional computational effort as well as more
energy sources. In the meantime, the estimated position
will be in relevance to the selected reference nodes, which
increases the need for adequate choice of reference nodes to
increase the accuracy and reduce the energy.
The proposed localization algorithm is organized in
two main phases, the initialization phase, and the localization phase. During the initialization phase, RSSI values between all available nodes are measured. Then,
each deployed node will update the lookup table, where
information related to connected sensor nodes are available. In fact, the lookup table contains the reference ID,
the received RSSI, the remaining energy, and the number of connected neighbors. The purpose of this phase is
to construct the prediction model that will be exploited
for the estimation of target position during the localization phase. The fuzzy-based technique does not require
any additional hardware, which makes it ideal as cost
effective solution. The main advantage of fuzzy logic
system is that it can process a large number of input
data, which can be imprecise or incomplete, based on
modeling their reaction and behavior rules. The second
main advantage is interpretability and simplicity as it
uses simple logic relation to estimate the output values,
which reduces the computational complexity of the system. In fact, fuzzy controller can automatically refine an
initial approximate set of fuzzy rule, when new data or
rules are added to the system, then no need to retrain the
system once again.
In our approach, Mamdani's method [25] is selected to
generate an output distribution. The fuzzy model considers
node density to ensure that at least three available reference
nodes are in the communication range of the unknown
node. Also the fuzzy system considers the battery level to
maintain the load balance of the network. Also, inference
system includes highest RSSI values to ensure its closeness
to the unknown node. This proposed model does not require

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MARCH 2019

Aerospace and Electronic Systems - March 2019

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