61,402 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
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Fixed-time Distributed Optimization under Time-Varying Communication Topology
This paper presents a method to solve distributed optimization problem within
a fixed time over a time-varying communication topology. Each agent in the
network can access its private objective function, while exchange of local
information is permitted between the neighbors. This study investigates first
nonlinear protocol for achieving distributed optimization for time-varying
communication topology within a fixed time independent of the initial
conditions. For the case when the global objective function is strictly convex,
a second-order Hessian based approach is developed for achieving fixed-time
convergence. In the special case of strongly convex global objective function,
it is shown that the requirement to transmit Hessians can be relaxed and an
equivalent first-order method is developed for achieving fixed-time convergence
to global optimum. Results are further extended to the case where the
underlying team objective function, possibly non-convex, satisfies only the
Polyak-\L ojasiewicz (PL) inequality, which is a relaxation of strong
convexity.Comment: 25 page
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
The propagation of sound in a shallow water environment is characterized by
boundary reflections from the sea surface and sea floor. These reflections
result in multiple (indirect) sound propagation paths, which can degrade the
performance of passive sound source localization methods. This paper proposes
the use of convolutional neural networks (CNNs) for the localization of sources
of broadband acoustic radiated noise (such as motor vessels) in shallow water
multipath environments. It is shown that CNNs operating on cepstrogram and
generalized cross-correlogram inputs are able to more reliably estimate the
instantaneous range and bearing of transiting motor vessels when the source
localization performance of conventional passive ranging methods is degraded.
The ensuing improvement in source localization performance is demonstrated
using real data collected during an at-sea experiment.Comment: 5 pages, 5 figures, Final draft of paper submitted to 2018 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
15-20 April 2018 in Calgary, Alberta, Canada. arXiv admin note: text overlap
with arXiv:1612.0350
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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