Network Diffusion Model Reveals Recovery Multipliers and Heterogeneous Spatial Effects in Post-Disaster Community Recovery

Community recovery from hazards and crises occurs through various diffusion processes within social and spatial networks of communities. Existing knowledge regarding the diffusion of recovery in community socio-spatial networks, however, is rather limited. To bridge this gap, in this study, we created a network diffusion model to characterize the unfolding of population activity recovery in spatial networks of communities. Using data related to population activity recovery durations calculated from location-based data in the context of 2017 Hurricane Harvey in the Houston area, we parameterized the threshold-based network diffusion model and evaluated the extent of homogeneity in spatial effects. Then we implemented the network diffusion model along with the genetic algorithm to simulate and identify recovery multipliers. The results show that the spatial effects of recovery are rather heterogeneous across spatial areas; some spatial areas demonstrate a greater spatial effect (spatial interdependence) in their recovery compared with others. Also, the results show that low-income areas demonstrate a greater spatial effect in their recovery. The greater spatial effects in recovery of low-income areas imply more reliance on resources and facilities of neighboring areas and also explain the existence of slow recovery hotspots in areas where socially vulnerable populations reside. Also, the results show that low-income and minority areas are community recovery multipliers; the faster the recovery of these recovery multipliers; the faster the recovery of the entire community. Hence, prioritizing these areas for recovery resource allocation could expedite the recovery of the overall community and promote recovery equality and equity.Comment: 20 pages, 9 figures, 2 table

Load-Balanced CDS Construction in Wireless Sensor Networks Via Genetic Algorithm

A Connected Dominating Set (CDS) is used as a virtual backbone for Wireless Sensor Networks (WSNs). Most existing works focus on a Minimum CDS (MCDS), a k-connect m-dominating CDS, a minimum routing cost CDS or a bounded-diameter CDS, ignoring the load-balance factor of CDSs. In this paper, we propose a novel problem the Load-Balanced CDS (LBCDS) problem, in which constructing an LBCDS and load-balancedly allocating dominatees to dominators are investigated simultaneously. A Genetic Algorithm based strategy called LBCDS-GA is proposed to construct an LBCDS. Building an LBCDS and load-balancedly allocating dominatees to dominators can prolong network lifetime through balancing the workloads of all the dominators. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by 65% on average compared with the best and latest MCDS construction algorithm