Context aware ad hoc network for mitigation of crowd disasters

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<ul><li><p>itig</p><p>ghaeetham</p><p>Available online xxxx</p><p>Keywords:Context awareCrowd behavior estimationWireless sensor network</p><p>focusile p</p><p>of a stampede during crowd disasters. This proposed sensor network consists of mobile</p><p>Ns) ccapab</p><p>sideration of our recent research work in WSN is to im-prove the quality and reliability of the sensed</p><p>includes nmunication</p><p>width and nearby resources such as printers, dand workstations. User context species the userslocation, and people nearby, even the current sociation. Physical context includes lighting, noise levels, trafcconditions and temperature.</p><p>Design and deployment of context aware systems inopen and dynamic environments is raising a new set of re-search challenges such as Sensor Data Acquisition, Context</p><p>1570-8705/$ - see front matter 2013 Elsevier B.V. All rights reserved.</p><p> Corresponding author. Address: AMRITA Center for Wireless Net-works &amp; Applications, Amrita Vishwa Vidyapeetham (AMRITA Univer-sity), Amritapuri Campus, Clappana P.O., Kollam 690 525, Kerala, India.</p><p>E-mail addresses: (M.V. Ramesh), (A. Shanmughan), (R. Prabha).</p><p>Ad Hoc Networks xxx (2013) xxxxxx</p><p>Contents lists available at SciVerse ScienceDirect</p><p>Ad Hoc Ne</p><p>journal homepage: www.els information is extremely important to take criticalaction in various real world situations. Hence, the key con-</p><p>physical context [30]. Computing contextconnectivity, communication costs, comPlease cite this article in press as: M.V. Ramesh et al., Context aware ad hoc network for mitigation of crowd disasters, Ad Hoc(2013),,l situa-physical world with the digital world by capturing andrevealing real-world phenomena and converting theseoccurrences into a format that can be processed, storedand acted upon [31]. These sensors are low power devicesthat can be used for a wide range of applications benecialfor society. The accurateness and the timeliness of this</p><p>ployed. The key focus of our research work is to integratethe concept of CAC for WSN (CAC-WSN) to predict the on-set of abnormality within a crowd.</p><p>Context aware computing is the key enabling technol-ogy for pervasive computing. The major classication ofcontext includes computing context, user context and1. Introduction</p><p>Wireless Sensor Networks (WSnumber of sensor nodes which aredevices 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-timecrowd dynamics modeling and real-time activity modeling have been achieved by imple-menting the algorithms developed for Context Acquisition and multi-context fusion.Dynamic crowd monitoring was achieved by implementing the context based region iden-tication and grouping of participants, distributed crowd behavior estimation, and stam-pede prediction based on distributed consensus. The implementation of the proposedarchitecture in Android smartphone provides light-weight, easy to deploy, context awarewireless services for effective crowd disaster mitigation and generation of an in time alertto take measures to avoid the occurrence of a stampede. The system has been tested andillustrated within a group of people for stampede prediction by using empirically collecteddata.</p><p> 2013 Elsevier B.V. All rights reserved.</p><p>onsists of largele of linking the</p><p>information by utilizing the potential of Context AwareComputing (CAC). This context awareness helps to cong-ure the WSN to adapt and react according to the real timesituation of the physical space in which the sensors are de-Received in revised form 24 December 2012Accepted 22 February 2013</p><p>machine-to-machine (M2M) communications. A mobile sensor network system integratedwith wireless multimedia sensor networks (WMSNs) was designed for effective predictionContext aware ad hoc network for m</p><p>Maneesha Vinodini Ramesh , Anjitha ShanmuAMRITA Center for Wireless Networks &amp; Applications, AMRITA Vishwa Vidyap</p><p>a r t i c l e i n f o</p><p>Article history:Received 14 August 2012</p><p>a b s t r a c t</p><p>Our research workscontext-aware mobation of crowd disasters</p><p>n, Rekha Prabha(AMRITA University), India</p><p>es on the design and implementation of a novel ubiquitous multihone sensing network for mitigation of crowd disasters using</p><p>tworks</p><p>evier .com/locate /adhocNetw.</p></li><li><p>2 M.V. Ramesh et al. / Ad Hoc Networks xxx (2013) xxxxxxData Fusion, Context Modeling and Reasoning, Service Dis-covery, and Execution of Services for the user. One of thecrucial requirements of context-aware applications isreal-time collection and aggregation of sensor data fromsensors distributed in the environment.