A Modified MPRR Protocol for WSN in Agricultural Scenario
Marco Cagnetti
, Mariagrazia Leccisi
and Fabio Leccese
Dipartimento di Scienze, Università degli Studi “Roma Tre”, via della Vasca Navale n.84, Rome, Italy
Keywords: WSN, Routing Protocols, Precision Agriculture, Castalia, Simulations, Energy Management.
Abstract: This paper analyses the use of a WSN for agricultural scenario, referencing to the modality of communication
between the network nodes, and proposes a modified version of Multipath Ring Routing (MPRR) to improve
performances and robustness of the network on the long period. Through simulations with Castalia, some
limits of the standard MPRR have been highlighted, and through the possibility to modify the algorithm itself,
improvements have been made that make the modified MPRR suitable for our scenario.
Precision Agriculture is a Management System
integrated with the aim of optimizing the efficiency
of the agricultural production, product quality and
profitability, increase climate and environmental
sustainability, using tools and innovative
Italy has provided for Rural Development
Programs in various Italian regions, through
intervention strategies related to the spread of
agronomic management methods and approaches.
The Ministry of Agricultural Policies and Europe
itself invested many funds on precision agriculture
and agricultural engineering (Ministero delle
Politiche Agricole Alimentari e Forestali, 2017).
The development of applications more and more
suitable for national productions is important for
many reasons: from production and quality
optimization to the reduction of business costs, from
minimizing environmental impacts with seeds,
fertilizers, agro pharmaceuticals up to cutting water
use and fuel consumption. In fact, the monitoring of
the environmental parameters permits to manage
many interesting areas as:
the use of pesticides and harmful substances,
the quality of air and water
monitoring of fires and atmospheric events or
natural and non-natural disasters etc.
The research field of precision agriculture is
having an increasing interest from the scientific
community due to its importance and the possibility
of using technology and IoT to improve processes.
For example, the use of an optoelectronic sensors
to evaluate the radial growth of a fruit, monitoring
fruit production, has been evaluated (Thalheimer
2016), while in (Pahuja et al., 2013) has been
monitored the climatic trend in commercial
greenhouses, in order to evaluate the production trend
and the health of the plants, through Wireless Sensor
Networks (WSN). In (Leccese et al., 2019) has been
studied the development of a WSN for smart
monitoring of pesticides on agricultural land,
designing an electronic nose starting from an array of
commercial gas sensors developed for other
environmental applications. In (Kim et al., 2008) a
distributed Wireless Sensor Network has been used to
remotely control an irrigation system. In (Nisio et al.,
2020) fast detection of olive trees affected by xylella
fastidiosa from uavs using multispectral imaging has
been implemented. In (Giaquinto et al., 2019) a
sensor for leak detection in underground water
pipelines has been developed. In (Cagnetti et al.,
2020) a comparison between the most suitable routing
protocols for WSNs applied in wide agriculture
scenarios is shown and it evidences the most suitable
protocol for a particular scenario.
In our paper, we are going to analyze the use of a
WSN for the agriculture, referencing to the modality
Cagnetti, M., Leccisi, M. and Leccese, F.
A Modified MPRR Protocol for WSN in Agricultural Scenario.
DOI: 10.5220/0010374701430150
In Proceedings of the 10th International Conference on Sensor Networks (SENSORNETS 2021), pages 143-150
ISBN: 978-989-758-489-3
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
of communication between the network nodes, and
proposing a modified version of Multipath Ring
Routing (MPRR) to better performances and
robustness of the network on the long period.
Through Castalia, a free simulation tool, the limits
of a standard MPRR have been evidenced, giving us
the possibility of modify the implementation of the
algorithm, improving it significantly.
WSNs are widely studied and applied to many
various contexts (Gallucci et al., 2017; Spagnolo, et
al., 2020). They are composed by many sensors
(called nodes) and all is necessary to their functioning
and communications; each node communicates with
each other and with a sink that collects, analyzes,
tracks and eventually sends data to other platforms,
and typically is connected to Internet and mains
(Leccese et al., 2014; Leccese et al., 2017).
The node is a device formed by sensors, a
microcontroller to manage the communication
between nodes, transmitter to connect with the other
nodes and sink through an antenna and an adaptive
circuit, a power supply circuit, and some I/O
interfaces to manage signal from sensors.
The topology of a WSN (Figure 1) describes the
physical position of each node: it strictly depends on
scenario and on sensors. Nodes are spatially disposed
according to the area to monitor, and they
communicate, auto organize themselves and
coordinate with each other through a routing protocol.
Figure 1: A generic WSN architecture.
