Smart Environmental Monitoring
Environmental monitoring involves the capture of any type of data that contributes to showing how the world around us behaves, how it affects our lives, and how it can be controlled. Environmental monitoring data includes data from natural sources – for example rainfall or soil composition – and human or industrial processes, for example human waste or vehicle emissions.
Environmental monitoring is a function that falls within the scope of a smart environment, which is a specific implementation of the Internet of Things (IoT) and aims to make people’s lives more secure, comfortable, environmentally friendly, and productive. An implementation of the IoT is one that focuses on a specific area of usage in smart worlds, for example smart buildings, smart cities, smart retail, and smart industry.
In the natural world, environmental monitoring focuses on air, soil, and water. For example, in air monitoring, sensor networks and geographical information systems (GISs) monitor pollution, topographical, and meteorological data to analyze air pollutants. In water monitoring, water samples are analyzed to measure chemical, radiological, and biological data against population demographics. In soil monitoring, soil grabs are monitored for salinity, contamination, and acidity to analyze soil quality in farming and to predict the potential for erosion, flooding, and threats to environmental biodiversity.
In people’s homes and urban communities, environmental monitoring includes other types of data tracking and analysis, for example traffic volumes, population demographics, security, resource scarcity, building and home health, city infrastructure, and food security.
Environmental monitoring extends to global monitoring of the environment. For example, the monitoring of greenhouse gases (GHGs). GHGs cause climate change and contribute to extreme weather conditions, food supply disruptions, and health issues caused by high levels of smog and pollution.
The biggest challenge in smart environments is the massive amounts of data that need to be sifted, monitored, analyzed, and proactively used to create solutions for everyday challenges.
Environmental monitoring can be purely about monitoring the functionality of sensors, for example using CCTV to monitor the behavior of people. But to be of real value, environmental monitoring applications must have the ability to understand measured values and devise solutions for everyday challenges. Everyday challenges include poor crops in farming, climate change, health issues from pollution in cities, and low productivity in labor-intensive factories.
Fine dust in the atmosphere
One application of environmental monitoring is air quality. Original fine dust usually has a natural origin. Secondary fine dust is created by complex chemical reactions from gaseous substances such as emissions from cars, heating systems, agricultural processes, and industrial processes. Environmental monitoring helps scientists understand the precise make up of the dust in specific areas, for example in urban areas versus those in industrial areas, and globally. Environmental monitoring of dust can assist healthcare researchers to assess the effect of dust pollutants on people’s health. Environmental monitoring of dust helps city planners and the manufacturing industry create regulations for acceptable levels of dust emitted by vehicles and from industrial processes.
Crop conditions in agriculture
Data from “wet leaf sensors” in the agriculture industry measures the moisture on crop leaves, which is analyzed to provide information to farmers about the current condition of their crops and show patterns over several seasons, and years, about the effect of weather conditions on production.
Water quality in mining
In the mining industry, water samples are analyzed to formulate viable water management plans and to predict the impact of mining operations on the environment.
There are two main types of environment monitoring applications in smart environments.
Monitoring and control
First, there are applications to monitor and control environmental events and industrial processes that may negatively affect the environment, like natural disasters and toxic waste from industrial processes. These applications also measure the impact of human activity, like population growth, on the environment. Monitoring and control applications analyze diverse inputs, for example marine biologists studying the effect of fishing quotas on seafood stocks and the impact of plastic waste on marine life. Environmental psychologists analyze environmental data to measure its impact on people’s well being and behavior, for example their motivation to recycle.
Agents for environmental improvements
Second, there are applications to improve the way certain industries in the broader environment operate. Smart environmental monitoring provides information to create sustainable guidelines for the regulation of mission-critical systems like energy and transport grids; food security ecosystems like agriculture and marine biology; and water and waste management processes. Environmental monitoring also enables the ecologically friendly design and maintenance of connected homes, buildings, and cities.
Composition of a smart environment
Smart environments have physical elements and non-physical elements. Physical elements include IoT devices that can connect to the internet like wearables, computer chips, interfaces, sensors, actuators, robots, and computers. Non-physical elements that underpin smart environments include data, computer code, machine learning models, and software protocols, standards, and theoretical frameworks.
An IoT device is one that has the capability to connect, wirelessly or using a wired connection, to the internet. In smart worlds, IoT devices are usually, but not always, wireless. An IoT device integrates with technology that supports network connections, with functional software like APIs, and with sensors and actuators. IoT devices enable the automatic transfer of information between objects, people, and software without human intervention.
Sensors and actuators
In smart environments, data is gathered from multiple sources, for example software applications, and sensors and actuators.
Temperature, proximity, gas, smoke, and water and air quality sensors provide the input data for environmental monitoring applications. A sensor is a physical device – for example a motion detector or a light switch – that converts physical events or characteristics. An actuator is a physical device – for example a switch or a valve – that converts electrical signals into physical events, for example air conditioning.
