Difficulties Associated with Indoor Location-Based Services

The use of indoor positioning systems makes it possible to track the location of people or items inside buildings.

FREMONT, CA: Indoor positioning's primary consumer benefit is the expansion of location-aware mobile computing indoors. As mobile devices become more prevalent, developers have prioritized contextual awareness. However, most applications currently rely on GNSS and perform poorly indoors. Indoor location-based services' open challenges and disadvantages can be classified into three categories: indoor localization, indoor mapping, and indoor spatial information modeling.

Indoor Localization Difficulties

The primary disadvantage of indoor localization is the absence of widely used positioning technology. Each technology has several advantages and disadvantages. Take a look at the most prevalent technologies;

Indoor Localization Using Bluetooth Low Energy (BLE) Beacons: Such specialized hardware is a resource-intensive technology, as it must be densely installed. As a result, they are only suitable for installation in large building structures (e.g., airports), which is complicated. They are primarily battery-powered, making them an energy-constrained technology, and there is no clear business case for such investment. Their clocks are incompatible with synchronization, which complicates precise localization.

Ultra-Wideband (UWB) for Indoor Localization: UWB has the same advantages and disadvantages as Bluetooth Low Energy (BLE) beacons; for example, it lacks a clear economic driver. They are more expensive than BLE, have a slower adoption rate, and cannot coexist with other radio-based technologies.

On the other hand, magnetic field-based localization requires permanent structures within a building (e.g., walls) to reach structural steel elements, depending on the steel content and structure. This is not always the case. Additionally, the disturbances tend to occur near walls, preventing the technique from being used in large indoor spaces such as large halls. Additionally, current techniques for mapping magnetic field landmarks are insensitive to user orientation and velocity.

Localization via WiFi: While these technologies appear to be ubiquitous, they are only effective in certain circumstances. Algorithms that employ trilateration for positioning—in which the distance to the target is estimated using RF propagation time—presuppose the synchronization of the access points (APs), which is difficult to achieve due to high clock crystal oscillations or insufficient transmission bandwidth for device-to-device synchronization. The angle of arrival algorithms requires optimized antennas for localization and therefore cannot be used with existing smartphones. Localization algorithms that rely on received signal strength may be influenced by the presence of people, as the human body can absorb the microwave frequency used in WLANs. Additionally, mapping areas using their unique characteristics colloquially referred to as "fingerprints," is a resource-intensive process that frequently suffers from heterogeneity due to the differences in WiFi antennas on smartphones.

Computer Vision-Based Localization: Computer vision-based approaches have the highest power consumption, the highest processing demand, the slowest response time for localization queries, and the highest user involvement, as they require users to capture multiple photos of an area at a time—resulting in a poor user experience, while also requiring a large upfront investment, as photos of the entire area must be captured with specialized equipment and updated whenever changes occur.

Vertical localization remains an unsolved problem. All preceding approaches have been limited to planar localization, and only a few have been validated in a multi-story environment. The issue with vertical localization is that existing methods have difficulty distinguishing between floors, and phones equipped with barometric sensors cannot use them for vertical localization due to the high sensitivity of atmospheric pressure to temperature, humidity, and other environmental constraints. Additionally, reference pressure, temperature, and humidity are required for altitude estimation with such sensors, as is calibration between the two sensors.