Most times people are unprepared to face natural disasters resulting in chaos, increased number of deaths, etc.Emergency responders need an efficiently working communication network to get in touch with the emergency services like hospitals, police, fire and rescue as well as people who are stranded. Such a network is also the need of the hour for survivors to contact their near and dear ones. One of the major barriers of communication during an emergency is the destruction of network elements. In case the communication devices survive the calamity, odds of the network getting congested are certainly high because almost everyone will be trying to use the same network resources. An important factor when dealing with emergency situations is the calls for an immediate response and an efficient Emergency Communication Systems (ECS). Currently there is a capability gap between existing ECS solutions and what we dream of achieving. Most current solutions do not meet cost or mobility constraints. An inexpensive, portable and mobile system will fulfill this capability gap. The main purpose of this research is to optimize the altitude and received signal strength of an aerial base station to provide maximum radio coverage on the ground as well as propose the best fit radio propagation channel model to carry out the experiment for the current scenario.

A call taker is the first point of contact in the emergency service system and thus the interface between the caller and ambulance dispatch. Misunderstandings in an emergency call have implications for the survival of patients. Using an applied conversation analytic approach this study examined participants’ use of conversational repair as an interactional strategy. Data included 101 calls from a South African emergency medical services call centre. The data set was comprised of two distinct subsets, namely: the 107 and public corpora. The 107 corpus (53 calls) contained calls from a general emergency call centre. The 107 caller thus served as a mediating party on behalf of the public caller. The public corpus (48 calls) comprised calls received directly from members of the public. The data subsets afforded a unique opportunity to analyse ways in which participants to an emergency call manage asymmetries of knowledge. Differential patterns of the type and purpose of repair were tracked across both data sets and similarities and differences were explored. Both data sets showed that participants’ choice of interactional strategies was customized based on an ongoing assessment of knowledge asymmetries. However, whilst knowledge asymmetries posed some constraints an overriding interactional constraint, inherent within the institutional nature of the emergency call, was a rigid adherence to routinized protocols. The call taker’s dilemma was thus identified as the management of these constraints through the frequent use of conversational repair. Although a level of responsiveness is required to glean quality information from callers, high volumes of emergency calls would not be possible without routinized protocols. However, increased orientation to routinized protocols led to a decreased orientation to responsiveness. This research therefore showed that knowledge symmetry is not necessarily more advantageous but that successful call trajectory is reliant on the call taker’s ability to maximize the collaborative nature of the interaction and effectively negotiate through the judicious use of repair and other relevant interactional strategies. This has important implications for call taker training.

This thesis is a case analysis of the nature of delays in emergency room admissions and the effects on ambulance dispatching and availability as it occurred in Southern California.

Indiana University-Purdue University Indianapolis (IUPUI) / Collaborative helping via social networking conversation threads can pose serious challenges in emergency situations. Interfaces that support complex group interaction and sense-making can help. This research applies human-computer interaction (HCI), computer-supported cooperative work (CSCW), and collaboration engineering in developing an interactive design, the Mega-Collaboration Tool (MCT). The goal is to reduce the cognitive load of a group’s growing mental model, thus increasing the general public’s ability to organize spontaneous collaborative helping. The specific aims of this research include understanding the dynamics of mental model negotiation and determining whether MCT can assist the group’s sense-making ability without increasing net cognitive load. The proposed HCI theory is that interfaces supporting collaborative cognition motivate contribution and reduce information bias, thus increasing the information shared. These research questions are addressed: 1. Does MCT support better collaborative cognition? 2. Does increasing the size of the shared data repository increase the amount of information shared? 3. Does this happen because group members experience 1) a greater sense of strategic commitment to the knowledge structure, 2) increased intrinsic motivation to contribute, and 3) reduced resistance to sharing information? These questions were affirmed to varying degrees, giving insight into the collaborative process. Greater content did not motive group members directly; instead, half of their motivation came from awareness of their contribution’s relevance. Greater content and organization improved this awareness, and also encouraged sharing through increased enthusiasm and reduced bias. Increased commitment was a result of this process, rather than a cause. Also, MCT increased collaborative cognition but was significantly hampered by Internet performance. This challenge indicates MCT’s system components should be redesigned to allow asynchronous interaction. These results should contribute to the development of MCT, other collaboration engineering applications, and HCI and information science theory.

In recent years, social media platforms such as Twitter and Facebook have emerged as effective tools for broadcasting messages worldwide during disaster events. With millions of messages posted through these services during such events, it has become imperative to identify valuable information that can help the emergency responders to develop effective relief efforts and aid victims. Many studies implied that the role of social media during disasters is invaluable and can be incorporated into emergency decision-making process. However, due to the "big data" nature of social media, it is very labor-intensive to employ human resources to sift through social media posts and categorize/classify them as useful information. Hence, there is a growing need for machine intelligence to automate the process of extracting useful information from the social media data during disaster events. This dissertation addresses the following questions: In a social media stream of messages, what is the useful information to be extracted that can help emergency response organizations to become more situationally aware during and following a disaster? What are the features (or patterns) that can contribute to automatically identifying messages that are useful during disasters? We explored a wide variety of features in conjunction with supervised learning algorithms to automatically identify messages that are useful during disaster events. The feature design includes sentiment features to extract the geo-mapped sentiment expressed in tweets, as well as tweet-content and user detail features to predict the likelihood of the information contained in a tweet to be quickly spread in the network. Further experimentation is carried out to see how these features help in identifying the informative tweets and filter out those tweets that are conversational in nature.