Asynchronous mesh networks for continuous methane leak monitoring

Spero Analytics is a research-driven IoT startup which builds mesh networks for automated, continuous surveillance of greenhouse gas emissions in landfills and energy facilities. As those networks are power efficient and operate using radio communication, they can be deployed in remote industrial areas with limited cellular/power infrastructure. The existing system architecture consists of a gateway connected to a number of downstream nodes arranged in a multi-hop mesh configuration and equipped with a high-accuracy methane sensor. Every hour, the nodes come online, transmit their sensor reading via their on-board antenna, then go into a “deep sleep” mode to conserve power. This existing arrangement allows us to deploy robust, scalable greenhouse gas surveillance networks that can operate for years with minimal operator intervention. The challenge, however, emerges when there is an anomalous methane reading (due to a leak) which occurs during the hour that the nodes are in deep sleep mode. In that case, the operator will not be alerted about the anomaly until the next reporting cycle, thereby introducing a delay in leak detection and repair. This project proposes the development of an algorithm which will allow the mesh network to react to those intra-duty-cycle anomalies without compromising power efficiency.

Faculty Supervisor:

Jorg Liebeherr

Student:

Partner:

Spero Analytics

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program: