Data Loss Prevention Method Based on Multiprotocol Connectivity for IoT

1. Introduction

Figure 1. The extent of the Internet of Things.

Figure 1. The extent of the Internet of Things.

Figure 2. Application of the Internet of Things.

Figure 2. Application of the Internet of Things.

2. Problem Definition

3. Related Works

4. IoT Infrastructure

Figure 3. Internet of Things Infrastructure.

Figure 3. Internet of Things Infrastructure.

4.2. Edge Computing

Figure 4. Edge Computing and IoT [26].

Figure 4. Edge Computing and IoT [26].

Faced with the need for more processing speed to take action in certain use cases that must be in real time and knowing the limitations of the Cloud in terms of latency since the information has to be transmitted to process and return, Edge Computing is an alternative that comes to face these limitations and essential needs. [19]

In fact, bringing the data processing close to the action taking devices or called actuator through the Edge Computing, reduces the response time compared to the communication and storage in the Cloud. In addition, Edge Computing contribute to more efficient data management, as data processing in the Edge will allow a much better quality of filtering and finish the priorities more efficiently of the data. Indeed, by eliminating unnecessary data and presenting only the important data and good quality for processing and backup in the Cloud which gives rise to better analytics and prediction.

4.3. Cloud Computing

Figure 5. Cloud Computing and IoT [25].

Figure 5. Cloud Computing and IoT [25].

In the Internet of Things systems, huge amounts of data are generated, these data, put aside the need for their storage for prior consultation, they must be processed and structured in order to derive relevant and useful information [17,18].

This need has made the Cloud and the Internet of Things become two very closely associated future Internet technologies and the convergence of these two technologies has brought many benefits among them citing, the third-party infrastructure that offer the Cloud for Internet of Things systems, the huge storage spaces for sharing and saving data for use, increasing performance with available resources that enable better performance for data processing and data transmission and to finish the benefit of the payment method for Cloud platforms as you only pay for what you use, that save money where users do not have to worry about buying storage, they can easily scale storage as the data generated increases and pays for the storage that they consume.

4.4. Big Data

Figure 6. Big Data and IoT.

Figure 6. Big Data and IoT.

According to studies, Cisco IBSG [20] estimated that 50 billion devices will be connected to the Internet from 2011 to 2020 (physical objects), which implies more and more devices connected and data generated, which must be collected, processed, analyzed and stored in the fastest way possible.

Indeed, it has already been proven that the advantage of the Internet of Things comes from taking advantage of these generated data otherwise its use becomes limited and its expansion would not be as important.

It is at this level that the concept of Big Data comes into play, the technologies put in place to meet the challenges of the continuous increase in the quantity of data and allow the improvement and the increase of the process of treatment and storage of this data source characterized by its volume, velocity and variety, to follow up on better results when making decisions. [21]

The processing of the data starts with the collection of data generated from connected objects. These generated data can be unstructured or semi structured and are characterized by their velocity, variety and volume. Then, this large amount of data is stored in large data files. After that, the data stored will be analyzed by specific tools. And finally, these data analyzed will give rise to results in several forms such as reports and statistics.

The advantage of Big Data is that it will allow to examine, reveal trends, find invisible patterns, find hidden correlations, and reveal new information.

4.5. Machine Learning (ML) and Artificial Intelligence (AI)

Figure 7. Machine Learning and IoT.

Figure 7. Machine Learning and IoT.

Deploying thousands of sensors at different locations results in the accumulation of huge amounts of data and information sources for organizations and businesses. However, the exploitation of this data source is not obvious until the processes are automated because the human capacity in terms of the speed of processing and analysis may be greatly exceeded by the machines. In order to take advantage of such data source Machine Learning and Artificial Intelligence comes to end this problem and make the most of this data source and information. Indeed, thanks to Machine Learning, the generated data will be analyzed and processed to build models and understand behavior in order to data to follow up on results, findings and predictions made. This is what made many companies like Siemens and others effectively apply the predictive maintenance for their products which made them reduce maintenance costs, unexpected failures and downtime for repairs in one hand. And increase production, safety and profits, on the other hand. [22,23,24,25]

5. Proposed Solution

Figure 8. Circuit overview: (a) Circuit overview; (b) Circuit component overview.

Figure 8. Circuit overview: (a) Circuit overview; (b) Circuit component overview.

5.1. Circuit Overview

Our solution is composed of three physically independent modules with each module that includes different components and offering diversified functionalities. Nevertheless, these modules will communicate effectively and exchange data through different communication protocols. Each module plays a role in our system and contributes to the efficiency and smooth functioning of the system.

