27,857 research outputs found
From Sparse Signals to Sparse Residuals for Robust Sensing
One of the key challenges in sensor networks is the extraction of information
by fusing data from a multitude of distinct, but possibly unreliable sensors.
Recovering information from the maximum number of dependable sensors while
specifying the unreliable ones is critical for robust sensing. This sensing
task is formulated here as that of finding the maximum number of feasible
subsystems of linear equations, and proved to be NP-hard. Useful links are
established with compressive sampling, which aims at recovering vectors that
are sparse. In contrast, the signals here are not sparse, but give rise to
sparse residuals. Capitalizing on this form of sparsity, four sensing schemes
with complementary strengths are developed. The first scheme is a convex
relaxation of the original problem expressed as a second-order cone program
(SOCP). It is shown that when the involved sensing matrices are Gaussian and
the reliable measurements are sufficiently many, the SOCP can recover the
optimal solution with overwhelming probability. The second scheme is obtained
by replacing the initial objective function with a concave one. The third and
fourth schemes are tailored for noisy sensor data. The noisy case is cast as a
combinatorial problem that is subsequently surrogated by a (weighted) SOCP.
Interestingly, the derived cost functions fall into the framework of robust
multivariate linear regression, while an efficient block-coordinate descent
algorithm is developed for their minimization. The robust sensing capabilities
of all schemes are verified by simulated tests.Comment: Under review for publication in the IEEE Transactions on Signal
Processing (revised version
From Dumb Wireless Sensors to Smart Networks using Network Coding
The vision of wireless sensor networks is one of a smart collection of tiny,
dumb devices. These motes may be individually cheap, unintelligent, imprecise,
and unreliable. Yet they are able to derive strength from numbers, rendering
the whole to be strong, reliable and robust. Our approach is to adopt a
distributed and randomized mindset and rely on in network processing and
network coding. Our general abstraction is that nodes should act only locally
and independently, and the desired global behavior should arise as a collective
property of the network. We summarize our work and present how these ideas can
be applied for communication and storage in sensor networks.Comment: To be presented at the Inaugural Workshop of the Center for
Information Theory and Its Applications, University of California - San
Diego, La Jolla, CA, February 6 - 10, 200
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Views from the coalface: chemo-sensors, sensor networks and the semantic sensor web
Currently millions of sensors are being deployed in sensor networks across the world. These networks generate vast quantities of heterogeneous data across various levels of spatial and temporal granularity. Sensors range from single-point in situ sensors to remote satellite sensors which can cover the globe. The semantic sensor web in principle should allow for the unification of the web with the real-word. In this position paper, we discuss the major challenges to this unification from the perspective of sensor developers (especially chemo-sensors) and integrating sensors data in real-world deployments. These challenges include: (1) identifying the quality of the data; (2) heterogeneity of data sources and data transport methods; (3) integrating data streams from different sources and modalities (esp. contextual information), and (4) pushing intelligence to the sensor level
An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks
A high degree of reliability for critical data transmission is required in
body sensor networks (BSNs). However, BSNs are usually vulnerable to channel
impairments due to body fading effect and RF interference, which may
potentially cause data transmission to be unreliable. In this paper, an
adaptive and flexible fault-tolerant communication scheme for BSNs, namely
AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to
provide reliable data transmission when channel impairments occur. In order to
fulfill the reliability requirements of critical sensors, fault-tolerant
priority and queue are employed to adaptively adjust the channel bandwidth
allocation. Simulation results show that AFTCS can alleviate the effect of
channel impairments, while yielding lower packet loss rate and latency for
critical sensors at runtime.Comment: 10 figures, 19 page
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