Membership Category
- Regular
Institution
- École de Technologie Supérieure - ETS
Discipline(s)
- Electrical Engineering and Electronic Engineering
- Industrial Engineering
Expertises
- IoT for the circular economy
- Sustainability
- Resource efficiency
Scientific activities and affiliations
- Systems Engineering Department
- Synchromedia Laboratory
Biography
Lokman Sboui is currently Assistant Professor in the Systems Engineering Department at the École de technologie supérieure (ÉTS) in Montreal, Canada. He received his master's and doctoral degrees in electrical engineering from King Abdullah University of Science and Technology (KAUST) in 2013 and 2017, respectively. He was a postdoctoral researcher in the electrical engineering department at KAUST and a mobile certification analyst at Videotron, a Canadian telecommunications operator. Professor Sboui's research intersects technology and sustainable practices, focusing on energy-efficient wireless communications that support the circular economy. He is deeply invested in the Internet of Things (IoT), advocating its application in smart and sustainable urban development, precision agriculture and resource management. His expertise extends to the emerging field of Urban Air Mobility (UAM), the innovative use of Low Earth Orbit (LEO) satellites and the development of cognitive radio systems that optimize spectrum use.
Affiliated research axes
Change and Transition Management
Planning Optimization
Resource and Product Maximization
Policy levers
Projects funded by the RRECQ
Smart and Sustainable Spent Coffee Grounds (SCG) Management System: from waste to resources
Description
The coffee-making process generates large quantities of coffee grounds. CGS is often disposed of as general garbage and transported to landfill sites, producing methane, a greenhouse gas 25 times more potent than carbon dioxide. Consequently, an efficient waste management system based on the circular economy and the recovery of its waste avoids high operating costs and emissions by reducing unnecessary collection trips and long haul journeys. This proposal sets out a vision for an intelligent waste management framework that uses real-time data and makes decisions accordingly. The decision, via decision support tools, includes a flexible design and configuration of a reverse logistics network and an intelligent vehicle routing system to decide when to send the truck for collection and which route to take. This new system will prevent bin overflow and waste contamination, and reduce operating costs.
Themes
- Greenhouse gas management
- Industry 4.0
- Internet of Things
- Optimization
- Reverse Logistics
