CANS towards Congestion – Adaptive and Small Stretch Emergency Navigation with WSNs
CANS towards Congestion – Adaptive and Small Stretch Emergency Navigation with WSNs One of the major applications of Wireless Sensor Networks (WSNs) is the emergency evacuation navigation service, whose goal is to help people escape from a hazardous region safely and quickly when an emergency occurs.
Most of the existing solutions focus on finding the safest path for each person while ignoring possible large detours and congestions caused by a lot of people rushing to the exit. We present CANS, a C ongestion-adaptive and small stretch emergency navigation algorithm with WSNs in this paper.
Specifically, CANS leverages the idea of a level-set method to track the evolution of the exit and the boundary of the hazardous area, so that people near the hazardous area achieve a mild congestion at the cost of a slight detour, while people distant from the hazard avoid unnecessary detours.
CANS also considers the situation in the event of emergency dynamics by incorporating a simple local status updating scheme. To the best of our knowledge, CANS is the first WSN-assisted emergency navigation algorithm achieving both mild congestion and small stretch, where all operations are performed in-situ by cyber-physical interactions between humans and sensor nodes.
CANS does not require location information or reliance on any particular communication model. It is also distributed and scalable to the network size with limited storage on each node. Both experiments and simulations validate CANS ‘ effectiveness and efficiency.
In this paper, we presented CANS, a novel algorithm distributed with WSNs for congestion-adaptive and small stretch emergency navigation. CANS does not require prior knowledge of location or distance information or reliance on any particular communication model.
It is also scalable as our algorithm’s time and message complexities are linear to the size of the network. Both small-scale experiments and extensive simulations demonstrate the efficiency and effectiveness of the proposed algorithm.