ESNAP: Enabling Sensor Network Automatic Positioning in IoT Lighting Systems

Abstract

The configuration of sensor nodes in Internet-of-Things (IoT) systems is a time-consuming and labor-intensive process, often due to the lack of user interfaces in embedded sensor devices or the large number of nodes in a network. A crucial step in configuring an IoT node is mapping its identification (ID) to its physical location within the deployment area. This step is more pronounced in lighting systems, where both lighting control and sensing data need to be supported. In this article, we propose, eSNAP , an ID-location mapping method that enables the automatic configuration of wireless sensor nodes in connected lighting systems. Such mapping allows setting up and maintaining a secure and reliable IoT network with little human intervention, while providing valuable contextual information for the sensor data, which is critical in most IoT applications, including lighting. Our proposed method combines available digitized building planning/design information with theories of the Euclidean distance matrices and combinatorial optimization to enable the automatic configuration of IoT nodes. Furthermore, we leverage the channel and time diversities of the received signal measurements obtained by off-the-shelf wireless RF modules to enhance the positioning accuracy over time. We evaluate and validate the proposed method using state-of-the-art wireless mesh networking standards on two real-world setups: 1) a 30-node lab setup implemented using the Bluetooth mesh standard-compliant embedded platform and 2) a real-world connected lighting system based on ZigBee mesh in a corporate office building.