Galgus is boosting wireless performance in all possible WiFi scenarios thanks to its patented Cognitive Hotspot Technology (CHT). Our Research team is focusing on developing novel indoor localisation technology, which together with the benefits of CHT will make your WiFi network perform better than ever, even in the most demanding situations.
Galgus’ Hybrid Solution to Wireless Indoor Positioning
As we have seen in previous posts, none of the aforementioned solutions shown here are free from drawbacks. For that reason, Galgus’ Research Department is focusing on developing novel hybrid solutions to the indoor location problem, which fuse several approaches in a unified algorithm.
The data fusion is done by using machine learning statistical tools, where the uncertainty in position estimation is reduced by combining guesses from different methods. Thanks to this hybrid approach, we are able to estimate the location of several users even when power-driven methods (like those based on RSSI estimation) are not accurate, like in indoor environments with multiple walls (hotels, cruises, offices, …). The location method uses advanced signal processing, geometry and statistics to reduce the estimation error.
Exclusive Advantages of Galgus’ Indoor Positioning
The main advantages of Galgus’ solution versus other commercial systems for indoor positioning are:
- The location is based exclusively on existing WiFi infrastructure, without the expensive deployment of additional APs or routers, beacons, and additional WiFi or non-WiFi devices (Bluetooth, Zigbee, LiFi, or even proprietary systems).
- Our methods are prepared to take advantage of idle times of the devices, so they do not have a negative impact on the performance of the network. The QoS for the clients is not affected, in terms of goodput, number of clients connected, number of retransmissions, and other KPIs (Key Performance Indicators). The network administrators are still able to locate their clients while they are enjoying their multimedia content.
- The location process may be done without disturbing the clients, requesting them to install odd applications, or modifying their terminals. Moreover, the users do not notice that they are being located, as long as the network administrator guarantee their privacy and the confidentiality of their communications.
- The system does not rely on fingerprinting techniques, so it does not need calibration nor expensive previous work to measure the radio characteristics of the map. The clients just start using their devices and they are automatically located by the network.
- Our location algorithm is based on machine learning techniques which combine information from different communication data between STAs and APs. Thanks to this property, the indoor positioning system exploits the most reliable information for each propagation environment. For example, RSSI-based approaches are not reliable in indoor scenarios with thick walls, while ToF-based solutions are not reliable when the strongest path is not the one with LoS. Our solution combines different position estimations to maximize the reliability.
- Our method does not require that STAs support special amendments like 802.11k, 802.11r, or 802.11v. In practice, these capabilities are not supported in many of the commodity terminals, especially the cheaper ones. However, if there are high-end tablets, smartphones or laptops with these features, our algorithms take into account to gather better information about the surroundings of the client.
- The location technique is fully scalable, so it adapts automatically to the number of access points. In fact, the accuracy of the position estimation improves as the number of deployed APs and wireless routers grow.
Applications of Wireless Indoor Positioning
Of course, after the position estimation has been done, the system administrator may use this information in many different ways. For example, we can apply statistical inference to make predictions about users’ density or roaming behaviors inside the building. We can also gather information about consumer habits and time spent on certain points of the map. In addition, it is easy to monitor the most frequented and crowded areas in an office.
This data may be used to deploy new services like restaurants or vending machines within an airport or to add contextual advertisements. Finally, we may obtain an automatic heat map of the coverage of the WiFi network itself by correlating SNIR and STA locations. This shall help network architects to detect shadow zones and to improve the dissemination of APs within an industrial facility.
Conclusions
Galgus manufactures smart WiFi solutions. We develop technology that enables performance improvement of high-density 802.11 networks, reducing the noise level in the environment, and promoting a more efficient and responsible use of resources.
With its recognised CHT (Cognitive HotspotTM Technology), Galgus provides to various vertical markets a round solution that solves the main challenges that WiFi networks are facing nowadays. Among the main benefits of our patented technology, we shall highlight that CHT increases network capacity, reduces interference, prevents congestion and slow data rates, reduces EMIs (Electro-Magnetic Interference) to critical systems, and enables automatic configuration / automatic failure detection/recovery.
In this article, we have analysed the potential of indoor localisation methods in wireless communications scenarios. As we have seen, none of the aforementioned solutions shown here is free from drawbacks. For that reason, Galgus’ Research Department is focusing on developing novel hybrid solutions to the indoor location problem, which fuse several approaches in a unified algorithm.
