Smart IoT sensors are devices capable of sensing real-world conditions (temperature, vibration, motion, light, air quality, etc.) and communicating the sensed data to a system that can monitor it and perform an action in response. The “smart” aspect of these sensors is their ability to process and make decisions based on the collected data, rather than simply sending the raw data to another device for processing.
The majority of smart IoT sensors include a sensing component, a microcontroller, a power management unit, and communication capabilities via one of the following protocols (Wi-Fi, BLE, Zigbee, LoRaWAN, NB-IoT, 5G). Some IoT sensors also include “edge AI,” a small machine learning model that runs on the sensor or a gateway. With edge AI, the sensor can identify anomalies (e.g., a machine’s bearings failing) and transmit only that information, reducing bandwidth costs and improving response speed.
Better, real-time visibility and faster decision making is what smart IoT sensors bring to many industries. Manufacturing uses sensors that measure vibrations and currents to enable predictive maintenance and detect signs of wear before they cause serious problems. Sensors in buildings measure occupancy, CO2 levels, and humidity to make heating, ventilation, and air conditioning (HVAC) systems and lighting systems run more efficiently while maintaining comfortable conditions.
Wearable devices in healthcare monitor patients’ heart rates, sleep patterns, and activity to support remote patient monitoring. GPS and environmental sensors in logistics protect the integrity of cold chain shipments by tracking their location and any temperature fluctuations during shipment. Soil moisture and weather sensors in agriculture provide data to automatically irrigate crops and avoid wasting water.
Value is derived from improved visibility into operations and more timely actions. Real-time data from smart IoT sensors can help to prevent downtime, create safer environments, and enable smarter automated processes. By integrating this data with an analytics platform, it can serve as the basis for creating dashboards, generating alerts, and developing long-term optimization strategies.
There are also some drawbacks. The battery will run out of power, limiting the deployment of these devices, especially in rural areas. The quality of the data collected will matter — if the sensor is poorly calibrated or placed incorrectly then the results may not accurately reflect the situation. Security is important — these types of devices need strong authentication, encrypted communication, and regular firmware updates. Also, getting all devices to communicate with one another using the same standards and formats can sometimes be difficult.
To achieve the best performance from your smart IoT sensors, identify a specific use case you want to solve and select the required connectivity based on the devices’ range and power requirements. In addition, you will need to develop a scalable method to manage devices and establish a secure pathway from the sensor to the cloud (or the edge) and ultimately to the application.
Your motion-sensor floodlight likely turns on every time something passes by (including a cat, a car, or an extremely windy day). Although this is somewhat helpful, it does not really utilize the concept of being “smart”. This type of product uses a very basic trigger system that cannot determine what matters.
The next step would be for that light to understand the difference. Think about this… that light only alerts you when someone walks up the path and ignores every single other little disturbance that occurred during the day. That is the key to all of today’s “Smart” technology.
There is one major distinction between making a sensor intelligent vs. simply reporting a condition (i.e., motion detected!) – an intelligent IoT sensor acts as a brain, making decisions based on its analysis of data before sending it to you or taking action. Although this technology may seem futuristic at first glance, it is currently powering many smart home technologies, enabling your thermostat to learn your daily routine so it can conserve energy and modern security cameras to differentiate between a person and a pet. This move from being “Connected” to being “Intelligent” is where this technology is changing the way we live in our homes.
What’s the Difference: From a “Dumb” to an “Intelligent” Sensor?
The term “smart” is often used loosely in the technology field; however, in the context of sensors, “smart” or “dumb” can be defined by a series of upgrades, each adding to a growing list of capabilities. For example, a basic sensor may be able to sense something, but it requires connectivity (the ability to report) and intelligence (to be considered a “smart” sensor).
This three-stage process helps describe how this occurs. First, there is a local sensor that operates independently. Second, the same sensor now has an Internet connection, allowing it to send information back to you. Third, the sensor now has the capability to act independently, which is the final step in creating a smart sensor from a simple switch.