The environmental monitoring is a typical
application of WSNs, for example to monitor
archeological sites or museums (Leccese et al., 2017;
D’alvia et al., 2017; Leccese et al., 2018), for
surveillance (Morello et al., 2010; Islam et al.2012;
Caciotta et al., 2014), in aerospace scenarios
(Pasquali et al., 2016; Cagnetti et al., 2020) or street
light control (Leccese, 2013). They are very flexible
and robust structures that can be configured in the
correct way according to the scenario; in fact, an ideal
network cannot exist since each scenario has some
characteristics that should be evaluated (Pasquali et
al., 2016; Pasquali et al., 2016).
The definition of the operative scenario is
fundamental to design a WSN correctly; in fact, each
scenario has some characteristics we need to know
and analyze, and a wide number of parameters should
be considerate and studied.
It is important to guarantee the transmission, that
must be reliable and safe (a node sensor can stop
working compromising the stability of the network or
requiring a new configuration), and the number of
nodes must be compatible with the area to be
monitored (too few sensors could decrease accuracy
and too many sensors could increase energy
consumption). The scenario is an agricultural field
where the sink is positioned at the center of the
structure, and is connected to the national electrical
grid and to internet.
Sensors are arranged in a radial pattern around the
sink, their number is between 9 and 15 each hectare,
spaced about 25/50 m; the sink provides to send them
to specific system for data analysis through internet
Various specific constraints should be evaluated:
Open field position of devices: sensors are
often on open field, so they are exposed to
meteorological events. They needs to be made
in a very simple and a light way, using robust
and reliable components as Commercial-off-
the-Shelf (COTS).
Energy saving: the evaluation of the supply
type of network is an important part of
agricultural scenario; in fact, the connection to
the National electric grid could be not
provided. A cabled power supply is not
recommended due to the possible damages of
cables caused by water, or animals, but
batteries have limits to their life and
dimensions. To improve energy saving, the
choice of electronic components and of
communication modality, as routing protocol
and strategies for a limited use of resources, is
fundamental. E.g., a sensor can be activated
for a limited time, to avoid a continuous use of
WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture
batteries, or network can be configured using
low-energy routing protocols.
Sensor maintenance: a structure that does not
require great maintenance should be used;
sensors arranged in open spaces are subjected
to problems that would require heavy and/or
expensive maintenance.
Economy: the structure must have affordable
costs for maintenance and commercial
The network is static or dynamic: the sensors
are placed in the field and remain in place until
they are naturally switch off.
The hardware of sensors and sink can be technically
composed by specific low energy components, and
the adaptive structures can be designed to minimize
energy consumption, but the network management
can be optimized searching for the best
communication modality between nodes and sink.
The consumption of a sensor is due to the
transmission phase, so an energy saving can be
obtained using specific protocols that guarantee
reliability of transmission, accuracy of information,
low consumption and maximize the life of the nodes.
A high consumption can be due to the overlapping
of information that are lost and should be transmitted
again, to the listening time of a node or the reception
of wrong packet.
Routing algorithms are a central point to work on
to save energy. Routing works managing the hops
between nodes and sink, creating the shorter path as
possible. Some protocols are designed to use
clustering, aggregating data before the dispatch. The
data aggregation is very useful to decrease energy
consumption, limiting the transmission time of a
Analyzing literature and according to our
experience, the most suitable protocols to limit
consumptions are the LEACH or Low-Energy
Adaptive Clustering Hierarchy (Leccese et al., 2019),
the PEGASIS Power Efficient Gathering in Sensor
Information Systems (Shekar, 2012), the AODV or
Ad hoc On Demand Distance Vector (Maurya et al.,
2012) and the Multipath Ring Routing or MPRR
(Pandya & Mehta, 2012).
LEACH is a cluster based protocol, in which
sensors are divided into clusters and each one
contains a cluster head that collects data and sent
them to sink, limiting the time of transmission and
consequently the consumption. The node disposition
is a limitation because some nodes could be very far
from sink.
The AODV protocol is specifically used for
mobile networks and creates a table of shorter path
between nodes when requested by them. If the
network topology changes, paths are rebuilt. It’s
limitation is due to a bad managing of network
Multipath ring routing uses multiple propagation
path between each sensors, to maximize network
reliability and avoid the loss of packets. The sink
provides a configuration signal to the nearest nodes,
assigning them a hierarchy. They are set with a level
of ring 1, so they send the signal configuration to the
nearest nodes that configure them as level 2 and so
on, until a level number, called ring number,
characterizes each sensor. At the end, network
provides almost the best paths between nodes. During
the transmission phase, a signal from node N is sent
towards all the N-1 nodes that send it toward the N-2
nodes and so on until it arrives to the sink. A sensor
could break or switch off; in this case the multiple
paths ensure that the signal reaches the sink through
another node of the same level, but its limitation is
due to initial configuration that could spent some
energy and time.
