KPI Deployment for Enhanced Rice Production in a Geo-Location Environment using a Wireless Sensor Network

International Journal of Wireless & Mobile Networks (IJWMN), Vol.14, No.3, June 2022
DOI:10.5121/ijwmn.2022.14303 35
KPI DEPLOYMENT FOR ENHANCED RICE
PRODUCTION IN A GEO-LOCATION ENVIRONMENT
USING A WIRELESS SENSOR NETWORK
Oyibo Uchechukwu Moses and Nosiri Onyebuchi Chikezie
Department of Electrical and Electronic Engineering,
Federal University of Technology, Owerri, Nigeria
ABSTRACT
Rice production plays a significant role in food security in the globe. The automation of rice production
remains the paradigm shift to meet up with the consumer demand considering the tremendous increase in
consumption rate. The paper aimed at implementing some selected key performance indicators (KPIs) for
enhanced rice production by addressing five major challenges that face rice farmers, especially in Nigeria.
The Non-availability of water/rain for year-round cultivation, disproportionate application of fertilizer,
weed control/prevention, pest/disease control, and rodents and bird’s invasion are outlined as observed
constraints. A Zigbee-based Enhanced Wireless Sensor Network (eWSN) was used to model various
network scenarios to demonstrate data sensing of different environmental variables in a given farm land.
This was achieved by varying network devices at different scenarios using OPNET simulator and
understudying the network performances. Each new set of network devices was integrated to a Zigbee
Coordinator (ZC) which assigns an address to its members and forms a personal area network (PAN), thus
representing data sensing of a particular environmental variable. Three different scenarios were designed
and simulated in the study. Each of the temperature and humidity, motion and soil nutrient sensors
generated about 29bps of traffic. At the Coordinators, steady stream of traffic was received. The
temperature and humidity Coordinators, received a traffic of 64bps each, while the soil nutrient
Coordinator received data traffic of 96bps. The outcome of the design demonstrates effective
communication between different network components and provides insight on how WSN could be used
simultaneously to monitor a number of different environmental variables on a farm field. By implementing
the KPIs, the simulation result provided an estimated yield increase from 2.2 to 8.7 metric ton per hectare
of a rice farm.
KEYWORDS
Enhanced Wireless Sensor Network(eWSN), Zigbee, Key Performance Indicators(KPI), OPNET Simulator.
1. INTRODUCTION
Food remains one of the basic necessities of life sustenance and which at all times require the
need for improved production strategy [1]. The country Nigeria has been a mono-economic
country, with revenue from oil accounting for over 90% of her foreign exchange earnings [2].
Due to the failure of her successive governments to properly explore and develop other sectors of
the economy especially agriculture, the need for improved system of agricultural sector
development cannot be over emphasized considering the exploding population in the face of
dwindling oil revenue.
Rice has become a highly strategic and priority commodity for food security in Africa [3].
Consumption is growing faster than that of any other major staple food on the continent because

