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SN Computer Science (2021) 2:36 https://doi.org/10.1007/s42979-020-00412-8

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ORIGINAL RESEARCH

A Prototype Modeling of Smart Irrigation System Using Event‑B

Rahul Karmakar1  · Bidyut Biman Sarkar2

Received: 10 September 2020 / Accepted: 24 November 2020 / Published online: 12 January 2021 © Springer Nature Singapore Pte Ltd 2021

AbstractTraditional irrigation operates on a preset programmed schedule and timers. Smart irrigation monitors weather, soil condi-tions, evaporation, and plant water use to automatically adjust the watering schedule to actual conditions of the site. Deploy-ment of IoT based sensors, GPS systems, and usage of solar energy greatly contributes to the cause. In order to monitor large irrigation area with different harvests, seasons, and parameters demand a formal model for higher yields. In this paper, a prototype of a smart irrigation system is modeled using Event-B, and verification is done by the RODIN tool support.

Keywords Event-B modeling · Smart irrigation · Smart agriculture · IoT · RODIN

Introduction

One-third of the world population suffers from water scar-city. Approximately 70% of the total withdrawal water is used for irrigation. 60% of water meant to be used in irriga-tion is lost either due to evaporation, land runoff, or simply inefficient, primitive usage methods. Smart irrigation is the key component of precision agriculture and can be a solu-tion for water conservation [1]. The use of the internet of things (IoT) in Smart irrigation [6, 17, 19] can save up to 45% of the water during the dry season and around 80% in rainy season compared with manual irrigation [1, 12]. Solar energy is a free renewable energy source. This energy can be used in agriculture especially in tropical areas. Precision irrigation is used in Precision Agriculture. It is the com-bination of science, art, and technology that improves the yields by effective management of water. IoT technology plays an important role in decision making towards precision

agriculture depending on the different metrological factors like temperature, humidity, etc [9, 13]. The proposed sys-tem deals with two important facets. The solar energy for irrigation and precision irrigation for water conservation. Solar energy reduces the water pump operating cost and on the other hand smart pumps saves the water wastage. The data collected from the sensors will help in decision mak-ing. The agricultural irrigation is primarily dependent on climate factors, crop growth, crop stage, and crop types [5]. The proposed system allows farmers to plan year-wise farm-ing effectively. This model can be applied to both precision irrigation and sprinkling irrigation [7, 8, 16]. Event-B is an incremental modeling approach, which supports incremental design and verification. Event-B has a tool support RODIN with different plugins [11, 14]. Event-B allows modeling the static and the dynamic parts separately. It models not only the software part but the entire system. We model for all the system requirements including the environmental properties of the system [2]. The model has water and power conser-vation goals and has a huge impact on the socio-economic structure.

The paper is organized as follows: The related works are summarized in “Related work”. The requirements are docu-mented in “System description” with the overall architecture of the system.The Event-B method is discussed in “Event-B method” and the Event-B model of the system is represented in “Event-B model of the system” followed by the model analysis in “Model analysis and discussion” and conclusion in “Conclusion”.

This article is part of the topical collection “Applications of Software Engineering and Tool Support” guest edited by Nabendu Chaki, Agostino Cortesi and Anirban Sarkar.

* Rahul Karmakar rkarmakar@cs.buruniv.ac.in

Bidyut Biman Sarkar bidyutbiman@gmail.com

1 Department of Computer Science, The University of Burdwan, Burdwan 713104, India

2 Department of Computer Application, Techno International College, Rajarhat, India

http://orcid.org/0000-0002-6607-2707http://crossmark.crossref.org/dialog/?doi=10.1007/s42979-020-00412-8&domain=pdf

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Related Work

Evaporation (ET) based irrigation controller for smart irri-gation is described in [3] for both sprinkle and drip. The ET sensors help to intelligent irrigation and conserve water for the arid season. An automatic irrigation scheduling is presented with the constant monitoring of the weather from the weather station. The authors conducted the case study in the season 2009–2010 in the tomato plant to get all the data. The proposed system ultimately saves up to 27% water. The effectiveness of the system is hugely dependent on the proper installation of the technologies used in the system.

We find a work in [4] where the authors present the importance of the sensor and network technology in pre-sent and future agriculture. A comparative study is done for different sensors used in agriculture and how the different parameters like size and cost affect agriculture. Based on the results the unsupervised smart irrigation is proposed with multi-factor considerations.

