On the Performance of Nash Equilibria for Data Preservation in Base Station-less Sensor Networks

Base station-less sensor networks (BSNs) refer to emerging sensing applications deployed in challenging environments (e.g., underwater exploration). As installing a base station in such an environment is not feasible, data generated in the BSN will be preserved in the network before being collected by the uploading opportunities. This process is called data preservation in the BSN. Considering that sensor nodes are intelligent and selfish, this paper studies the Nash Equilibrium (NE) for data preservation in BSNs. We design a suite of data preservation algorithms, examine whether they achieve NEs, and rigorously analyze the performance of the NEs using existing (i.e., price of anarchy and price of stability) and our own designed metrics (i.e., rate of efficiency loss). We find that a minimum cost flow-based algorithm produces a NE that achieves the social optimal with minimum energy consumption in the data preservation process. We show the NE from one of our straightforwardly designed greedy algorithms achieves a price of anarchy of 3. On the other hand, we prove that a greedy algorithm always exists (although non-straightforward), achieving the socially optimal NE. Finally, we conduct extensive simulations to investigate the performances of various data preservation NEs and validate our theoretical results under different network parameters.