36
of high population growth, rapid urbanization and changes in eating habits [4]. Rice is one of the
most consumed staples in Nigeria, with a consumption per capita of 32 kg, it is proven to be the
single most important source of dietary energy in West Africa and the third most important for
Africa as a whole [3].
The importance of rice in Nigeria is no longer the question but rather how the growing demand
can be met to reduce its importation and be self-sufficient [5]. Many theories and hypotheses
were tried in the past for the rice production systems yet the self-sufficiency level has not been
achieved [6]. Recently, it was realized that rice production in Nigeria has significantly improved
and has recorded a peak of 4.9 million Metric Tonnes (MT) produced by farmers in Nigeria,
despite the production growth, it has not been able to meet the national demand on rice
consumption which stands an all-time high of 7 million Metric Tonnes (MT) [7]. This means that
there is a gap of 2.1 million MT to be cushioned, which is realizable if the observed limited
factors are improved. The limited capacity of the Nigerian rice sector to meet the domestic
demand has been attributed to several factors; notable among them is the declining productivity
due to low adoption of improved production practices [8].
In-lieu of the rice supply shortage in Nigeria occasioned by poor production output by Nigerian
farmers, the author looks at developing an environmental monitoring system using some selected
key performance indicators (KPIs) that utilize wireless sensor network technology, capable of
alleviating the production deficit of the country and engendering for export. The proposed
solution is a multi-functional and integrated system. It is an enhanced Wireless Sensor Network
(eWSN) technology solution that can do more than just irrigation work as has been widely
reported by many researchers on the subject matter. Consequently, the researcher designed an
eWSN system that is capable of:
i. Ensuring automated irrigation of a rice field for year-round production.
ii. Adaptable for disease control/prevention via automated application of pesticides.
iii. Adaptable for weed control/prevention via automated application of herbicides.
iv. Adaptable for rodents and birds’ control/prevention via automated buzzer activation
mechanism for scaring animals.
v. Adaptable for even and right proportion of fertilizer application.
2. LITERATURE REVIEW
The authors of [9] conducted a study to demonstrate the practical ways of using mobile phones in
conjunction with WSNs to enable farmers in Nigeria monitor and control their farm and hence
increase their productivity. Wireless sensor network was applied in conjunction with GSM
technology and was used to monitor and control various environmental factors. The model
monitored temperature, humidity, soil moisture and water level which were evaluated to activate
or deactivate the designed irrigation system with set threshold values [9]. In [10], demonstrated
the implementation of embedded system for automatic irrigation which has a wireless sensor
network placed in the root zone of the plant for real time in-field sensing and control of an
irrigation system. Data was received, identified, saved and displayed at the base station and if it
exceeds the desired limit. the control will be enabled by an android smart phone via GSM
network. In the works of [11], [12] analyzed the simulation on wireless sensor network for low-
cost wireless controlled and monitored irrigation solution using Zigbee/IEEE802.15.4. The
authors implemented a simulation model approach for monitoring and controlling of water and
irrigation systems. A Beacon Cluster-based mesh topology was introduced in their study which
significantly lowered power consumption with a higher battery life and a good delivery ratio.
The authors of [13] designed and implemented an Automatic Irrigation System Based on
Monitoring Soil Moisture. The method employed was to continuously monitor the soil moisture

37
level to decide whether irrigation was needed, and how much water was needed in the soil. A
pumping mechanism was used to deliver the needed amount of water to the soil. The work was
grouped into four subsystems namely; power supply, sensing unit, control unit and pumping
subsystems which made up the automatic irrigation control system [14]. The authors of [15],
designed a farmland environment information collection and monitoring system based on NB-IoT
to solve the problems associated with waste water resources. Their research study was aimed at
reducing the high labour intensity and unscientific irrigation challenges during farmland
irrigation and the shortcoming of conventional technologies in the water saving irrigation system
network.
The reviewed works has shown the extent of some research work on the subject matter. Scholars
have done a great deal of extensive work including implementation of automatic irrigation system
using WSN application. However, lack of water resource is not the basic challenge that limits
crop production especially rice. Such other challenges like pest and disease invasion, problem
with method of fertilizer application, birds/rodent’s invasion, and weed control could be among
major factors to contend with. On that premise, the study takes a step further to suggest the
design of a single multi-functional and integrated WSN application in agriculture adaptable to
irrigate, control pests and weeds, apply fertilizer and deter birds and rodents in a rice farm at the
same time. Selected key performance indicators for enhanced production, using OPNET Modeler
14.5A to design and simulate a model of the farm on Zigbee based Wireless Sensor Network was
introduced.
3. MATERIAL AND METHODS
3.1. Materials
The basic materials implemented for the realization of the research study are the Optimized
Network Engineering Tools (OPNET) and Circuit Wizard. The various sensor types were
represented as Zigbee End Devices (ZED) and the following were proposed based on their
comparative advantages:
i. Motion sensors for sensing the presence of birds and rodents. Passive InfraRed (PIR)
sensor has been adjudged to be best suitable for motion sensing. Panasonic’s AMN41121
PIR sensor was proposed because of its comparative advantages of extremely compact
with built-in amplifier, adjustable sensitivity, and noise withstanding capability.
ii. Temperature and Humidity sensors to detect change in temperature and to measure the
amount of water vapour within the farmstead. Sensirion Inc. SHT75 was proposed because
of its ability to measure temperature and humidity to the highest precision; and it is
relatively inexpensive with impeccable continuity and minimal size.
iii. Biological sensors: Biological sensor has the ability to sense the presence of weed, pests,
insects, eg. Weed Seeker.
iv. Soil moisture sensor to measure the amount of moisture on the farmstead. VG400 (a
frequency domain reflectometry sensor) from Vegetronix Inc. is proposed because it is less
expensive and uses less power.
v. Soil nutrient sensor to measure soil micronutrients such as nitrogen (N), phosphorus (P)
and potassium (K). Teralytic(R)
sensors are proposed. They can measure Soil electrical
conductivity, moisture, pH, Nitrates, Phosphates, Potassium, and temperature at 3 different
depths and sample every 15 minutes.

