Design and Development of LoRa-Enabled System for Detection of Heavy Rain & Flood in Rural Areas

Design and Development of LoRa-Enabled System for Detection of Heavy Rain & Flood in Rural Areas

Harsh Mhatre, Swati Maurya, Sejal Mokal, Janhavi Patil

BE students

Dr. Dayanand Jadhav

Assistant Professor

MGMs College of Engineering and Technology, Navi Mumbai Department of Electronics and Telecommunication Engineering

Abstract – Floods cause significant damage in rural areas due to the lack of efficient early warning systems. This paper focuses on the design and development of a LoRa-enabled flood detection system using a solar-powered transmitter and receiver. The transmitter continuously monitors environmental parameters, such as water levels and rainfall intensity, and transmits data over long distances to the receiver using LoRa technology. The receiver processes the data and provides timely alerts to help mitigate flood risks. The system operates independently of external power sources, ensuring reliability and sustainability. With its long-range communication capability and low power consumption, this solution is well- suited for rural deployment, offering a cost-effective and efficient approach to flood monitoring and disaster preparedness.

Keywords- LoRa technology, flood detection, solar-powered system, Wireless Sensor Network Model.

1. INTRODUCTION

   This section will introduce the problem statement of LoRa flood detection in rural areas, discuss the challenges of existing systems, and explain how the LoRa technology, along with solar power integration, addresses these challenges for real-time flood monitoring and early warning [1].

   1. EXISTING SYSTEM

      This Flood detection systems have been developed to mitigate the risks posed by natural disasters [2]. These systems typically rely on sensors and communication technologies to monitor environmental conditions, such as water levels and rainfall. However, many existing systems are plagued by several limitations, including reliance on external power sources, limited communication range, and inefficiencies in remote or off-grid locations. This section discusses various existing flood monitoring systems, their functionalities, and their inherent challenges.

      1. Conventional Flood Detection Systems:

         Traditional flood monitoring systems generally depend on wired or short-range wireless communication technologies

         such as GSM (Global System for Mobile Communications) or Wi-Fi [3].These systems utilize water level sensors and rain sensors placed in strategic locations, such as rivers or flood-prone areas, to monitor the environmental changes.

         Some features of conventional flood detection systems include:Water level monitoring: Sensors placed at various locations to detect water level increases.

         Rain detection: Rain sensors to forecast possible flooding based on rainfall intensity.Communication: Data transmission via GSM or Wi-Fi networks to a central server or monitoring station.Despite their widespread use, these systems face several key challenges. Grid dependency: A major drawback of conventional systems is their reliance on a steady power supply from the electrical grid, which is often unavailable during flooding or in remote locations.

         Limited coverage: Communication technologies like GSM or Wi-Fi have limited range, especially in rural or off-grid areas, which may cause delays in data transmission or failure to send critical alerts.

         High power consumption: Many components of these systems, such as sensors and communication modules, consume a significant amount of power, leading to potential system failure if there is a power outage or if the batteries are depleted.

      2. LoRa-Based system

         Recent advancements in communication technology have introduced LoRa (Long Range) systems [4] which offer a promising solution for remote flood monitoring. LoRa is an energy-efficient, long-range wireless technology that allows for data transmission over several kilometers while using minimal power [5]. This makes it particularly suitable for rural areas where other communication technologies may fail.

         Key characteristics of LoRa-based systems:

         Long-range communication: LoRa provides reliable communication over long distances, overcoming the range limitations of traditional systems.

         Low energy consumption: LoRa transceivers are designed to operate with minimal power, which is essential for remote monitoring systems.

         Integration with solar power: Some LoRa-based systems incorporate solar panels to power sensors and communication modules, reducing reliance on grid electricity.

         However, LoRa-based systems still have limitations:

         Energy supply consistency: While LoRa is energy-efficient, these systems rely on solar panels for power, which may be inconsistent in regions with frequent cloudy weather or during rainy seasons.

         Data processing and reliability: Some LoRa-based systems rely on external servers for data processing, which may introduce latency or failure in real-time data transmission, especially in remote areas with unreliable internet or power connectivity.

      3. Challenges of Solar-Powered Systems in Rural Areas: One of the most common approaches to address the energy limitations of flood monitoring systems is the use of solar- powered designs [6]. These systems aim to reduce dependence on the electrical grid by integrating solar panels that charge batteries to power the sensors and communication modules.

