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.
 Emergency applications have recently become widely available on modern smart phones. Nearly all of these commercial applications have focused on providing simple accident information in outdoor settings. AR in indoor environments poses unique challenges, due to the unavailability of GPS indoors and WiFi-based positioning limitations. In this paper, we propose the use of Rescue Me, a novel system based on indoor mobile AR applications using personalized pedometry and one that recommends the most optimal, uncrowded exit path to users. We have developed the Rescue Me application for use within large scale buildings, with complex paths. We show how Rescue Meleverages the sensors on a smart phone, in conjunction with emergency information and daily-based user behavior, to deliver evacuation information in emergency situations. Cloud Computing, Mobile Technology, and SocialNetworking Services such as Facebook and Twitter has becomean integral part of society during the event of an emergency ordisaster. In the wake and aftermath of a disaster, atremendous number of people used social networking sites topost and share information. The Department of Health andHuman Services (HHS) had sponsored a challenge for softwareapplication developers to design a Facebook application to helppeople prepare for emergencies and to obtain support fromfriends and families during its aftermath. Lockheed Martinhad responded to this challenge by creating a cloud and mobilebased Facebook application called the Personal EmergencyPreparedness Plan (PEPP). This paper discusses the designand integration of the PEPP Facebook App with the intentionof serving as reference architecture for developing socialnetworking applications using cloud computing and mobiletechnology. Natural and man-made emergencies pose an ever-present threat to the society. In response to the growing number of recent disasters, such as the Indonesian volcanic eruption, Gulf of Mexico oil spill, Haitian earthquake, Pakistani floods, and in particular, the Red River crest that causes flood almost every year here in Fargo, North Dakota, we propose a community-based scalable cloud computing infrastructure for large-scale emergency management. This infrastructure will coordinate various organizations and integrate massive amounts of heterogeneous data sources to effectively deploy personnel and logistics to aid in search and rescue. The infrastructure also will aid in damage assessment, enumeration, and coordination to support sustainable livelihood by protecting lives, properties and the environment.