38
3.2. Method
The method employed in the research study involves computer-based simulation design
approach, it provides varied conditions and investigation on the resultant outcome.
3.2.1. Conceptual Model for Selected Key performance Indicators (KPIs)
Stochastic model was adopted to determine the effect of the additional technology input to the
overall yield of a rice farm. The Stochastic frontier for crop yield response is viewed as a good
approximation and is widely used in crop yield response analysis [16].
Let a farmer’s amount of produce (yield) per a hectare of rice farm be a function of his adoption
of best technology practice.
This can be represented as:
Yeft = f(Xeft) + α (1)
Where Yefjt = yield per hectare (ton) on farm e for farmer f in season (time) t,
Xeft= technology input variables,
And α = unforeseen natural/environmental factors (other agronomic conditions).
For a multi-technology input farm, yield for a given farming season can be given as:
Yeft = X0eft + X1eft (Irr.) + X2eft (Irr.+ Fer) + X3eft (Irr. + Fer. + Pst.) + X4eft (Irr.+Fer.+Pst. +Herb) +
X5eft (Irr.+Fer.+Pst.+Herb.+Bird & Rdnt Ctrl) + α (2)
where
X0eft = Rainfed variable
X1eft (Irr.) = Irrigation variable
X2eft (Irr.+ Fer) = Irrigation and Fertilizer application variable.
X3eft (Irr. + Fer. + Pst.) = Irrigation, Fertilizer and Pesticide application variable.
X4eft (Irr.+Fer.+Pst. +Herb) = Irrigation, Fertilizer, Pesticide and Herbicide application variable.
X5eft (Irr.+Fer.+Pst.+Herb.+Bird & Rdnt Ctrl) = Irrigation, Fertilizer, Pesticide, Herbicide
application and Birds/Rodents Control variable.
The general form of equation 2 can be written as:
𝑌𝑒𝑓𝑡 = ∑ 𝑋𝑒𝑓𝑡 + 𝛼
𝑛
𝑖 (3)
3.2.2.Data Evaluation from Technological Input variable
Data from comparative study of yield for rainfed (Lowland) and irrigated rice farm by [17] and
Potential Yield from [18] for some selected Nigerian states was used. The average yield (ton/ha)
for rainfed rice farm stood at 2.2, while average yield (ton/ha) for irrigated rice farm was 3.5.
This shows a percentage difference of 37.14%.
Drawing from the model equation and using the data above as basis, the computation of the effect
of additional technology-input variable results are represented in table 1.

