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Time Analysis of Air Quality using LoRA and Cloud Computing

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How environmental air quality monitoring can be improved with IoT, cloud computing and big data technologies and with low cost of deployment?

What consequences does IOT, big data technologies and cloud computing has on monitoring air quality with low cost of deployment?


With the IoT coming into existence, it has significantly impacted the standard of living of people that loves to play around with technologies, in terms of health, lifestyle, fitness etc. To achieve better and healthy lifestyle of citizen, air quality in which they are living plays a vital role. Air quality monitoring becomes a vital area to be focused on to achieve “smart city”. As regular contact with densely polluted air quality can cause eye, nose, and throat irritation and hence exposure to this polluted air for long time can cause some serious health issue. Indoor environmental sensing and managing plays a crucial role in public health. There are various areas such as Industries, schools, buildings, offices, houses etc. which requires indoor environmental monitoring. There are number of researches carried out on monitoring air quality with the help of smart sensors around the network. Mostly ZigBee and Bluetooth technology are used, which is good but, their range is very short, and cost of deployment associated with it is also high. This paper focuses on how sensors and gateways integrated with LoRA technology can be used as an alternative for environmental sensing. The proposed architecture elements are LoRA enabled sensors modules, gateways and cloud computing, big data technologies to store and process data. The model is divided into three categories: data collection, data storing and data processing.

Keywords: LoRA, MapReduce, Big Data, Environment sensors, Data Analysis, HBase, The Things Network.


With the IoT coming into existence, it has significantly impacted the standard of living of people that loves to play around with technologies, in terms of health, lifestyle, fitness etc. The cognitive devices and sensors (IOT) plays vital role in the city to become smart city, with the help of these “smart things” we can monitor every event taking place around the city and intelligently using it for making decision which will have significant impact on resource management [4]. Wireless Sensor Network (WSN) are on the rise among researchers due to its capability of providing automating infrastructures for various controlling and monitoring applications [5]. These simple low-cost sensors network allow monitoring application centrally, simultaneously and with less human interference [5]. The present period is supposed to be the time of cities. Recent researches indicate that due to urbanization urban population surpasses the rural population and it will continue to grow as predictions are made that it will grow nearly about 70% till 2050 [4]. Due to urbanization, there is urge of growing efficiency, development and Quality of Life (QoL). To achieve a healthy lifestyle, the urge of environmental monitoring system is growing significantly with precisely smart monitoring and control system [1]. Most of the population have perception that air pollution is mainly outside like near industries, forest fires, exhaust fumes from vehicles etc. But, recent studies found that indoor conditions like schools, industries, offices, houses, buildings etc. have air pollutant level two to five times greater than outdoor level of pollution [3]. This is mainly due to materials used in building can be volatile chemicals, cleaning products, smoking cigarettes, burning of fuels from oven and stove while cooking [3]. Due to continuously exposure to such polluted air one can have effects on health in coming few of the years. There are existing solutions for monitoring air quality with the help of Zigbee and Bluetooth which are good, but they are limited in terms of range, security, power and capacity of data transmission per base stations. These solutions cost of deployment is relatively high and requires maintenance with every sensor you must deploy gateways with it and in today’s world of IoT, sensors are relatively cheaper as compared to gateways. While making selection of WSN for applications various criteria is followed such as frequency, range, data rate, power and security [5]. LoRA come into existence when various wireless network such as ZigBee, Wi-Fi and Bluetooth fail to meet the long-range performance. By analyzing pros and cons of existing technologies, we decided to use open source LoRA technology embedded sensors and gateways combined with big data and cloud computing environment for monitoring air quality. There are mainly few of gases which level is to be monitored they are majorly present in air such as Carbon Monoxide (CO), Oxygen (O) and Carbon Dioxide (CO2). Along with gases, sensors will also monitor air pressure, humidity and temperature after each interval of time.