The identification of the components included in each module of the system were chosen after having carried out a study and in-depth analysis of the needs of the solution. This process allowed us to identify the elements to be included in each part based on the objective of the system. The system parameters such as security, the most critical and risky elements, the specificities of each component, rate and transmission delay, the expected result and the available equipment to eventually come up with three modules as follows:

First Module (Figure 9): This module includes an Arduino Uno [27], REYAX RYLR896 LoRa module [28], SIM5320A 3G cellular module [29], along with various sensors and actuators.

Figure 9. First module.

Figure 9. First module.

Second Module (Figure 10): This configuration incorporates an Arduino Uno, REYAX RYLR896 LoRa module, ESP8266 Wi-Fi module [30], and a range of sensors and actuators.

Figure 10. Second module.

Figure 10. Second module.

Third Module (Figure 11): The third module features an ESP32 Wi-Fi and Bluetooth module [30] and a Raspberry Pi 3 Model B [31], equipped with sensors and actuators.

Figure 11. Third module.

Figure 11. Third module.

5.2. Server Configuration

Figure 12. Client / server architecture.

Figure 12. Client / server architecture.

Apart from the circuits, the configuration and the development, we propose the installation of the servers. Indeed, we configure an MQTT broker [32] for the transmission of data between our system and web clients through WebSocket.

This communication method allows the user to follow the status in real time through a responsive web interface that will offer the ability to perform control and monitoring of data.

In addition, we will set up our web server and our database server, so that data that will be transferred can be collected, processed and stored in databases for later use for reporting. Indeed, reports and statistics will be made available to the user who will allow the prediction and the state analysis.

5.3. Proposed Solution Architecture

Figure 13. Proposed Solution Architecture.

Figure 13. Proposed Solution Architecture.

As shown in the fig. 13 above, we will deploy sensors and actuators on two groups.

The first group will be controlled by an Arduino Uno linked to a LoRa transmitter / receiver and has a SIM5320A card for cellular communication. The Arduino Uno, on one side, will control its first group of objects either by performing automatic actions or having received the order from the user who will be able to control the system through the web interface or through SMS, and on other side, will pass the data to the second group of objects.

The second group, controlled by the ESP8266, includes sensors/actuators connected to the network via Wi-Fi. It receives client requests for action through the web interface, facilitated by the MQTT broker. In addition to the reception of the actions, it will carry out the transmission of the received data either of the first group of objects, passing by an Arduino Uno integrating a transmitter / receiver LoRa which will communicate for the exchange of data with the Arduino Uno of the first group-through-the LoRa protocol, or the transmission of sensor data directly attached to it.

Initially, connected to the web thanks to its Wi-Fi module that will allow him to interact with the MQTT broker to which he will receive and transmit the data, in case of problems and loss of this communication he will switch for the transmission of data to the ESP32.

Indeed, playing the role of an access point, the ESP32 will offer a local network on which the ESP8266 can connect and transmit data. Subsequently, these data transmitted to the ESP32 will in turn be transferred to the Raspberry Pi through Bluetooth.

At the level of the Raspberry Pi, upon receipt of the data, they will be processed and stored in a database configured locally. Once the connection to the network is restored, synchronization with the remote database can be made, which will keep the compliance of the data and the continued smooth operation of the system and leading to high-level results for statistics and predictions.

The configuration of web, MQTT and database servers will allow interaction with the user and database storage.

Apart from interfacing the components of our system and the configuration and implementation of servers. We proceeded to the implementation on several levels.

Indeed, firmware have been developed and integrated to automate the processes of the various components of our system. Then we did the design and created database for remote storage and reproduced a copy of this database locally near the hardware. The latter will allow the temporary storage of transmitted data in case of problems.

After that, synchronization with the remote database will be performed when needed to allow updating the data collected and processed to maintain the compliance of the stored data to give better analytics results.

Finally, we proceeded to the development and implementation of a responsive web interface allowing system status monitoring, as well as the provision of reports and different graphical forms for statistics to users.

6. Experimental Validation

6.1. Experiment for LoRa data transmission

Table 3. Experimental results for LoRa data transmission.

Table 3. Experimental results for LoRa data transmission.