39
Table 1. Estimated Average Rice yield(ton/ha) by additional technology-input variable
Production System Major Nig. State Covered Average
Yield (metric
ton/hectare)
Yield %
Change
Rainfed (Lowland) Benue, Ebonyi, Cross River,
Niger
2.2 0
Irrigated Benue, Ebonyi, Cross River,
Niger
3.5 37.14
Irrigated + Fertilized Benue, Ebonyi, Cross River,
Niger
4.8 64.22
Irrigated +Fert. +Pest. Appl. Benue, Ebonyi, Cross River,
Niger
6.1 85.53
Irrigated +Fert. +Pest. + Herb. Benue, Ebonyi, Cross River,
Niger
7.4 103.09
Irrigated +Fert. +Pest. + Herb. +
Birds/Rodent Control
Benue, Ebonyi, Cross River,
Niger
8.7 118.03
3.2.3. Wireless Sensor Network Modeling
Modeling of various network scenarios were deployed to demonstrate data sensing of different
environmental variables in a given farm land. This was achieved by varying network devices at
different scenarios using OPNET simulator and understudying the network performances such as
traffic sent (bits/sec), traffic received (bits/sec), end-to-end delay(second), throughput (bits/sec)
and media access control (MAC) load (bits/sec). The idea of varying network devices is a design
approach adopted in the study to demonstrate integration of different sensor types, monitoring
different environmental variables simultaneously, yet constituting a single unit of WSN working
cooperatively. Each new set of network devices are integrated to a Zigbee Coordinator (ZC)
which assigns an address to its members and forms a personal area network (PAN), thus
representing data sensing of a particular environmental variable. Mesh topology was adopted for
the design because of its ability to cover limitless area with the power to route data across
different paths[19].
The modeling of the eWSN was based on Zigbee standard (IEEE 802.5.4) using OPNET Modeler
14.5A. The Zigbee wireless sensor network consists of three types of nodes: the end device
nodes, the router nodes, and the gateway node (coordinator). The end device and router nodes
were used to manage the data collection of various environmental variables (temperature &
humidity, soil nutrients level, soil moisture level, presence of pests and rodents) and then the
collected data were sent to the coordinator for processing, and control.
Design Assumption: it is assumed that OPNET has the capability to be configured to allow for
received data analysis of packets sent from different sensor types – packet containing
temperature, humidity, soil nutrient level, soil moisture, etc.
3.2.4. System Block Diagram
Figure 1 shows the block diagram of the WSN model. Sensed data from individual sensor types
are routed through the router to the coordinator (Sink node) for further processing and control.
The monitoring sub-network is equally connected to the coordinator for both on-the-premise and
remote monitoring as maybe deemed necessary. Irrigation, pesticide application, herbicide
application, and soluble fertilizer application could be done from any of the 4 compartments
(Liquid A - D) connected to a water source through the irrigation pipe by the activation of the

40
solenoid depending on the type of instruction received from the controller (coordinator). The
other actuator systems could be for the alarming system to deter birds and rodents from the farm.
Fig. 1. The System Block Diagram
3.2.5. System Flow Chart
Figure 2 is the sensor designed flow chart showing sequence of events for system realization.
Fig 2. Sensor Design Flowchart

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3.2.6.Model Farm Network Design
A farmland of 100m x 100m was used as a baseline for the study. Sensors were sparsely
distributed across the farmland consisting of Zigbee end devices (ZED), Zigbee routers (ZR),
Zigbee coordinator (ZC) and actuators. The WSN was connected to a monitoring point via an
access point gateway, with a wireless database server and a PC for on-the-premise monitoring
while a host computer was connected via an internet protocol (IP) cloud for remote monitoring.
Figure 3 shows the block diagram of the model farm network.
Fig.3. Block Diagram of the Model Farm Network
3.2.7. Configured Network Scenarios
Three network scenarios were created to demonstrate data sensing of different environmental
parameters by varying number of network devices while watching out for network performance.
New set of Zigbee devices were added to the ideal network (network of one sensor type) and
configured to form a personal area network with an identifier for its members.
Scenario 1: consists of 4 Sensor Nodes, 2 Routers, and 1 Coordinator; to represent data sensing
of temperature and humidity variables.
Scenario 2: consists of 8 Sensor nodes, 4 Routers, and 2 Coordinators. The second Coordinator is
for the new set of sensor types; representing data sensing of soil nutrients, it is configured to
route its traffic to the central Coordinator.
Scenario 3: consists of 12 Sensor nodes, 6 Routers, and 3 Coordinators. Again, the third
Coordinator is for the next new set of sensor types; representing data sensing of motion variable,
while the first Coordinator remains the central Coordinator while traffic from Nut_Coordinator is
equally configured to be routed to it.

