8,203 research outputs found
Optimal Policies for Status Update Generation in a Wireless System with Heterogeneous Traffic
A large body of applications that involve monitoring, decision making, and
forecasting require timely status updates for their efficient operation. Age of
Information (AoI) is a newly proposed metric that effectively captures this
requirement. Recent research on the subject has derived AoI optimal policies
for the generation of status updates and AoI optimal packet queueing
disciplines. Unlike previous research we focus on low-end devices that
typically support monitoring applications in the context of the Internet of
Things. We acknowledge that these devices host a diverse set of applications
some of which are AoI sensitive while others are not. Furthermore, due to their
limited computational resources they typically utilize a simple First-In
First-Out (FIFO) queueing discipline. We consider the problem of optimally
controlling the status update generation process for a system with a
source-destination pair that communicates via a wireless link, whereby the
source node is comprised of a FIFO queue and two applications, one that is AoI
sensitive and one that is not. We formulate this problem as a dynamic
programming problem and utilize the framework of Markov Decision Processes to
derive optimal policies for the generation of status update packets. Due to the
lack of comparable methods in the literature, we compare the derived optimal
policies against baseline policies, such as the zero-wait policy, and
investigate the performance of all policies for a variety of network
configurations. Results indicate that existing status update policies fail to
capture the trade-off between frequent generation of status updates and
queueing delay and thus perform poorly
Architecture for Mobile Heterogeneous Multi Domain Networks
Multi domain networks can be used in several scenarios including military, enterprize networks, emergency networks and many other cases. In such networks, each domain might be under its own administration. Therefore, the cooperation among domains is conditioned by individual domain policies regarding sharing information, such as network topology, connectivity, mobility, security, various service availability and so on. We propose a new architecture for Heterogeneous Multi Domain (HMD) networks, in which one the operations are subject to specific domain policies. We propose a hierarchical architecture, with an infrastructure of gateways at highest-control level that enables policy based interconnection, mobility and other services among domains. Gateways are responsible for translation among different communication protocols, including routing, signalling, and security. Besides the architecture, we discuss in more details the mobility and adaptive capacity of services in HMD. We discuss the HMD scalability and other advantages compared to existing architectural and mobility solutions. Furthermore, we analyze the dynamic availability at the control level of the hierarchy
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
- …