User-centric and context-aware resource management in wireless sensor networks for the Internet of Things

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The Internet of Things (IoT) is expected to connect billions of objects and devices through a dynamic global information exchange leading to diverse applications that will impact every aspect of our society. Sensing and data collection tasks in IoT systems are usually performed by wireless sensor networks (WSN) which represent a fundamental part of any IoT system. However, heterogeneous sensor technologies, dynamic user behaviour, and limited resources in terms of energy, bandwidth, and computing make IoT system design and deployment very challenging. To address these issues, this research program proposes effective resource management algorithms and tools in WSN for IoT systems. In particular, we propose joint optimization of energy, spectrum, and computing resources based on context information and user experience. Our approach is based on distributed learning and adaptation across the WSN, where nodes exchange information about the available and predicted resources, environment conditions, and user behaviour. Local and global optimization algorithms are then employed to optimize resource allocation at the node, cluster, and network levels. New dynamic hierarchical clustering techniques as well as Sensing-as-a-Service strategies will be investigated. Moreover, virtual clustering will be investigated in heterogeneous IoT networks to bridge data silos between the different IoT verticals. Here we propose joint optimization of data computing resources across WSN nodes, network edge (Fog nodes), and the Cloud.***State-of-the-art IoT facility at Carleton University (funded through Cisco Chair, NSERC/Cisco IRC Chair and CFI) will be used for hardware implementation of the proposed algorithms and protocols. The facility offers real-world IoT deployment targeting public safety and emergency response and residential energy management which will be used to test and validate our prototypes. ***This program will provide extensive hands-on training to 6 PhD, 6 MSc and 10 undergraduate students. Student training will closely couple research and practical development by integrating fundamental research, technology development, and hardware implementation. Through the breadth, depth, and innovation of this research program, students will acquire theoretical, applied, and system-oriented skills. There is a strong demand for these HQP and their future employment will accelerate the dissemination of IoT technology to Canadian industry and enhance Canada's leadership role in these sectors. Integrating user and context information in the design and operation of IoT systems, aside from yielding crucial technical contributions, will have ramifications into standardization bodies, service providers, and end user communities. This will contribute to making IoT as a main-stream technology in Canada that can be easily adopted and deployed by end users in various sectors of our society.