Smart thermostats provide a real-world example of three types of sensors (dumb sensor, connected sensor, and intelligent sensor) and how each type of sensor thinks differently:
- Dumb Sensor: Your old beige wall thermostat is a good example of this type of sensor. It has only two functions: it turns on when it feels cold and off when it feels warm. That’s all.
- Connected Sensor: This type of sensor is the most common smart thermostat with Wi-Fi connectivity. In addition to sensing temperature, this type of thermostat sends the temperature reading to a mobile app, where you can view it and turn the heater on/off from anywhere. However, you still have to decide whether or not to turn the heater on/off.
- Intelligent Sensor: The third type of sensor is a very advanced smart thermostat that is capable of not only sensing the temperature, but also learning your daily habits. For example, if you consistently get up and leave the house at 8 AM, the heating/cooling will automatically be adjusted to conserve energy.
Therefore, the last “jump” from simply sending data to making a decision itself is the defining characteristic of a smart device. Also, the ability to process and make decisions on the device itself, referred to as the “edge,” is the determining factor of a smart device versus a simply connected device. So how do small devices “learn” a schedule or “make” a decision? The answer is based on how these devices are designed to think.
Smart IoT Devices: Sensors That Think and Respond
Smart IoT devices are an integrated system for collecting and interpreting data through connectivity with the environment. The Smart IoT device provides immediate action based on data collected from the environment. Examples of Smart IoT Devices include: a thermostat that adjusts its temperature setting to match your daily routine, and a factory monitor that alerts you to potential issues with your factory’s machinery. Smart IoT devices can enable real-time decision-making across all industries. Most Smart IoT devices use sensors to capture signals such as temperature, motion, vibration, sound, air quality, pressure, and location.
The additional “thinking” of today’s devices occurs in the data path. Smart IoT Sensors can send measurements directly to an on-device logic or edge AI model, which can analyze the collected data for patterns, filter out unnecessary information (noise), and identify anomalies. In addition, many Smart IoT devices do not send all their raw data to the cloud. They instead send summaries of the most important information, such as “anomalous vibration detected” or “occupancy has changed”.
By doing so, the bandwidth required to transfer the summarized data is greatly reduced, and the response time is also increased. The use of summarized data is particularly useful when the devices must operate continuously (uptime), when there is an urgent need to respond quickly (safety), or when low latency is required.
The Internet of Things (IoT) allows practical automation in many everyday environments. Smart IoT devices help automate tasks such as turning lights on when you enter a room, managing your home’s energy usage, and monitoring your home’s security.
Commercial buildings benefit from smart IoT devices that help control both comfort and efficiency, using sensors to monitor occupancy, CO2 levels, humidity, and temperature to determine how best to provide comfortable working conditions while maintaining high efficiency. Industrial applications of smart IoT devices tend to prioritize reliability; smart IoT sensors monitor motor current, heat, and vibration to support predictive maintenance, helping prevent failures that cause downtime.
While all smart IoT devices rely on connectivity to perform their duties, the type of connectivity required will vary by deployment. Smart IoT devices that require a higher connection rate may use WiFi, Ethernet, or other connections that offer faster speeds. On the other hand, smart IoT devices that require a longer connection range and/or lower power consumption may utilize Bluetooth Low Energy, Zigbee, LoRaWAN, or Cellular. Strong authentication, encryption of communications, and timely firmware updates are required to maintain the integrity and security of smart IoT device fleets from unauthorized access.
In addition to security, there are other considerations to achieve lasting value from smart IoT sensors. Reliable data collection begins with proper placement and calibration of smart IoT sensors to ensure that analytics accurately reflect the physical environment. Reliable data collection and responsible design enable smart IoT devices to identify changes in their environment, understand the context of those changes, and take actions to improve safety, efficiency, and resiliency in their respective operational environments.
How Do AI-Powered Sensors Actually “Think” On Their Own?
We don’t use the term “thinking” to imply that a sensor is conscious or has emotions, but rather to describe its ability to analyze information in real time with its own microprocessor — essentially a mini-computer on board the device. Consider this to be having an expert on-site (the processor) instead of sending all data to a remote location for analysis (i.e., “HQ”), where someone else will have to sift through it. This local analysis is a fundamental component of edge computing (Edge AI), allowing the sensor to determine whether an event is significant enough to send a signal, thereby significantly reducing the amount of data sent across networks.
