Real-Time Locating

The term “real-time locating” stems from wireless navigation or radio positioning. Real-time locating or real-time localization refers to the ability to track and determine the current location of an object or person in real-time.

In the context of real-time locating, both positioning and localization are crucial for accurately tracking the movement and location of objects or individuals. Real-time locating systems (RTLS) use a combination of positioning and localization techniques to continuously monitor and update the real-time location of targets. These systems typically employ a variety of sensors, such as GPS, Radio Frequency Identification (RFID), Wi-Fi, or inertial sensors, along with sophisticated algorithms to achieve accurate and reliable real-time locating capabilities.

Real-time locating is used in many branches of the industry. In factories, the location of tools and machinery can be tracked in real-time in order to minimize downtime. By implementing RTLS, companies can achieve precise tracking of their forklift fleet, ensuring that each unit is optimally utilized and easily located within large warehouses or manufacturing facilities. This technology enhances operational efficiency, reduces downtime, and improves overall productivity by providing real-time data on the location and status of all transport units. Worker safety is also optimized through RTLS tracking.

Only authorized workers may enter hazardous zones. These applications demonstrate digitalization in industry and manufacturing, and are similar to the applications for digitalization in the construction industry. On construction sites, real-time locating systems monitor the location of equipment and personnel, preventing unauthorized use of machinery and enhancing worker safety by ensuring they remain in safe zones.

In a distribution center, the location of pallets and items are tracked in real-time from receiving to shipping, reducing lost items and optimizing inventory management. This is an example of digitalization in logistics.

Real-time locating is used in the digitalization of agriculture. It involves the tracking of tractors and other farm equipment, for example. The location of livestock can also be tracked in real-time.

As part of digitalization in healthcare, real-time locating systems are often used to track medical equipment, patients, and staff. This ensures that critical medical devices are available when required and also enhances patient and staff safety.

In a smart city (digital city), applications of real-time localization include the tracking of people and assets within smart buildings. Real-time locating systems are used to enable quick response to safety-related incidents.

Real-time data refers to information that is transmitted and provided directly to users, without delay or lag, after the data generation and collection process. There are two types of real-time data: Event data and streaming data.

Real-time event data refers to individual pieces of data that represent specific occurrences or changes of state within a system. Each event is a discrete unit of data that is generated by an action or occurrence. Events are typically timestamped to mark the exact time they occurred and are processed as soon as they occur to trigger immediate actions or responses (e.g., sending a notification when a threshold is crossed). Applications that use real-time event data include monitoring and alerts (e.g., fraud detection, system health monitoring), and event-driven architectures.

In the context of real-time locating, real-time event data is used to detect and respond to specific occurrences or changes of state. For example, when an asset enters or exits a predefined zone, an event is generated to trigger an alert or action.

Real-time streaming data involves a continuous flow of data that is constantly being generated and delivered in a sequence. This data is ingested, processed, and analyzed in real-time. The data stream can be aggregated, filtered, and analyzed continuously to extract real-time insights. Applications that use real-time streaming data include real-time analytics (e.g., real-time business intelligence, operational dashboards), data pipelines (e.g., Extract, Transport, Load (ETL) processes for big data environments), and live data feeds (e.g., news feeds, live sports scores).

In the context of real-time locating, real-time streaming data is used to continuously monitor the location of assets, people, or vehicles. This allows for the creation of a live map showing the current positions of all tracked entities.

There are four main steps real-time data goes through:

1. Data Capture

Companies capture data in real-time by using different technologies and methods. Information is captured as it is generated. These technologies and methods include sensors and IoT devices, web tracking and analytics, mobile apps, social media monitoring tools, machine learning (ML) and artificial intelligence (AI) algorithms, application programming interfaces (APIs) and data streams, distributed systems and edge computing devices, customer feedback tools, and data aggregation and visualization tools.

In a real-time locating application, the process begins with data generation from various sources like RFID tags, GPS devices, and BLE beacons attached to assets, people, or vehicles. These devices continuously transmit location data, which is ingested through data streams or APIs into the system.

2. Data Streaming

After data capture or ingestion, streaming platforms and technologies such as Apache Kafka, RabbitMQ, or Amazon Kinesis are used for the transmission of real-time location data in the form of data streams or messages.

3. Data Processing

Stream processing engines like Apache Flink, Apache Storm, and Apache Spark Streaming handle real-time data processing, while rules engines like Drools apply business rules and logic to the data. This involves filtering out unwanted data, aggregating data to form meaningful metrics, and adding additional context such as geolocation or user details.

