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物体和设备，从而产生影响我们社会各个方面的各种应用。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，无线传感器网络是任何物联网系统的基本组成部分。然而，异构传感器技术，动态用户行为以及能源，带宽和计算方面的有限资源使物联网系统设计和部署极具挑战性。为了解决这些问题，本研究计划提出了有效的资源管理算法和工具，在物联网系统的无线传感器网络。特别是，我们提出了基于上下文信息和用户体验的能量，频谱和计算资源的联合优化。我们的方法是基于分布式学习和适应整个无线传感器网络，其中节点交换信息的可用和预测的资源，环境条件和用户行为。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。新的动态层次聚类技术，以及传感器作为一个服务的战略将被调查。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的联合优化。 卡尔顿大学最先进的物联网设施（由思科主席，NSERC/思科IRC主席和CFI资助）将用于拟议算法和协议的硬件实现。该设施提供针对公共安全和应急响应以及住宅能源管理的真实物联网部署，将用于测试和验证我们的原型。 该计划将为6名博士，6名硕士和10名本科生提供广泛的实践培训。学生培训将通过整合基础研究，技术开发和硬件实现将研究和实践开发紧密结合起来。通过这个研究计划的广度，深度和创新，学生将获得理论，应用和系统导向的技能。对这些HQP的需求很大，他们未来的就业将加速物联网技术向加拿大工业的传播，并加强加拿大在这些领域的领导作用。在物联网系统的设计和操作中集成用户和上下文信息，除了产生关键的技术贡献外，还将对标准化机构、服务提供商和最终用户社区产生影响。这将有助于使物联网成为加拿大的主流技术，可以很容易地被我们社会各个部门的最终用户采用和部署。

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.