Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks During Optimization

TL;DR

This paper employs temporal network analysis to visualize and quantify the evolving topology of bio-inspired wireless sensor networks, focusing on obstacle and noise effects on connectivity and efficiency.

Contribution

It introduces the use of temporal graphs and network measures to analyze dynamic network performance and compares these with traditional coverage metrics.

Findings

Temporal graphs reveal network evolution patterns.

Network centrality measures differentiate deployment strategies.

Coverage and distance metrics correlate with network characteristics.

Abstract

Temporal network analysis and time evolution of network characteristics are powerful tools in describing the changing topology of dynamic networks. This paper uses such approaches to better visualize and provide analytical measures for the changes in performance that we observed in Voronoi-type spatial coverage, particularly for the example of time evolving networks with a changing number of wireless sensors being deployed. Specifically, our analysis focuses on the role different combinations of impenetrable obstacles and environmental noise play in connectivity and overall network structure. It is shown how the use of (i) temporal network graphs, and (ii) network centrality and regularity measures illustrate the differences between various options developed for the balancing act of energy and time efficiency in network coverage. Lastly, we compare the outcome of these measures with the less abstract classification variables, such as percent area covered, and cumulative distance travelled.