1,481 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
Edge Offloading in Smart Grid
The energy transition supports the shift towards more sustainable energy
alternatives, paving towards decentralized smart grids, where the energy is
generated closer to the point of use. The decentralized smart grids foresee
novel data-driven low latency applications for improving resilience and
responsiveness, such as peer-to-peer energy trading, microgrid control, fault
detection, or demand response. However, the traditional cloud-based smart grid
architectures are unable to meet the requirements of the new emerging
applications such as low latency and high-reliability thus alternative
architectures such as edge, fog, or hybrid need to be adopted. Moreover, edge
offloading can play a pivotal role for the next-generation smart grid AI
applications because it enables the efficient utilization of computing
resources and addresses the challenges of increasing data generated by IoT
devices, optimizing the response time, energy consumption, and network
performance. However, a comprehensive overview of the current state of research
is needed to support sound decisions regarding energy-related applications
offloading from cloud to fog or edge, focusing on smart grid open challenges
and potential impacts. In this paper, we delve into smart grid and
computational distribution architec-tures, including edge-fog-cloud models,
orchestration architecture, and serverless computing, and analyze the
decision-making variables and optimization algorithms to assess the efficiency
of edge offloading. Finally, the work contributes to a comprehensive
understanding of the edge offloading in smart grid, providing a SWOT analysis
to support decision making.Comment: to be submitted to journa
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