Pazl: A mobile crowdsensing based indoor WiFi monitoring system

Abstract

WiFi in indoor environments exhibits spatiotemporal variations in terms of coverage and interference in typical WLAN deployments with multiple APs, motivating the need for automated monitoring to aid network administrators to adapt the WLAN deployment in order to match the user expectations. We develop Pazl, a mobile crowdsensing based indoor WiFi monitoring system that is enabled by a novel hybrid localization mechanism to locate individual measurements taken from participant phones. The localization mechanism in Pazl integrates the best aspects of two well known localization techniques, pedestrian dead reckoning and WiFi fingerprinting; it also relies on crowdsourcing for constructing the WiFi fingerprint database. Compared to existing WiFi monitoring systems based on static sniffers, Pazl is low cost and provides a user-side perspective. Pazl is significantly more automated than wireless site survey tools such as Ekahau Mobile Survey tool by drastically reducing the manual point-and-click based measurement location determination. We implement Pazl through a combination of Android mobile app and cloud backend application on the Google App Engine. Experimental evaluation of Pazl with a trial set of users shows that it yields similar results to manual site surveys but without the tedium.

Wearable Electronics Sensors: Current Status and Future Opportunities

“…Some of the advantages of using Wi-Fi are marked by its speed, coverage of transmission of data and security. The Wi-Fi is also gaining its popularity [123,124] in local area network and private area network. The heterogeneous sensors used in smart homes can be connected at different locations in the house, and the data can be simultaneously collected from all the nodes to the receiving node via separate identification number of nodes.…”

Why is fingerprint-based indoor localization still so hard?

“…Pazl [7] presents this innovative combination, but even with sufficient number of samples accurate location estimation is still difficult because the system only use one fingerprint and has to proceed rapidly to combined with PDR. Moreover, this system can be quite expensive from an energy consumption perspective if solely relied on for continuous location tracking.…”

Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering

“…In existing work, researchers simply select these common APs with the largest signal strength for matching [2][3][4] or set the corresponding RSS entity to a small value (e.g., −110 dBm) if no RSS reading is found for an AP [5][6][7]. In fact, these approaches either introduce some false information or omitted some useful information.…”

“…The results in Figure 3 show that it is very rare to have the same visible APs at different times even at the same position. Using AP sets similarity or RSS distance alone to represent fingerprint distance results in undesirable outcomes: AP sets similarity only provides coarse-grained information about two reference points, while RSS distance may delete some useful signal characteristics [2][3][4] or add artificial information [5][6][7]. Therefore, we propose to combine AP sets similarity and RSS distance to calculate the distance between two fingerprints.…”

“…Existing work typically uses one of the two methods to work around this issue. One is to select the APs shared by online radio measurements and reference points (RPs) in radio map, and then these APs with the strongest RSS will be used for final matching [2][3][4]. The other method is to assign a small RSS value to a nonobserved AP [5][6][7].…”