Location-aware Sensor Networks Research Articles

A CMOS 6–10 GHz impulse radio UWB transmitter based on gated oscillator with switching pulse shaper

In an impulse radio ultra-wideband (IR-UWB) system for low-power location-aware sensor networks, the output pulses must meet the FCC spectral emission mask. In addition, to increase battery life, the IR-UWB transmitter must be energy efficient. In this paper, a CMOS ultra-wideband (UWB) transmitter designed using a 0.11-μm process is presented for 6–10 GHz applications. To reduce energy consumption, the transmitter adopts a gated voltage-controlled oscillator (VCO) that directly drives 50 Ω output impedance. A gated VCO that uses a trapezoidal pulse for on/off gating of the tail current source satisfies the spectrum mask without requiring an additional pulse shaping filter. The active energy consumption is 13.4 pJ/pulse at a pulse repeating frequency (PRF) of 40 MHz. The side-lobe rejection performance is more than 30 dB.

Accuracy of Range-Based Cooperative Positioning: A Lower Bound Analysis

Accurate location information is essential for mobile systems such as wireless sensor networks. A location-aware sensor network generally includes two types of nodes: Sensors whose locations to be determined and anchors whose locations are known a priori. For range-based cooperative positioning, sensors' locations are deduced from anchor-to-sensor and sensor-to-sensor range measurements. Positioning accuracy depends on the network parameters such as network connectivity and size. This paper provides a generalized theory that quantitatively characterizes such a relation between network parameters and positioning accuracy. We use the average degree as a connectivity metric and use geometric dilution of precision (DOP) to quantify positioning accuracy. Under the assumption that nodes are randomly deployed, we prove a novel lower bound on expectation of average geometric DOP (LB-E-AGDOP) and derives a closed-form formula that relates LB-E-AGDOP to only three parameters: Average anchor degree, average sensor degree, and number of sensor nodes. The formula shows that positioning accuracy is approximately inversely proportional to the average degree, and a higher ratio of average anchor degree to average sensor degree yields better positioning accuracy. Furthermore, the paper shows a strong connection between LB-E-AGDOP and the best achievable accuracy. Finally, we demonstrate the theory via numerical simulations with three different random graph models.

Lightweight Localization Using Trilateration for Sensor Networks

Localization is an unavoidable procedure in location aware sensor networks. In such networks, management of a large amount of location information along with its processing and updating is highly desirable at a central station of the network. In this paper, we have discussed the implementation of software layer to be run on various types of sensor nodes in the localization network, which has been dealt with extensively along with some of the addressed problems and their respective solutions. In addition, the article discusses implementation of an already mathematical formulation of least squares trilateration, which has not yet been attempted in the space of wireless sensor networks. To support our clam we performed experimental analysis on $$telosb$$ motes. Experimental results of proposed framework shows that average error with respect to the physical location estimation can be reduced upto 46.66 % using 4 anchor node as compare to three anchor nodes at outdoor scenario and upto 40 % in indoor scenario.

NLOS identification and mitigation for localization based on UWB experimental data

Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

Multiple-access insights from bounds on sensor localization

In ad hoc location-aware sensor networks, unlocalized sensors can estimate their locations based on the triangulation of range measurements from location-aware sensors or “anchors”, whose locations are known or estimated a priori. In this paper, we investigate the relationship between multiple-access design parameters and the accuracy of location estimation in ad hoc sensor networks. Bounds on the average localization accuracy in a packet-based sensor network with time-of-arrival (TOA) based distance estimation are used to investigate the connection between the average effective throughput of packets and the average localization accuracy. On this basis, we (i) develop an analytical framework that allows us to analyze the relationship between network parameters and the average localization accuracy obtained in ad hoc sensor networks with a spread-spectrum physical layer, and (ii) show that, for such networks, minimizing the average localization error is equivalent to maximizing the average effective throughput. We further demonstrate that the developed framework allows the extraction of the optimal network parameters that maximize localization accuracy. The trends in the localization accuracy with respect to the network parameters observed through analysis are then validated via simulation studies. Finally, some aspects of the modeling of location-aware sensor networks that warrant investigation and require more sophisticated modeling strategies are listed.

Processing [formula omitted] nearest neighbor queries in location-aware sensor networks

Efficient search for k nearest neighbors to a given location point (called a KNN query) is an important problem arising in a variety of sensor network applications. In this paper, we investigate in-network query processing strategies under a KNN query processing framework in location-aware wireless sensor networks. A set of algorithms, namely the geo-routing tree, the KNN boundary tree and the itinerary-based KNN algorithms, are designed in accordance with the global infrastructure-based, local infrastructure-based and infrastructure-free strategies, respectively. They have distinctive performance characteristics and are desirable under different contexts. We evaluate the performance of these algorithms under several sensor network scenarios and application requirements, and identify the conditions under which the various approaches are preferable.