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Parameter Description for the Simulation of Scenario 1
The global frequency and maximum bit rate of the network parameter of the coordinator is set at
2.4GH and 250kbps respectively (Zigbee standards).
Media Access Control (MAC) Parameters
Figure 4 shows the MAC configuration of the network. The maximum back-off exponent of the
MAC parameter is assigned to 4 and the minimum back-off exponent is assigned to 3. The value
of the maximum back-off exponent executes Carrier Sense Multiple Access with Collision
Avoidance (CSMA/CA) algorithm for 4 times, while the minimum back-off exponent ensures 3
attempts of executing before declaring the channel access failure. Both values were chosen for
convenience’s sake. Channel sensing duration which is the duration each channel will be scanned
for beacons after the beacon request has been sent is assigned to 0.1 seconds.
Fig. 4. MAC parameters configuration
Network Parameters of the Coordinator
The network configuration parameters for the coordinator is shown in figure 5. The maximum
children number specifies the number of sensor nodes, routers and actuators that can be supported
by a coordinator or a router. Maximum depth means the number of network trees the coordinator
could have while router discovery timeout is the length of time allowable for the network to keep
route discovery entries. In scenario 1, the coordinator established its network with personal area
network identifier (PAN ID) of 0; representing data sensing of a single environmental variable
(temperature and humidity sensing).

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Fig. 5. Network parameter of the coordinator
Physical Layer Parameters
The physical layer parameters is shown in figure 6. In order to determine whether a node is dead
or alive, a packet reception-power threshold is set to -76 dBm (considered optimal).
Fig. 6. Physical Layer Parameters
Application Traffic Attributes
The application traffic parameters used as shown in figure 7. The packet size of the sensed
environmental variables is set to 32 bits; however, they could be an overhead added by each layer
of the open system interconnection (OSI) model. The sensed nodes select a random destination
within its own PAN. All traffic is started, followed by a distribution of uniform (60, 61) seconds
after the simulation starts and traffic generation stops at the end of the simulation.

44
Fig. 7. Application Traffic Parameters
Transmit Power Configuration
Figure 8 shows the transmit power parameter used. A value of 0.06 mW was chosen because it is
considered as optimal transmit power in terms of achieving maximum traffic sent (Shah Nawaz,
2015).
Fig 8. Transmit Power Parameter

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Table. 2. Summary of Network Parameter Configuration.
Scenari
o
No.
of
ZE
D
No
. of
ZR
NO
. of
ZC
MAC Layer PHY
Layer
APPL Layer Transm
it
Power
(mWatt
)
ZC
PA
N
ID
Max.Bac
k-off
Expon.
Min.
Back-
off
Expon
.
Sensing
Duratio
n (sec)
Pkt.
Receptio
n Power
(dBm)
Pkt
size
(bits
)
Distri
.
Time
(sec)
Scenari
o 1
4 2 1 4 3 0.1 -76 32 60,61 0.06 0
Scenari
o 2
8 4 2 4 3 0.1 -76 32 60,61 0.06 1
Scenari
o 3
12 6 3 4 3 0.1 -76 32 60,61 0.06 2
Simulation Setup of Scenario 1
Figure 9 shows the device configuration for the simulation of scenario 1. It consists of 4 sensor
nodes, 2 routers, and a coordinator. The number of routers is chosen to be 2 to ensure self-healing
mechanism of mesh topology should one fail while minimal number of ZED were used for visual
simplicity.
Fig. 9. Simulation Setup of Scenario 1
Figure 10 shows the device configuration of Scenario 2. It consists of 8 sensor nodes, 4 routers,
and 2 coordinators. The topology of scenario 2 differs from that of scenario 1 in that the number
of sensor nodes and routers doubled in size but the second coordinator (Motion Coordinator) is
set to establish network with its ZED and with the two new routers (Motion Router 1 & Motion
Router 2) with a PAN ID of 1 (PAN ID_1). These new number of network devices added to the
network forms a network sensing different environmental variables (motion detection) from the
first set of nodes. Data routed through Motion Coordinator from its ZEDs and ZRs are configured
to route to the central Coordinator. Sensor nodes and routers have similar parameter
configuration as in scenario 1.