1.1 Why LoRA technology?

  • Long Range: the sensors can be connected to gateways in highly dense urban and deep indoor conditions, along with over 15 kms distance in open rural areas. LoRA provides a consistent RF connectivity between sensing infrastructure and LoRA WAN based network with very low bandwidth.
  • Extremely low Power: It enables battery lifespan of about 10 years.
  • Large Capacity: It supports millions of messages or packets per base station.
  • Geolocation: It comes with inbuilt enabled GPS tracking system without additional power usage.
  • Security: It has end-to-end- AES -128 encryption of data privacy and protection
  • Cost: Decreases advance infrastructure cost of deployment, along with operating and end node cost.
  • Complexity: LoRA is easy to install and deploy. Due to its low complexity, development time is less.
  • Applications: Uniquely designed for battery operated sensors and for low power applications like IOT, M2M, Smart City, Sensor Network, Industrial Automations etc.


Air contamination is responsible for extensive range of health conditions. Traditional air quality monitoring solutions comes with costly base stations and their range for measuring is also limited to short distance. Due to the limitations of range and high cost of deployment of these base stations, it is not practically possible for cities to monitor air quality across widespread area. To become a typical “smart city”, there is a need for significant approach to implement a better air quality monitoring system. The solution by overcoming all these limitations can be implementing sensors and gateways integrated with LoRA technology and a smart low power WAN based on LoRAWAN protocol. With the help of LoRA technology integrated with cloud computing, the steps for measuring air quality can be improved with low cost of deployment and long range. LoRA Technology is extremely economical and easy to deploy for industrial applications and for personal use as well. [3]

The literature review in this direction shows that, most of the system uses ZigBee WSN for environment sensing. Legacy systems are limited in terms of range, accuracy and power. So, to enhance monitoring LoRA WAN can be an alternative due to its enhanced features and advantages. Our proposed architecture mainly focuses on real-time data analysis by using big data technologies, for that we need to deploy LoRA enabled hardware gas sensor board for monitoring and LoRA enabled gateways for communicating, collecting and transmitting data to cloud [1].

The paper is divided into following sections (2) State of the art. (3) Research Question (4) Proposed approach (5) Proposed Implementation (6) Proposed Evaluation (7) Conclusions

State of the Art

paper demonstrates that how critical is indoor air quality monitoring in terms of hospitality management. Traditional air monitoring system does not provide real time analysis of air quality data. This paper [10] proposes “Intelligent Environmental Monitoring System (IEMS)” integrated with wireless sensing system and information portal. This proposed system measures threshold value of Carbon Dioxide (CO2) factor for air quality index. The information portal is used to display all the analyzed data showing which area needs improvement. For this approach, ZigBee WSN was used to monitor the air quality samples placed across the different areas of hospital. For gathering the data gas sensor was used and enabled between 09:00 -18:00 hr. Air quality data such as CO2, temperature and relative humidity was collected. All the data was stored in the MySQL database and analysis were performed. The data was collected after time interval of 60 minutes. For notification and measuring, back-end server is built on Java code. But this method is used for limited amount of data and performance of MySQL database was not up to mark. To overcome all these limitations, Big Data tools can be used to store and process large amount of data within quick time. [10]

[6] paper proposed a model which has IoT, Block Chain and LoRA for environmental sensing. The proposed architecture is distributed system which is used for automatic measurements, collection, storage and monitoring environmental sense data. The architecture is splitted into two parts, one part consists of LoRA embedded sensors and gateways to surpass the limitations of high power consumption and long range. Other part makes use of distributed Ethereum Block Chain for gathering and storing the data from IoT sensors. The work presented in [6] demonstrates that implementing ELC with LoRA enabled gateways serves significant advantages paired with Block Chain and IOT devices. But installing block chain nodes with the IoT sensors is little bit on complex side and deployment cost is on higher side. With the limitations of storing, processing and power resources this model is not suited. On contrary [1] demonstrates of using ZigBee WSN combined with IoT sensors, big data technologies and cloud platform. The model is called “Intelligent Indoor Environment Monitoring System (iDEMS)” which makes use of ZigBee WSN to collect and transmit data from sensors and HBase for storing and processing the data. After storing the data in HBase, with the help of big data technologies data analysis is performed to get the desired output. All these tasks are performed on OpenStack Virtual Machine. [1] monitors temperature, humidity, CO2 and pressure of air. The model visualizes the output from MapReduce task and user can filter the output based on days, week and month. [1] the result obtained is good, but there are certain limitations of using ZigBee such as range, frequency, power consumption and expensive cost of deployment. By comparing [1] and [6], it can be concluded that range, complexity, power and cost of deployment is vital factor for considering any applications.

