Graduate Research Positions

The following funded Research Projects in Mobile Mesh Networking Evaluation are available (start dates ranging between January 1, 2019 and September 1, 2019). The projects will remain open until appropriate candidates are identified, pending funding. Given the nature of this research program, the projects listed are subject to change.

Research Background

Connectivity in remote, and rural communities in Canada and around the world has led to a digital divide where millions lack access to online resources and the digital economy due to insufficient or non-existent infrastructure.

Left, an award-winning start-up company located in Maple Ridge, British Columbia (a suburb of Vancouver) has developed a novel approach known as RightMesh to bridge this digital divide. Specifically, RightMesh capitalizes on the Wi-Fi, Wi-Fi Direct, and Bluetooth technologies that exist in most modern mobile devices. RightMesh turns individual user devices into “nodes” in a mobile mesh network, which can be accessed by local users to connect to the internet. RightMesh enables data sharing and connectivity in the absence of, or in parallel with internet and/or cellular infrastructure.

To ensure that Left continues to lead in this domain, we are seeking PhD students to explore, implement, and evaluate network role assignment optimization, including, but not limited to such topics as device density, switching optimization, routing optimization, traffic balance optimization, caching protocols, and device energy conservation.

Projects Available

The following PhD research projects are currently available. Strong MSc candidates are also encouraged to apply. The goal is to fill two of these positions each semester for the next three semesters:

  • Research Obj1.2 Device Density Optimization [PhD/MSc]
    It is expected that the utility and usability of the mesh network will depend on appropriate coverage within a community, and a user’s ability at connecting with the network. Presently, it is not known how dense the network needs to be (especially in communities where devices and people are transient), how many super peer devices are required to support networks of varying size, nor how the density might affect data transfer speeds from one device to another. As such, the goal of this research question is to explore the density dependence of the network to optimize performance given different densities, movement of nodes, and other factors.
  • Research Obj1.3 Switching Optimization [PhD/MSc]
    The mesh network relies on Bluetooth, Wi-Fi and Wi-Fi Direct technologies to connect devices. As a network expands, devices are assigned different roles to govern the movement of data from source node to its intended delivery node. It is necessary to evaluate the utility of Bluetooth, Wi-Fi and Wi-Fi Direct to act as a signalling agent, and further develop methods to optimally time the switching to achieve close to full two-way transmission of data (despite the switching). Further, it’s necessary to understand how network performance is affected as the number of switching nodes increases. Thus, the goal of this research question is 1) to develop and evaluate methods to optimize switching in a dynamic mesh, 2) to evaluate the effect of switching on network performance, and 3) to inform and update the mesh protocols.
  • Research Obj1.4 Routing Optimization [PhD/MSc]
  • Depending on the density of the mobile mesh network, optimizing data packet routes becomes necessary to minimize wait times and to prevent or bypass data backlogs. To achieve this, it is necessary to identify the best metrics for route selection that consider current mesh density, current traffic load, and multiple device technologies (e.g. Bluetooth, Wi-Fi, Wi-Fi direct, etc.), and then to use this information to optimize routing within the network, possibly through the assignment or reassignment of role’s such that network coverage and capacity are maximized, while delays are minimized. The goal of this research question is to update the mesh protocol to achieve optimal routing given mesh density, traffic load, and the technologies present in the mesh.
  • Research Obj1.5 Traffic Balance Optimization [PhD/MSc]
  • With keeping network density, dynamic role assignment, and optimization routines for switching and routing data in mind, there is a need to develop a framework for selecting the most appropriate algorithm for a given situation. This will allow Left to manage traffic balance on the mesh. Specifically, it is essential to optimize traffic balance across multiple paths and considering the novel Left switching system. As such, the student will work to identify different traffic balancing optimization algorithms in the literature and evaluate them given different densities and dynamic role assignments.
  • Research Obj2.3 Data Mining Best Practices [PhD/MSc]
  • Many of the mesh applications that will be developed for rural and remote communities in northern Canada will support Indigenous-led community based monitoring programs with the goal of collecting environment and health data in response to climate change impacts. In addition to data available through remote sensors and weather stations, vast amounts of both quantitative and qualitative health and environment data will be collected through mesh based apps. Using data mining methods, these data will be used to support public health, wellness, and environmental stewardship decision making. However, it remains to be determined how best to process the qualitative and quantitative data sets in a manner that supports Indigenous self-determination and sovereignty, while providing the most accurate information for decision makers pertaining to the status of the environment and health. As such, there is a need to explore and develop a set of best practices for data mining within this context. The goal of this research question will be to develop a set of best practices for Indigenous data mining.
  • Research Obj3.1 Engagement through Competition [PhD/MSc]
    Gamification, or the act of introducing elements of gaming to non-gaming environments, has been shown to improve engagement in several domains. However, it is unknown if these tools can be used to support the development of mesh-based applications, especially those that are used to support community-led monitoring programs in remote, rural, and Indigenous communities. As such, mesh apps will be developed that explore the utility of self-competition and social-competition will be developed to increase user engagement. Ultimately, the findings of this research question will be the development of best practices for designing mesh-based software and user engagement.

In addition to developing and evaluating these algorithms, the MSc and PhD students will be responsible for working with Postdoctoral Fellows, as well as coordinating undergraduate research assistants who will research mesh network performance.

For more information, see the following links: