Big Data Processing with Minimal Delay and Guaranteed Data Resolution in Disaster Areas

Big data analysis is very important to support rescue activities when natural disaster happens, through understanding various situations, such as power/water outage regions. The traditional way to process big data is based on high-performance computation/storage resources in a cloud center. However, this is hard to be guaranteed in a disaster scenario due to destruction of communication infrastructure. Meanwhile, high latency between local sensing devices and cloud center sets a big obstacle enabling a near real-time big data analysis. On the other hand, movable base station, such as vehicle-based movable & deployable ICT resource unit (MDRU) developed by NTT, is a possible solution to reconstruct an emergency communication network and process data at the edge sites with reduced data transmission time. In this paper, we study the optimal overall delay in a fog/edge-computing platform constructed by vehicle-based MDRUs with guaranteed data resolution. We formalize the problem as a mixed-integer nonlinear program, which is a well-known NP-hard problem, and then relax the original problem to an mixed integer linear programing (MILP). Finally, we propose a two-stage heuristic algorithm to solve it in a time-efficient manner. Through evaluation, the effectiveness of the proposed heuristic approach has been validated in terms of minimizing overall delay with sufficient given data resolutions.