Distributed Sensor Logging: As Easy As a Mesh of Yoyos*

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Distributed Sensor Logging: As Easy As a Mesh of Yoyos* Int. J. Communications, Network and System Sciences, 2013, 6, 309-315 http://dx.doi.org/10.4236/ijcns.2013.66033 Published Online June 2013 (http://www.scirp.org/journal/ijcns) Distributed Sensor Logging: As Easy as a Mesh of Yoyos* Corey Wallis1, Alison Hutton2, Steve Brown1,3, Romana Challans1, Paul Gardner-Stephen1 1School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide, Australia 2School of Nursing and Midwifery, Flinders University, Adelaide, Australia 3Tourism Department, School of Humanities, Flinders University, Adelaide, Australia Email: [email protected], [email protected], [email protected], [email protected], [email protected] Received April 16, 2013; revised May 20, 2013; accepted June 14, 2013 Copyright © 2013 Corey Wallis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT The Mass Gathering Data Acquisition and Analysis (MaGDAA) project involved the development of hardware and software solutions to facilitate the rapid and effective collection of autonomous and survey based data during mass gathering events. The aim of the project was the development and trial of a purpose-built Open Hardware based envi- ronment monitoring sensor prototypes using IOIO (pronounced “yoyo”) boards. Data from these sensors, and other de- vices, was collected using Open Source software running on Android powered mobile phones, tablets and other open hardware based platforms. Data was shared using a Wi-Fi mesh network based on an Open Source project called The Serval Project. Additional data in the form of survey based questionnaires were collected using ODK Collect, one of the applications in the Open Data Kit suite. The MaGDAA project demonstrated that it is possible for researchers (through the use of Open Source software and Open Hardware) to own, visualise, and share data without the difficulties of set- ting up and maintaining servers. MaGDAA proved to be an effective infrastructure independent sensor logging network that enables a broad range of data collection (demographic, predispositions, motivations, psychosocial and environ- mental influencers and modifiers of audience behaviour, cultural value) in the field of mass gathering research. Keywords: Sensor Logging; Open Source Software; Open Source Hardware; Automated Data Acquisition; Surveying 1. Objective 2. Background The Mass Gathering Data Acquisition and Analysis 2.1. Mass Gathering Research (MaGDAA) project has been focused on the develop- In 2004 Arbon proposed a conceptual model that was ment of Open Source Software and Open Hardware to founded on the idea that mass-gathering health can be support research in the area of mass gatherings. By using understood as an inter-relationship between three do- open technologies and standards the project has been mains: 1) the biomedical; 2) the environmental; and 3) able to develop prototype hardware and software in a the psychosocial [1]. Mass gatherings are defined in relatively short time period. terms of the number of people who attend an event where The use of the MaGDAA sensors and survey man- people gather and where there is potential for a delay agement tools on Android powered devices were devel- response to emergencies or medical care [2]. To date the oped to improve the speed, automation and overall effec- science of mass gatherings has focused on the environ- tiveness of the collection of data, particularly in a way mental and biomedical domains with limited knowledge that could be incorporated into the overall dataset for a to support understanding of the psychosocial domain. wide range of events. The psychosocial domain captures the psychological and Additionally, through the use of mesh networking, the social influences within mass gatherings mass gatherings sensor network can be used without the need for addi- and includes both individual and audience behaviour and tional infrastructure and can also support distributed data motivation [1]. management. In the past there has been recognition that the nature of *This project was funded by a Flinders University Health Science Infra- the audience will impact directly on its health and safety, structure Grant. however the majority of published research focuses on Copyright © 2013 SciRes. IJCNS 310 C. WALLIS ET AL. audience behaviour in a negative context such as vio- attending these large public events and support the de- lence or conflict [3,4]. Additionally there is no agreement velopment of theory. Part of this strategy is to measure on what audience behaviour, mood and type actually audience behaviour through a wide range of variables mean. At the same time these elements have a number of (including density, humidity and temperature) and relate applications, including event design and management, these back to the observed health status of individuals. emergency planning and mass gathering