[2] this paper proposes a fuzzy based system to monitor indoor conditions effectively. The model makes use of following hardware Arduino UNO integrated with TMP36 senor and Raspberry Pi 2 embedded with several component along with software system developed on JAVA 8 which uses JFuzzyLogic libraries. The Architecture consists of Data collection, Data processing and Actuators. The system uses RESTful web services to interact between sensor and system. The data is collected through RASPBERRY PI 2 embedded with temperature sensor which measures the temperature and humidity. The data is collected after time interval of one hour i.e. 60 minutes. After collecting, the data is transmitted to data processing layer via REST web services. The actual values of temperature and humidity is transformed into fuzzy values with the help of fuzzy set values libraries and linguistic variables. With the obtained value after conversion, fuzzy logic helps the system to make decisions. After processing data, the decision is transferred to the actuators to perform associated actions. But the results show’s that due to the fuzzy logic conversion there would be error in critical situations. So more accurate sensors and systems are required to monitor and analyze the indoor environment. [2]

[14] paper proposes a novel approach for monitoring air quality and predicting the pattern from which quality is degraded. To make air quality monitoring better, [14] uses “SEMANTIC ETL (Extraction-Transformation-Load) framework” deployed on cloud platform to make analysis and prediction. The proposed ETL framework consists of computing nodes that executes analytics algorithm for data mining and storage nodes for storing, retrieving and processing analyzed data. [14] architecture consists of LoRA based PM2.5 monitor sensor for gathering air data, MongoDB as database which is used to store data and “Hadoop Distributed File System (HDFS)” with Apache Spark running on top of HDFS is setup as processing environment which is efficiently used for analytics and data mining. The data is collected in CSV format which makes it more reliable and easier for retrieval, store and preprocess. After performing analysis and storing data in MongoDB, it makes use of RESTFUL web service API to visualize and display the information to the user [14]. On the other hand, paper [15] proposes the similar architecture of LORA WAN to monitor Particulate Matter PM2.5 across urban cities. To monitor [15] implemented six Air Quality IoT devices across the city with four different PM sensor. But, the approach presented in [15] only monitors the data and stores it i.e. performs basic operation of collecting and storing data. Both researchers in [14] and [15] only monitors and analyze Particulate Matter (PM2.5) in air quality so it is limited. [14] does not analyses more air quality such as temperature, humidity, Oxygen and CO2 which is vital factor for human comfortability in indoor conditions.

[4] paper illustrates the use of open sensorized platform combined with hardware and software to monitor resources intelligently within the boundary of smart city. It makes use of GIScience, “which uses Geographic Information System as a tool to understand world” along with “Open Geospatial Consortium (OGC)”. GIScience can offer a great way to connect to the range of Things with an interoperable connection and can be helped to build smart cities. The proposed technique was used to measured metrological and air quality metrics. It makes use of smartUJI platforms, which are built on different layers i.e. content layer, Services layer and application layer.

Proposed Approach

Semtech LoRA enabled technology allows the system live data analysis, connectivity, alerting and integrated enhance function of geolocation (GPS Enabled chips).

Workflow for Air Quality analysis.

