Curran, K

Furey, E

McKevitt, P

English

Using Wi-Fi signals is an attractive and reasonably affordable option to deal with the currently unsolved problem of widespread tracking in an indoor environment. These systems however suffer due to the underlying characteristics of radio waves (i.e. multipath effects) and due to infrastructural requirements. HABITS (History Aware Based Indoor Tracking System) overcomes these by modeling the historical movement habits of people in a workplace environment and then learns from these and intelligently predicts next location using a discrete Bayesian filter. This knowledge not only improves on currently available systems in terms of accuracy, yield and latency but also can be used as an input to building automation (heating, lighting) systems as an energy saving feature

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IX.  PublicationsIII.  EkahauI.  Research AimV.  Bayesian Filtering· Bayes filter is commonly used in robotics as a method to infer the position of a robot.  · This recursive algorithm enables a position estimate to be continuously updated by including the most recent sensor readings.  · A general form of the Bayes filter, which may be used for a discrete case like this, is outlined in pseudo code below.II.  Positioning SystemsjkIV.  Multisensor data Fusion VII.  Implementation Graphing/MappingVIII.  Probablistic MatricesFig 3: Probabilistic estimation in Ekahau Fig 2: Map showing areas where a user may travel· Multi sensor data fusion can facilitate bringing together data from different sources. · Using a combination of the live data from Ekahau, historical movement data and dead reckoning information, an enhanced live track may be produced which would be continuous (Ekahau has 5 sec updates at its fastest) and would continue to track when signals were poor - black spots. · A number of filtering techniques may be used to produce a new estimate of the state of a system.  · The more HABITS is used the more accurate it should become. · When a critical set of historical movement data has been gathered it can be analysed for patterns.· Probabilistic functions can be calculated for the decision points. Fig 8 shows the initial probability of moving from one state to a neighbouring one.· This data will then be used to update the various weights/inputs to the HABITS algorithm . · Movement patterns of a particular user or type of user can facilitate profiling which can be used for a number of ambient intelligent applications.Figure 4 gives the flow of data in HABITS. · The Ekahau API enables a position estimate from the Ekahau system to be fed into the fusion algorithm. · This information will then be compared to the data stored in the historical database. · From this a prediction of the movement steps for the next 5 seconds will be calculated.  · These predicted positions will be plotted on the map.  Figure 5 shows the context in which HABITS will be used.Ekahau enhanced with HABITS· Positioning is a process to obtain the spatial position of a target.  · In recent years the need has arisen for the development of Location Based Services (LBS) which work in an indoor environment.  Large public buildings; universities, hospitals and shopping centres have become target areas.  · Due to the poor performance of Satellite and Cellular systems indoors, a separate system is required. · 802.11 Wi-Fi networks as specified by the IEEE are available in many large buildings.  The signals transmitted by the Access Points (APs) provide a readily available network of signals which may be used for positioning· HABITS can be tested by comparing its tracking capability with the standard Ekahau system in terms of Accuracy, Precision, Yield and Latency.· The Ekahau RTLS (Real Time Location System) is used to provide the position of a Wi-Fi device.  · It does not rely on proprietary infrastructure or readers in order to track devices. · The existing 802.11 Wi-Fi network is used for all tracking with signal strengths of the Access Points (APs) being recorded as shown in fig 1. · Ekahau Site Survey records RSSI data of the test area.  · This data is mapped to a model which shows the areas where a Wi-Fi enabled device may travel (Fig 2). · The test area would need to be mapped in a similar way to Ekahau.  These maps would contain all the possible routes that a person may travel.  · A graph may be used as a means of representing the movement of people indoors (Fig 6).· This graph of the area will be used along with the historical movement data of a person (Fig 7) in order to calculate the transition matrix · The observed Wi-Fi signal strength data is recorded at each location. A probability is then assigned to each location based on this data as Fig 3 shows.  · The aim of this research is to create an algorithm that enhances Wi-Fi tracking capability in an indoor environment.  · The HABITS (History Aware Based wi-fi Indoor Tracking System) algorithm will allow for real-time continuous tracking in areas where this was not previously possible due to signal black spots.  Historical movement patterns will be used to probabilistically facilitate this.Fig 1: Heat Map showing areas of similar RSS values· Curran, K., Furey, E., (2007). “Pinpointing Users with Location Estimation Techniques and Wi-Fi Hotspot Technology”. Int Journal of Network Management · Furey, E., Curran, K., Mc Kevitt, P., (2008) “HABITS: A History Aware Based Wi-Fi Indoor Tracking System”. PGNET 2008 The 9th Annual Postgraduate Symposium: The Convergence of Telecommunications, Networking and Broadcasting 2008. Liverpool, John Moores University, UK· Petzold, J., Bagci, F., Trumler, W. And Ungerer, T., 2006. “Comparison of Different Methods for Next Location Prediction”. Lecture Notes In Computer Science, pp. 909Fig 4: HABITS overviewPlot on map of test areaHABITS data fusion algorithm (Bayesian Filter)Database containing movement historyPosition Estimate att+1,t+2,t+3,t+4Ekahau RTLS Position Estimate every 5 secsFig 5: Context of HABITSVI.  HABITS Overview and Context  General Algorithm for Bayes Filtering 1  Algorithm_filter(bel(xt-1), ut, zt): 2    for all xt do 3       bel (xt) =  p(xt | ut, xt-1) bel (xt – 1)  (PREDICTION STEP) 4        bel (xt) =  p(zt | xt) bel (xt) (UPDATE STEP) 5     end for 6     return bel (xt)  Inputs belief bel(xt-1) at t-1; most recent control ut + measurement zt.  Output is the belief bel (xt) at time t.  5421764512467Day 476421768,86712467Day 376421768,86712467Day 2764215645,546712467Day 1EveningAfternoonMorningMovement History of User 1Fig 7: Historical movement sequences through the nodes.Position Update from EkahauDecision PointAreas that can be travelled12111098765432101542123345766788Fig 6: Indoor map represented as a graphPredictive Indoor Tracking by the Probabilistic Modelling of Human Movement HabitsEoghan Furey, Kevin Curran, Paul Mc Kevitt Intelligent Systems Research Centre (ISRC), University of Ulster, Magee Campus0.500.500000800.50.50000070.150.150.500.200060000.50.50005000.20.10.500.204000000.50.503New State00000.20.10.50.220000000.50.5187654321Prior StateFig 8: Transition Matrix

Modeling user movement habits for intelligent indoor tracking

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Modeling user movement habits for intelligent indoor tracking

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