Take a smart home security camera, for example. The basic, non-intelligent camera continually sends a video feed to the cloud, requiring you to review hours of footage, from swaying trees to meaningful events. On the other hand, an intelligent camera performs the same type of analysis in real-time, asking itself, “Is that movement a human or just the wind?” In addition to this level of analysis, the intelligent camera reduces the amount of irrelevant data transmitted over networks by filtering out “noise” and delivering only a single, valuable piece of insight to the user (e.g., “a person was detected at your front door”). It takes a deluge of raw data and turns it into a clear and actionable alert.
In summary, using an intelligent camera system significantly improves onboard data processing, benefiting both privacy and the speed of alert receipt. As this type of individual thinking occurs across many devices, it creates the potential for new types of applications.
Sensor Data Analytics: Turning Raw Data into Intelligence
Sensor Data Analytics is the transformation of raw sensor data into actionable intelligence. Today’s systems collect enormous amounts of sensor data (e.g., temperature, vibration, pressure, motion, sound, and/or location) from smart IoT devices, but without Sensor Data Analytics, this data remains isolated as individual data points. With Sensor Data Analytics, the same data becomes actionable intelligence, such as an alert, prediction, or decision that improves performance or reduces risk.
The first step in a practical Sensor Data Analytics pipeline is collection and cleaning. Smart IoT sensors can generate noise in their output due to interference, drift, or improper placement. Sensor Data Analytics may include methods for filtering, smoothing, calibration checks, and handling missing data. The next component of the Sensor Data Analytics pipeline is adding context: timestamps, device IDs, and environmental conditions are included so that Sensor Data Analytics can compare normal behavior across machines, locations, or routes.
A key objective of Sensor Data Analytics is Anomaly Detection — identifying patterns that do not align with historical behavior. For example, Sensor Data Analytics using data collected from smart IoT sensors mounted on a motor can detect subtle vibration variations indicative of a bearing’s impending failure before actual failure. Similarly, in buildings, Sensor Data Analytics can identify anomalies in humidity or CO₂ levels that may indicate ventilation issues. Finally, Sensor Data Analytics can detect temperature excursions during shipping and provide the point(s) and time(s) at which they occurred.
Another important area of application of Sensor Data Analytics is prediction. Sensor Data Analytics learns from past trends and predicts future demand, equipment failures, and environmental changes. These predictions enable predictive maintenance, better inventory management, and improved operational safety. In addition, some Sensor Data Analytics applications run in the cloud to analyze large amounts of historical data, while others run at the “edge” (near the sensor) for rapid response and low-bandwidth communication.
For Sensor Data Analytics to make a difference, it must lead to some action. The output of Sensor Data Analytics may include sending notifications, opening a work ticket for maintenance, modifying an operating system, or updating an operator dashboard. This is where smart IoT sensors truly provide value: they produce timely, objective information that allows the people who rely on them to remain informed and their systems to operate reliably.
To accomplish this, teams will need to focus on ensuring data quality and security, and on establishing good data governance practices. Smart IoT sensors must have authentication mechanisms, communications should be encrypted, and access should be limited to ensure data reliability. When the data pipeline is both reliable and well-defined, Sensor Data Analytics transforms raw readings into actionable intelligence to support better decision-making every day.
Smart Sensing Technology: The Future of Real-Time Awareness
The way people and organizations perceive their surroundings has evolved with Smart Sensing Technology. Rather than conducting occasional inspections or waiting for periodic reports, Smart Sensing Technology continuously monitors and measures machine performance, building usage, vehicle operation, and public space activity through near-real-time signals. This enables quicker decision-making, safer operations, and smoother automation by integrating awareness directly into the environment.
The heart of Smart Sensing Technology is the networked sensor(s) that measure environmental factors (temperature, vibration, motion, air quality, pressure, noise, and/or location). Most of these networked sensors are smart IoT sensors capable of detecting change and transmitting it to software systems that may be able to analyze it. When deployed at scale, the total number of sensors creates a “live” condition map of an organization’s facility, supply chain, or city.
