900 research outputs found
Robust Component-based Network Localization with Noisy Range Measurements
Accurate and robust localization is crucial for wireless ad-hoc and sensor
networks. Among the localization techniques, component-based methods advance
themselves for conquering network sparseness and anchor sparseness. But
component-based methods are sensitive to ranging noises, which may cause a huge
accumulated error either in component realization or merging process. This
paper presents three results for robust component-based localization under
ranging noises. (1) For a rigid graph component, a novel method is proposed to
evaluate the graph's possible number of flip ambiguities under noises. In
particular, graph's \emph{MInimal sepaRators that are neaRly cOllineaR
(MIRROR)} is presented as the cause of flip ambiguity, and the number of
MIRRORs indicates the possible number of flip ambiguities under noise. (2) Then
the sensitivity of a graph's local deforming regarding ranging noises is
investigated by perturbation analysis. A novel Ranging Sensitivity Matrix (RSM)
is proposed to estimate the node location perturbations due to ranging noises.
(3) By evaluating component robustness via the flipping and the local deforming
risks, a Robust Component Generation and Realization (RCGR) algorithm is
developed, which generates components based on the robustness metrics. RCGR was
evaluated by simulations, which showed much better noise resistance and
locating accuracy improvements than state-of-the-art of component-based
localization algorithms.Comment: 9 pages, 15 figures, ICCCN 2018, Hangzhou, Chin
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
Localizability of Wireless Sensor Networks: Beyond Wheel Extension
A network is called localizable if the positions of all the nodes of the
network can be computed uniquely. If a network is localizable and embedded in
plane with generic configuration, the positions of the nodes may be computed
uniquely in finite time. Therefore, identifying localizable networks is an
important function. If the complete information about the network is available
at a single place, localizability can be tested in polynomial time. In a
distributed environment, networks with trilateration orderings (popular in real
applications) and wheel extensions (a specific class of localizable networks)
embedded in plane can be identified by existing techniques. We propose a
distributed technique which efficiently identifies a larger class of
localizable networks. This class covers both trilateration and wheel
extensions. In reality, exact distance is almost impossible or costly. The
proposed algorithm based only on connectivity information. It requires no
distance information
On Sensor Network Localization Using SDP Relaxation
A Semidefinite Programming (SDP) relaxation is an effective computational
method to solve a Sensor Network Localization problem, which attempts to
determine the locations of a group of sensors given the distances between some
of them [11]. In this paper, we analyze and determine new sufficient conditions
and formulations that guarantee that the SDP relaxation is exact, i.e., gives
the correct solution. These conditions can be useful for designing sensor
networks and managing connectivities in practice.
Our main contribution is twofold: We present the first non-asymptotic bound
on the connectivity or radio range requirement of the sensors in order to
ensure the network is uniquely localizable. Determining this range is a key
component in the design of sensor networks, and we provide a result that leads
to a correct localization of each sensor, for any number of sensors. Second, we
introduce a new class of graphs that can always be correctly localized by an
SDP relaxation. Specifically, we show that adding a simple objective function
to the SDP relaxation model will ensure that the solution is correct when
applied to a triangulation graph. Since triangulation graphs are very sparse,
this is informationally efficient, requiring an almost minimal amount of
distance information. We also analyze a number objective functions for the SDP
relaxation to solve the localization problem for a general graph.Comment: 20 pages, 4 figures, submitted to the Fields Institute Communications
Series on Discrete Geometry and Optimizatio
Network Localization by Shadow Edges
Localization is a fundamental task for sensor networks. Traditional network
construction approaches allow to obtain localized networks requiring the nodes
to be at least tri-connected (in 2D), i.e., the communication graph needs to be
globally rigid. In this paper we exploit, besides the information on the
neighbors sensed by each robot/sensor, also the information about the lack of
communication among nodes. The result is a framework where the nodes are
required to be bi-connected and the communication graph has to be rigid. This
is possible considering a novel typology of link, namely Shadow Edges, that
account for the lack of communication among nodes and allow to reduce the
uncertainty associated to the position of the nodes.Comment: preprint submitted to 2013 European Control Conference, July 17-19
2013, Zurich, Switzerlan
Technologies and solutions for location-based services in smart cities: past, present, and future
Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas
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