medicine. These To achieve this a data collection system is being de- questions are, therefore, worthy of exploration. signed to capture and compare real-time data on the wide An important component of this early model is the po- range of variables of audience behaviour at mass gather- tential impact that the psychosocial domain may have on ings that can influence the health of individuals and are the incidence of injury and illness due to features tradi- measured as patient presentation (PPR) and transport to tionally described as crowd mood and type. This poten- hospital rates (TTHR). tial arises from the interaction of key features of the The maintenance of public order at mass gatherings is event and understanding the audience’s demographic, often referred to as audience management [11]. The size predispositions and behaviour can lead to the develop- of the audience and the type of event can impact on the ment of interventions that are likely to reduce risk and workload of emergency services [12]. Therefore gaining enhance the safety of the event. Yet this potential has an understanding of audience behaviour is important in been little explored until now and there is a dearth of supporting timely and appropriate management princi- literature describing how the audience’s behaviour, mood, ples in the planning and provision of emergency services type and motivation impacts on their health and safety at at mass gathering. There is a large theory to practice gap mass gatherings. In addition there is limited evidence of in relation to crowd/audience psychology within the mass models with practical applications to monitor and meas- gathering setting [3]. ure crowd at mass gatherings [5]. This assertion is sup- The purpose of the research is to develop an evidence ported by Emergency Management Australia (2005) who base to support theoretical and conceptual models to pro- state that “practical applications to monitor and measure vide a better understanding of the interaction between crowd type and mood have received limited attention” key characteristics of individual behaviour and the health (Emergency Management Australia (p. 14) in the past. effects of mass gatherings. This data will contribute to Crowd (or audience) behaviour has been defined by [5] more accurate predictive models for patient presentation as the demonstrable factor that requires assessment and rate (PPR) and transport to hospital rate (TTHR) for monitoring to underpin management actions at mass mass-gatherings. gathering events. Zeitz et al. [5] proclaim the term refers The purpose of the MaGDAA project is to automate to the gathering of a large number of people; not de- the collection of as much of the environment data as pos- pendent on the reason for the gathering. Whereas “type” sible, while at the same time supporting the collection of is a social descriptor of the sub-culture (society) and empirical data using surveys that are accessed using tab- “mood” is a psychological descriptor of an audience. let based computers. Additionally a Wi-Fi mesh network Type and mood impact on behaviour. Impacting factors is used to support the distribution of data to each device on behaviour also include the nature of the activity, mo- in the network to reduce the risk of data loss and support tivation of the crowd, security, weather and the density of easier data management activities. the audience (e.g. people per square metre) [2,6]. As audience behaviour is the most visible feature of 2.2. MaGDAA System Architecture the psychosocial domain of mass gatherings it is surpris- The core of the MaGDAA system architecture is a Wi-Fi ing that is has not been measured in a rigorous fashion. mesh network. A mesh network within the context of the In the mid-nineties two models where developed to as- MaGDAA project is a Wi-Fi network that supports peer- sess crowd mood and crowd type; Berlonghi (USA) [7] to-peer communication and where the network can be and Pines and Maslach (UK). In Australia Berlonghi’s created, used and maintained independently of the pres- classification of crowd types is now promoted by the ence of any traditional wireless network infrastructure. EMA (1999) and the work of Pines and Maslach [8] was The formation and management of the Wi-Fi mesh net- adapted by Zeitz et al. [5] to analyse the effect crowd work is undertaken by the open source Serval Mesh mood may have on emergency services workload at mass software developed by the Serval Project. gatherings. In the past few years Brown and Hutton [9,10] have 2.2.1. The Serval Project been examining practical strategies to develop accurate The Serval Project has developed software, which uses models to understand crowd behaviour at mass gather- the Wi-Fi capabilities of mobile devices powered by the ings. The outcomes of this research will enable more Android operating system to form a wireless mesh net- accurate predictions on the health care needs of people work.
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