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

物联网（IoT）预计将通过动态的全球信息交换连接数十亿个对象和设备，从而产生各种应用，影响我们社会的各个方面。物联网系统中的传感和数据收集任务通常由无线传感器网络（WSN）执行，它代表了任何物联网系统的基本组成部分。然而，异构传感器技术、动态用户行为以及有限的能源、带宽和计算资源使得物联网系统的设计和部署非常具有挑战性。为了解决这些问题，本研究计划在物联网系统的WSN中提出了有效的资源管理算法和工具。我们特别提出了基于上下文信息和用户体验的能源、频谱和计算资源的联合优化。我们的方法基于整个WSN的分布式学习和适应，其中节点交换有关可用和预测资源、环境条件和用户行为的信息。然后采用局部和全局优化算法来优化节点、集群和网络级别的资源分配。新的动态分层聚类技术以及传感即服务策略将被研究。此外，虚拟集群将在异构物联网网络中进行研究，以弥合不同物联网垂直领域之间的数据孤岛。在这里，我们提出了跨WSN节点、网络边缘（雾节点）和云的数据计算资源联合优化。

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.