A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <s> The ontology has many merits, of which the most notable are the excellent extensibility, and high expression power. Thus, many systems in the Ubiquitous environment are developed using ontologies. Usually, ontologies are used for modeling context that the systems should collect and analyze. Also, they use only OWL and other ontology languages based on Description Logic, so can not properly handle rules expressed in Horn-Logic. We define the context-type for establishing a context model that is adequate for inference and learning services. Second, we develop the context model using OWL and SWRL. SWRL is a rule language submitted by the W3C(World Wide Web Consortium), used for expressing rules. Finally, a framework is developed to handle context information with the developed ontology and learning algorithm, like the DT(decision-tree) or NN(neural-network). <s> BIB001 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <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> BIB002 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <s> Recent years have witnessed the emergence of Smart Environments technology for assisting people with their daily routines and for remote health monitoring. A lot of work has been done in the past few years on Activity Recognition and the technology is not just at the stage of experimentation in the labs, but is ready to be deployed on a larger scale. In this paper, we design a data-mining framework to extract the useful features from sensor data collected in the smart home environment and select the most important features based on two different feature selection criterions, then utilize several machine learning techniques to recognize the activities. To validate these algorithms, we use real sensor data collected from volunteers living in our smart apartment test bed. We compare the performance between alternative learning algorithms and analyze the prediction results of two different group experiments performed in the smart home. <s> BIB003 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <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> BIB004 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <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> BIB005 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <s> Mobile ad hoc peer-to-peer (P2P) applications become popular for providing the file sharing, voice communicating, and video streaming services due to entertainments and disaster recovery. However, both the topology of wireless network and the overlay of P2P network are dynamic, so the middleware is proposed to integrate such architectures of service-oriented applications. Therefore, we propose context-aware cooperative application (CACA) to overcome the frequent churn and high mobility problems. CACA proposes a cross-layer middleware to integrate DHT-based lookup, anycast query, and P2P delivery via the IPv6 routing header. Through anycast query, the response delay can be shortened and the query duplication can be minimized. Via IPv6 routing header, the delivery efficiency can be improved. Through the cross-layer design, the finger table in overlay layer is combined with the routing table in network layer to heighten proximity. The simulation results demonstrate that CACA has the outstanding performances of short download delay, high playback continuity, and low signaling overhead in mobile ad hoc network. <s> BIB006 </s> A systematic literature review on activity recognition with context-awareness techniques for mitigation of disasters <s> Research activity on crowd DM issue (Q4) <s> We propose a novel context aware anomaly detection process applied to human behaviour.We evaluate our method upon 2 different datasets.We utilise a novel social connections metric and a scene model as contextual information.The use of scene contextual information improves the detection of subtle anomalies.The use of social contextual information improves the detection of abnormal behaviour. This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour. <s> BIB007
In Figure 6 (a), few of the selected primary studies from the entire publications addressed crowd disaster issues and other related disaster control which is four (4%) of all the 75 publications. This indication further explains why there is need for further research in the domain. Despite the overwhelming interest of researchers in CAC and AR, efforts in MPS for CAC with AR BIB003 BIB005 ) is still very low. This prompted the recent development for the existing approach as earlier stated in Section 4.3.6. The distribution of other research focus showed in Figure 6 (a) include, 44 (41%), 26 (24%), 24 (23%) and nine (9%) on application BIB006 BIB007 , ARAC, context interaction and context reasoning    BIB001 respectively. Meanwhile, stampede is a major cause of crowd disaster BIB004 . According to , search from media account and research results reported 215 human stampede identified worldwide, from 1980-2007 with over 7,067 deaths and over 14,000 injured. This pathetic situation influenced the interest of researchers worldwide in finding ways to reduce these lingering dangers in our society. Although risk from human natural hazards cannot be eliminated completely, rather it can be minimised through a systematic approaches using disaster risk reduction (DRR) BIB002 ). This effort prompted the different methods, models, techniques and frameworks proposed in one study or the other using the technology under consideration. Consequently, the mapping of the research contributions and research types using the research main focus is summarised in Figure 6 (d).