2,044 research outputs found
Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail
We are motivated by the problem of impromptu or as- you-go deployment of
wireless sensor networks. As an application example, a person, starting from a
sink node, walks along a forest trail, makes link quality measurements (with
the previously placed nodes) at equally spaced locations, and deploys relays at
some of these locations, so as to connect a sensor placed at some a priori
unknown point on the trail with the sink node. In this paper, we report our
experimental experiences with some as-you-go deployment algorithms. Two
algorithms are based on Markov decision process (MDP) formulations; these
require a radio propagation model. We also study purely measurement based
strategies: one heuristic that is motivated by our MDP formulations, one
asymptotically optimal learning algorithm, and one inspired by a popular
heuristic. We extract a statistical model of the propagation along a forest
trail from raw measurement data, implement the algorithms experimentally in the
forest, and compare them. The results provide useful insights regarding the
choice of the deployment algorithm and its parameters, and also demonstrate the
necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201
Doctor of Philosophy
dissertationIn wireless sensor networks, knowing the location of the wireless sensors is critical in many remote sensing and location-based applications, from asset tracking, and structural monitoring to geographical routing. For a majority of these applications, received signal strength (RSS)-based localization algorithms are a cost effective and viable solution. However, RSS measurements vary unpredictably because of fading, the shadowing caused by presence of walls and obstacles in the path, and non-isotropic antenna gain patterns, which affect the performance of the RSS-based localization algorithms. This dissertation aims to provide efficient models for the measured RSS and use the lessons learned from these models to develop and evaluate efficient localization algorithms. The first contribution of this dissertation is to model the correlation in shadowing across link pairs. We propose a non-site specific statistical joint path loss model between a set of static nodes. Radio links that are geographically proximate often experience similar environmental shadowing effects and thus have correlated shadowing. Using a large number of multi-hop network measurements in an ensemble of indoor and outdoor environments, we show statistically significant correlations among shadowing experienced on different links in the network. Finally, we analyze multihop paths in three and four node networks using both correlated and independent shadowing models and show that independent shadowing models can underestimate the probability of route failure by a factor of two or greater. Second, we study a special class of algorithms, called kernel-based localization algorithms, that use kernel methods as a tool for learning correlation between the RSS measurements. Kernel methods simplify RSS-based localization algorithms by providing a means to learn the complicated relationship between RSS measurements and position. We present a common mathematical framework for kernel-based localization algorithms to study and compare the performance of four different kernel-based localization algorithms from the literature. We show via simulations and an extensive measurement data set that kernel-based localization algorithms can perform better than model-based algorithms. Results show that kernel methods can achieve an RMSE up to 55% lower than a model-based algorithm. Finally, we propose a novel distance estimator for estimating the distance between two nodes a and b using indirect link measurements, which are the measurements made between a and k, for k ? b and b and k, for k ? a. Traditionally, distance estimators use only direct link measurement, which is the pairwise measurement between the nodes a and b. The results show that the estimator that uses indirect link measurements enables better distance estimation than the estimator that uses direct link measurements
Key Generation in Wireless Sensor Networks Based on Frequency-selective Channels - Design, Implementation, and Analysis
Key management in wireless sensor networks faces several new challenges. The
scale, resource limitations, and new threats such as node capture necessitate
the use of an on-line key generation by the nodes themselves. However, the cost
of such schemes is high since their secrecy is based on computational
complexity. Recently, several research contributions justified that the
wireless channel itself can be used to generate information-theoretic secure
keys. By exchanging sampling messages during movement, a bit string can be
derived that is only known to the involved entities. Yet, movement is not the
only possibility to generate randomness. The channel response is also strongly
dependent on the frequency of the transmitted signal. In our work, we introduce
a protocol for key generation based on the frequency-selectivity of channel
fading. The practical advantage of this approach is that we do not require node
movement. Thus, the frequent case of a sensor network with static motes is
supported. Furthermore, the error correction property of the protocol mitigates
the effects of measurement errors and other temporal effects, giving rise to an
agreement rate of over 97%. We show the applicability of our protocol by
implementing it on MICAz motes, and evaluate its robustness and secrecy through
experiments and analysis.Comment: Submitted to IEEE Transactions on Dependable and Secure Computin
Spatial Performance Analysis and Design Principles for Wireless Peer Discovery
In wireless peer-to-peer networks that serve various proximity-based
applications, peer discovery is the key to identifying other peers with which a
peer can communicate and an understanding of its performance is fundamental to
the design of an efficient discovery operation. This paper analyzes the
performance of wireless peer discovery through comprehensively considering the
wireless channel, spatial distribution of peers, and discovery operation
parameters. The average numbers of successfully discovered peers are expressed
in closed forms for two widely used channel models, i.e., the interference
limited Nakagami-m fading model and the Rayleigh fading model with nonzero
noise, when peers are spatially distributed according to a homogeneous Poisson
point process. These insightful expressions lead to the design principles for
the key operation parameters including the transmission probability, required
amount of wireless resources, level of modulation and coding scheme (MCS), and
transmit power. Furthermore, the impact of shadowing on the spatial performance
and suggested design principles is evaluated using mathematical analysis and
simulations.Comment: 12 pages (double columns), 10 figures, 1 table, to appear in the IEEE
Transactions on Wireless Communication
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