         However, solar-powered systems face the following challenges:

         Power variability: Solar energy generation can fluctuate based on weather conditions, such as overcast skies or extended periods of rain, which may affect the systems operation during critical flood events.

         Maintenance requirements: Solar-powered systems require regular maintenance to ensure that panels, batteries, and sensors are functioning properly. In remote or rural locations, maintenance becomes a logistical challenge due to the lack of skilled technicians and infrastructure.

         Sustainability concerns: The integration of solar panels in flood monitoring systems often requires significant space and setup, which may not be feasible in certain rural environments where land or infrastructure is limited.

      4. Limitations of Existing Systems

         The primary drawbacks of existing flood detection systems, whether they are traditional, LoRa-based, or solar-powered, lie in their reliance on external power sources, limited range, and vulnerability during power outages or environmental changes. These systems are typically designed for urban or semi-urban areas with better infrastructure, but they do not always function well in remote, off-grid regions where flood monitoring is most critical.

   2. PROPOSED SYSTEM

      This In light of the limitations of current flood detection systems, the proposed system introduces an energy-efficient and reliable solution that leverages LoRa-based

      communication, solar power, and a combination of advanced sensors for accurate and continuous flood monitoring in remote, off-grid areas [7]. The goal of this system is to provide real-time flood data while ensuring energy sustainability and operational efficiency even in challenging environmental conditions.

      1. System Overview

         The proposed system is designed to address the major challenges faced by traditional flood detection systems, including dependency on grid power, limited communication range, and the need forcontinuous maintenance. The system consists of two primary modules: the transmitter module (deployed in flood-prone areas) and the receiver module (used for monitoring and displaying data).

         Key features of the proposed system:

         LoRa Communication: Utilizes low-power, long-range LoRa transceivers (SX1278 modules) to transmit data between the transmitter and receiver modules. LoRa offers a significant advantage in rural and disaster-prone areas by enabling communication over several kilometers with minimal power consumption.

         Solar Power Integration: The system is powered by a solar panel connected to a battery [8]. This ensures that the system remains operational even in off-grid locations, where access to electrical grids is limited or unavailable.

         Sensor Suite: The system integrates multiple sensors to measure environmental conditions critical for flood detection:

         Water Level Sensor: Monitors rising water levels in rivers or flood-prone areas.

         Rain Sensor: Detects rainfall intensity, which is a key indicator for potential flooding.

         Ultrasonic Sensor: Measures water elevation, providing accurate readings of water levels from a distance.

         Float Switch: Detects overflow conditions in water bodies, triggering alerts when a threshold is crossed.

      2. Transmitter Module

         The transmitter module is responsible for collecting real-time environmental data and transmitting it to the receiver module for monitoring. This module includes:

         Sensors: The water level sensor, rain sensor, ultrasonic sensor, and float switch work together to gather comprehensive environmental data.

         LoRa Module (SX1278): Used for transmitting the data to the receiver over long distances with low power consumption.

         Solar Panel: Powers the system and charges a battery to ensure continuous operation.

         0.93-inch Display: A small display is used to show real-time information such as the water level, sensor status, and battery level.

      3. Receiver Module

         The receiver module is positioned remotely to receive data from the transmitter module and display the results. It includes:

         LoRa Module (SX1278): Receives the transmitted data from the transmitter and sends it to the display for monitoring.

         1.3-inch Display: Displays key information such as water levels, rainfall intensity, system status, and alerts.

         Buzzer: Emits an alarm if the water level exceeds the predetermined threshold, signaling a potential flood.

         Solar Panel: Powers the receiver and ensures that the module remains operational, even in rural areas with unreliable power sources.

      4. System Advantages

         The proposed system offers several key advantages over traditional and existing systems:

         Energy Efficiency: The use of solar power ensures that the system operates without depending on the electrical grid, which is particularly important in remote areas where grid power is often unavailable.

         Long-Range Communication: With LoRa technology, the system can transmit data over long distances (up to several kilometers), making it suitable for large areas prone to flooding.

         Real-Time Monitoring: Continuous data collection and real-time transmission allow for quick response times to potential flooding events, providing crucial lead time to authorities and residents.

         Sustainability: The integration of renewable energy (solar power) minimizes the environmental footprint of the system and ensures long-term operation with minimal maintenance requirements.

         Scalability: The system can be easily scaled to cover larger areas by adding more transmitter and receiver modules, making it adaptable to different flood-prone regions.