46
Figure 11 is the device configuration of scenario 3. The network model of scenario 2 was
replicated with 12 sensors, 6 routers, and 3 coordinators. The third Coordinator (Soil
Nut_Coordinator) establishes a network with its members (Soil Nut_Sensor 1 – 4 and Soil
Nut_Routers 1&2) with a PAN ID of 2 (PAN_2). Sensed data (traffic) from Soil Nut_sensors are
routed through the Soil Nut_Routers to the Soil Nut_Coordinator. The Soil Nut_Coordinator is
configured to route its traffic to the central Coordinator. By so doing, another set of
environmental variables (soil nutrients: NPK, etc.) different from those of scenarios 1 and 2 are
sensed.

47
Simulation Run-Time
The simulation run-time information for the investigation of the network performance for the
three scenarios simulated for 10 minutes is shown in figure 12.

48
Fig. 12. Simulation Run-time for the three scenarios
Functional Description of Metrics
In order to collect the statistics for the performance metrics, the simulation was run using OPNET
Modeler 14.5A. Table 3 is a description of the global and object metrics collected.
Table 3. Performance Metrics
SN Name Description Group Capture
Mode
Draw
Style
Filter
1. Traffic
Sent
(bits/sec)
Application traffic sent by the
layer in bits/sec.
ZigBee
Applicati
on
bucket/defau
lt
total/sum_ti
me
Linear time
average
2. Traffic
Received
(bits/sec)
Application traffic received by
the layer in bits/sec.
ZigBee
Applicati
on
bucket/defau
lt
total/sum_ti
me
Overl
aid
time
average
3. 802.1544_
MAC
Throughp
ut
(bits/sec)
Represents the total number of
bits (in bits/sec) forwarded from
802.15.4 MAC to higher layers
in all WPAN nodes of the
network.
ZigBee
802.15.4
MAC
bucket/defau
lt
total/sum_ti
me
Linear As Is

49
4. 802.15.4_
MAC
Load
(bits/sec)
Represents the total load (in
bits/sec) submitted to 802.15.4
MAC by all higher layers in all
WPAN nodes of the network.
ZigBee
802.15.4
MAC
bucket/defau
lt
total/sum_ti
me
Linear As Is
5. End-to-
End
Delay(bits
/sec)
Represents the entire delay
between the invention and
reception of application packets
(bits/sec)
ZigBee
802.15.4
MAC
bucket/defau
lt
total/sum_ti
me
Linear As Is
4. RESULTS AND DISCUSSIONS
The outcome of the Implementation of Conceptual Model of Selected Key performance
Indicators (KPIs) is represented in figure 13
Fig. 13. Average Yield (ton/ha)
The intent of this study was to understudy the effect of the introduction of each of the selected
KPIs on the overall yield of a rice farm. Rice yield across incremental addition of the KPIs as
production inputs followed priori expectation and corroborates the result of previous studies that
adoption of improved agricultural production techniques increases yield per hectare of a
farmland. For instance, the result showed that with irrigation as an input variable, a yield increase
of 1.3 metric ton (MT) per hectare is possible. This representing about 37.14% increase in yield
from the rainfed production system. In that order, the combination of the other selected KPIs
increased yield per hectare up to 8.7MT as shown in figure 13.
4.1. WSN Modeling and System Simulation Results
Data sensing and transmission by wireless devices were modeled using OPNET 14.5A.
Simulation was run to collect results as follows:
i. Traffic sent by the 3 sensor types used in the scenarios (bits/sec);
ii. Traffic received at the individual Coordinators (bits/sec);
iii. The network End-to-End delay (sec);
iv. Medium Access Control (MAC) Throughput (bits/sec), and
v. MAC load per PAN (bits/sec).