4. Data Visualization & Action

Real-time analytics tools like Grafana or Power BI visualize the location data on dashboards, showing the real-time positions of assets, people, or vehicles. These dashboards are dynamically updated to reflect the latest data, providing users with an immediate and accurate overview of the situation. They often include interactive maps, charts, and alerts, which allow users to drill down into specific details, monitor movement patterns, and identify any issues promptly.

Here is an example scenario:
In a large warehouse, a locating system is used to track the location of forklifts equipped with RFID tags in real-time. The dashboard displays the current positions of all forklifts on an interactive map of the warehouse, highlighting areas where forklifts are operating. If a forklift enters a restricted area, an alert is immediately sent to the warehouse manager, both on the dashboard and via notifications. The dashboard can also show historical movement patterns, helping managers optimize routes and improve operational efficiency. This real-time visualization and alerting system ensure that the warehouse operates safely and efficiently, demonstrating the dynamic and responsive nature of real-time data processing in real-time locating applications.

A real-time locating system does not refer to a specific type of technology or system. It is a combination of systems that have the goal of asset localization and management. Real-time locating systems often use one or a combination of different technologies as listed below.

Global Positioning System (GPS)

GPS is a satellite-based navigation system that provides accurate positioning information outdoors, typically within a few meters. It is widely used for real-time locating in outdoor environments where line-of-sight to satellites is available.

Radio-Frequency Identification (RFID) Systems

RFID utilizes radio waves to identify and track tags attached to objects or individuals. It is commonly used for real-time locating in indoor environments and has applications in asset tracking, inventory management, and access control.

Wi-Fi Positioning Systems

Wi-Fi positioning leverages Wi-Fi access points to determine the location of devices within range. By triangulating signals from multiple access points, Wi-Fi positioning systems can provide indoor navigation and positioning with reasonable accuracy.

Bluetooth Low Energy (BLE) Systems

BLE beacons are small devices that transmit signals to Bluetooth-enabled devices nearby. By measuring signal strength and triangulating beacon locations, BLE-based systems can provide indoor navigation and positioning, as well as proximity detection in real time.

Ultra-Wideband (UWB) Systems

UWB technology uses short pulses of radio waves to measure the time it takes for signals to travel between devices. By precisely calculating these time-of-flight measurements, UWB systems can achieve highly accurate real-time locating, especially in indoor environments.

Systems with Inertial Measurement Units (IMUs)

IMUs consist of sensors such as accelerometers and gyroscopes that measure changes in acceleration and orientation. By integrating these measurements over time, IMUs can estimate the position and movement of objects in real-time, particularly in dynamic environments such as robotics or sports tracking.

Cellular Network Systems

Cellular networks can also be utilized for real-time locating using techniques such as triangulation based on signal strength and timing advance. While less accurate than GPS or other dedicated positioning technologies, cellular-based locating can provide rough estimates of location in urban or suburban areas with cellular coverage.

Omlox Systems

Omlox is an open, interoperable standard for location tracking and positioning in industrial environments. It aims to standardize and simplify the integration of various positioning technologies, such as RFID, UWB, 5G, BLE, and GPS, into a unified system. Omlox provides a common framework for communication and data exchange between different location-aware devices and systems. This enables seamless interoperability and integration across diverse industrial environments.

There are three basic components of a real-time locating system: transponders, receivers, and software for data interpretation. Depending on the locating technology used, different hardware (transponders and receivers) and software are required.

Transponders are data carriers that are either attached to assets or people, or placed in fixed locations. Examples of transponders include RFID tags, Bluetooth LE beacons, Wi-Fi tags, GPS tags, ultrasound and infrared tags, as well as other smart devices.

Receivers are connected to a network. They send and receive signals from transponders and typically have their own power source. Captured data is then forwarded to back-end databases or host computers. Examples of receivers include readers, location sensors, access points, and beacons.

There are three main types of software used for real-time locating applications. This includes firmware, application software, and middleware. Firmware is the software found within the hardware. Application software is found on the back-end server or computer. The middleware is used to connect the application software to the firmware.

Technologies like RFID are more cost-effective for real-time location solutions compared to other technologies like GPS, for example. According to a report by the market research and management consulting company “Global Market Insights”, the RFID segment of the global market for real-time locating systems had 30 percent of total market share in 2022. This was the largest market share compared to that of alternative location technologies. In terms of components, the hardware segment held the highest market share at approximately 50 percent. In terms of regions, North America held the highest market share in the real-time locating systems market at over 45 percent.

According to a report by “Grand View Research”, the global market for real-time location system specifically within the healthcare industry, the hospitals and healthcare facilities segment had the largest market share at around 88 percent. In the US, approximately 25 to 30 percent of healthcare systems use some form of asset tracking. This shows that RTLS is being increasingly deployed in hospital settings.

