Positioning and Localization

The terms “localization” and “positioning” are closely related. Positioning refers to the methods and techniques used to determine the position or coordinates of an object or person. This includes tool tracking or tool positioning, for example. These methods include satellite-based locating systems such as Global Positioning Systems (GPS), cellular positioning, Wi-Fi positioning and inertial navigation systems (INS). Positioning technologies and systems provide information about the relative position of an object, typically in the form of latitude, longitude, and altitude.

Localization, on the other hand, refers to a specific aspect of positioning. It involves determining the exact location of an object or entity within a limited area, often with high accuracy. In contrast to positioning, which can refer to the determination of absolute coordinates over a large area, localization is more concerned with determining the relative position within a smaller, defined space.

Localization is carried out by means of indoor positioning systems that use technologies such as Wi-Fi, Bluetooth Low Energy (BLE) and ultrasonic or magnetic field sensors to enable precise positioning indoors. RFID positioning systems enable precise object localization and tracking. IoT location devices equipped with sensors and GPS modules can also be used. LoRaWAN, real-time location systems (RTLS) and ultra-wideband technology (UWB) are also used for localization.
Geozones, in the context of localization and positioning, refer to predefined geographical areas or zones defined by specific coordinates or boundaries. Geozones are commonly used in location-based services, asset tracking, fleet management, and geofencing applications.

Vehicle localization is a sub-area of the topic of “positioning” that involves the monitoring and tracking of vehicles. A real-time vehicle localization system makes it possible to determine the exact position of a vehicle in real time and to monitor it continuously. These systems use GPS technology to capture location data and transmit it to a central platform. This allows fleet managers and companies to view the location of their vehicles at any time, optimize route planning and increase operational efficiency. Real-time vehicle tracking systems also offer increased security, as they enable rapid localization and response in the event of theft or emergencies.

A positioning or localization system can be created with GPS trackers, Bluetooth beacons, LoRaWAN-enabled devices, RFID tags and wireless RFID readers, Wi-Fi access points, UWB tags and anchors, cellular IoT modules, as well as IoT gateways and edge devices.

GPS trackers are often attached to objects, vehicles or people (via clothing or name tags) and use satellite signals to determine their location outdoors. BLE beacons are placed indoors and are often attached to walls, ceilings, or furniture to create a network of reference points for short-range localization. RFID tags are attached to equipment, products, or people. They can also be embedded directly into product packaging or integrated into name tags or wristbands. UWB tags are attached to systems, devices or people for high-precision positioning. Wi-Fi access points are installed anywhere indoors, usually on ceilings, walls or at regular intervals on poles. This ensures sufficient coverage and signal strength to establish a wireless connection and enable Wi-Fi positioning.

IoT gateways and edge devices are often installed in central locations within a facility, such as server rooms or equipment racks. They facilitate data aggregation and transmission to centralized platforms. In a LoRaWAN network, multiple gateways are distributed across a geographic area. These gateways receive signals from LoRaWAN-enabled devices, such as sensors or trackers. Multilateration technology can be used to estimate the location of devices within this network.

Positioning and localization, terms that are often used synonymously in the media, are enormous growth markets. This is mainly due to technological advancement. According to a report by the Canadian/Indian provider of market insights “Precedence Research”, the global market for location based services was valued at over 49 billion USD in 2022. In terms of components, the hardware segment had the largest market share in 2022 at 45 percent. In terms of technology, the GPS segment had 31 percent of the market share in 2022. In terms of application, 35 percent of market share was generated by the mapping and navigation segment.

The market for indoor navigation and location is also growing. According to a report by the market research and industry consulting firm “Spherical Insights”, the global market for indoor location solutions is segmented into different technologies, including BLE, Wi-Fi, UWB, RFID and more. The BLE segment has the largest market revenue share at 32.6 percent in 2022 and is predicted to maintain the largest market share until 2032. In terms of end user segments, the transportation and logistics segment held the largest share in revenue in 2022 at 38.2 percent. This shows the growing importance of indoor positioning in order to locate, monitor, and identify assets in this sector.

Are there practical examples that show how wireless IoT technologies are used for localization? Yes! In the fields of healthcare, logistics, and construction, wireless IoT technologies generate numerous benefits.

Wireless IoT technologies will continue to make localization and positioning more efficient, scalable, and precise. The localization of people and objects in real time will be made easier. LPWAN technologies like LoRaWAN and mioty, for example, are increasingly being used in smart city applications for more precise positioning of assets or people outdoors.

In the future, machine learning will play a big part in the development of localization and positioning solutions. Deep learning (DL), a subset of machine learning, can be used to predict device locations based on data patterns. In terms of IoT positioning, DL is used to analyze data from GPS trackers and sensors to improve the localization accuracy.

In order to predict the position of an IoT device, ‘neural networks’, another subset of machine learning, analyzes data patterns from captured data. Another future innovation includes indoor positioning based on fingerprints in combination with DL methods.

There are various challenges when setting up IoT-based localization systems indoors and outdoors.

There are often numerous physical barriers indoors and also in some outdoor areas. Indoors, these include walls, furniture, doors, and machines. In outdoor areas, these include buildings and mountains. These barriers can interfere with the radio signals used for localization, resulting in inaccurate location data. It is important to identify potential sources of interference and strategically place beacons or access points to minimize interference.

Another challenge, especially in outdoor areas, is exposure to harsh conditions such as extreme temperatures, humidity, and dust. These factors can affect the performance and lifespan of IoT devices. It is important that IoT devices installed outdoors have robust housings.

Data management and data security for companies is another major challenge when it comes to IoT-based localization solutions and systems. IoT devices in the field are vulnerable to security threats such as unauthorized access, data breaches, and physical tampering. Security measures such as device authentication, data encryption, and secure boot mechanisms should be implemented to protect against cyberattacks both indoors and outdoors. Tamper-proof seals can also prevent physical tampering and unauthorized access to the IoT devices.

The collection and processing of indoor location data also raises privacy concerns, particularly in workplaces, healthcare facilities, and retail stores. According to a study by Gemalto, 50 percent of consumers have no control over their personal data. 54 percent of consumers are very concerned about the lack of privacy from connected devices. 51 percent of consumers are afraid of data breaches by unauthorized parties such as hackers. Companies need to implement privacy policies and security measures to protect sensitive location data and comply with regulations such as GDPR and CCPA.

In the rapidly evolving field of IoT localization and positioning, several future trends will change the perception and use of location data. The localization of the future with IoT includes edge computing, AI and machine learning, as well as blockchain.

As the number of network nodes increases, so does the volume of data and the computing power required for IoT localization. Edge computing in the digitalized supply chain will play a crucial role in IoT localization as it processes data closer to the source, reducing latency and bandwidth usage. Edge-based localization algorithms enable devices to make real-time decisions without relying heavily on cloud resources. This makes them ideal for applications that require low latency, such as augmented reality (AR) and robotics.

AI and machine learning algorithms will further improve the accuracy and reliability of IoT localization systems. Advanced localization algorithms, such as deep learning techniques, will analyze large amounts of data from sensors to extract patterns and make accurate location predictions, even in challenging environments such as urban areas or indoor spaces.

Blockchain technology will play a crucial role in securing location data and ensuring data integrity in IoT localization systems. Distributed ledger technology (DLT) will enable tamper-proof recording of location data, increasing trust and transparency in location-based services while protecting user privacy.