Range (m)	Heat		Humidity
< 1	Sample	Value	Sample	Value
	1	23,42	1	32
	2	23,42	2	32
	3	23,42	3	32
	4	23,35	4	32
	5	23,32	5	32
<= 5	Sample	Value	Sample	Value
	1	23,56	1	32
	2	23,42	2	31
	3	23,56	3	32
	4	23,56	4	32
	5	23,56	5	32
<= 10	Sample	Value	Sample	Value
	1	23,42	1	32
	2	None	2	None
	3	22,85	3	33
	4	23,56	4	33
	5	23,56	5	32
<= 15	Sample	Value	Sample	Value
	1	None	1	None
	2	21,81	2	34
	3	22,55	3	33
	4	23,45	4	32
	5	23,45	5	32
Average	23		32

The results in the table 3 show different samples of values for temperature and humidity taken at various distances for the LoRa transmission. These values demonstrate that despite the presence of obstacles in our test environment, LoRa remains able to transmit data between the two ends with an acceptable rate of precision. The only downside is that the longer the distance, the more you need to wait for the retransmission to have a reliable value.

6.2. Experiment for Our System Response to Alerts

Table 4. Experimental results for our system response to alerts.

Table 4. Experimental results for our system response to alerts.

Range (m)	Fire		Obstacle
< 1	State	Alerts	State	Alerts
	ON	Web: Trigger in less than 10s SMS: Trigger in less than 5s	ON	Web: Trigger in less than 10s SMS: Trigger in less than 5s
	OFF	Web: Trigger in less than 10s SMS: Trigger in less than 5s	OFF	Web: Trigger in less than 10s SMS: Trigger in less than 5s
<= 5	State	Alerts	State	Alerts
	ON	Web: Trigger in less than 10s SMS: Trigger in less than 5s	ON	Web: Trigger in less than 10s SMS: Trigger in less than 5s
	OFF	Web: Trigger in less than 10s SMS: Trigger in less than 5s	OFF	Web: Trigger in less than 10s SMS: Trigger in less than 5s
<= 10	State	Alerts	State	Alerts
	ON	Web: Trigger in less than 10s SMS: Trigger in less than 5s	ON	Web: Trigger in less than 10s SMS: Trigger in less than 5s
	OFF	Web: Trigger in less than 10s SMS: Trigger in less than 5s	OFF	Web: Trigger in less than 10s SMS: Trigger in less than 5s
<= 15	State	Alerts	State	Alerts
	ON	Web: Trigger in less than 15s SMS: Trigger in less than 5s	ON	Web: Trigger in less than 15s SMS: Trigger in less than 5s
	OFF	Web: Trigger in less than 15s SMS: Trigger in less than 5s	OFF	Web: Trigger in less than 15s SMS: Trigger in less than 5s

The results in the table 4, show different reaction times of the system to the alerts, taken at different distances for the LoRa transmission. These values demonstrate that the longer the distance, the more time it takes to launch alerts on the web. In fact, given that information is transmitted from one point to another at the beginning through LoRa and then on the web, it is normal to notice this delay. By cons, given the SIM5320A card is attached directly to the Arduino managing the sensors, launching alerts by SMS is faster.

6.3. Experiment for Our System Data Flow

Table 5. Experimental results for our system data flow.

Table 5. Experimental results for our system data flow.

Time interval (s)	Data received
0-60	✓
60-120	✓
120-180	✓
When the Internet connection is lost, transfer data to the local database.
180-250	None
250-310	✓
310-370	✓
Upon restoration of the Internet connection, synchronize the data with the remote server.
370-425	None
425-485	✓
485-545	✓

As shown in Table 5, the data flow through our system in both situations with and without an Internet connection. When the Internet connection is available, the data continues to be processed and stored in the remote machine (Cloud). Then we forced the loss of the Internet connection, which led our system to a total of more than 60 seconds of data loss for the time it took to move to local storage. Once the Internet connection was restored, data transmission returned to normal with a switching latency of 55 seconds of data loss. This delay could be improved with performing more optimization techniques.

6.4. Experiment on Interaction Between User/System Through SMS

Table 6. Experimental results for interaction between user/system through SMS.

Table 6. Experimental results for interaction between user/system through SMS.

	Interaction	Send	Receive	Response time (s)
Ask for a menu	User to system	✓		1 - 5
Menu options	System to user		✓	1 - 5
Choose option	User to system	✓		1 - 5
React to choose option	System to user		✓	5 - 10
Alerts	System to user	✓		5 - 10

The table 6 shows the response time for interaction between the system and the user through text messages. The response time is around 1 and 10 seconds which is acceptable and can be improved and remains dependent on the Cellular service provider.

6.5. Summary of functionalities depending on both situations with and without an Internet connection

Table 7. Summary of functionalities.

Table 7. Summary of functionalities.