As already mentioned in the sections above, real-time locating systems are used in almost all industries. This includes manufacturing and Industry 4.0, logistics, construction, agriculture, healthcare, sports, and in smart cities, for example.

Below are solutions based on success stories from the real world in industry, healthcare, and logistics.

The market for real-time locating systems has been on the rise in recent years. This upward trend will continue to grow and evolve. New applications and technologies will emerge with the rise of digital twins and IoT technologies. The integration of advanced technologies such as IoT sensors and AI algorithms, for example, is enhancing the accuracy and efficiency of tracking assets and people in real-time. This technological advancement is crucial in meeting the increasingly complex demands of modern industries.

In the healthcare sector, there is a substantial increase in the adoption of real-time locating systems. These systems are pivotal in tracking medical equipment, patients, and staff. This improves operational efficiency and patient care. The ability to provide real-time location information is proving invaluable in enhancing the overall functionality and safety of healthcare facilities.

The increasing demand for safety and security in many industries like the oil & gas industry, production, and healthcare, will contribute tot he growth of the market for real-time locating systems. Precise location data becomes crucial to ensure human safety.

Additionally, there is a rising trend towards cloud-based real-time locating solutions. These solutions offer scalability, flexibility, and cost-effectiveness, making them attractive to a wide range of industries. The ability to manage and access data remotely through the cloud enhances the appeal of these solutions, especially for businesses looking to optimize their operations without significant upfront infrastructure investments.

Another crucial aspect driving the market for real-time locating systems is the focus on interoperability and standardization. Efforts are underway to ensure that real-time locating systems can seamlessly integrate with other technologies, fostering a more interconnected and efficient environment. This standardization is key to broad adoption and the development of more comprehensive solutions.

There are a few challenges associated with real-time localization.

The first challenge relates to high initial costs required for acquiring the necessary hardware, software and RTLS infrastructure. These costs may prevent smaller companies from adopting real-time localization solutions. RTLS infrastructure providers should be consulted before installation.

In addition to costs, there is also the complexity of integration. Real-time locating systems may need to be integrated into existing systems, a process that may be complex. A real-time locating system requires anchors which need to be supplied with power. This is usually provided via Power Over Ethernet (POE). The existing IT infrastructure of a facility must be able to provide power and signal access to RTLS anchors.

The next challenge involves data security and privacy concerns. One primary concern is the potential for unauthorized tracking, where individuals or entities without proper authorization gain access to sensitive location data. This can lead to misuse, such as stalking or unauthorized surveillance, compromising the privacy and safety of individuals. Additionally, an RTLS system often involves collecting and processing sensitive personal information, including the movements and locations of individuals, which raises serious concerns about how this data is protected and utilized. Ensuring compliance with stringent data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, and the California Consumer Privacy Act (CCPA), adds another layer of complexity. These regulations require robust data protection measures and impose significant penalties for non-compliance.

Factors like walls, floors, and other obstacles interfere with signals, reducing the accuracy of location data. Devices operating in the same frequency range, such as Wi-Fi networks, Bluetooth devices, and other IoT equipment, cause signal interference. This leads to degraded performance in real-time localization. This interference can result in signal degradation or loss, affecting the accuracy and reliability of location data. Additionally, environmental factors like metal structures, electronic noise, and even weather conditions can also contribute to signal interference. Such challenges are particularly pronounced in industrial settings where numerous electronic devices and machinery operate simultaneously, potentially creating a noisy and complex signal environment.

The future of real-time localization involves digital twins, edge computing, and artificial intelligence. The goal: Faster and improved decision-making and predictive capabilities.

The integration of real-time localization with digital twins is transforming various industries by creating virtual replicas of physical assets and environments. This allows for real-time monitoring, simulation, and optimization of operations, improving decision-making and predictive maintenance. Digital twins leverage precise location data to provide accurate, real-time insights into asset performance and utilization.

Edge computing enhances real-time localization by processing data closer to the source, reducing latency and bandwidth usage. This enables faster, more efficient decision-making and real-time responses to changes in the environment. Edge servers and gateways enable companies to analyze location data in real-time. This leads to higher reliability and performance in data-intensive applications.

Artificial intelligence (AI) is increasingly being integrated with real-time localization systems to enhance accuracy and predictive capabilities. AI algorithms analyze vast amounts of location data to identify patterns, optimize routes, and predict maintenance needs, improving operational efficiency. By integrating AI into real-time localization systems, logistics companies can minimize delivery times and optimize shipping routes, for example. The AI enables the prediction of prevention of shipment delays.