Efficient Path Rescheduling of Heterogeneous Mobile Data Collectors for Dynamic Events in Shanty Town Emergency Response

Abstract

To investigate reported emergency incidents and provide better situational awareness during an emergency response effort in a shanty town, we envision the use of volunteers with networked sensing devices employed as Mobile Data Collectors (MDCs). These MDCs are heterogeneous depending upon the type of roads they can access. They gather information about events reported dynamically at random and relay it to central command center. We consider the problem of minimizing the Travel Time of such heterogeneous volunteer MDCs and maximizing the gathering of event data before its expiry time. We model this problem as a Dynamic Vehicle Routing Problem with Time Windows (DVRPTW), which reduces to a Combinatorial Optimization Problem and is NP-Hard to solve. In this paper, we developed two algorithms, Minimum Deviated Walk and Ortho Walk, to dynamically route or reroute the path of these MDCs to capture the data efficiently. We tested the effectiveness of these algorithms with three different classes of MDCs on simulated non- deterministic random sets of events applied to a real road map of Dharavi, a shanty town in Mumbai, India. We show that both these algorithms are capable of capturing 20% more data than a naive algorithm as well as more than 90% of the events generated within a specified time.