6,611 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
Selective Fair Scheduling over Fading Channels
Imposing fairness in resource allocation incurs a loss of system throughput,
known as the Price of Fairness (). In wireless scheduling, increases
when serving users with very poor channel quality because the scheduler wastes
resources trying to be fair. This paper proposes a novel resource allocation
framework to rigorously address this issue. We introduce selective fairness:
being fair only to selected users, and improving by momentarily blocking
the rest. We study the associated admission control problem of finding the user
selection that minimizes subject to selective fairness, and show that
this combinatorial problem can be solved efficiently if the feasibility set
satisfies a condition; in our model it suffices that the wireless channels are
stochastically dominated. Exploiting selective fairness, we design a stochastic
framework where we minimize subject to an SLA, which ensures that an
ergodic subscriber is served frequently enough. In this context, we propose an
online policy that combines the drift-plus-penalty technique with
Gradient-Based Scheduling experts, and we prove it achieves the optimal .
Simulations show that our intelligent blocking outperforms by 40 in
throughput previous approaches which satisfy the SLA by blocking low-SNR users
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