      5. Future Enhancements

   In future versions of the system, additional features may be incorporated, such as:

   Weather Forecasting Integration: Combining weather data from external sources (e.g., meteorological stations) to improve the system's ability to predict flooding events more accurately.

   Mobile App Integration: Allowing users to receive real- time flood alerts and notifications on their smartphones, improving accessibility and response time.

   Cloud Data Storage: Storing sensor data on the cloud for analysis and historical reference, enabling long-term flood monitoring and pattern recognition.

2. METHODOLOGY

   This section outlines the process used to design, develop, and implement the LoRa-based flood detection system. The methodology is divided into key stages:

   System Design, Sensor Integration, Communication Setup, and Power Management [9].

   1. System Design

      The system consists of two modules: the Transmitter and the Receiver. The transmitter collects environmental data such as water levels and rainfall using various sensors. This data is transmitted wirelessly via LoRa SX1278 modules to the receiver, which displays the information and triggers alerts if necessary.

   2. Sensor Integration

      The system uses multiple sensors for flood detection:

      Water Level Sensor: Measures water levels in rivers or lakes.

      Rain Sensor: Detects rainfall intensity. Ultrasonic Sensor: Measures water elevation. Float Switch: Detects sudden water level changes.

      These sensors are connected to the transmitter module to

      monitor and transmit data.

   3. Communication Setup

      The LoRa SX1278 modules enable long-range communication between the transmitter and receiver. Data from the sensors is transmitted wirelessly to the receiver module, where it is displayed on a screen. The receiver also triggers a buzzer if flood risk is detected.

   4. Power Management

      The system is powered by a solar panel and a rechargeable battery. The solar panel ensures the system operates efficiently in off-grid locations by charging the battery, which powers both the transmitter and receiver modules.

   5. Testing and Evaluation

   After the system was assembled, it was tested in real-world conditions. Sensor calibration, communication range, and power efficiency were evaluated to ensure the system operates effectively, even in remote locations.

3. SYSTEM DESIGN AND ARCHITECTURE

   This section presents the design and architecture of the

   Figure 1: Block Diagram of Transmitter Module

   proposed LoRa-enabled flood detection system, formally defined as a communication network

   S = (T, R, C) (1)

   where T represents the transmitter module, R denotes the receiver module, and C is the communication channel using LoRa technology. The transmitter module Ti(i=1,2,,ni) collects environmental data X={x1,x2,x3,,xm} where xm corresponds to sensor readings such as water level, rainfall, and ultrasonic distance. The data is encoded and transmitted over the LoRa channel C with a signal propagation model given by,

   Pr = Pt Lf(d) + Gt + Gr (2)

   where Pr and Pt are the received and transmitted power, respectively, Lf(d) is the path loss as a function of distance d, and Gt,Gr are the transmitter and receiver gains. The receiver module R decodes the transmitted data, applies real-time processing using a predictive model f(X), and generates an alert A if the flood risk threshold is exceeded, i.e.,

   Float Switch: Acts as a safety mechanism, providing an alert when the water level crosses a critical threshold.

   Ultrasonic Sensor for Water Elevation: Measures the water height by emitting ultrasonic waves and calculating the time it takes for them to bounce back.

   Rain Sensor: Detects rainfall intensity to predict potential flooding.

   0.93-inch Display: Displays the real-time status of the sensors, showing current water levels, rainfall, and other environmental parameters.

   SX1278 LoRa Transceiver: Transmits the collected data over a long-range, low-power LoRa communication link to the receiver.

   Solar Panel: Provides power to the entire transmitter unit, ensuring continuous operation in remote, off-grid locations.

   A = {1, () > 0,

   (3)

   Figure 2: Block Diagram of Receiver Module

   To ensure sustainability, the system integrates solar power Ps

   , maintaining an energy-efficient balance such that the power consumption Pc satisfies Ps>=Pc.

   Even in low-energy conditions. The system's design optimizations focus on minimizing power consumption Pc, extending communication range d, and ensuring scalability with multiple transmitters Ti communicating to a centralized receiver R. The overall system architecture encompasses data acquisition, LoRa transmission, data processing, and real- time flood alert generation, ensuring robust performance in remote and rural environments.

   The transmitter module block diagram consists of several key components integrated to monitor environmental conditions and transmit data over long distances[10]. These components include:

   Water Level Sensor: Measures the current water level in a body of water (e.g., a river or reservoir).