In smart environments, sensor data is uploaded to cloud databases and data lakes where it is monitored and analyzed and may be used to develop intelligent, self-learning applications.
IoT operating systems (OSs) are embedded in most IoT devices, enabling them to connect to IoT device management applications. Examples of IoT OSs include Nucleus RTOS, TinyOS, and Amazon FreeRTOS .
Wireless sensor network (WSN)
WSN is a technology that is often used in IoT systems like smart environments. In a WSN system, a large collection of sensors, for example a mesh network, is used to gather and send data through a router to the internet in an IoT system. While an IoT network may include wired devices, a WSN may not include wired devices.
An example of a WSN is a network of wireless sensors that monitor rainfall in an area and that may or may not be connected to a smart environment network.
Closed-circuit television (CCTV)
The technologies used in smart environments are transparent even to non-technical users and CCTV plays a large role in enabling this transparency.
Specialist video surveillance software can be added to smart environment systems to provide functionality such as motion detection, face tracking, and data storage. An example of CCTV surveillance in smart environments is to monitor security in houses, offices, and factories. CCTV may provide documentary proof of criminal activities but is also used to research urban activity, for example the volume of urban and foot traffic. Understanding traffic volumes helps city planners to improve the design of urban infrastructure.
Benefits accruing to solutions built on IoT networks include capabilities for the remote operation of connected devices, like power line communication systems and CCTV security.
Smart environments automate the collection of field data in the natural environment, like air quality, rainfall, energy use, and waste management. Action taken from the analysis of environmental monitoring data can reduce GHGs and create a healthier planet.
Smart environments provide organizations with the ability to create new business models based on big data and predictive insights for protecting the environment, increasing production, enhancing the quality of life, enabling informed decisions to be made, and promoting workplace safety.
Analytics applications in smart environments monitor traffic, pollution, waste, water and energy usage, crime, and infrastructure. This provides additional capabilities to convert raw data into automated alerts, insights, and actions, for example switching off a water supply where a leak is detected.
The measurement of air quality is mandatory in most cities and industries to comply with environmental regulations.
In smart agriculture, the benefits of environmental monitoring include automated detection and eradication of pests; better crop quality and yield; improved crop, livestock, climate, and soil condition monitoring; and greenhouse, irrigation, fertilization, and livestock monitoring.
The detection of leaks in a smart environment can support water conservation.
Payment for waste removal above a specified limit can result in the reduction of solid waste and promote recycling practices.
Environmental monitoring has increased environmental awareness of the impact of human life on the planet and has resulted in a global drive to reduce the use of plastics, reduce greenhouse emissions, and promote food security.
Publicly available studies indicate that many organizations that have adopted the IoT find it difficult to generate value from the data they extract and to use it to improve business decision-making processes. The inability to manage and monitor IoT systems due to inexperience and a lack of best practice guidelines is exacerbated in companies where the IoT systems lack maturity and employees lack specialist IoT experience.
Quality control can be difficult in some areas of the IoT, for example in the manufacturing sector where products and services from different countries have different standards for quality, security, and regulatory compliance.
In some cases, and because IoT is evolving so rapidly, government regulations often cannot keep pace with the current state of technology and there are insufficient controls in place to regulate environmental standards like for vehicle emissions. In addition, not all countries agree on the international laws that attempt to regulate acceptable levels of pollution, and monitor the sustainability of scarce resources.
There is no universal standard for IoT devices, for example ZigBee was a challenge to Bluetooth’s mesh network offerings. Without a universal standard, compatibility and security issues may arise. In addition, some IoT systems use diverse protocols and technologies, creating overly complex configurations that are difficult to maintain.
IoT networks rely on a centralized client-server paradigm to authenticate, authorize, and connect different nodes. As the number of nodes increase, there is a risk of a server-side bottleneck. In the future, the solution to bottlenecks may be to adopt fog computing models, where IoT devices function as hubs for time-critical operations and cloud servers perform data manipulation and analytic tasks.
The IoT uses and generates unstructured and structured data. Legacy systems are unable to process unstructured data, so IoT systems may require additional interfaces to manage this data.
Flaws in machine learning (ML) algorithms can create false positives or false negatives, which results in additional manual work in mission-critical systems.
Understanding the environment and the data it produces without monitoring it is like using a PC without a monitor. Because of the complexity of smart world systems, organizations have the formidable task of monitoring databases, applications, performance, cloud services, hardware, and services, as well as specialized technologies from niche industries.
The solution is a platform that provides intelligent insights into all areas of a smart world network, and automated notifications and alerts. PRTG Monitor is a platform that provides plug-and-play monitoring functionality for any smart world niche and any type of technology or hardware.
The approach of PRTG to environmental monitoring combines conditional monitoring and contextual monitoring. Conditional monitoring refers to the monitoring the functionality of sensors (for example CCTV sensors), and contextual monitoring refers to the recording and understanding of the respective measured values.