STEP 1: Sensors are placed inside the premises of homes and building. All the air quality data are collected by the sensors enabled and integrated with LoRA technology. The sensor will be programmed using sleep and wake protocol after each interval of time in our case it would be 300 seconds. [3]

STEP 2: Air quality data gathered by the sensors are periodically sent to a LoRA enabled gateway. The gateways act as a bridge between the sensor and internet. From Lora gateway the data is transferred to The Things Network (TTN). The Things Network is a open source community platform for building LoRA based network for IoT which generates massive amount of data [12]. With the help of TTN it is very easy to gather data and transmit it to the desired system. [3]

STEP 3: The data transferred from gateways to TTN is then forwarded to AWS Cloud server on which the virtual machine is hosted. Air quality data processing task is performed. AWS EC2 virtual machine is setup on Ubuntu OS and Hadoop environment. For configuring Hadoop environment on VM, followed the tutorials provided in academic “Programming for Data Analytics” module. [3]

STEP 4: The hosted application server on which the data processing task is performed sends alerts and notification based on environmental aspects such as threshold value of CO2 level, air pressure level, humidity and temperature. For storing the air quality data HBase as the database is used due to the advantage that it stores data across wide range of clusters and it runs on vertex of Hadoop Distributed File System. The HBase stored data is then loaded into Apache Spark to perform MapReduce task. In PIG, all the queries based on desired output is executed. After executing queries, the output result is stored and graphically displayed on Front end page using MQTT and Apache thrift. It will notify the user based on human comfortability. [3]

(5) Proposed Implementation

Our proposed Implementation is divided into three models.

[7] Model 1: Setting up and configuring LoRA embedded sensors and Gateways.

The main advantage of LoRA is Long Range, low cost and easy to implement.

Technologies used to create this model

SEMTECH Technology

PNucleo Board

XNucleo Multi Sensor Board (STN-32)

LoRA Gateway (SX1301)



56 Euros

18 Euros

200 Euros

274 Euros

*Please Note: All the products are available through SEMTECH opensource LoRA Alliance Partners.

Configuring SEMTECH LoRA Gateway with The Things Network (TTN):

  • Setting up a free account on The Things Network “https://www.thethingsnetwork.org/”
  • TTN is used for collecting the data from gateways and processing and forwarding it to the desired storage or system.
  • After setting up the account we need to register our LoRA Gateway at TTN to start service of collecting sensed data.

Software and Packages used to build:

  • MCU Embedded Software: I-Cube LRWAN
  • Software Development Tools: STSW-Link007

To setup Model 1, followed the tutorial provided by The Things Networks (TTN) available at [7]

[1][7] Model 2: In this model, virtual machine and database is setup and configured on Amazon AWS.

  • Creating and configuring AWS EC2 instances. Launching Ubuntu Server in EC2.
  • To connect to created EC2 instances, make use of PuTTY and configuring according to our requirement.
  • Creating and setting up Amazon Web Services AWS IoT. Create things IoT here.
  • Integrating The Things Network setup in Model 1 to AWS IoT.

In AWS EC2 instance, setup Hadoop and MapReduce environment for storing and processing data collected from The Things Network.

  • [1] Storage: Apache HBase database is setup in virtual machines. The main advantage of storing data in HBase is that is stores data distributively in the form of clusters and it is different from commonly used relational databases. The data in HBase is processed more efficiently. [1]
  • SQOOP: It is a tool used to transfer data between HDFS and database in Hadoop environment.

[1] Model 3: To process stored data from Apache HBase, Hadoop MapReduce environment and Apache Spark is used.

  • Setting up Hadoop MapReduce environment and integrating it to Apache PIG with the help of bash profiles in Hadoop.
  • After configuring Spark with MapReduce, all the queries and scripts are run in Spark grunt shell.
  • The output from the Spark is saved in to HBase with the help of Spark Storage.

To visualize the desired information following tools is used:

  • Cloudera Manager: Deployment and configuration of Hadoop is easy.
  • Front End technologies JavaScript, HTML, CSS and API.
  • Apache Thrift: to connect Hadoop environment to front end development.

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