13,029 research outputs found
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
Ergodic Randomized Algorithms and Dynamics over Networks
Algorithms and dynamics over networks often involve randomization, and
randomization may result in oscillating dynamics which fail to converge in a
deterministic sense. In this paper, we observe this undesired feature in three
applications, in which the dynamics is the randomized asynchronous counterpart
of a well-behaved synchronous one. These three applications are network
localization, PageRank computation, and opinion dynamics. Motivated by their
formal similarity, we show the following general fact, under the assumptions of
independence across time and linearities of the updates: if the expected
dynamics is stable and converges to the same limit of the original synchronous
dynamics, then the oscillations are ergodic and the desired limit can be
locally recovered via time-averaging.Comment: 11 pages; submitted for publication. revised version with fixed
technical flaw and updated reference
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
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