27,507 research outputs found
Adaptive data synchronization algorithm for IoT-oriented low-power wide-area networks
The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications
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
A Middleware for the Internet of Things
The Internet of Things (IoT) connects everyday objects including a vast array
of sensors, actuators, and smart devices, referred to as things to the
Internet, in an intelligent and pervasive fashion. This connectivity gives rise
to the possibility of using the tracking capabilities of things to impinge on
the location privacy of users. Most of the existing management and location
privacy protection solutions do not consider the low-cost and low-power
requirements of things, or, they do not account for the heterogeneity,
scalability, or autonomy of communications supported in the IoT. Moreover,
these traditional solutions do not consider the case where a user wishes to
control the granularity of the disclosed information based on the context of
their use (e.g. based on the time or the current location of the user). To fill
this gap, a middleware, referred to as the Internet of Things Management
Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications
(IJCNC) Vol.8, No.2, March 201
Leveraging Edge Computing through Collaborative Machine Learning
The Internet of Things (IoT) offers the ability
to analyze and predict our surroundings through sensor
networks at the network edge. To facilitate this predictive
functionality, Edge Computing (EC) applications are developed
by considering: power consumption, network lifetime and
quality of context inference. Humongous contextual data from
sensors provide data scientists better knowledge extraction,
albeit coming at the expense of holistic data transfer that
threatens the network feasibility and lifetime. To cope with this,
collaborative machine learning is applied to EC devices to (i)
extract the statistical relationships and (ii) construct regression
(predictive) models to maximize communication efficiency. In
this paper, we propose a learning methodology that improves
the prediction accuracy by quantizing the input space and
leveraging the local knowledge of the EC devices
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