Title:Dynamic Resource Management in Massive Heterogeneous IoT
Keywords: IoT, resource allocation, heterogeneous technologies, 5G, machine learning
Altran Technologies SA is a French group designated as the world leader in consulting, engineering and research and development and innovation (R & D & I). Founded in 1982, the group now has more than 45,000 employees in more than 30 countries. Altran is involved in many sectors: aerospace, automotive, defense, energy, railway, finance, life sciences, telecommunications, etc.
Altran research department (Vélizy-Villacoublay, France) is the entity responsible for research, development, and innovation within the Group. The main objective of the department is the development of high added-value services, innovative products, new tools and new methodologies to train the Group internal teams and support its customers in all phases of their projects.
Laboratoire ETIS UMR 8051, Université Paris Seine, Université Cergy-Pontoise, ENSEA, CNRS, 6 avenue du Ponceau, 95014 Cergy-Pontoise, France.
The use of the Internet of Things in all areas in Smart Homes continues to grow. Several communications technologies are implemented: WiFi, ZigBee, Bluetooth, Zwave, Insteon, NB-LTE etc. This diversity represents a real problem for an easy use of things-based functions for an end-user point of view as interoperability between the communications technologies. Indeed, it puts a communication barrier between the different connected objects. As a result, it is critical that IoT applications address the challenge of heterogeneity and enable the exchange of information between different platforms and applications. This is the main objective of the project CoBox. Indeed, CoBox is a research project relying on the Internet of Things and 5G domains. The final product is a unified interface for controlling a large number of connected objects belonging to one or more clusters. This interface will be able, through various plans (control, management, supervision, security ...), to communicate with connected objects. It will enable different IoTs to communicate transparently and will be the way to overcome the technology barrier preventing these objects to communicate.
The main objective of the PhD thesis is to develop dynamic resource allocation algorithms within the CoBox framework, where a massive number of IoT devices from different technologies is deployed. The features of each IoT device (or cluster), as well as the priority of the real-time transmitted messages, should be considered in the proposed algorithms. Machine learning tools are expected to deal with this high-dimensional optimization problem.
The PhD applicant should hold a Master degree (or equivalent) in Electrical engineering or Computer science or any related field. Knowledge of machine learning tools and optimization theory is a plus. The applicant should send an email with his master’s degree transcripts, Curriculum Vitae and recommendation letters to the contact below.
Expected starting date: Early 2019 (flexible)
Duration: 36 months
(c) GdR 720 ISIS - CNRS - 2011-2018.