We focused our attention on MPRR, providing more
simulations through Castalia, a very useful tool for
studying WSNs.
It is a free application that includes the
implementation of some routing protocols that are
applied to the specific topology of the scenario
defined by user. For this reason, we could compare
the routing protocols, according to the specific
scenario: each scenario has some characteristics that
can be evaluated through a simulation.
We also worked for a visual tool to show the
network physical topology and package trend during
a transmission time.
We focused our attention on MPRR for its
characteristics of being very robust and efficient,
reducing possibility of losing information. In fact,
although it provides multiple propagation paths, if the
nodes of a certain level shut down, the nodes of the
previous level could not receive messages to send
toward the sink. A multi-hop structure can be used; in
this case, the dead node is bypassed, permitting to the
message to arrive; obviously, it is very expensive for
consumption because of the more distances between
nodes and can create problem of overlapping.
A Modified MPRR Protocol for WSN in Agricultural Scenario
For our scenario, MPRR is not the first choice
because of the consumption during the initial
configuration phase, and the transmission can bring
to an early shut down of the network (Leccese et al.,
2014). However, the arrangement of the nodes is
compatible with MPRR, so we ask if it was possible
to improve this algorithm to make it more efficient.
Our simulations highlighted these problems and
allowed us to evaluate the configuration of the nodes
in MPRR.
In fact, the network configuration occurs only one
time and it sets the best routes for each sensor. In this
case, a little percentage of node could be wrong or no
configured: these nodes could not ever communicate
between the other nodes and the sink. This problem is
caused by the contemporary node’s transmission, so
during the configuration phase, an overlapping of
signal, and a reduction of signal/noise ratio, could
occur, causing a bad configuration of some sensors.
Castalia tool showed clearly and visually this
configuration problem, in which some nodes
appeared to be inactive.
The wrong configuration can be characterized by:
one or more nodes that are not configured,
making them invisible to the network;
one or more nodes that can acquire a wrong
ring number, incrementing the route to the
Our scenario provides a central sink and many
nodes disposed around it, equally spaced and with the
same initial energy.
Figure 2 shows the configuration: a central sink
sends the configuration message to the nearest nodes.
Figure 2: The topology of our scenario.
During the configuration, the sink sends a
message to the nodes that configure themselves as
level 1, then the nodes of level 1 (A1,B1 …) send
messages to the nearest nodes that configure
themselves as level 2 (A2, B2…) and so on until each
node is configured.
Only one configuration message could be
necessary to configure all nodes correctly, but in the
reality, the distance between nodes is not the same, so
these situations can happen:
more packets collide between each other
(Figure 3). In this situation, the configured
node A1 and B1 transmit the message at the
same time towards the node N2 that does not
configure itself as level 2 because it cannot
decode the message. When node A3 will send
the configuration packet, N2, which has not
been configured yet, will configure itself as
level 4 instead of level 2, incrementing the hop
number from the sink.
Figure 3: The collision between two packets makes N2 not
Nodes transmit in every direction (Figure 4).
The configuration packet from A1 is
intercepted by A2 but it can be intercepted also
by A3 that configure itself as level 2 instead of
level 3. Therefore, nodes already configured
are not newly modified, but nodes of higher
level could configure themselves wrongly.
Figure 4: The signal from A1 is intercepted by A3, which is
incorrectly configured.
WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture
Our simulations have confirmed the inefficiency,
due to the bad configuration; through a visual tool, we
monitored the configuration of all nodes, evidencing
the overlapping of signal and the loss of configuration
To avoid a bad configuration, we have modified
Castalia’s core to manage:
a little delay to avoid the contemporary
transmission of packet by nodes, reducing
the probability of interferences;
a double configuration signal. The first
configuration packet starts from the sink
toward the nearest sensors to configure the
whole network according to the standard
MPRR, while a second signal checks the
network and re-configure it when the first
signal failed, warranting a correct
configuration of all nodes.
According with these modifications, in case of
Figure 3, the node N2 that was not configured by the
first configuration signal will be configured as level 2
by the second signal. In case of Figure 4, node A3 is
configured wrongly as level 2 by the first signal from
A1, but the second configuration signal rechecks the
ring number and re configures node A3 as level 3. The
reconfigured nodes will reconfigure also all nodes of
higher levels. Therefore, compared to an initial
increase of energy consumption due to the
reconfiguration, at the end, the network is correctly
configured and it does not need configuration
Figure 5 shows the initial topology for a
simulation of 108 sensors. No sensor is configured,
because it is waiting inactive for a configuration
packet from sink.