Smart Sensors have the potential to become intelligent through additional layers of intelligence above the sensing function. Lightweight AI models and edge computing technologies enable systems to rapidly determine whether certain sensed conditions represent a pattern. For example, smart IoT sensors installed on industrial equipment could detect unusual vibrations or other conditions indicating an impending mechanical failure before the failure occurs.
Additionally, smart buildings can utilize Smart Sensor technology to adjust lighting and ventilation based on current occupant numbers and indoor air quality. Finally, Smart Sensing Technology is used to remotely monitor patients by detecting meaningful changes in patient activity or vital signs.
The design of both connectivity and power is a significant element in the development of Smart Sensing Technology. Based on the application, Smart Sensing Technology can use either Wi-Fi or Ethernet for high-throughput applications, or Bluetooth Low Energy, ZigBee, LoRaWAN, and Cellular IoT for longer-range applications. Most smart IoT sensors are designed to be energy-efficient and often use sleep modes or event-driven reporting to extend battery life.
Data and Security (trustworthiness) must be real-time; therefore, security and data quality cannot be an afterthought. To function at its best, Smart Sensing Technology requires that all devices be properly authenticated, all data be encrypted, and all firmware be current. Proper placement and calibration are also important to ensure smart IoT sensors are installed properly so that sensor readings reflect actuality, rather than “noise”.
As Smart Sensing Technology continues to mature, it will transition from a “Monitor and Alert” model to a “Predict and Adapt” model, enabling continuous learning and response with minimal delay.
Intelligent Sensor Networks: Connected Intelligence at Scale
Intelligent Sensor Networks are distributed sensor networks that can collect information about their environment and share it to create an overall picture of events, processes, and/or states in real time across a wide area or within complex processes/operations. Unlike traditional point-to-point sensor networks, in which each sensor provides a separate report in its own “silos”, Intelligent Sensor Networks leverage the “network effect” by combining multiple sensor signals to provide greater clarity, faster detection, and more intelligent responses to changing conditions.
At the periphery of these systems are intelligent IoT sensors that monitor environmental factors, including vibration, temperature, humidity, pressure, location, and air quality. In environments with thousands of intelligent IoT sensors (such as a factory, a hospital campus, or a city district), Intelligent Sensor Networks can combine data from each sensor to distinguish between ordinary fluctuations and significant changes in the monitored state.
For example, while individual temperature and vibration measurements may be relatively unimportant, if a temperature measurement increases slightly and, at the same time, vibration increases slightly, those two measurements become important indicators of a possible problem.
The potential for Intelligent Sensor Networks is to scale up. With Intelligent Sensor Networks, the routing of data to support sensor coordination and the bandwidth required for that coordination are both minimized through gateways, mesh network topologies, and event-based reporting.
Therefore, instead of sending all raw sensor readings back to a central processor, the intelligent IoT sensor will send a message only when a threshold is exceeded, or a pattern of readings appears. As a result of this capability, Intelligent Sensor Networks can operate effectively in environments with limited power, spotty or unreliable connectivity, or high sensor concentrations.
In addition to providing better coverage, intelligent sensor networks are also resilient. If a node fails, other nodes can still provide adequate coverage and even reroute communications using a mesh design. The predictive maintenance of equipment is another application for intelligent sensor networks. By continuously monitoring an item’s condition over time, intelligent sensor networks allow industries to identify when maintenance is required before it becomes necessary.
Another example is in smart buildings, where intelligent sensor networks adjust lighting, heating, ventilation, and air conditioning (HVAC) systems based on the number of people present and indoor air quality readings. Finally, in the area of logistics and cold-chain delivery, smart IoT sensors monitor the product’s temperature and detect whether it has been subjected to any shocks during transit. However, intelligent sensor networks can provide end-to-end visibility and traceability for the entire shipping process.
Security and governance become extremely important when working with large-scale sensor networks. For instance, intelligent sensor networks should require strong device identification for each device, encrypt all data flowing through the network, and strictly control network access to prevent hackers from spoofing or tampering with data.
