6,778 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
SymbioCity: Smart Cities for Smarter Networks
The "Smart City" (SC) concept revolves around the idea of embodying
cutting-edge ICT solutions in the very fabric of future cities, in order to
offer new and better services to citizens while lowering the city management
costs, both in monetary, social, and environmental terms. In this framework,
communication technologies are perceived as subservient to the SC services,
providing the means to collect and process the data needed to make the services
function. In this paper, we propose a new vision in which technology and SC
services are designed to take advantage of each other in a symbiotic manner.
According to this new paradigm, which we call "SymbioCity", SC services can
indeed be exploited to improve the performance of the same communication
systems that provide them with data. Suggestive examples of this symbiotic
ecosystem are discussed in the paper. The dissertation is then substantiated in
a proof-of-concept case study, where we show how the traffic monitoring service
provided by the London Smart City initiative can be used to predict the density
of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging
Telecommunications Technologie
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
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