2 research outputs found
Energy-Efficient Localization and Tracking of Mobile Devices in Wireless Sensor Networks
Wireless sensor networks (WSNs) are effective for locating and tracking
people and objects in various industrial environments. Since energy consumption
is critical to prolonging the lifespan of WSNs, we propose an energy-efficient
LOcalization and Tracking} (eLOT) system, using low-cost and portable hardware
to enable highly accurate tracking of targets. Various fingerprint-based
approaches for localization and tracking are implemented in eLOT. In order to
achieve high energy efficiency, a network-level scheme coordinating collision
and interference is proposed. On the other hand, based on the location
information, mobile devices in eLOT can quickly associate with the specific
channel in a given area, while saving energy through avoiding unnecessary
transmission. Finally, a platform based on TI CC2530 and the Linux operating
system is built to demonstrate the effectiveness of our proposed scheme in
terms of localization accuracy and energy efficiency.Comment: IEEE Transactions on Vehicular Technology (submitted
Inverting Systems of Embedded Sensors for Position Verification in Location-Aware Applications
Abstract—Wireless sensor networks are typically deployed to monitor phenomena that vary over the spatial region the sensor network covers. The sensor readings may also be dual-used for additional purposes. In this paper, we propose to use the inherent spatial variability in physical phenomena, such as temperature or ambient acoustic energy, to support localization and position verification. We first present the problem of localization using general spatial information fields, and then, propose a theory for exploiting this spatial variability for localization. Our Spatial Correlation Weighting Mechanism (SCWM) uses spatial correlation across different phenomena to isolate an appropriate subset of environmental parameters for better location accuracy. We then develop an array of algorithms employing environmental parameters using a two-level approach: first, we develop the strategies on how the subset of parameters should be chosen, and second, we derive mapping functions for position estimation. Our algorithms support our theoretical model for performing localization utilizing environmental properties. Finally, we provide an experimental evaluation of our approach by using a collection of physical phenomena measured across 100 locations inside a building. Our results provide strong evidence of the viability of using general sensor readings for location-aware applications. Index Terms—Localization, sensor networks, wireless networks. Ç