An IoT enabled wireless sensor-based framework for smart irrigation is presented in [16]. Different sensors are used for soil moisture, environment temperature and humid-ity, and CO2 checking in the agricultural area. This model is proposed for the areas with water deficiency for automatic irrigation. The proposed model is based on Fuzzy logic. The proposed system successfully controls the valve and predicts the accurate soil moisture.

In another work [18] presents an automatic decision sup-port system to manage irrigation. A setup is proposed and two machine learning models ANFIS and PLSR predictive model are combined to get the smart irrigation prediction. The results are analyzed accordingly.

In another work worth mentioning here [20], the authors proposed a service-oriented Architecture (SOA) and busi-ness process management (BPM) framework base approach for precision irrigation in this work. An effective decision support system is modeled. The services are presented in this work and implemented using Java.

A hidden Markov model (HMM) based decision model is presented in [10]. A generalized Markov decision based (MDI) irrigation system is proposed. The model checks the different humidity levels of the soil in different weather for different crops. The model is checked against the existing threshold level checking values of the water and power sav-ing. It is shown that the proposed system can save energy and water up to 40% while maintaining the humidity level. The model can be reused for different farming factors.

In [15] a automatic water pump controller is designed using Event-B for domestic use.

A limited number of works have been done in agriculture using formal modeling. A few mathematical and machine learning models are used but a lot of work can be done in the area of formal verification. Smart irrigation is a huge setup for a large agricultural area, so formal verification of the system requirement is an effective approach to system development.

System Description

System Requirements

The Fig. 1 shows the prototype of the proposed system. A large agricultural area can be covered by replicating the prototype. The system requirements (REQ) are documented below.

Fig. 1 The proposed system architecture

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REQ 1: The pump will on and off automatically with the availability of the power supply.

REQ 2: The soil moisture sensors are connected to the cor-responding transceiver in the field.

REQ 3: The average reading from the soil moisture sensors is calculated.

REQ 4: The sensor value is checked with the predefined standard value or threshold value.

REQ 5: The power-banks are connected.REQ 6: When a particular power bank is drained then

power can be borrowed from another power bank that has the maximum power.

REQ 7: Statistical Analysis of day-wise month-wise and year-wise power and water consumption.

System Architecture

All the power banks can be connected and controlled by a controller for the effective use of power. A whole agricul-tural area can be covered by replicating the model and con-nected the power banks. Figure 2 shows the whole system planning. Each area consists of a solar panel (SP) which is connected to the corresponding power bank (PB). Soil moisture sensors (S) are placed in the field and through the transceiver (TC) all the sensor readings are collected. A pump (PUMP) is placed in the area which will be operated by the controller (C) automatically. The controller checks the power availability and transceiver value to on or off the pump for a certain time. The same setup is installed in a large agricultural area by connecting the power banks. A central controller will monitor the whole system. The sensor data can be collected using the IoT enabled technology. The controller or the central server is placed in a remote location. The overall operations are highlighted bellow.

(a) The entire agricultural land can be portioned into sev-eral plots.

(b) The number of solar panels is placed in the agricultural field (plot-wise) at a certain distance.

(c) The number of power banks is placed corresponding to the solar panels.

(d) All the power banks are connected like the mesh design.(e) The number of water pumps is placed and connected to

the corresponding power banks (plot-wise).(f) The number of soil moisture sensors are placed in the

plots of the agricultural field.(g) A transceiver is placed on each of the plots for collect-

ing the sensor data of that plot.(h) The number of pump-controllers in the pumps are

connected with the corresponding power bank and the transceiver (plot-wise).

(i) All the pump-controllers are connected to the central-ized controller.

(j) A centralized controller will maintain all the opera-tions.

(k) The transceiver value and the availability of the power supply are checked by the pump-controller.

(l) The pump-controller has the predefined calculated standard values of the soil moisture.

(m) The pump-controller automatically switches on and off the pump depending on the transceiver value.

(n) The pump will borrow power from the other power bank which has the maximum power if the correspond-ing power bank is drained or not in use.

(o) The statistical analysis of the water and power con-sumption are done using day-wise, month-wise, and year-wise consumptions.

