Aerospace and Electronic Systems - March 2019 - 59

Khriji et al.

Figure 3.
Block diagram of the proposed energy efficient routing algorithm.

energy are selected as final CHs. In [9], a multiclustering
algorithm based on fuzzy logic is presented. The residual
energy and number of neighbors are used as fuzzy descriptors for the CH selection. Nodes with higher fuzzy output
are selected as CHs. The same CHs remain as CHs in two
consecutive rounds. Then, in the third round, the process of
CH selection is carried on, and new CHs are chosen. By
reducing the number of CH selection, the number of sent
messages is reduced while guaranteeing increased capacities to save energy within the network.
In this paper, clustering and network organization of
WSNs for low-energy consumption is investigated. It proposes a fuzzy-based energy aware unequal clustering algorithm named FEAUC. Compared to previous clustering
algorithms, some noticeable features of FEUAC can be
listed and then described in the next sections:
Sensor nodes are distributed into deterministic rings,
which can provide efficient coverage over entire area.
The optimal cluster radius calculation is carried out
based on an energy analysis model before network
deployment.
Energy efficient and low-cost localization techniques based on RSSI for nodes position determination is proposed.
Nonprobabilistic CH selection is proposed, where a
fuzzy logic system is employed. Node residual
energy, node density, and node centrality are considered to compute the fuzzy cost of CHs.

PROPOSED ROUTING ALGORITHM FOR AGRICULTURAL
APPLICATION
The WSN test-bed consists of a number of sensor nodes
deployed in an uniform distribution with a density . These
nodes can operate in two different modes: Sensing mode and
CH mode. In sensing mode, they measure some environmental parameters and send them to the associated CH. In CH
mode, nodes collect data, compress it, and forward it directly
to the BS in single-hop communication or to another CH in a
MARCH 2019

multihop communication. The
network is assumed as a circle,
where the BS is in the center.
The choice of a circular
partitioning scheme is based
on the energy consumption
analysis performed in [10],
which proves that circular
network model have better
accuracy in terms of energy
consumption compared with
rectangular
partitioning
schemes. The use of rectangular shape is inaccurate for
the variable dimensions compared to circular partitioning scheme. The whole area is then partitioned into a
defined number of rings with specific radii. After being
deployed in the field, the BS as well as sensor nodes are
stationary and all nodes haves the same initial energy.
Generally, WSN nodes have three possible energy
consumption states, which are sleep, transmit, and receive.
Based on the energy model presented in [11], the energy
consumption during the sleep, transmit, and receive modes
can be presented, respectively, as follows:
Esleep ¼ Psleep Â Dtsleep

(1)

ETx ¼ PTx Â DtTx

(2)

ERx ¼ PRx Â DtRx

(3)

where Psleep , PTx , and PRx are the consumed power during
the sleep state and when transmitting and receiving data
packets. Dt is the state duration in seconds for each state.
A new energy-efficient routing algorithm is proposed,
which is based on two main phases: Offline phase and
online phase (see Figure 3). During the offline phase, localization is carried out in order to estimate real nodes position,
which enables us to have an overview of the overall of the
network. Once the nodes are located, the online phase is
applied. The online phase itself is composed of two subsystems: The clustering phase and data transmission phase.
During the clustering, each node is capable to recognize its
respective neighbors and then can elect the most adequate
CH for each defined nodes group. While, in data transmission phase, protocols for communication and data aggregation between nodes, CHs, and the BS are defined. Details
about each phase are provided in the following sections.

OFFLINE PHASE
The proposed algorithm considers the real nodes position in the network to look for the shortest and optimal
path to apply urgent decision. Nodes are deployed in
the network and data circulate from one node to

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Aerospace and Electronic Systems - March 2019

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