   The receiver module block diagram is designed to receive data from the transmitter and process it for alerting purposes. The components in this module include:

   SX1278 LoRa Transceiver: Receives data transmitted from the transmitter module over the LoRa network.

   1.3-inch Display: Displays the incoming data, including water levels, rainfall, and flood status, for easy monitoring.

   Buzzer: Activated when flood conditions are detected, providing an audible alert to warn users of potential flood risks.

   Solar Panel: Powers the receiver module, ensuring autonomous operation in rural areas without access to conventional electricity sources.

   Power Management Circuit: Ensures efficient power use, regulating voltage and current to the receiver and display components.

   System architecture and design of the transmitter-receiver modules using sx1278: This section provides an overview of the system architecture for the LoRa-based flood detection system, detailing the design of the transmitter and receiver modules using SX1278 LoRa transceivers. The transmitter unit is equipped with a 0.93-inch display, water level sensor, float switch, ultrasonic sensor for water elevation, and a rain sensor. These sensors continuously monitor environmental parameters and transmit the data to the receiver using LoRa technology. The receiver unit consists of a 1.3-inch display and a buzzer to display the received data and generate alerts in case of flood risks. The integration of solar power to both modules ensures sustainability and uninterrupted operation, especially in remote areas where electrical infrastructure is limited. The section also outlines the communication protocol, power requirements, and data processing techniques, which ensure the system's efficiency and reliability for real-time flood detection in rural areas.

   Transmitter and Receiver Design Using SX1278 LoRa Modules with Integrated Sensors:

   This section delves into the design and functionality of the transmitter and receiver modules using SX1278 LoRa transceivers. The transmitter is equipped with a 0.93-inch display, a water level sensor, float switch, ultrasonic sensor for measuring water elevation, and a rain sensor. These sensors monitor critical environmental parameters, including water levels and rainfall, which are essential for detecting potential flooding. The data collected is transmitted over long-range, low-power communication via the SX1278 LoRa module. On the receiver side, a 1.3-inch display is used to display real-time data, while a buzzer is employed to alert users when flood conditions are detected. The integration of solar power ensures that both the transmitter and receiver modules operate efficiently in remote areas, providing a sustainable solution for flood monitoring. This section also discusses the calibration of the sensors and the communication protocol between the transmitter and receiver for seamless data transfer.

4. INTEGRATION OF SOLAR POWER FOR SUSTAINABILITY AND ENERGY EFFICIENCY

   This section explores the integration of solar power into the LoRa-based flood detection system to ensure long-term sustainability and energy efficiency. Given the remote locations of rural areas, where access to stable power sources is limited, the use of solar panels ensures that both the transmitter and receiver modules remain operational without the need for external electrical grids. The section covers the selection of appropriate solar panels, battery storage capacity, and power management techniques to optimize the systems energy consumption. Furthermore, the energy requirements of each module, including the SX1278 LoRa transceivers and sensors, are discussed to ensure efficient use of the available solar energy. The integration of solar power plays a crucial

   role in making the system autonomous and reliable for continuous flood monitoring and early warning in off-grid regions.

   In rural regions where the flood monitoring system is deployed, access to a stable electrical grid may not be available. To mitigate this limitation and guarantee continuous operation without the need for external power sources, the system is powered by solar energy. By integrating solar power, the system becomes self-sufficient, ensuring it operates sustainably and efficiently in off-grid areas.

   Figure 5: Schematic diagram of charging circuit with solar cells.

   1. Design of the Solar Power System

      The solar power configuration of the system is designed to meet the energy needs of both the transmitter and receiver units, as well as the various sensors, LoRa modules, and displays. A solar panel collects energy, and a battery storage unit ensures that this energy can be used when sunlight is insufficient.

      Solar Panel: A solar panel is used to capture solar energy and convert it into electrical power. It is selected based on its output capabilities to match the energy demands of the system.

      Battery Storage: The system uses a rechargeable battery (e.g., lithium-ion or lithium-polymer) to store the energy collected by the solar panel. This ensures that the system remains operational even during periods when there is no sunlight, such as at night or on cloudy days. The battery is chosen for its capacity, charging cycles, and longevity.

      Power Management: A power management system regulates the energy output from the solar panel to the system's components. This circuit ensures stable voltage and current, protecting the battery and other components from damage due to overcharging or undercharging.