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Traffic Sent by the 3 Sensor Types Used in the Scenarios (bits/sec)
The focus of this study is to determine if the different sensor types used in the scenarios were
able to generate and transmit their traffic (data). Figure 14 shows the traffic sent by
motion_sensor1, soil Nut_sensor1 and Temp & Humility sensor1 to their respective routers.
Fig. 14. Traffic Sent by the 3 Sensor Types (bits/sec)
Data traffic sent is defined as the total number of data bits sent by the source to destination per
unit time irrespective of the condition whether all the data bit reach the destination or not [20]. It
can be seen that each of the sensor type was able to generate and transmit an average traffic of
29kbps to its router destination. In each instance of the sensors, there was a delay of about 54s
from when the simulation starts during which the sensor senses its data and determines the best
path to route it.
Traffic Received at the Individual Coordinators (bits/sec)
Figure 15 shows the traffic received at the individual Coordinators used in the simulation. Traffic
received is defined as the total number of data bit received per unit time.

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Fig. 15. Traffic Received at the Individual Coordinators (bits/sec)
The statistics was collected as object statistics and presented as overlaid. It can be seen that each
of the coordinators received steady stream of data without disruption. The amount of data
received by Temp & Humidity coordinator (blue) and that of the Motion Coordinator (red) is
64bps for each, hence the overlap while that of the Soil Nut_Coordinator (green) is 96bps.
Network End-to-End (ETE) Delay (seconds)
Figure 16 shows the End-to-End (ETE) delay for the three PANs of the network. End-to-End
delay is an OPNET global statistics. It is the entire delay between the invention and reception of
application packets.
Fig. 16. Network End-to-End Delay (second)
Global statistics give relevant information concerning the overall system and measures the effect
in real time monitoring. As can be seen, the average delay for the 3 PANs shows a consistency in
the amount of ETE delay. It shows that the Zigbee end devices (blue line) of PAN 0 connect to

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their routers with a delay of 0.01s, PAN 1(red line) with a delay of 0.003s and PAN 2 (green line)
with a delay of 0.007s. On the average, the ETE of the network stood at about 0.007s.
Significantly, the average ETE of the network is low due to ability of mesh routing process to
find more efficient route.
Medium Access Control (MAC) Throughput (bits/sec)
Figure 17 shows the MAC throughput of the 3 scenarios. The MAC throughput was collected as a
global statistic. It is the number of bits or packets successfully acquired or transmitted by the
receiver or transmitter channel per second. The spike for each of the scenarios at the beginning
and at some point, of the simulation are indications of management and control traffic sent and
received to determine the presence of devices as well as the optimal route. As can be seen, the
throughput for the scenarios showed same pattern with the farm of one sensor type (blue line)
having a throughput of 1368bps, 3192bps for the farm of two sensor types (red line) and 3977bps
for the farm of three sensor types (green line).
Fig. 17. Medium Access Control of the 3 scenario (bits/sec)
MAC Load per PAN (bits/sec)
Figure 18 is the graph of the global MAC load per PAN of the simulation. MAC load represents
the forwarding load for each PAN to transfer the packets to the IEEE 802.15.4 MAC layer, i.e.,
physical layer, by the upper layers [21]. The MAC load for PAN 0 (blue line) is 1976 bits/sec,
while that of PAN 1 (red line) is 304 bits/sec and PAN 2 (green line) is 1849 bits/sec
respectively. There is a spike in each of the PAN at about 6 seconds when the simulation started
and later at about 60 seconds. This is due to the routing messages being broadcast at those times.

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Fig. 18. MAC Load per PAN (bits/sec)
5. CONCLUSION
The study aimed at implementing the selected Key Performance Indicators (KPIs) for enhanced
rice production towards addressing five major challenges that face rice farmers in developing
countries like Nigeria. The result of the design and simulation indicated the possibility of
integrating different sensor types to work cooperatively to sense different environmental
variables simultaneously. The learning experience in the course of the study is that an integrated
and enhanced wireless sensor network (eWSN) is realizable and suitable for improved rice
production since it has the capability of managing and maintaining the scarce resources at the
farmer’s disposal.
Future studies should consider developing protocols and standards for ease of interoperability and
low-cost efficient energy harvesting mechanisms for energy sustainability of the wireless network
communication architecture which is envisaged as a limiting factor on the proposed system.
CONFLICTS OF INTEREST
The Author declares no conflict of interest
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