12,570 research outputs found
Performance Analysis of a System with Bursty Traffic and Adjustable Transmission Times
In this work, we consider the case where a source with bursty traffic can
adjust the transmission duration in order to increase the reliability. The
source is equipped with a queue in order to store the arriving packets. We
model the system with a discrete time Markov Chain, and we characterize the
performance in terms of service probability and average delay per packet. The
accuracy of the theoretical results is validated through simulations. This work
serves as an initial step in order to provide a framework for systems with
arbitrary arrivals and variable transmission durations and it can be utilized
for the derivation of the delay distribution and the delay violation
probability.Comment: ISWCS 201
Understanding the limits of LoRaWAN
The quick proliferation of LPWAN networks, being LoRaWAN one of the most
adopted, raised the interest of the industry, network operators and facilitated
the development of novel services based on large scale and simple network
structures. LoRaWAN brings the desired ubiquitous connectivity to enable most
of the outdoor IoT applications and its growth and quick adoption are real
proofs of that. Yet the technology has some limitations that need to be
understood in order to avoid over-use of the technology. In this article we aim
to provide an impartial overview of what are the limitations of such
technology, and in a comprehensive manner bring use case examples to show where
the limits are
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
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