Simulation-driven emulation of collaborative algorithms to assess their requirements for a large-scale WSN implementation

Abstract

Assessing how the performance of a decentralized wireless sensor network (WSN) algorithm's implementation scales, in terms of communication and energy costs, as the network size increases is an essential requirement before its field deployment. Simulations are commonly used for this purpose, especially for large-scale environmental monitoring applications. However, it is difficult to evaluate energy consumption, processing and memory requirements before the algorithm is really ported to a real WSN platform. We propose a method for emulating the operation of collaborative algorithms in large-scale WSNs by re-using a small number of available real sensor nodes. We demonstrate the potential of the proposed simulation-driven WSN emulation approach by using it to estimate how communication and energy costs scale with the network’s size when implementing a collaborative algorithm we developed in for tracking the spatiotemporal evolution of a progressing environmental hazard

Predictive Tracking of Continuous Object Boundaries Using Sparse Local Estimates

“…As the hazard progresses, we have shown how the deployed sensor nodes can be dynamically re-organized into small-size ad-hoc clusters (node triplets) and compute, within their local area, estimates of the hazard's characteristics. Moreover, we have shown how the sensor nodes can update the local front model parameters using closed-form expressions (analytical solutions of a Bayesian parameter estimation problem) that are easy to implement using their commodity microprocessors [30]. Importantly, a unique feature of our WSN-based environmental monitoring methodology is that each sensor node can also estimate the uncertainty associated with the local front estimates it produces as the hazard progresses [29].…”