27,444 research outputs found
Routing Diverse Evacuees with Cognitive Packets
This paper explores the idea of smart building evacuation when evacuees can
belong to different categories with respect to their ability to move and their
health conditions. This leads to new algorithms that use the Cognitive Packet
Network concept to tailor different quality of service needs to different
evacuees. These ideas are implemented in a simulated environment and evaluated
with regard to their effectiveness.Comment: 7 pages, 7 figure
Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation
State-of-the-art emergency navigation approaches are designed to evacuate
civilians during a disaster based on real-time decisions using a pre-defined
algorithm and live sensory data. Hence, casualties caused by the poor decisions
and guidance are only apparent at the end of the evacuation process and cannot
then be remedied. Previous research shows that the performance of routing
algorithms for evacuation purposes are sensitive to the initial distribution of
evacuees, the occupancy levels, the type of disaster and its as well its
locations. Thus an algorithm that performs well in one scenario may achieve bad
results in another scenario. This problem is especially serious in
heuristic-based routing algorithms for evacuees where results are affected by
the choice of certain parameters. Therefore, this paper proposes a
simulation-based evacuee routing algorithm that optimises evacuation by making
use of the high computational power of cloud servers. Rather than guiding
evacuees with a predetermined routing algorithm, a robust Cognitive Packet
Network based algorithm is first evaluated via a cloud-based simulator in a
faster-than-real-time manner, and any "simulated casualties" are then re-routed
using a variant of Dijkstra's algorithm to obtain new safe paths for them to
exits. This approach can be iterated as long as corrective action is still
possible.Comment: Submitted to PerNEM'15 for revie
Towards Flight Trials for an Autonomous UAV Emergency Landing using Machine Vision
This paper presents the evolution and status of a number of research programs focussed on developing an automated fixed wing UAV landing system. Results obtained in each of the three main areas of research as vision-based site identification, path and trajectory planning and multi-criteria decision making are presented. The results obtained provide a baseline for further refinements and constitute the starting point for the implementation of a prototype system ready for flight testing
A Cooperative Emergency Navigation Framework using Mobile Cloud Computing
The use of wireless sensor networks (WSNs) for emergency navigation systems
suffer disadvantages such as limited computing capacity, restricted battery
power and high likelihood of malfunction due to the harsh physical environment.
By making use of the powerful sensing ability of smart phones, this paper
presents a cloud-enabled emergency navigation framework to guide evacuees in a
coordinated manner and improve the reliability and resilience in both
communication and localization. By using social potential fields (SPF),
evacuees form clusters during an evacuation process and are directed to
egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm.
Rather than just rely on the conventional telecommunications infrastructures,
we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong
the life time of smart phones, that adaptively searches optimal communication
routes between portable devices and the egress node that provides access to a
cloud server with respect to the remaining battery power of smart phones and
the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by
ISCIS 2014 (29th International Symposium on Computer and Information
Sciences
- …