In-Network Convolution in Grid Shaped Sensor Networks

Gathering information is the primary purpose of a Sensor Network. The task is performed by spatially distributed nodes equipped with sensing, processing, and communication capabilities. However, data gathered from a sensor network must be processed, and often the collective computation capability of nodes forming the sensor network is neglected in favor of data processing on cloud systems. Nowadays, Edge Computing has emerged as a new paradigm aiming to migrate data processing close to data sources. In this contribution, we focus on the development of a sensor network designed to detect a person's fall. We named this sensor network the smart floor. Fall detection is tackled with a Convolutional Neural Network, and we propose an approach for in-network processing of convolution layers on grid-shaped sensor networks. The proposed approach could lead to the development of a sensor network that detects falls by performing CNN inference processing on the edge. We complement our work with a simulation using the simulator ns-3. The simulation is designed to emulate the communication overhead of the proposed approach applied to a wired sensor network that resembles the smart floor. Simulation results provide evidence on the feasibility of the proposed concept applied to wired grid shaped sensor networks.