30,593 research outputs found
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
Privacy preserving distributed optimization using homomorphic encryption
This paper studies how a system operator and a set of agents securely execute
a distributed projected gradient-based algorithm. In particular, each
participant holds a set of problem coefficients and/or states whose values are
private to the data owner. The concerned problem raises two questions: how to
securely compute given functions; and which functions should be computed in the
first place. For the first question, by using the techniques of homomorphic
encryption, we propose novel algorithms which can achieve secure multiparty
computation with perfect correctness. For the second question, we identify a
class of functions which can be securely computed. The correctness and
computational efficiency of the proposed algorithms are verified by two case
studies of power systems, one on a demand response problem and the other on an
optimal power flow problem.Comment: 24 pages, 5 figures, journa
Efficient Computations of a Security Index for False Data Attacks in Power Networks
The resilience of Supervisory Control and Data Acquisition (SCADA) systems
for electric power networks for certain cyber-attacks is considered. We analyze
the vulnerability of the measurement system to false data attack on
communicated measurements. The vulnerability analysis problem is shown to be
NP-hard, meaning that unless there is no polynomial time algorithm to
analyze the vulnerability of the system. Nevertheless, we identify situations,
such as the full measurement case, where it can be solved efficiently. In such
cases, we show indeed that the problem can be cast as a generalization of the
minimum cut problem involving costly nodes. We further show that it can be
reformulated as a standard minimum cut problem (without costly nodes) on a
modified graph of proportional size. An important consequence of this result is
that our approach provides the first exact efficient algorithm for the
vulnerability analysis problem under the full measurement assumption.
Furthermore, our approach also provides an efficient heuristic algorithm for
the general NP-hard problem. Our results are illustrated by numerical studies
on benchmark systems including the IEEE 118-bus system
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