As artificial intelligence (AI) moves closer to the edge, intelligent sensor networks will move away from “monitor and alert” to “detect, predict, and coordinate.” This will turn large numbers of individual sensors into a large amount of connected intelligence that operates in real time.
The Power of Teamwork: How Intelligent Sensors Create a Truly Smart System
A single intelligent sensor can be impressive, but it is through collaboration that its real potential is reached. An individual smart sensor is akin to having a single expert on a job site. However, a group of smart sensors working collectively forms a team of experts that works in unison. Instead of sending the results of each individual investigation to a centralized location, they share the data with one another, creating a far more comprehensive and precise view of the events unfolding. It is this ability to collaborate that transforms a multitude of devices into a true intelligent system.
Consider that your smart thermostat determines the afternoon sun has warmed a particular room, and that the intelligent sensor on the window simultaneously realizes the window has been left open. Rather than blindly blowing cold air, the smart thermostat receives a message from the window sensor and delays its action.
This integration of various data flows to create a superior decision is referred to as sensor fusion (simply put, using both of your senses – sight and hearing- to develop an understanding of a situation). Smart Home Scenario is arguably one of the strongest IoT Edge Intelligence Use Cases, as it eliminates wasted energy and money without your input.
The same collaborative process is at work in modern vehicles, where camera and radar sensors work together to keep you safely in your lane. This same methodology is also transforming large-scale operations, such as optimizing city traffic flow and advancing industrial automation by employing smart sensors to prevent accidents on a factory floor. The same collaborative process that enables a smart device to become a smart environment.
Intelligent Monitoring Systems: From Detection to Prediction
Intelligent Monitoring Systems are moving beyond simple “alarm and detect” systems to become predictive platforms capable of identifying potential failures before they occur. Early monitoring was based on a threshold approach — if a temperature or pressure exceeded a predetermined limit, an alarm would fire. In contrast, today’s intelligent monitoring systems use continuous data collection, advanced analytics, and workflow automation to enable teams to identify trends and take proactive steps to reduce downtime and respond more quickly.
Much of the significant progress in these areas is being driven by Smart IoT Sensors. These devices collect signals from the physical world (e.g., vibration, current draw, humidity, air quality, motion, and location) and send them to a dashboard, an Edge Gateway, or a Cloud Platform. As smart IoT sensors are placed throughout an organization’s equipment and facilities, an intelligent monitoring system will begin to establish a baseline of what constitutes “normal” behavior and can identify subtle variations that may go undetected by humans.
The move toward predictive intelligence, rather than solely relying on detection, is facilitated by Pattern Recognition techniques. Rather than simply waiting for a limit to be reached, intelligent monitoring systems use tracking rate of change, correlation among multiple variables, and repeated signature patterns that frequently indicate impending failure as part of their predictive methodology.
For instance, smart IoT sensors on a motor could detect a gradual increase in vibration at specific frequencies, while related temperature and power readings would further support that trend. With sufficient historical data, an intelligent monitoring system can estimate Remaining Useful Life (RUL) and recommend optimal maintenance timing to minimize business disruption.
Where an intelligent monitoring system processes information (that is, where it is hosted) also plays a role. Data processing in the cloud enables in-depth historical analysis, report generation, and predictive model training. However, edge processing enables intelligent monitoring systems to send real-time alert notifications with reduced latency, even in environments with poor connectivity. In either deployment type, smart IoT sensor technology benefits from event-based reporting and/or filtering to reduce noise and increase the amount of signal sent to the central monitoring location.
Predictive modeling requires that Intelligent Monitoring Systems are deployed using trustworthy and secure methods and practices, which include proper installation and calibration of the smart IoT sensors, synchronized clock times across all devices, and security measures such as device authentication, encryption of communication, and authorized access control to ensure the accuracy of the predictions generated by the system; otherwise, the results could potentially be false or the system could be compromised.
As organizations deploy increasing amounts of automation in their operations, Intelligent Monitoring Systems will continue to provide a greater degree of closed-loop functionality to enable them to identify potential risks, predict the potential impacts of those risks, and take corrective action, such as adjust set points, open service requests, order parts, or schedule maintenance crews — thereby enabling Intelligent Monitoring Systems to convert passive monitoring into proactive operational intelligence.
