39,844 research outputs found
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Novel vehicular communication methods are mostly analyzed simulatively or
analytically as real world performance tests are highly time-consuming and
cost-intense. Moreover, the high number of uncontrollable effects makes it
practically impossible to reevaluate different approaches under the exact same
conditions. However, as these methods massively simplify the effects of the
radio environment and various cross-layer interdependencies, the results of
end-to-end indicators (e.g., the resulting data rate) often differ
significantly from real world measurements. In this paper, we present a
data-driven approach that exploits a combination of multiple machine learning
methods for modeling the end-to-end behavior of network performance indicators
within vehicular networks. The proposed approach can be exploited for fast and
close to reality evaluation and optimization of new methods in a controllable
environment as it implicitly considers cross-layer dependencies between
measurable features. Within an example case study for opportunistic vehicular
data transfer, the proposed approach is validated against real world
measurements and a classical system-level network simulation setup. Although
the proposed method does only require a fraction of the computation time of the
latter, it achieves a significantly better match with the real world
evaluations
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