Figure 5: Initial topology for a network of 108 nodes. The
sink is at the centre of the network.
When the sink starts to transmit, the nearest node
configure themselves as level 1. Figure 6 shows in
green the nodes of level 1 just configured and, in red,
the nodes of level 2. The black circles represent the
configuration packet expanding for the entire
network, while Figure 7 shows a completely
configured network.
Figure 6: The configuration signal starts from the sink to
the sensors, which self-configure to create the best paths.
Figure 7: The whole network is configured.
Figure 8 shows a detail of the first configuration
phase: green nodes are configured as level 1, while
red nodes are configured as level 2.
Figure 9 shows the reconfiguration of nodes 2, 3,
7 and 8: in Figure 8 they were configured as level two
(red), while after the second configuration signal are
reconfigured as level one (green).
A Modified MPRR Protocol for WSN in Agricultural Scenario
Figure 8: The configuration of levels 1 and 2 by the first
configuration signal.
Figure 9: The re-configuration of nodes after the second
configuration signal.
Through Castalia we evaluated:
the number of transmitted and sent packets
between nodes and sink;
the life span of nodes, caused by excessive
consumption of batteries or physical
the energy consumption of the network.
Performances information can be extracted from
simulations, studying the trend of the number of
packets received by the sink, and dividing it by the
number of nodes remaining active during the
network’s life.
The performance index η
is defined as:
= N
/ S – D
where N
is the number of packets received by the
sink, S is the number of initial node and D is the
number of dead nodes after a fixed time.
Higher values show energy inefficiency (due to
more transmission toward sink) and more reliability,
while lower value show a greater number of death
nodes but less energy consumption.
The abscissa represents the temporal evolution; it
is expressed in epochs and each epoch corresponds at
2 months of network working.
The ordinate axis represents the performance η
value. Figure 10 shows the average of about 50
simulation cycles, considering about 108 nodes.
Figure 10: Comparison between standard and modified
MPRR for a network of 100 nodes.
The comparison between the standard MPRR and
the modified MPRR is well highlighted; in the first
epoch, standard and Modified MPRR are both
inefficient, with a very large packet redundancy and
a very high consumption. In fact, each node transmits
its package toward sink through more than one path
(one level has more nodes), so the sink receives
duplicated information that will be managed by the
sink itself.
From the second Epoch, nodes start to switch off
for damages or low batteries, and in the MPRR, the
nodes nearest to the sink switch off before than the
nodes that are more distant because of the great
number of packets.
They do not switch off at the same time, thanks to
the network configuration, so at least one node
remains alive to send message to sink: on the long
time, the number of packets received by the sink
decreases. The standard MPRR, for this scenario,
provides a not completely correct configuration, so
the nodes nearest the sink shutdown faster than the
WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture
modified MPRR. After 8 epochs, the network based
on standard MPRR is dead, while the network based
on the Modified MPRR is still alive. On the long
period, the modified MPRR seems to be the first
choice to guarantee the best compromise between
energy saving and robustness of network. This
situation is confirmed incrementing the number of
node. Figure 11 shows the average of about 50
simulation cycles for about 200 nodes; it confirms the
modified MPRR robustness on the long time for a
wide network, while a WSN using standard MPRR
shut down earlier.
Figure 11: Comparison between standard and modified
MPRR for a network of 200 nodes.
In order to correctly design a WSN, it is important to
study various parameters, including how the nodes
communicate with each other and with the sink.
During the evaluation of the best routing algorithm
for our agricultural scenario, we identified some
critical issues in the use of the MPRR standard.
Therefore, we tried to understand if it was
possible to modify the MPRR so that it could be used
effectively in an agricultural scenario. Some changes
have been made to the routing algorithm, bringing a
clear improvement in its performance.
Through Castalia, a free tool for studying WSN
routing protocols, we evaluated the comparison
between the standard MPRR and its modified version
to exceed the MPRR limits due to the characteristics
of agricultural scenario.
Compared with the standard MPRR, the modified
MPRR provides two signal for configuring the
network, a first signal makes the network configured
in the standard way, while the second signal
reconfigures the wrong nodes to improve the routing
paths between nodes and sink.
In order to reduce the possibility of interferences
and loss of information, even a little delay during
transmission has been adopted.
Performances of the modified MPPR have been
evaluated, evidencing the better life span, despite a
bad initial performance.
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WSN4PA 2021 - Special Session on Wireless Sensor Networks for Precise Agriculture