Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization

Abstract

-Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption.

Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

“…Many different mobility prediction techniques have been proposed for a variety of wireless networks, such as cel lular [27][28][29][30][31][32], WLANs [10-13, 33, 34], ad hoc networks [35,36], and mesh networks [5], and applied to reduce handoff latency [8,12,13,37], provide efficient resource reservation [27][28][29][30][31][32][33], improve routing protocols [35], and conserve power [36]. However, these methods tend to be general and thus do not consider the special characteristics of WLANs, such as highly overlapped cell coverage, MAC contention, and variations in link quality.…”

SmartFix: Indoor Locating Optimization Algorithm for Energy-Constrained Wearable Devices

“…Perceiving environment information to dynamically adjust the rate of data collection is the main idea of the current energy-saving mechanism, such as using velocity of the nodes to dynamically adjust the frequency of acquiring the signal strength [10,[21][22][23][24], reducing the rate of use channel responses from multiple OFDM subcarriers [25], and using the environment information [26,27]. There are other methods to reduce the total energy consumption in a system, like GreenLoc [28].…”

A Survey of Localization in Wireless Sensor Network

“…Assisted by multiple-transmit-power information, it outperforms the existing algorithms that do not utilize multiplepower information. You et al [72] proposed a specified positional error tolerance, the sensor-enhanced and energyefficient adaptive localization system in an application. This localization system dynamically sets sleep time for the nodes and adapting the sampling rate of target's mobility level.…”