Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation

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1 Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation Huibo Bi and Erol Gelenbe Intelligent Systems and Networks Group Department of Electrical and Electronic Engineering Imperial College London

2 Cloud Enabled Emergency Navigation Using Fasterthan-real-time Simulation Outlines Overview Related Work System Framework Routing Algorithms Simulation Model Experimental Results Conclusions

3 Overview Objective Current emergency navigation approaches direct evacuees to exits in a real-time manner and casualties caused by the poor decisions are only apparent at the end of the evacuation process and cannot then be remedied. Our research aims to evaluate evacuation routes through a cloud-based simulator and generate new routes for simulated casualties. Graph model As an example of our approach, consider: A Graph: Representing three story canary wharf shopping mall. Vertices (Points of Interest): Positions with landmarks where evacuees can easily identify their locations by uploading snapshots to cloud servers. Edges: Physical paths for evacuees such as corridors.

4 Related work Cloud enabled emergency response system Compared with wireless sensor network based counterparts, cloudbased emergency navigation systems offer advantages such as high processing power, large storage and high interoperability. Use on-site sensors or portable devices to gather sensory data and offload intensive computations to cloud servers. Create a delayed feedback loop between live sensory data and routing decisions. Faster-than-real-time simulators in emergency management Most research focuses on inferring the spreading of hazard based on predictive models and live sensory data.

5 System Framework System architecture User layer Deployed on handhold devices. Collect sensory data. Cloud infrastructure layer Deployed on cloud severs. Consist of a data interpretation module and a navigation module. Data interpretation module extracts landmarks from the uploaded photos and matches them with pre-stored images. Navigation module is simulator that contains many inter-connected servers.

6 Routing Algorithms Components Cognitive Packet Network with time metric (CPNST) CPNST pursues safe routes with shortest time to the exit. We use CPNST as the basis algorithm to guide evacuees. Time-dependent Dijkstra s algorithm A variant of Dijkstra s shortest path algorithm. Replace the original distance metric with a time metric. Procedures 1. Perform CPNST to generate routes for evacuees in the simulator; 2. Reassign routes for perished evacuees in the simulation; 3. Send the generated routes to evacuees.

7 Cognitive Packet Network Cognitive Packet Network (CPN) Intelligent capabilities for routing and flow control are concentrated in the packets. Basis Random Neural Networks Components Smart Packets (SPs) Search paths and collect information with regard to pre-defined goal functions. Acknowledgements (ACKs) Bring back the information collected by SPs. Dumb Packets (DPs) Carry the payload. In the context of emergency evacuation, evacuees are considered as DPs. Variations for a cloud-based environment Each CPN node is deployed on a cloud server and SPs are used to gather interested information from other servers. Each server is also associated with a position with landmarks.

8 Time-dependent Dijkstra s algorithm Time-dependent metric T E ( ( i ), ( i 1) ) q ( ( i ), ( i 1) ) t ( ( i ) ) c V Term E e (π i, π i + 1 ) is the effective length of the edge between node π i and π i + 1. Term V S represents the average speed of civilians. e Term t c q (π i ) denotes the queueing time after an evacuee reaches node π i. s Queueing time at a node t q c ( ( i ) ) N c q Term N q c is the number of queued civilians when the evacuee arrives and d π(i) is the departure rate of node π(i). d ( i )

9 Simulation model DBES Existing Tool in Prof. Gelenbe s Group. Agent based. Graph based. Assumptions A data center with massive servers is used for decision support. Evacuees can communicate with the cloud over 3G or WI-FI. Fire starts near a main channel. Evacuees are randomly scattered in the building. Cloud servers can obtain the position of individuals by matching the image snapshots uploaded by evacuees.

10 Results: Average Percentage of Survivors CPN with time metric (CPNST) performs better than Dijkstra s algorithm in higher occupancy rates due to its embedded congestion-ease mechanisms. The proposed simulation-based algorithm achieves higher survival rate than CPNST. This is because CPNST may take the risk to traverse potential hazard areas in order to reduce the evacuation time and our proposed algorithm can generate new paths for perished evacuees in the simulation.

11 Results: Number of two-way information exchanges One two-way information exchange is defined as the process of an evacuee uploading a snapshot and gaining the suggested path from the Cloud. As expected, our proposed algorithm achieves the lowest number of data exchanges with cloud servers because can send final paths to evacuees when a disaster breaks out rather than making periodically decisions based on live sensory data.

12 Conclusions Conclusions The proposed simulation-based routing algorithm can improve the survival rate of an evacuation process. Owing to the hazard predictive model, the proposed algorithm can calculate desired paths only based on the initial fire location and distribution of evacuees. Hence, the information exchanges between evacuees and cloud severs is reduced.

13 Cloud Enabled Emergency Navigation Using Faster-thanreal-time Simulation Thank you!