Real-World Magic: 3 Examples of AI Sensors You Might Be Using Now
We are already surrounded by this advanced, smart “Sense, Decide, Act” technology, which will make everyday life easier and safer. These advanced examples of AI-enabled sensors are already appearing in small devices all around you. Many people have probably used an example of this kind of AI-enabled sensor or application without realizing it. These AI-enabled sensors leverage their own intelligence to better understand our surroundings.
Here are a few examples of how AI can be found in everyday products:
- Your Watch: An average watch simply tracks how many times it has been moved (i.e., how many steps). However, a smartwatch can track your movement rate, determine whether you have switched from running to cycling to swimming, and then use this information to correctly start tracking your workout. A smartwatch uses machine-learning-based sensors to track your unique movement patterns and provide real-time feedback on your performance.
- Your Doorbell: A standard camera-based doorbell will send you an alert anytime a vehicle passes by. An advanced doorbell camera sends you alerts based on the type of activity taking place near your home (e.g., package delivery vs. someone walking up to the door). When a person enters the frame of the doorbell camera, it sends you a message stating something like “person detected.” This type of advance warning lets you respond to situations before they occur, rather than after.
- Your Newer Vehicle: As you travel down the highway, a camera in your vehicle detects the painted lines on the road. The computer system inside your vehicle determines when you have drifted over the centerline of the road and then acts accordingly, either by applying gentle pressure to the steering wheel to get you back on course or by vibrating your seat to remind you to adjust your position.
The AI’s decision-making ability provides users with greater assistance than simply detecting a condition (e.g., “you have drifted off course”). On a broader level, this type of technology is now being utilized to address issues before they become problems.
IoT Sensor Applications: Real-World Use Cases Across Industries
IoT sensor applications have a wide variety of uses because connected sensing enables the resolution of many real-world challenges (visibility, safety, efficiency, etc.) and allows decision-making based on real-time information rather than assumptions. IoT sensor applications accomplish this by installing smart IoT sensors at the point of work and then transmitting their data to an analytics tool. IoT sensor applications use smart sensors to measure temperature, vibration, motion, humidity, pressure, air quality, noise, and location.
IoT sensor applications in manufacturing provide predictive maintenance and quality control through the placement of smart IoT sensors on motors, pumps, and conveyor systems that track vibration and current usage to determine if there is excessive wear; also, by using smart IoT sensors to track temperature and torque on production lines, IoT sensor applications help manufacturers reduce defects and rework.
IoT sensor applications in the energy and utility sectors help monitor grid equipment, pipelines, and substation equipment. Through distributed sensing, operators can identify overheating, pressure drops, or irregular loading patterns and take action before an outage or leak develops.
Smart building IoT sensor applications help optimize both comfort and costs. Smart building IoT sensor applications help provide better ventilation through occupancy and CO2 tracking, and improve HVAC control through humidity and temperature monitoring. Smart building IoT sensor applications enable automated adjustments that reduce energy consumption while maintaining acceptable indoor quality.
Healthcare IoT sensor applications include remote patient monitoring, fall detection, and asset tracking. Healthcare IoT sensor applications that use wearables and/or room sensors will flag changes in movement and/or vital sign patterns, enabling quicker responses and continuous care.
IoT sensor applications offer many opportunities in logistics and supply chain management as well. Temperature, shock, and shipment location may be monitored using smart sensors. Cold chain items such as vaccines and food may be protected using this technology. Real-time monitoring of vehicle health, driver behavior, and delivery conditions is achieved through IoT Sensor Applications in fleet management.
In agriculture, IoT Sensor Applications help manage resources better. Smart sensors that measure soil moisture, weather, and crop health enable more precise irrigation and fertilization, resulting in less waste and higher yields. IoT Sensor Applications used in environmental monitoring and smart city applications monitor and report on air quality, noise, traffic patterns, and water levels, which support public safety and planning.
