Our research span core system building blocks, data and network management tools and services, mathematical principles, and application of wireless communication and networking to scientific, industrial and health-care disciplines. The main themes are:

  • Cyber physical systems: CPS is a system featuring a tight combination of, and coordination between, the system’s computational and physical elements. Within CPS, our research is primarily application driven -- we look into ways that computational, sensory or (wireless) networking technologies can improve targeted physical systems, and conversely, devise new methodogies to address the demands from the physical systems. Specific application domains we investigate include: 1) Civil structural monitoring, 2) Smart grid, 3) Data centre monitoring, and 4) Building energy management
  • Mobile computing concerns human-computer interaction on the move. It involves mobile communication, mobile hardware, and mobile software. We are primarily interested in developing key enabling technologies to faciliate such interactions: 1) Location discovery, location privacy, location-based services, and 2) Mobile crowdsourcing
  • Wireless networking: To make wireless networks more robust under uncertainty in channel quality, dynamics in traffic loads and users, and device heterogeneity, we incorporate learning in the resource management decisions  in wireless networks and investigate co-existence issues among multiple wireless technologies in the context of safe-critical applications

Location, Location, Location

in localization, mapping, SLAM

Humans spend majority of their time indoor. And yet, floor plans, indoor localization and location-based services are nowhere close in pervasiveness, accuracy, and maturity compared to their outdoor counter-part. We aim to close the gaps by devising effective algorithms and systems solutions. Specifically, we work on several related projects one indoor mapping, localization and location-based services.

Mobile Crowdsensing

in active learning, crowdsensing, data cleaning, incentivization, mobile

Recently, with the proliferation of mobile devices with rich sensor peripherals and computation capability, mobile crowdsensing, a special form of crowdsourcing where communities contribute sensing information and human intelligence using mobile devices to form a body of knowledge, has gained much interest in a variety of environmental, commercial and social applications. The goal of this project is to develop novel algorithmic, data analytics and system solutions to lowering the barriers to participation and monetization in mobile crowdsensing.

This project received 3-year funding from NSERC SPG in partnership with OverAir Proximity Technologies and CompuClever Systems.

Autonomous Data Center Monitoring

in data center, DCIM, wireless sensor networks

The project develops three enabling technologies towards a wireless based solution for autonomous monitoring of data center operations: (1) inexpensive & modular monitoring hardware that record and wirelessly transmit health and performance metrics of various DC components, (2) wireless networking protocols for real-time communications of monitoring data, commands and alarms, and (3) algorithms for fully autonomous monitoring and diagnosis enabled by the real-time data collected and efficient thermal modelling.

This project received 5-year funding from NSERC CRD in collaboration with Cinnos Mission Critical Incorporated.

MacQuest - An Indoor Navigation App

in Indoor navigation, mobile app

MacQuest is an APP developed by the Wireless System Research Group (WiSeR) from the Department of Computing and Software, McMaster University. It provides on-campus navigation and other campus-related services to visitors to McMaster University, McMaster students, staff and faculty. 

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Sequential Learning Framework for Resource Management in Wireless Networks

in multi-armed bandit, resource management, sequential learning, wireless networks

Many resource management solutions in wireless networks operate on the as- sumption that the decision makers have the complete knowledge of system states and parameters (e.g., channel states, network topology, user density, etc.).
The primary goal of the research program is to advance the fundamental understanding and development of efficient robust solutions for cyber physical systems, and wireless and mobile data networks. The specific objective of the project is to develop a sequential learning framework for a variety of resource management problems in wireless networks.

This project is currently funded by NSERC Discovery.

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