CANS: Towards Congestion-Adaptive and Small Stretch Emergency Navigation with Wireless Sensor Networks


One of the major applications of wireless sensor networks (WSNs) is that the navigation service for emergency evacuation, the goal of which is to assist individuals in escaping from a hazardous region safely and quickly when an emergency occurs. Most existing solutions specialise in finding the safest path for every person, while ignoring doable giant detours and congestions caused by lots of folks dashing to the exit. During this paper, we tend to present CANS, a C ongestion-Adaptive and tiny stretch emergency Navigation algorithm with WSNs. Specifically, CANS leverages the thought of level set method to trace the evolution of the exit and the boundary of the hazardous space, so that individuals nearby the hazardous area achieve a gentle congestion at the price of a slight detour, whereas people distant from the danger avoid unnecessary detours. CANS additionally considers matters within the event of emergency dynamics by incorporating a local yet simple standing updating scheme. To the simplest of our information, CANS is the primary WSN-assisted emergency navigation algorithm achieving each delicate congestion and little stretch, where all operations are in-situ allotted by cyber-physical interactions among people and sensor nodes. CANS does not require location data, nor the reliance on any specific Communication model. It is additionally distributed and scalable to the size of the network with restricted storage on each node. Each experiments and simulations validate the effectiveness and potency of CANS.

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