Network lifetime optimization in wireless healthcare systems: Understanding the gap between online and offline scenarios

In this paper, we study the network Lifetime Maximization problem in Mobile healthcare sensor systems (LMM). For the healthecare system, we consider a dynamic scenario where users are mobile at their own wills and periodically report their personal health information (PHI) to a static sink, e.g. a powerful server, for further processing and distributing. The objective is to optimize the network lifetime by flow scheduling. The major difficulty lies in the time-dependent network topologies. Therefore, we propose a novel temporal-spatial network modeling method by extending current model with time dimension. Based on this model, we show that if the movement of users are known in advance (i.e. offline case), the problem can be optimally solved in polynomial time by a linear programming. However, the online LMM problem is much more difficult to tackle, since we prove that there exists no online algorithm with a constant performance ratio to the offline optimal algorithm in terms of the network lifetime. We further design simulations to show the performance gap between online and offline LMM. Considering the user mobility within a given scenario follows some certain patterns, we show the potential improvements of using a prediction-based method. This investigation provides certain insights on designing efficient online algorithms for the LMM problem.