Estimating one’s own position has always been a central problem in human history. While the need for obtaining the physical position of humans and objects has been an old request for society, it becomes apparent that in the future, the importance of localisation will ever grow. New breakthroughs in sensors, electronics, and information technology, enable ever new applications that demand real-time accurate location information at low cost. Furthermore, many of these applications are situated indoors, as human and machines tend to spend more and more time of their days within buildings.
Besides traditional applications, indoor localisation has experienced additional efforts in recent years with the appearance of ubiquitous computing. In various scenarios, objects and humans must be located or tracked, e.g. in an industrial or medical environment. Additionally, people localisation enables a large number of applications and services. Customers and employees can be observed, intruders detected, elderly people supported, and patients tracked. In public places like subway stations or airport terminals, safety concerns can be addressed by an emergency guidance system. A similar situation may occur in a hotel or in a shopping centre. A guidance system fed with position information of humans may work more efficiently than a blind one. Finally, besides distributed sensing, indoor positioning allows for context-aware information to customers and may improve network efficiency and low power demands.
This article aims to expose current indoor positioning systems based upon existing wireless infrastructure. The deployment of WiFi networks which rely on IEEE (Institute of Electrical and Electronic Engineers) 802.11 standard has experienced an overwhelming success in the last decade, powered by the explosion of high-density networks with users demanding instant, high-quality, multimedia content. It supposes a fast, reliable, and cheap way of providing Internet service to indoor scenarios. Furthermore, it is expected that the number of WiFi connected devices shall grow exponentially in the next decade, as Internet of Things (IoT) penetrates into our society.
“It is expected that the number of WiFi connected devices shall grow exponentially in the next decade”
By installing wireless positioning technology on already deployed infrastructures, the operational cost reduces to the minimum. Thus, network administrators are able to access to a vast variety of information from users within their buildings, obtaining confident analytics and reliable accuracy, while preserving the privacy. However, there are two paradigms for indoor positioning, depending on which devices of the RAN (Radio Access Network) gather and processes the information:
As the 802.11 standard evolves, it is complemented with amendments which aim to provide novel management mechanisms. We refer the reader to the documents describing 802.11v, 802.11r and 802.11k, among others. They present smart mechanisms for a simple interchange of advanced management and control frames between network nodes. However, this article advocates on presenting feasible solutions for the indoor location problem, even when the devices on the network only support standard WiFi amendments, like 802.11n or 802.11ac standards. This is usually constrained by old STAs, or even by devices which have those advanced features inhibited.
The technological challenge is to obtain the relevant information and to apply it to an estimation algorithm. There are many approaches to the problem, but all are generally based on exploiting the geometry of the network infrastructure. If we know the positions of the APs with respect to a fixed coordinate system, it is sufficient to find the distances from some of them to the desired STA. However, estimating distances in a wireless environment is not straightforward. In fact, there are several electromagnetic phenomena like dispersion, multipath, interferences, fast and slow fading, and things even worse. In addition, the indoor location problem inherently presents a moving and changing environment, where people may be wandering, and furniture and walls may not be fixed.
“If we know the positions of the APs with respect to a fixed coordinate system, it is sufficient to find the distances from some of them to the desired STA”
To overcome these problems, we present here some of the most successful methods for wireless indoor positioning. We have focused on real-world, feasible solutions, which rely on user transparency, and zero changes on the deployment of the WiFi infrastructure. The following four families of methods are arranged from the easiest to the most difficult one:
It should be noted that we have deliberately omitted techniques involving complex and expensive processes like fingerprinting, where one has to create a previous database of all the spots and their corresponding radio propagation features, by manually collecting the data. In addition, these fancy methods do not work when there are obstacles which change their positions, like people or furniture.
In our next articles, we will delve into the details of these promising techniques, as well as show how Galgus combines them to solve the WiFi-based indoor location problem.
“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.”