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

物联网无线传感器网络中以用户为中心和上下文感知的资源管理

• 批准号: RGPIN-2017-06968
• 负责人: Ibnkahla, Mohamed
• 金额: $ 1.75万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637371
• 关键词: User; centric; context; aware; resource

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.

物联网（物联网）有望通过动态的全球信息交流将数十亿个对象和设备连接起来，从而导致各种应用程序会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，该网络代表任何物联网系统的基本部分。但是，异质传感器技术，动态用户行为以及能源，带宽和计算的资源有限，使IoT系统设计和部署变得非常具有挑战性。为了解决这些问题，该研究计划提出了用于物联网系统的WSN中有效的资源管理算法和工具。特别是，我们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，在该学习中，NODES交换有关可用资源，环境条件和用户行为的信息。然后，使用本地和全局优化算法来优化节点，群集和网络级别的资源分配。将研究新的动态分层聚类技术以及将传感策略进行研究。此外，将在异质的物联网网络中研究虚拟聚类，以在不同的物联网垂直方面桥接数据筒仓。在这里，我们提出了跨WSN节点，网络边缘（FOG节点）和云的数据计算资源的联合优化。