Fig. 2 Block diagram of the whole system

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Event‑B Method

Event-B has two components, context, and machine. Con-text is the static part of the model and the Machine is the dynamic part. The state changes of the model are represented by the events. The context has sets, constants, and axioms to represent the static part of the system and the dynamic part is implemented using variables, invariants, and events in the machine. The event has guarded for condition checking and different actions are performed [2]. Figure 3a, b represent the Event-B context and machine. The machine context rela-tionship is represented in Fig. 3c. All the required properties are modeled and checked mathematically by the Event-B modeling language.

The Event‑B Modeling of the System

The system requirements are modeled using Event-B nota-tions. Event-B modeling is done incrementally. The system design starts with the abstract representations of the sys-tem and stepwise refinements are done by incorporating the detailed requirements [14]. The proposed system design starts with the initial model with the minimum requirements.

Initial Model: (REQ 1 is Modeled)

The initial model is the abstract representation of the system. The model only shows the automatic operation of the water pump. If a power supply is available then the pump is on otherwise off. This model satisfies the Requirement (REQ 1) given below. The model has two events PUMP_ ON and PUMP_OFF. The context C0 shown in Table 1 consists of two sets: first, the PUMP_OPERATION which is either ON

or OFF and second, the POWER_STATUS which is either AVAILABLE or NOT AVAILABLE. The system property is defined using invariant rules. Four invariant rules are estab-lished. These invariants and events are shown in Tables 2 and 3.

Refinement 1: (REQ 2, REQ 3, and REQ 4 are Modeled)

This model refines the initial model. Event-B has an incre-mental design strategy that allows detailed design system-atically and unambiguously. The requirements 2 and 3 are modeled using Event-B in the refined model. These require-ments are modeled using the following invariant rules and events. The static part of the model is defined in context C1 and is shown in Table 4. The connection between every transceiver with some soil moisture sensor is presented in axiom 7 of C1.

Table 5 represents all the invariant properties of the model.The new PUMP_ ON and PUMP_OFF events refine the PUMP_ ON and PUMP_OFF events of the initial model. The additional requirement is added i.e. when the transceiver value received from the soil moisture sensors is greater or less than the predefined standard value and the power sup-ply is available or unavailable then switch on and switch off the pump accordingly. Table 6 represents the events of the refined model.

An independent model is designed that deals with the requirements REQ 5 and REQ 6. Requirements 5 and 6 are designed using the following two invariants and two events Get_Power and Release_Power. The power bank is con-nected so, when a particular power bank is drained then the corresponding pump can use any one of the remaining power banks. The context C2 of this model is shown in Table 7.

Fig. 3 a The context, b the machine, c the mahine context relation

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All the power banks as connected are defined in axiom 7 of C2. The Get_Power event ensures the power receiving from any one of the power banks which has available power and Release_Power event ensures the release of power from the power bank. All the invariants and events are represented in Tables 8 and 9.

Statistical Analysis: (REQ 7 is Modeled)

This is an additional feature to get the water and power con-sumption data for future decision making. The machine sta-tistics is modeled. The system can store the statistical data, which allows optimized use of water and power for differ-ent crops. The model allows us to store the day-wise power consumption and water consumption records in the server. Requirement 7 is meant for statistical analysis of power and groundwater consumptions. The model has Context C3 with two sets PUMP and POWER. Seven constants TIME, DAY, MONTH, ON, OFF, AVAILABLE, and NOTAVAILABLE are used with thirteen axioms. The values are also set for the TIME, DAY, and MONTH with the axioms. The DAY is declared with the following axioms:

axm3∶ DAY = {d|d ≥ 0 ∧ d ≤ 30}

axm4 ∶ 0 ∈ DAY

Table 1 Context of the initial model

CONTEXTC0SETSPOWER_STATUSPUMP_OPERATIONCONSTANTSONOFFAVAILABLENOTAVAILABLEAXIOMSaxm1: ON ∈ PUMP_OPERATIONaxm2: OFF ∈ PUMP_OPERATIONaxm3: AVAILABLE ∈ POWER_STATUSaxm4: NOTAVAILABLE ∈ POWER_STATUSaxm5: ON ≠ OFFaxm6: AVAILABLE ≠ NOTAVAILABLEEND

Table 2 Invariants of the initial model

Inv1: pump_operation ∈ PUMP_OPERATIONinv2 : power_status ∈ POWER_STATUSinv3: power_status = AVAILABLE⇒ pump_operation =ONinv4: power_status = NOT AVAILABLE⇒ pump_operation = OFF