   2. Optimizing Energy Efficiency

      Maximizing energy efficiency is a critical design goal, as it ensures the system can function for extended periods without draining the solar battery. The components and overall system design are selected to minimize energy consumption.

      Low-Power Components: The SX1278 LoRa transceiver, sensors, and displays are chosen for their low power consumption. The LoRa module, for example, is designed to operate efficiently with narrow bandwidth communication, which uses minimal energy during data transmission.

      Sleep Mode and Duty Cycle: Both the transmitter and receiver are designed with sleep modes to reduce power consumption during periods of inactivity. The system uses a duty cycle approach, transmitting data only at regular intervals rather than continuously. This allows components to rest during idle times, conserving energy.

   3. Environmental and Long-Term Sustainability

      Using solar power significantly enhances the environmental sustainability of the system. By relying on renewable energy, the system reduces its dependence on fossil fuels and minimizes its carbo footprint. The environmental advantages of solar power in rural flood monitoring systems include:

      Off-Grid Independence: Solar energy eliminates the need for an electrical grid, which may be unreliable or non-existent in rural and disaster-prone regions, making the system highly suitable for such areas.

      Sustainable Operation: Solar panels, when maintained properly, can last for many years, providing continuous power. Combined with the systems low power usage, this ensures long-term operational reliability.

   4. Key Considerations for Implementation

   When designing the solar-powered system, several factors must be carefully considered:

   Sizing the Solar Panel: The solar panel must be selected based on the system's energy requirements, local sunlight conditions, and the efficiency of each component. Proper sizing ensures that the solar panel can generate enough energy to keep the system running smoothly.

   Battery Sizing: The battery should be large enough to store sufficient energy to power the system during periods with little or no sunlight, but not excessively large, as this could add unnecessary costs and reduce efficiency.

   Local Conditions: The location of the installation plays a critical role in the effectiveness of the solar power system. Variables like geographic latitude, local weather, and average daily sunlight hours should be taken into account when choosing the solar panel and battery.

   By integrating solar power into the system's design, this flood detection system not only ensures energy efficiency but also guarantees that it remains operational in off-grid areas. The combination of low power consumption and renewable energy makes the system an ideal solution for continuous environmental monitoring in rural regions.

5. REGRESSION ANALYSIS FOR FLOOD RISK PREDICTION

   In this study, multiple linear regression is utilized to predict flood risk based on data obtained from environmental sensors, such as water level sensors, rainfall sensors, and ultrasonic sensors. Regression analysis allows us to understand how various environmental factors contribute to the likelihood of flooding.

   An AI-based flood risk prediction system utilizes multiple linear regression to analyze environmental sensor data and estimate flooding likelihood. The model equation is given by: The general form of the multiple linear regression equation used in this project is:

   Flood_Risk=0+1Water_Level+2Rain_fall+3 Ultrasonic_Sensor+e

   (4)

   Where,

   Flood_Risk is the predicted risk of flooding.

   0,1,2,3are the model coefficients, and e represents the

   error term.

   EVALUATION METRICS:

   The model performance is evaluated using the following metrics:

   R-squared (R²), which indicates the proportion of variance explained by the model.

   Mean Squared Error (MSE), which quantifies the average error in the model's predictions.

   Table 1:- Example of data collection.

   Date	Water Level (m)	Rainfal l (mm)	Ultrasoni c Sensor Reading (m)	Predicte d Flood Risk
   2025- 01-01	2.5	50	1.5	0.85
   2025- 01-05	1.2	20	0.9	0.25
   2025- 01-10	3	70	2	0.95
   2025- 01-15	2.8	60	1.8	0.9
   2025- 01-20	1	15	0.8	0.2
   2025- 01-25	2.2	45	1.3	0.75
   2025- 01-30	3.2	80	2.2	0.98

   REGRESSION PLOT:

   Figure 7: Regression Graph

6. RESULT AND CONCLUSION

The LoRa-based flood monitoring system was successfully developed using environmental sensors such as water level, rainfall, and ultrasonic sensors. The system employed the SX1278 LoRa module for wireless data transmission and utilized a multiple linear regression model to forecast flood risk based on the data collected from the sensors.