The reliability and usability of IoT sensor data across these areas will determine the success of IoT Sensor Applications. Therefore, it is important to select smart sensors based on their suitability for the environment in which they will be deployed; ensure a secure method of connecting them (and the devices they are connected to); and employ analytical tools that provide alerts, forecasts, and workflow triggers from sensor data. The successful use of IoT Sensor Applications converts everyday activity measurements into quantifiable improvements in both performance and resilience.
Beyond the Home: Fixing Problems Before They Happen with Smart Sensors
In addition to the applications in our homes and automobiles, the logic behind “Sense, Decide, Act” is also used at a massive scale to prevent disasters and increase the efficiency of our world through a process known as predictive maintenance. Predictive maintenance is similar to when you hear a strange, new noise coming from your automobile’s engine and take it to the mechanic before you break down along the highway. The intelligent sensors can act as “super-listeners,” detecting problems across all types of equipment, from manufacturing machinery to city infrastructure.
For example, imagine a large wind turbine in a remote field. A device placed within the turbine uses smart sensor technology for predictive maintenance and can detect small, subtle vibrations (not visible to the human eye). The device does not send a flood of data. Rather, the device’s built-in intelligence identifies this vibration pattern as the first signs of a failing component, and then sends a single message: “Bearing #7 requires an inspection.” This enables a service crew to plan the repair for a less busy day, thereby preventing a disastrous failure and expensive downtime.
Predictive Maintenance is also being used to improve industrial automation through the application of smart sensors in unique ways. For example, in agriculture, intelligent soil sensors can measure moisture and nutrient levels, providing irrigation systems with precise information on which areas require additional water and the exact amount needed, thereby conserving a valuable resource and maximizing crop yields.
Examples of applying intelligent sensors in cities are numerous, including monitoring traffic flow and adjusting traffic signal timing to reduce congestion in real time. Whether the setting is a large turbine in a remote area or the kitchen in your home, the basic premise is the same: using intelligent sensing to enable smarter, faster decision-making.
How to Choose a Truly “Smart” Device for Your Home
With so many smart home devices available on the market, they’re not all alike in terms of quality. Some are just smart devices with app-based remote control, while others are designed as intelligent problem-solving solutions. When you shop for a smart sensor or device, the first step in determining whether a particular product will provide value is to learn to identify what truly qualifies as “smart.”
There’s no reason for you to become a technology guru in order to make sense of all this hype surrounding “intelligent” products. All you have to do is answer these three easy-to-ask questions before you spend your money:
- Will it get smarter as I use it (i.e., does it really learn), or will it simply rely on a set of pre-programmed rules? The former would be an example of intelligence; the latter, merely connectivity.
- Does it actually solve a real problem, or is it simply introducing another “feature”? For example, an intelligent video doorbell that can differentiate between people, packages, and cars eliminates the false alert problem. On the other hand, a Wi-Fi-enabled egg tray that lets me know when I’m low on eggs is likely a gimmick.
- Can the company clearly explain both my personal data and how it will protect it? A trustworthy company should be open and transparent about the types of data collected by its product(s) and the reasons for collecting them. Be very leery if the privacy policy is difficult to find or difficult to read/understand.
These types of questions will help you see through the marketing to the true added value of having convenient, peaceful smart sensors in your home. What truly makes a sensor smart is not just how it uses technology to function, but how much it can improve your day-to-day routine.
Your World is Getting Smarter: What This Means for You
You probably used to think that all the “magic” in your smart home was magical. Now, I am sure you understand that intelligent IoT sensors can actually make real-time decisions. They do not simply detect motion; they identify faces, packages, and pets, converting raw data into one actionable piece of information. It is this progression from observing to understanding that has made these devices so powerful.
The next time your smart watch buzzes or your thermostat changes, note the three pieces: What is it sensing? What localized decision is it making? And what resulting action is it taking? These three simple pieces (Sense, Decide, Act) are the tools for demystifying any new smart device you come across.
This is the direction that the future of smart technology will take – Not more notifications, but quiet competence. Edge AI’s job is to bring this intelligence directly into the world around us, creating truly useful devices rather than noisier ones. At this point, you should be able to witness this transition as it occurs and see how simple devices have evolved into sophisticated systems that clearly help our world.
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