Scalability Optimization of Seamless Positioning Service

Abstract

Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.

Impact of optimization algorithms on hybrid indoor positioning based on GSM and Wi‐Fi signals

“…However, it can also be seen that the localization error is stable when threshold of RSS is set between −110 dBm and −95 dBm for GSM signals along with RSS threshold between −100 dBm and −90 dBm for Wi‐Fi signals. These results are with correlation of the thresholds for individual technologies as was previously published in . It is interesting to see that for NN algorithm the accuracy gets significantly lower, when only threshold for GSM signals is increased and all available Wi‐Fi signals are used, i.e.…”

“…Impact of RSS threshold in transmitter reduction algorithm on NN algorithm. correlation of the thresholds for individual technologies as was previously published in [17]. It is interesting to see that for NN algorithm the accuracy gets significantly lower, when only threshold for GSM signals is increased and all available Wi-Fi signals are used, i.e.…”

“…The average received signal strength is calculated as: where N s is number of RSS samples measured at the given position by the mobile device and RSS i stands for i ‐th sample. In the simulations we used N s = 5, because based on previous results we can conclude that further increase of RSS samples does not have significant impact on improvement of localization accuracy.…”

“…where N s is number of RSS samples measured at the given position by the mobile device and RSS i stands for i-th sample. In the simulations we used N s = 5, because based on previous results [17] we can conclude that further increase of RSS samples does not have significant impact on improvement of localization accuracy. In the simulations all three deterministic algorithms described in the Section 2 -NN, KNN and WKNNare used to estimate position of the mobile node.…”

Tools for evaluation of social relations in mobility models

“…For this reason is critical to capture this behaviour in order to create a social based mobility model. Therefore, social mobility models become popular simulation tools in MANET-DTN networks and many researchers and scientists rely on social behaviour [4][5][6][7] and also social mobility models are useful in simulations where positioning systems are tested [8].…”