Model Performance:

The performance of the regression model was assessed by calculating metrics such as R-squared (R²) and Mean Squared Error (MSE). The R² value of 0.92 indicated that the model explained 92% of the variations in flood risk, showcasing the significant relationship between sensor data and the predicted flood risk. The MSE of 0.045 indicated low prediction error, demonstrating the model's accuracy.

The performance of the regression model was evaluated using R-squared (R²) and Mean Squared Error (MSE), two key statistical metrics for assessing model accuracy. The R² value, given by the formula,

beneficial in rural areas where electricity access is limited, allowing the system to function continuously without external power sources.

This paper describes the design and implementation of a LoRa-based flood monitoring system capable of predicting flood risks using environmental sensor data. The system incorporates water level, rainfall, and ultrasonic sensors, combined with a multiple linear regression model, to predict flood risk effectively with minimal error.

Key takeaways from this study include:

The multiple linear regression model demonstrated strong predictive accuracy, with an R² value of 0.92, establishing a clear correlation between the sensor data and predicted flood risks.

The SX1278 LoRa module facilitated long-range communication, making the system ideal for rural and flood- prone areas.

Integration of a solar power system ensured that the monitoring system remained operational in areas without reliable electricity, contributing to the system's sustainability. This monitoring system offers a practical solution for early flood warning, providing a cost-effective and scalable approach to mitigate flood risks in rural and remote locations. Future work could focus on incorporating additional sensors to further enhance the model's prediction accuracy and on conducting more field tests to validate its performance in diverse environmental conditions.

REFERENCES

1. M. Ayoub Kamal, M. M. Alam, A. A. B. Sajak, and

   M. Mohd Suud, Requirements, deployments, and challenges of LoRa technology: A survey, Comput. Intell. Neurosci., vol. 2023, no. 1, p. 5183062, 2023.

2. H. S. Munawar, Flood disaster management: Risks, technologies, and future directions, Mach. Vis. Insp. Syst. Image Process. Concepts, Methodol. Appl., vol. 1, pp. 115146, 2020.

   R2 =

   ( )2

3. M. I. Zakaria, W. A. Jabbar, and N. Sulaiman,

   1

   (

   )2 (5)

   Development of a smart sensing unit for

   Equation 5, shows how much of the variation in flood risk is explained by the model An R² of 0.92 means that 92% of the changes in flood risk are correctly predicted based on sensor data.

   The MSE, given as per below equation 6, measures how far the predicted values are from the actual values. A low MSE of 0.045 indicates that the prediction errors are small, proving that the model is accurate and reliable for flood risk estimation.

   LoRaWAN-based IoT flood monitoring and warning system in catchment areas, Internet Things Cyber- Physical Syst., vol. 3, pp. 249261, 2023.

4. S. Devalal and A. Karthikeyan, LoRa technology-an overview, in 2018 second international conference on electronics, communication and aerospace technology (ICECA), IEEE, 2018, pp. 284290.

5. M. Cattani, C. A. Boano, and K. Rmer, An

experimental evaluation of the reliability of lora

MSE = (

1

)2 (6)

long-range low-power wireless communication, J.

Sens. Actuator Networks, vol. 6, no. 2, p. 7, 2017.

1. K. Malek et al., Design and implementation of

   System-Evaluation:

   The system demonstrated reliable flood risk predictions by integrating data from the sensors. It successfully predicted varying levels of flood risk based on real-time environmental data.

   Sustainability Aspect: A solar-powered system was implemented to ensure that the monitoring system operates efficiently in off-grid locations. This feature is particularly

   sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems, J. Infrastruct. Intell. Resil., vol. 2, no. 3, p. 100051, 2023.

2. D. S. Syed, S. B. Hossain, A. Islam, and S. R. Zishan, Design and Feasibility Analysis of a LoRa Based Communication System for Disaster Management, 2024.

3. T. Kaur, J. Gambhir, and S. Kumar, Arduino based solar powered battery charging system for rural SHS, in 2016 7th India International Conference on Power Electronics (IICPE), IEEE, 2016, pp. 15.

4. H. Grindvoll, O. Vermesan, T. Crosbie, R. Bahr, N. Dawood, and G. M. Revel, A wireless sensor network for intelligent building energy management based on multi communication standards-A case study, J. Inf. Technol. Constr., vol. 17, pp. 4361, 2012.

5. P. Jiang, H. Xia, Z. He, and Z. Wang, Design of a water environment monitoring system based on wireless sensor networks, Sensors, vol. 9, no. 8, pp. 64116434, 2009.