11,945 research outputs found
Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs
Future Connected and Automated Vehicles (CAVs) will be supervised by
cloud-based systems overseeing the overall security and orchestrating traffic
flows. Such systems rely on data collected from CAVs across the whole city
operational area. This paper develops a Fog Computing-based infrastructure for
future Intelligent Transportation Systems (ITSs) enabling an agile and reliable
off-load of CAV data. Since CAVs are expected to generate large quantities of
data, it is not feasible to assume data off-loading to be completed while a CAV
is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be
in the range of an RSU only for a limited amount of time, necessitating data
reconciliation across different RSUs, if traditional approaches to data
off-load were to be used. To this end, this paper proposes an agile Fog
Computing infrastructure, which interconnects all the RSUs so that the data
reconciliation is solved efficiently as a by-product of deploying the Random
Linear Network Coding (RLNC) technique. Our numerical results confirm the
feasibility of our solution and show its effectiveness when operated in a
large-scale urban testbed.Comment: To appear in IEEE VTC-Spring 201
Channel Impulse Response-based Distributed Physical Layer Authentication
In this preliminary work, we study the problem of {\it distributed}
authentication in wireless networks. Specifically, we consider a system where
multiple Bob (sensor) nodes listen to a channel and report their {\it
correlated} measurements to a Fusion Center (FC) which makes the ultimate
authentication decision. For the feature-based authentication at the FC,
channel impulse response has been utilized as the device fingerprint.
Additionally, the {\it correlated} measurements by the Bob nodes allow us to
invoke Compressed sensing to significantly reduce the reporting overhead to the
FC. Numerical results show that: i) the detection performance of the FC is
superior to that of a single Bob-node, ii) compressed sensing leads to at least
overhead reduction on the reporting channel at the expense of a small
( dB) SNR margin to achieve the same detection performance.Comment: 6 pages, 5 figures, accepted for presentation at IEEE VTC 2017 Sprin
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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