Social Equality-Aware Resource Allocation for Post-Disaster Communication Restoration

Disasters are constant threats to humankind, and beyond losses in lives, they may cause many implicit yet profound societal issues such as wealth disparity and digital divide. Among those recovery measures in the aftermath of disasters, restoring communication services is of vital importance. Although existing works have proposed many architectural and protocol designs, none of them have taken human factors and social equality into consideration. Recent sociological studies have shown that people from marginalized groups (e.g., low income) are more vulnerable to communication outages. In this paper, we make efforts in integrating human factors – extracted from our collected dataset after Hurricane Harvey in 2017 in Texas, US – into an empirical optimization model to determine strategies for post-disaster communication restoration. We cast the design into a mix-integer non-linear programming problem, which captures the essential features of the design but is proven too complex to be solved. To find approximate solutions, we leverage a suite of convex relaxations and then develop heuristic algorithms to efficiently solve the transformed optimization problem. Based on our collected dataset, we further evaluate and demonstrate how our design could prioritize communication services for vulnerable people and promote social equality compared with an existing modeling benchmark.