Personalized Nudges with Edge Computing

Abstract

This thesis aims to investigate the role of edge computing in a smart nudging system. A smart nudging system has requirements for efficient data processing of personal and context-aware data from heterogeneous sources. Furthermore, a smart nudging system needs to protect and preserve the privacy of data within the system. Edge computing has been proposed as a computing paradigm in a smart nudging system to accommodate some of these requirements. The edge computing paradigm makes promises of low latency, context-aware data collection and contributions to privacy when running on an edge device. However, edge computing has limitations in resources for heavy computations and storage. Therefore, a smart nudging system, NuEdge, has been proposed to utilize edge computing resources integrated with cloud computing, a local server, and IoT devices for better performance, privacy storage, and data off-loading. Further, a prototype of the NuEdge system has been implemented to discover the possibilities and limitations of the prototype in a real-world scenario. The primary nudge goal of the system is to improve physical activity for inactive users. By gathering research on edge computing and smart nudging, combined with the implementation's observations, has edge computing's role in smart nudging been evaluated. Edge computing has significantly contributed to efficient data collection in a smart nudging system and lower latency for data transmissions. Future work should include a large-scale prototype and new technologies like 5G to investigate the limitations of edge device capabilities such as power consumption, storage, and computational power.