Table 3 The events of the intitial model

PUMP_ON eventWHENgrd1 : powerstatus= AVAILABLETHENact1 : pump_operation ∶ = ONENDPUMP_OFF eventWHENgrd1: power_status = NOTAVAILABLETHENact1 : pump_operation ∶ = OFFEND

Table 4 Context of the refinement 1

CONTEXTC1CONSTANTSSTANDARD_VALUETRANSRECEIVERSENSORSfAXIOMSaxm1: STANDARD_VALUE ∈ Naxm2: SENSORS = t|t ∈ Naxm3: TRANSRECEIVER = s|s ∈ Naxm4: 0 ∈ TRANSRECEIVERaxm5: 0 ∈ SENSORSaxm6: STANDARD_VALUE > 0axm7: f ∈ TRANSRECEIVER ⟼(partial Surjection) SENSORSEND

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The MONTH and TIME are declared in a similar way. The PUMP will be either in ON or OFF state and the correspond-ing axioms are declared as

The POWER is either AVAILABLE or UNAVAILABLE and is declared the same way as PUMP is declared. Three events are used to get the day-wise, month-wise, and year-wise usage of power and water. The season-wise consump-tion can be calculated from the day- and month-wise data. These data can be further analyzed for different crop-wise water usage. This also enables the water conservation for the dry seasons. The power consumption can easily be calculated using the water consumption data. The power consumption and the corresponding cost can also be cal-culated by simple steps. These three events are represented in Table 10.

Model Analysis and Discussion

Table 11 summarizes the overall relationships between the Event-B components and the system requirements.

Table 12 depicts the proof obligations generated using the RODIN tool. The initial model has the machine M0 and all

axm10∶ PUMP = {ON,OFF}

axm11 ∶ ON ≠ OFF

Table 5 The invariant properties of refinement 1

inv1: TR_VALUE ∈ TRANSCEIVERinv2: sensors_value ∈ SENSORSinv6: power_status ∈ POWER_STATUSinv4: TR_VALUE > STANDARD_VALUE ⇒ pump_operation =

ONinv5: STANDARD_VALUE≤ TR_VALUE ⇒pump_operation =

OFFinv7: TR_VALUE = sensors_value ÷ x // average value of sensorsinv8: x ∈ N

Table 6 The PUMP_ ON and PUMP_OFF events refines the initial model

PUMP_ON eventREFINESPUMP_ONWHENgrd1: STANDARD_VALUE > TR_VALUE ∧ power_status=

AVAILABLETHENskipENDPUMP_OFF eventREFINESPUMP_OFFWHENgrd1: STANDARD_VALUE≤ TR_VALUE ∧ power_status=NOTA-

VAILABLETHENskipEND

Table 7 Context C2 for power bank status

CONTEXTC2SETSPower_bank_statusCONSTANTSPOWER_BANK_CHARGE DRAINEDMAXPOWERBANKfAXIOMSaxm1: POWER_BANK_CHARGE ∈ Naxm2: power_bank_status = DRAINED,MAXaxm3: DRAINED≠ MAXaxm5: POWERBANK = p|p ∈ Naxm6: f ∈ POWERBANK × POWERBANKEND

Table 8 The invariants of the model

inv1: power ∈ Ninv2: status ∈ power_bank_status

Table 9 The events

Get_Power eventWHENgrd1: status= DRAINEDgrd2: power + 1 ∈ NTHENact1: power∶ = power+ 1ENDRelease_Power eventWHENgrd1: status= MAXgrd2: power− 1 ∈ NTHENact1: power∶ = power − 1END

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the proofs are discharged automatically. Machine M1 refines machine M0 and out of 14 proofs 13 are proved automati-cally. The Power Bank machine is designed and proved inde-pendently and the statistic machine is verified by the tool with 28 automatic proof obligations. The model can also be used to get the season-wise and crop-wise water consump-tions. Some part of the Event-B model is not consistent for all the automatic proof obligations. These requirements are proved separately. Also, soil PH sensor, temperature sensor, humidity sensor can be used for a better understanding of the weather condition and operate the pump accordingly. Dif-ferent governmental websites provide weather and other rel-evant information regarding agriculture. The data from the websites and the locally collected data can be compared and merged for further improvement of the model and empirical data analysis.

