A Software Defined Networking Architecture for the Internet-of-Things - Abstract and Introduction Summary

Sep 12, 2026

Growing interest in the Internet of Things (IoT) drives wide-area deployments of heterogeneous IoT subnetworks, spanning multiple wireless access technologies (cellular, WiFi, ZigBee, Bluetooth) and multi-hop ad-hoc/MANET routing. Managing open, geographically distributed, dynamic infrastructures requires techniques that provision different classes of IoT traffic over shared sensing and networking resources. The paper proposes an IoT SDN approach extending MINA with a layered controller, using Network Calculus and Genetic Algorithms to optimize task-specific flow scheduling and quality differentiation.

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Abstract

Introduction

Heterogeneous IoT deployments and challenges

Task classes and service requirements

A Software Deﬁned Networking Architecture for the Internet-of-Things Zhijing Qin∗ , Grit Denker§ , Carlo Giannelli‡, Paolo Bellavista‡, Nalini Venkatasubramanian∗ ∗ University of California, Irvine, USA ‡ University of Bologna, Italy § SRI International, USA Abstract—The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT trafﬁc across a common set of sensors and networking resources must be designed. In this paper, we will design a software-deﬁned approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reﬂective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate ﬂow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINASDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efﬁcient exploitation of the IoT multinetwork capabilities. I. INTRODUCTION The recent surge in popularity of the Internet of Things (IoT) across multiple domains has stemmed from the spread of networking-enabled consumer devices that are deployed on a geographically wide-scale. For instance, within the context of two large EU Artemis Joint Undertaking projects, called Internet of Energy for Electric Mobility [1] and Arrowhead [2], we are addressing wide-scale deployment scenarios of IoTand the industrial interest for the near future where a large fraction of vehicular trafﬁc will consist of electric vehicles. These scenarios give rise to many technical and organizational challenges-from the monitoring of current road trafﬁc to the optimization of travel paths based on recharging availability; from the locating of target vehicles to the dissemination of alert message in an audio or text format; from the identiﬁcation of spatio-temporal recharging patterns for enabling mass scale user behavior prediction to the optimization of smart grid management in order to adequately sustain the expected patterns of recharging requests from different geographical areas. 978-1-4799-0913-1/14/$31.00 􀀄c 2014 IEEE Real world IoT deployments are fundamentally heterogeneous; they are often derived from the integration of already independently deployed IoT sub-networks, characterized by very heterogeneous devices and connectivity capabilities. The co-existence of different types of network technologies is due to legacy motivations and to different specializationsin different sub-domains. Potential networks in the smart transportation case above may include single-hop wireless communications based primarily on Near Field Communications and ZigBee between neighboring cars and between cars and recharging sites (at le

FAQs

Which methods are used to optimize the usage of available IoT network opportunities?

The solution uses Network Calculus and Genetic Algorithms to optimize how network resources are utilized for IoT traffic differentiation.

Sep 12, 2026

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