72 research outputs found
Bayesian Outlier Detection in Location-aware Wireless Networks
Location-aware networks are a rapidly growing area of research with
a wide range of applications. The accuracy of localization depends
on the reliability of the information exchanged between devices in
the network. In practice, devices may fail or maliciously inject false
position information into the network. This paper aims to design and
test algorithms to verify the location consistency in wireless networks.
We propose a new method based on factor graphs. This method is flexible,
easily extendible to cooperative networks, and leads to significant
performance improvements compared to existing techniques that are
based on linear programming
Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study
The accuracy of indoor wireless localization systems can be substantially
enhanced by map-awareness, i.e., by the knowledge of the map of the environment
in which localization signals are acquired. In fact, this knowledge can be
exploited to cancel out, at least to some extent, the signal degradation due to
propagation through physical obstructions, i.e., to the so called
non-line-of-sight bias. This result can be achieved by developing novel
localization techniques that rely on proper map-aware statistical modelling of
the measurements they process. In this manuscript a unified statistical model
for the measurements acquired in map-aware localization systems based on
time-of-arrival and received signal strength techniques is developed and its
experimental validation is illustrated. Finally, the accuracy of the proposed
map-aware model is assessed and compared with that offered by its map-unaware
counterparts. Our numerical results show that, when the quality of acquired
measurements is poor, map-aware modelling can enhance localization accuracy by
up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless
Communications, 201
Intelligent Processing in Wireless Communications Using Particle Swarm Based Methods
There are a lot of optimization needs in the research and design of wireless communica- tion systems. Many of these optimization problems are Nondeterministic Polynomial (NP) hard problems and could not be solved well. Many of other non-NP-hard optimization problems are combinatorial and do not have satisfying solutions either. This dissertation presents a series of Particle Swarm Optimization (PSO) based search and optimization algorithms that solve open research and design problems in wireless communications. These problems are either avoided or solved approximately before.
PSO is a bottom-up approach for optimization problems. It imposes no conditions on the underlying problem. Its simple formulation makes it easy to implement, apply, extend and hybridize. The algorithm uses simple operators like adders, and multipliers to travel through the search space and the process requires just five simple steps. PSO is also easy to control because it has limited number of parameters and is less sensitive to parameters than other swarm intelligence algorithms. It is not dependent on initial points and converges very fast.
Four types of PSO based approaches are proposed targeting four different kinds of problems in wireless communications. First, we use binary PSO and continuous PSO together to find optimal compositions of Gaussian derivative pulses to form several UWB pulses that not only comply with the FCC spectrum mask, but also best exploit the avail- able spectrum and power. Second, three different PSO based algorithms are developed to solve the NLOS/LOS channel differentiation, NLOS range error mitigation and multilateration problems respectively. Third, a PSO based search method is proposed to find optimal orthogonal code sets to reduce the inter carrier interference effects in an frequency redundant OFDM system. Fourth, a PSO based phase optimization technique is proposed in reducing the PAPR of an frequency redundant OFDM system. The PSO based approaches are compared with other canonical solutions for these communication problems and showed superior performance in many aspects. which are confirmed by analysis and simulation results provided respectively. Open questions and future
Open questions and future works for the dissertation are proposed to serve as a guide for the future research efforts
Soft information for localization-of-things
Location awareness is vital for emerging Internetof-
Things applications and opens a new era for Localizationof-
Things. This paper first reviews the classical localization
techniques based on single-value metrics, such as range and
angle estimates, and on fixed measurement models, such as
Gaussian distributions with mean equal to the true value of the
metric. Then, it presents a new localization approach based
on soft information (SI) extracted from intra- and inter-node
measurements, as well as from contextual data. In particular,
efficient techniques for learning and fusing different kinds of SI
are described. Case studies are presented for two scenarios in
which sensing measurements are based on: 1) noisy features
and non-line-of-sight detector outputs and 2) IEEE 802.15.4a
standard. The results show that SI-based localization is highly
efficient, can significantly outperform classical techniques, and
provides robustness to harsh propagation conditions.RYC-2016-1938
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