A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Related crowd disaster researches <s> Wireless Sensor Networks raise the interest of different business domains, including public security [5]. The ability of WSNs to monitor and control physical environments such as football stadiums makes them very attractive. The integration of WSNs into Command and Control (C2) systems aims at avoiding catastrophes such as the Heysel Stadium disaster [16]. In this paper, the architecture and implementation of a prototype for stadium surveillance is presented. We demonstrate the notification of alerts processed on sensor nodes and routed toward a C2 system. The prototype aims at increasing situational awareness of decision makers and at providing real time information related to the occurrence of incidents within the stadium. Decision makers can react accordingly by assigning available first responders to the incidents. Such integration comes along with security requirements; the use of encryption-based access control and trustworthiness evaluation for the generated alerts to ensure their confidentiality and reliability is proposed. <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Related crowd disaster researches <s> Abstract Human stampedes have been identified as a major hazard that could occur during mass gathering events in India, as well as anywhere in the world where people crowd together. This study compiles and analyses stampede data from religious, entertainment and political gatherings over the last five decades in India to derive an effective risk reduction framework for mass gatherings. Religious gatherings and pilgrimages have been venues for 79% of the stampedes in India. Noticeable occurrences and casualties have been reported from 15 Indian states and there have been instances of recurrent stampedes at certain locations. Risk management strategies to tackle stampedes during religious festivals are quite inadequate and have failed consistently in India because of the large crowds and the constantly widening spread of the venue. The triggers of human stampedes have been identified from case studies and it is concluded that a simple accident, an intentional act or even a rumor can trigger a crowd disturbance. Hence the organizers have to take into consideration the causes of crowd disturbance for ensuring safety in mass gatherings. The vulnerability of religious gatherings is also increased due to venue inadequacies like remote or hazard-prone setting, poor facilities, or lack of basic infrastructure and medical care centers. The risk factors identified from the study of past incidents have lead to the development of a basic framework aligned on four interlinked compartments for inter-agency cooperation and multi-disciplinary contemplation ranging from hazard identification to the execution of mitigation measures for human stampede risk reduction. <s> BIB002 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Related crowd disaster researches <s> In many web information systems such as e-shops and information portals, predictive modeling is used to understand user's intentions based on their browsing behaviour. User behavior is inherently sensitive to various hidden contexts. It has been shown in different experimental studies that exploitation of contextual information can help in improving prediction performance significantly. It is reasonable to assume that users may change their intents during one web session and that changes are influenced by some external factors such as switch in temporal context e.g. 'users want to find information about a specific product' and after a while 'they want to buy this product'. A web session can be represented as a sequence of user's actions where actions are ordered by time. The generation of a web session might be influenced by several hidden temporal contexts. Each session can be represented as a concatenation of independent segments, each of which is influenced by one corresponding context. We show how to learn how to apply different predictive models for each segment in this work. We define the problem of discovering temporal hidden contexts in such way that we optimize directly the accuracy of predictive models (e.g. users' trails prediction) during the process of context acquisition. Our empirical study on a real dataset demonstrates the effectiveness of our method. <s> BIB003 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Related crowd disaster researches <s> Abstract Our research works focuses on the design and implementation of a novel ubiquitous multi context-aware mobile phone sensing network for mitigation of crowd disasters using machine-to-machine (M2M) communications. A mobile sensor network system integrated with wireless multimedia sensor networks (WMSNs) was designed for effective prediction of a stampede during crowd disasters. This proposed sensor network consists of mobile devices that are used as crowd monitoring participant nodes that employ light sensors, accelerometers, as well as audio and video sensors to collect the relevant data. Real-time crowd dynamics modeling and real-time activity modeling have been achieved by implementing the algorithms developed for Context Acquisition and multi-context fusion. Dynamic crowd monitoring was achieved by implementing the context based region identification and grouping of participants, distributed crowd behavior estimation, and stampede prediction based on distributed consensus. The implementation of the proposed architecture in Android smartphone provides light-weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation and generation of an in time alert to take measures to avoid the occurrence of a stampede. The system has been tested and illustrated within a group of people for stampede prediction by using empirically collected data. <s> BIB004
Human stampedes is an hazards from mass gathering events and it has witnessed less attention worldwide while formulating risk management strategies BIB002 . Research has shown that the mass gathering (crowd) originated from religious festivity BIB002 . Crowd monitoring originated in 1995 when CCTV were used as means of monitoring human activities in a crowd with pattern recognition techniques . The technique which faced with the following short coming such as manually coordination, with lots human effort (tedious and cumbersome) above utilised physical appearance of security officers. These limitations led the proposed wireless sensor networks with wireless communication techniques for better situational awareness of monitoring people in crowded places BIB001 ). However, it suffered from a high rate of false alarm, and the use of low power sensors in the study due to low temperature and acoustic sensors used makes the technique less reliable and undependable. The limitations of this study witnessed the introduction of an emerging technology with MPS by . Despite the novel contribution and solution proposed in , their approach suffers some weaknesses. Thus, the weakness and the stampede incident that claimed life of over 102 devotees in the State of Kerala in Southern India and the constant deaths of more than 100 people per year due to crowd disaster motivates the proposed CAC-WSN for a crowd DM by BIB004 . The study seeks to address HAR accuracy, poor real time information and high rate of false alarm for effective stampede prediction in order to mitigate crowd disasters as identified in BIB004 . However, BIB004 , was the first to introduce CA application using AR to investigate HAR accuracy as a basis for effective stampede prediction in mitigation of crowd disasters by developing multi-context-fusion which suffers some drawbacks led the study in this paper. CAness application availability on smartphone devices is a technology that helps to sensitise users of activities going on around them (Anguita et al., 2014) . It has been used in prediction of web analytics BIB003 .