The physical implication of the system would be the solar-based power availability in the rainy and foggy weather. In the worst-case, alternate power source can be the solutions. The whole system will be dependent on the availability of internet service. So internet availability is one of the impor-tant constraints and has a high implications. The software may be developed as a mobile app for farmers to operate the pump.The statistical data and the weather prediction data help to maintain the system effectively. Though the non-recurring cost to set up the system is high, but on the other hand, the recurring cost will be on the lower side.

Conclusion

System requirements are specified using Event-B notations and verified using the RODIN tool support. Finally, we can conclude that the formal modeling approach establishes the required properties of the smart irrigation system theoreti-cally and allows system development systematically and effectively. The requirements are formalized and the con-sistency between requirement and design is established. Event-B based formal modeling helps in early verification of software design. Early corrections reduce the cost and complexity of this kind of large irrigation system software.The implementation of the system can be done from the Event-B models which will indeed reduce the testing time of the software. A mobile app can also be designed for farmers for ready to use.

Table 10 Three events for day-wise month-wise and year-wise data analysis

EndOfDay eventWHENgrd1: time ∈ TIMEgrd2: day ∈ DAYgrd3: monthlyStatistics ∪ (day× ConsumedWaterToday) ⊂ DAY × Ngrd4: ConsumedWaterThisMonth ∈ Ngrd5: ConsumedWaterToday ∈ Ngrd6: day + 1 ∈ DAYgrd7: ConsumedWaterThisMonth+ ConsumedWaterToday ∈ Ngrd8: pump = ONTHENact1: time∶ = 0act2: day ∶ = day+ 1act5: ConsumedWaterToday ∶ = 0act3: monthlyStatistics∶ = monthlyStatistics ∪ (day × ConsumedWa-

terToday)act4 : ConsumedWaterThisMonth∶ = ConsumedWaterThisMonth +

ConsumedWaterTodayENDEndOfMonth eventWHENgrd1: day ∈ DAYgrd2: monthlyStatistics ⊂ DAY × Ngrd3: ConsumedWaterThisMonth ∈ Ngrd4: WATER_CONSUMPTION_Limit_PER_DAY ∈ N1grd5: yearlyStatistics ∪ (month × ConsumedWaterThisMonth) ⊂

MONTH × Ngrd6: month + 1 ∈ MONTHTHENact1: day ∶ = 0act2: month ∶ = month + 1act3: WATER_CONSUMPTION_Limit_PER_DAY ∶=WATER_

CONSUMPTION_Limit_PER_DAYact4: yearlyStatistics∶ = yearlyStatistics ∪ (month × ConsumedWa-

terThisMonth)act5: ConsumedWaterThisMonth ∶ = 0act6: monthlyStatistics ∶ ∈ ∅ENDEndOfYear eventWHENgrd1: yearlyStatistics ⊂ MONTH× NTHENact1: yearlyStatistics ∶ ∈ ∅END

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Acknowledgement We sincerely thank the Department of Computer Science and Engineering, University of Calcutta, India, for the assis-tance to pursue our research work.

Compliance with Ethical Standards

Conflict of interest The authors declare that they have no conflict of interest.

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Table 11 Lookup table of the overall Event-B model

Requirement Context Machine Event

REQ 1 C0 M0 Sees C0 PUMP_ON, PUMP_OFFREQ 2, 3, 4 C1 M1 Sees C1 Refines M0 PUMP_ON, PUMP_OFFREQ 5, 6 C2 M2 Sees C2 Get_Power, Release_PowerREQ 7 C3 M3 Sees C3 EndOfDay, EndOfMonth, EndOfYear

Table 12 Proof statistics using RODIN

Machine Total Automatic Manual Reverse Undis-charged

M0 6 6 0 0 0Events 3Invariant 4M1 refines M0 14 13 0 0 1Events 3Invariant 2Power Bank Connection 3 3 0 0 0Events 3Invariant 2Statistics 32 28 0 0 4Events 3Invariant 12

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A Prototype Modeling of Smart Irrigation System Using Event-BAbstractIntroductionRelated WorkSystem DescriptionSystem RequirementsSystem Architecture

Event-B MethodThe Event-B Modeling of the SystemInitial Model: (REQ 1 is Modeled)Refinement 1: (REQ 2, REQ 3, and REQ 4 are Modeled)Statistical Analysis: (REQ 7 is Modeled)

Model Analysis and DiscussionConclusionAcknowledgement References