Synchronous data acquisition from large-scale clustered wireless sensor networks

Wireless Sensor Networks (WSNs) have become a mainstream for observing various variables of interest for a wide variety of applications, ranging from monitoring environmental parameters to medical, military and structural health conditions. Severe resource constraints of WSNs necessitate an efficient software layer, which acts as an intermediary between the applications and hardware resources, in order to regulate the energy consumption and optimize sensor nodes’ longevity. Most of the existing software solutions lack several features, which are crucial for synchronous data acquisition tasks, including collaborative data distribution, decentralized task execution and most importantly data fusion based on the application of spatiotemporal requirements and operational modality. Therefore, WSN applications are often unable to remotely fulfil their data aggregation/mining requirements. Sensomax is an agent-based WSN middleware, which facilitates parallel data-gathering for multiple concurrent applications, in a decentralized and adaptive fashion. It autonomously disperses the applications’ data-related demands to multiple target sources (sensors), where further processing (potentially computational algorithms) applied by the subagents, and captured data from multiple sources get relayed back to the corresponding applications, either as raw data in batch or aggregated form . In this paper Sensomax’s data - gathering mechanism is applied to human-structure interaction modelling in order to capture several data streams from a single human subject, and replicate and remodel it for multiple subjects.