Optimization of Wi-Fi Access Point Placement for Indoor Localization

The popularity of location based applications is undiminished today. They require accurate location information which is a challenging issue in indoor environments. Wireless technologies can help derive indoor positioning data. Especially, the Wi-Fi technology is a promising candidate due to the existing and almost ubiquitous Wi-Fi infrastructure. The already deployed Wi-Fi devices can also serve as reference points for localization eliminating the cost of setting up a dedicated system. However, the primary purpose of these Wi-Fi systems is data communication and not providing location services. Thus their positioning accuracy might be insufficient. This accuracy can be increased by carefully placing the Wi-Fi access points to cover the given territory properly. In this paper, our contribution is a method based on simulated annealing, what we propose to find the optimal number and placement of Wi-Fi access points with regard to indoor positioning. We investigate its performance in a real environment scenario via simulations

On the Placement of Wi-Fi Access Points for Indoor Localization

Nowadays, the more and more popular location based applications require accurate position information even in indoor environments. Wireless technologies can be used to derive positioning data. Especially, the Wi-Fi technology is popular for indoor localization because the existing and almost ubiquitous worldwide Wi-Fi infrastructure can be reused lowering the expenses. However, the primary purpose of these Wi-Fi systems is different from being used for positioning services, thus the accuracy they provide might be low. This accuracy can be increased by carefully placing the Wi-Fi access points to cover the given territory appropriately. In this paper, we propose a simulated annealing based method to find, in a given area, the optimal number and placement of Wi-Fi access points to be used for indoor positioning. We investigate the performance of our method via simulations

Location-Quality-aware Policy Optimisation for Relay Selection in Mobile Networks

Relaying can improve the coverage and performance of wireless access networks. In presence of a localisation system at the mobile nodes, the use of such location estimates for relay node selection can be advantageous as such information can be collected by access points in linear effort with respect to number of mobile nodes (while the number of links grows quadratically). However, the localisation error and the chosen update rate of location information in conjunction with the mobility model affect the performance of such location-based relay schemes; these parameters also need to be taken into account in the design of optimal policies. This paper develops a Markov model that can capture the joint impact of localisation errors and inaccuracies of location information due to forwarding delays and mobility; the Markov model is used to develop algorithms to determine optimal location-based relay policies that take the aforementioned factors into account. The model is subsequently used to analyse the impact of deployment parameter choices on the performance of location-based relaying in WLAN scenarios with free-space propagation conditions and in an measurement-based indoor office scenario.Comment: Accepted for publication in ACM/Springer Wireless Network

Design of Indoor Positioning Systems Based on Location Fingerprinting Technique

Positioning systems enable location-awareness for mobile computers in ubiquitous and pervasive wireless computing. By utilizing location information, location-aware computers can render location-based services possible for mobile users. Indoor positioning systems based on location fingerprints of wireless local area networks have been suggested as a viable solution where the global positioning system does not work well. Instead of depending on accurate estimations of angle or distance in order to derive the location with geometry, the fingerprinting technique associates location-dependent characteristics such as received signal strength to a location and uses these characteristics to infer the location. The advantage of this technique is that it is simple to deploy with no specialized hardware required at the mobile station except the wireless network interface card. Any existing wireless local area network infrastructure can be reused for this kind of positioning system. While empirical results and performance studies of such positioning systems are presented in the literature, analytical models that can be used as a framework for efficiently designing the positioning systems are not available. This dissertation develops an analytical model as a design tool and recommends a design guideline for such positioning systems in order to expedite the deployment process. A system designer can use this framework to strike a balance between the accuracy, the precision, the location granularity, the number of access points, and the location spacing. A systematic study is used to analyze the location fingerprint and discover its unique properties. The location fingerprint based on the received signal strength is investigated. Both deterministic and probabilistic approaches of location fingerprint representations are considered. The main objectives of this work are to predict the performance of such systems using a suitable model and perform sensitivity analyses that are useful for selecting proper system parameters such as number of access points and minimum spacing between any two different locations

A new model for indoor WLAN positioning system

National audienceIn this paper we attempt to answer the following question : how to deploy a WLAN in order to guarantee the requested Quality of Service (QoS) while reducing the location error ? Such a problem includes two aspects : WLAN planning and positioning error reduction. To provide users an optimal wireless access to their local network, WLAN planning not only consists in selecting a location for each transmitter and setting the parameters of all sites, but also acts on allocating one of the available frequencies to each Access Point (AP) configuration [1]. And toward the indoor positioning system, once the Received Signal Strengths (RSSs) from all visible APs are measured and inputted, the location is estimated and outputted using the RSS distribution and machine learning technique [2]. We propose a new approach where WLAN planning and positioning error reduction are modeled as an optimization problem and tackled together during WLAN planning process

Performance investigation of the RBF localization algorithm

In the present paper the impact of network properties on localization accuracy of Rank Based Fingerprinting algorithm will be investigated. Rank Based Fingerprinting (RBF) will be described in detail together with Nearest Neighbour fingerprinting algorithms. RBF algorithm is a new algorithm and was designed as improvement of standard fingerprinting algorithms. Therefore exhaustive testing needs to be performed. This testing is mainly focused on optimal distribution of APs and its impact on positioning accuracy. Simulations were performed in Matlab environment in three different scenarios. In the first scenario different numbers of APs were implemented in the area to estimate the impact of APs number on the localization accuracy of the Rank Based Fingerprinting algorithm. The second scenario was introduced to evaluate the impact of APs placement in the localization area on the accuracy of the positioning using fingerprinting algorithms. The last scenario was proposed to investigate an impact of the number of heard APs and distribution of the RSS values on the accuracy of the RBF algorithm. Results achieved by the RBF algorithm in the first and second scenarios were compared to commonly used NN and WKNN algorithms

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in RSSI as function of position, distance, and number of people. RSSI decreased from 5 dBm to 1 dBm when people in LOS position, and start from 0.5 dBm to 0.3 dBm when people in NLOS position. The system accuracy decreases due to the presence of people. When the system in NLOS case (ΔRSSI = 0.5 dBm), the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case (ΔRSSI = 5 dBm)