	Available connection to Internet (data stored in a remote database)	Not Available connection to Internet (data stored in a local database)
Data stored in database	✓	✓
Synchronization	✓ move data from local to remote	None
Web control/ monitor/alerts/analytic	✓	None
SMS control/monitor/alerts	✓	✓
Detect obstacle	✓	✓
Detect motion	✓	✓
Detect fire	✓	✓
Detect touch	✓	✓
Get temperature	✓	✓
Get humidity	✓	✓
Control Fan	✓	✓
Control motor	✓	None
Obstacle alerts	✓	✓
Touch alerts	✓	None
Motion alerts	✓	None
Fire alerts	✓	✓
Temperature exceeded threshold alerts	✓	✓

The table 7 shows the different features available across the system and their availability depending on the existence of Internet connection or not. Indeed, the control through the web will be lost in case the Internet connection is lost while storing data will continue to perform in the local database as the system will continue to operate and will be available for consultation once the Internet connection is re-established thanks to our local storage strategy. As well as some monitoring and control related to the first module presented previously only will remain available through the exchange of SMS as the first module contains the SIM5320A card which allows users/system interactions through SMS in contrary to the second module which contains the ESP8266 card that allows users/system interactions through the web.

6.6. Monitoring and Control by SMS

Figure 14. System control by SMS.

Figure 14. System control by SMS.

In figure 14 shows the interaction with the system through SMS to follow up as follows:

• The user sends an SMS with the keyword “MENU”.
• The system will return a message containing 4 options with the code for each option like:
  • Start: To start the fan.
  • Humidity: To find out the humidity level.
  • Temperature: To obtain the temperature.
  • Stop: To stop the fan.
• The user replies with an SMS containing the number of the option chosen
• The system will return a response either the requested value or the confirmation of the start of an action

Figure 15. System monitoring by SMS: (a) System monitoring by SMS; (b) Received alerts by SMS.

Figure 15. System monitoring by SMS: (a) System monitoring by SMS; (b) Received alerts by SMS.

As presented in Fig. 15, our system allows the control and the follow-up by text messages. Indeed, thanks to the Sim5320A, we added the direct interaction with the system either for the follow-up of the temperature or the humidity or for the control of the fan.

In addition, we have programed our microcontroller to track the temperature and allow the automatic launch of the fan in the case where the temperature exceeds a threshold defined by users, as well as the transmission of alerts by text message to the user to notify him of any problem such as flame detection or the presence of obstacles.

The definition of the threshold of the temperature not to be exceeded may be modified by the user through the web interface.

6.7. Monitoring and control through web interface

Figure 16. Reports through a web interface: (a) Total actions executed; (b) Heat and Humidity variations by month.

Figure 16. Reports through a web interface: (a) Total actions executed; (b) Heat and Humidity variations by month.

Figure 17. Reports through a web interface: (a) Total alerts received; (b) Alerts received by month.

Figure 17. Reports through a web interface: (a) Total alerts received; (b) Alerts received by month.

Figure 18. Reports through a web interface.

Figure 18. Reports through a web interface.

Through the web interface, Fig. 16, 17 and 18, we offer statistics and reports for the user such as variations in temperature or humidity over time, the number of actions runs either automatically launches or manually launch as well as alerts through the stored data based on the threshold setting.

Figure 19. System monitoring and control through a web interface: (a) Real-time monitoring; (b) Real-time heat variations monitoring.

Figure 19. System monitoring and control through a web interface: (a) Real-time monitoring; (b) Real-time heat variations monitoring.

Figure 20. System monitoring and control through a web interface: (a) Real-time humidity variations monitoring; (b) System control interface.

Figure 20. System monitoring and control through a web interface: (a) Real-time humidity variations monitoring; (b) System control interface.

Figure 21. System monitoring and control through a web interface: (a) Real-time fire alerts monitoring; (b) Real-time motion alerts monitoring.

Figure 21. System monitoring and control through a web interface: (a) Real-time fire alerts monitoring; (b) Real-time motion alerts monitoring.

Figure 22. System control through a web interface.

Figure 22. System control through a web interface.

As shown in Fig. from 19 to 22, we also allow the user to get real-time tracking of temperature and humidity changes or alert tracking through communication with the MQTT broker.

In addition, we offer remote control to initiate actions such as starting a servo motor or a fan. Plus, the possibility to update for example, alert threshold for temperature.

Figure 23. System configuration through a web interface: (a) Check the connection to the MQTT Broker or begin storing data into the database; (b) Initiate synchronization from Edge to Cloud or halt data storage into the database.

Figure 23. System configuration through a web interface: (a) Check the connection to the MQTT Broker or begin storing data into the database; (b) Initiate synchronization from Edge to Cloud or halt data storage into the database.

Finally, we give the possibility through a web page for the system configuration, the ability to choose to store the data in databases or not, test the connection with the MQTT broker and manually start synchronization to recover the data stored in the local database and the transmission to the remote database, shown in Fig 23.

7. Conclusion

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