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Orchestration of pollution/QoS-aware control strategies for SD-IIoT

ABG-120458 Thesis topic
2024-02-19 Public funding alone (i.e. government, region, European, international organization research grant)
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Centre de Recherche en Automatique de Nancy ( CRAN )
Nancy - Grand Est - France
Orchestration of pollution/QoS-aware control strategies for SD-IIoT
  • Computer science
  • Engineering sciences
  • Telecommunications
Control of networks, IoT networks, SDN architecture, graph theory

Topic description

In recent years, the footprint of digital communication networks on the environment and society has
emerged as a major issue in the deployment of communication infrastructures (we are aiming at
environmentally aware, or even sober networks - a term used by the legislator, but without a real
definition). This aspect should not be reduced to the sole minimization of local energy consumption for
a single communication, but on the contrary to the whole infrastructure (of end-to-end services in a
potentially multi-actor and multi-technology context) and to other metrics including the different
sources of pollution (for example, the carbon cost per bit with the consideration of the mode of
production of the energy used or the radio-frequency pollution).
Moreover, the networks of the future (especially 5/6G), the pillars of digital ubiquity, must be able to
reconfigure themselves automatically, whether to support a new management strategy in the context
of the industry of the future or the provision of specific services during a temporary event such as a
sporting event. This is even more the case in industrial and wireless Internet of Things environments,
where the dynamics of traffic, mobility, QoS requirements (such as range or bandwidth) and
environment are massive.
This topic is at the confluence of these two themes, where it becomes necessary to implement
network control architectures (usually centralized Software/Intelligent-Defined Networking). Such
strategies must then optimize a budget shared by the whole network, concatenating both pollution and
QoS metrics. The scientific state of the art for such integrative strategies is still limited (either to traffic
evolution or to energy consumption optimization only), but first works at CRAN have highlighted their
interest. Thus, in the thesis Green metrics to improve sustainable networking, we find solutions by learning routing and capacity allocation leading to the selection of paths according to the mode of production of electrical energy (carbonization, renewable energy). It should also be noted that this is nowadays also found in smartphone charging strategies that shift the charging when the energy production is very carbonized (Clean Energy Charging - https://support.apple.com/en-us/HT213323).
In the context of a more sober architecture, the strategies lead to the partitioning of sub-networks, to
the reduction of capacities, and potentially to their putting on standby/shutdown. It is then necessary
to ensure that the translation of a strategy into an ordered set of rules for each equipment does not
generate inconsistencies in the data plane (need to define a migration order) and that the network
controller can recover the control of its equipment (how to access a part of the network that would
have been disconnected?). The problematic of this thesis covers more the ability to implement the
optimal solution computed by the controller. The literature is relatively incomplete here since it
essentially addresses the stability of the controller and the cost of reconfiguration, but not its implementation on the architecture. This problem is even more obvious when the communication
strategy between the controller and the devices is in-band, i.e. when it uses data transport links (and
not dedicated links), which reinforces the need to ensure a priori the durability of a communication
channel between the controller(s) and the devices. More generally, the issue of scaling and the
complexity of the selected algorithms remains a point to be evaluated.
Therefore, this thesis aims to orchestrate the different reconfiguration instructions of an IIoT
architecture to reduce its environmental footprint. It is structured as follows. The first step concerns
the state of the art of network control solutions in IoT (especially based on 5/6G protocols) and those
integrating QoS and Environmental Integration Quality metrics. Similarly, the candidate will identify the
processes in place in current SDN controllers to translate an infrastructure control strategy into a set
of rules. In the second step, a learning strategy to optimize the budget of an IoT infrastructure will be
defined and will be used as a reference for the rest of the work (the control plan could be based on
routing, the management of slices, or on the control of the transmission power).
The associated keywords will thus concern network metrology (e.g. energy), learning (e.g. channels),
and reactivity/reconfigurability. Then, we will analyze the impact of the topology (/graph) structure on
the ability to implement a given strategy. For a given structure, is it necessary to use a centralized,
decentralized, or distributed (multi-controller) architecture? Which controller placement is optimal?
Which links (and associated configurations) should be kept? Are there any constraints on the
formation of clusters? The answers to these questions will allow us to define a network reconfiguration
strategy (potentially based on intermediate data plans) that does not jeopardize its stability and its
future ability to reconfigure itself. Otherwise (i.e., if the topology structure does not offer enough
communication channels), we will have to define filtering strategies to rule out potential commands
that could lead to inconsistencies. For this design stage, we will also have to solve the issue of how
the network management plan takes into account the different dynamics and spatiotemporal
evolutions. Finally, we envisage that in this context of autonomous networks, the orchestration should
be able to explain the network reconfiguration decisions.

Starting date


Funding category

Public funding alone (i.e. government, region, European, international organization research grant)

Funding further details

Presentation of host institution and host laboratory

Centre de Recherche en Automatique de Nancy ( CRAN )

The Research Center for Automatic Control (CRAN) is a joint research unit between the University of Lorraine and the French National Scientific Research Center (CNRS) - Institute for Information Sciences and Technologies (INS2I).

Based on digital sciences, the laboratory is internationally recognized for its activities in the fields of signal and image processing, control and computer engineering, as well as for its work in health in connection with biology and neuroscience. Today, its fundamental and applied research enables it to accompany the changes in society and goes beyond the traditional industrial issues: energy production, management of the intelligent city or transport. In health, it is opening up to diagnosis and care in cancerology and neurology. They are crossing sociology, listening to social behaviors and opinion dynamics, and investing in the field of sustainable development, in the service of the circular economy and ecological systems.

Candidate's profile

- Master / Engineering degree in Computer Science/Networks  or a related field

- Knowledge in Networks

- Skills in Simulation tools and development

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