This level of simplicity will further the ubiquity of indoor localization in the age of smartphones. However, it is disappointing that none of these achievements have become a de-facto standard of indoor localization and the majority of billions of smartphone users in the world still do not benefit from an indoor localization technique in their living spaces. At the same time, it removes the necessity of a labor-intensive procedure of signal fingerprinting by roughly crowding the positions on a floor plan of randomly chosen users in the indoor structure.
In the indoor localization literature, several solutions have been proposed in the last two decades. However, it would not be practical in the sense that clients would have to install special applications or allow certain changes to their operating systems. Offline phase tasks refer to processes that need to prepare the system and are executed only once at the beginning, while online phase tasks are performed while tracking users.
Therefore, we perform the following steps to get the real distances and paths between all locations in the floor by considering all the physical constraints. In the evaluations, we determined the threshold using Otsu's method [39], which selects the threshold to minimize the intraclass variance of the thresholded black and white pixels. By calculating the distances between all pairs of sample points, we have the distance matrix D = [dij], where dij is the shortest distance calculated using Dijkstra's algorithm between two points pi and pj in the ground plan.
After cleaning the noisy or intrusive signal points, the system uses the fingerprint database M to retrieve the list of possible locations for each received signal strength data using the location estimator described in Algorithm 2. Then a tree structure of all possible paths created. constructed by connecting the consecutive list of locations.
Converting floor plan image to a graph of nodes
Finally, the binary matrix is divided equally into a mesh of lattices and we refer to each center of those lattices as a node. The length l of a grid can be 1-3 meters according to the general performance of fingerprint-based localization methods. Dots in the accessible area refer to nodes that are the result of dividing the space evenly into a mesh of grids.
Also, after converting the floor plan to a graph of nodes, we do one more step that helps us obtain realistic routes and distances while running the main algorithm. Simply taking a direct line between points in the floor plan will not give us the correct walking routes between them due to the block walls and other obstacles. The calculation is performed only once and stored in the matrix to quickly return realistic routes and distances in the later stages of the system.
This step will output the matrix R= [rij], where rij is the shortest path between two points mapi and pj in the floor plan. Channel (number) Label transmission channel (either 5 GHz or 2.4 GHz) AP MAC (string) MAC address of the AP. Radio BSSID (string) BSSID of the radio that detected the device Mon BSSID (string) BSSID of the AP with which the station is connected to the client MAC (string) MAC address of the station.
Building fingerprint database
The system then matches the timestamps of the real coordinate data with the raw signal data to begin creating the map matrix. Since 5 GHz and 2.4 GHz radio frequencies have different signal properties, we create 2 different matrices for each. We assume that this process of collecting fingerprints is quite simple and can be done by any person familiar with planimetry.
Also, the process is more 'realistic' in the sense that during the location estimation process the signals we receive will be from moving objects, not always static. However, most fingerprint-based solutions collect fingerprint data in a static manner, which may differ from online phase signal propagation. Using the data, we construct a matrix M which maps each AP ×RSSI history to a vector of corresponding P~ locations (nodes).
After these steps, we have the fingerprint database that stores corresponding locations for tuples of the form AP × RSSI, which were captured in the ground truth collection steps using the T raceT racker application. The matrix has the form shown in Figure 5. a) List of possible locations for each timestamp. Location 33 is missing in tree b) due to the high speed of 4.4 m/s to pass the shortest route.
Retrieve locations from RSS data
After Algorithm 2 has finished running, we have a list of possible locations (nodes) for each timestamp. The possible locations count for tracks are shown in Figure 8, where random 2 tracks are selected in Figure 8a and Figure 8b, each with about 5 minutes of track.
Minimum required speed
We identify problem points by taking the fastest possible path between successive signal points. Since each signal point was converted to a list of possible locations from the previous subsection 5.1, the minimum speed required to pass consecutive signal points is the closest distance between the consecutive list of locations, divided by a duration that must to pass the points.
P athT ree construction
P athT ree compressor
We implemented Multiverse using the RSSI data feed from the Cisco Wireless LAN controller (Cisco WLC 8500), which is designed to send information about client devices listening from the network on the campus of the Ulsan National Institute of Science and Technology. We have developed a framework that can enter the controller at the 4-second interval and dump the signal information with the data fields described in Table 1 of the selected devices. From all the signal attributes, we use the information related to each AP, the RSSI and its corresponding timestamp when the signal was heard.
Additionally, all devices treated as hashed entities without additional knowledge about them. The RSSI data feed is collected in our main server and all WiFi signal data is sent to a program written in MATLAB to send location information in packets. Figure 10.
Methodology
Localization error
Location error is defined as the Euclidean distance from the estimated location to the ground truth one. As the final output of Multiverse, RSS noise and map errors are taken into account simultaneously. Multiverse's average point-by-point distance error is 1.6 meters, which is about 30% smaller than Landmark-5 (5.1 meters), as shown in Figure 12.
The performance of Multiverse is significantly better than the state-of-the-art model-based approaches (larger than 5 meters) reported in [31] and EZ (larger than 7 meters) [6]. Some location errors are caused by the symmetrical structure of the rooms, but they are relatively small and do not contribute to the room error.
Fréchet distance error
System efficiency
Most indoor location systems rely on sophisticated signal information or device sensor data to accurately estimate the user's location and track. WiFi-based indoor localization technique was developed, which can achieve practical accuracy, easy to implement, and only utilizes existing WiFi infrastructure without requiring any access to data from mobile devices. Striegel, “Face-to-face proximity estimation using bluetooth on smartphones,” IEEE Transactions on Mobile Computing, vol.
Rhee, “FM-based indoor localization via automatic fingerprint construction and matching,” in ACM MobiSys, 2013. Liu, “Tagoram: Real-time tracking of mobile RFID tags with high precision using baby cribs,” in ACM MobiCom, 2014 .Venetsanopoulos, “Kernel-based positioning in wireless local area networks,” IEEE transactions on mobile computing, vol.
Chen, “Accurate and inexpensive location estimation using kernels,” in International Joint Conference on Artificial Intelligence, 2005. Jamieson, “Phaser: Enabling phased array signal processing on commodity wifi access points,” in ACM MobiCom, 2014. Steinbach, “ Graf -based data fusion of pedometer and wifi measurements for mobile indoor positioning,” in ACM UbiComp, 2014.
Rizos, "Differences in rssi readings made by different wi-fi chips: A limitation of wlan localization," in Localization and GNSS (ICL-GNSS). Van, "Ssd: A robust rf location fingerprint addressing mobile device heterogeneity," IEEE Transactions on Mobile Computing, vol. Chen, “Using homogeneous features to address the device heterogeneity problem in fingerprint localization,” IEEE Sensors Journal , vol.