</p><p>The proposed Ubiquitous Multi-Context Model (UMM)consists of three major phases, namely the Context Acqui-sition phase, the Context Modeling and Inference phase,and the Context based Action Generation phase. The keyproblem under consideration of our model is to develop adistributed context aware architecture with key featuressuch as reusability, non-redundancy and wide area cover-age of context information.</p><p>The recent advancements in the eld of WSN make useof mobile phones, specically smartphones, to act as sen-sor nodes. The mobile phone penetration rate is increasingtremendously all over the world, and in India it is expectedto reach up to 97% of the total population in the year 2014[10]. The increased penetration rate of smartphones ineveryday life has resulted in the development of manypromising applications utilizing low cost embedded sen-sors such as accelerometers, microphone, camera, gyro-scope, and light sensors. This new research area isreferred to as mobile phone sensing.</p><p>The implementation of our UMM is performed on anAndroid framework using mobile phone sensing andCAC-WSN. The objective of our research work is to makeuse of UMM for applications suitable for both personalsensing (e.g. individual activity) and group sensing (e.g.crowd monitoring).</p><p>The demand for security and safety within public spacesis gaining attention nowadays due to the increase in crowddisasters. The brief history of crowd disasters in publicgatherings for pilgrimage, sports events, etc. from 1989to 2011 is reported in the reference paper [9]. India expe-riences more than 100 deaths per year due to crowd disas-ters. The major motivation for our research work is thestampede of pilgrims that occurred at the hilltop Sabari-mala shrine in the state of Kerala in southern India, whichtook the life of 102 devotees [9]. The stampede was set offwhen a jeep drove into the crowd of pilgrims. The pilgrim-age area was ooded with people and the situation wentuncontrollable. The other causes of crowd disasters are ter-rorist activities that are focused on public gatheringsresulting in the loss of life of many innocent people.</p><p>Integration of context aware computing with mobilephones makes the devices capable of sensing the physicalworld, process the current scenario, and adapt and reactto dynamic changes of the environment. The proposed sys-tem uses the above-mentioned capability to develop anapplication suitable for crowd management, real-timemonitoring of crowd behavior, and dissemination alertsand instructions for crowd control.</p><p>2. Context and context aware computing</p><p>Researchers dened the term context in a slightly dif-ferent manner such as, Schmidt dened context as knowl-edge about the state of the users IT devices, along withinformation on the surroundings, situation, and to a lessextent, location [3], Chen and Kotz dened context as thePlease cite this article in press as: M.V. Ramesh et al., Context aware(2013), of environmental states and settings that either deter-mined an applications behavior or in which an applicationevent occurred and is interesting to the user [4], Dey et al.gave the denition of context as any information that isuseful to highlight the interaction between a user and anapplication and also used to characterize the situation ofan entity such as a person, place or object [5]. The knowl-edge of data along with its context will provide insightsinto the complex patterns hidden in those data. Thisunderstanding lead to the development of the new areaof context awareness in ubiquitous computing.</p><p>The term context-awareness in ubiquitous computingwas initially introduced by Schilit in 1994 [1,2]. Accordingto Schilit [1], context awareness is the capability of a sys-tem to adapt according to the location of the user, the col-lection of information about nearby people, hosts,accessible devices, as well as the changes to such thingsover time. Pascoe et al. [6] dene context-awareness asthe ability to detect, gather, interpret and react to contextchanges in the users surrounding and changes in its device.</p><p>The research paper [6] discusses the importance of con-text-aware sensing and provides a general overview ofacquiring context information and various context awareapplications.</p><p>These context aware frameworks detailed several openresearch challenges such as:</p><p> Context Acquisition Issues Determination of appropriate sensors and the type</p><p>of context to be acquired. Some of the sensors shouldinitially be chosen for recognizing context becausemost of the applications do not require context datafrom all the available sensors. Use redundant sensorsonly in case of uncertainty. In this way, data trans-mission over the network decreases together withthe processing demand.</p><p> Real time management of the sensors. Providingfeedback to the sensors and dynamically managingthem during the operational phase can improve theapplications performance, improving the overallresult.</p><p> Determination of who the context capturers will beand the number required. Assess the need for prede-termined participants or new users of the applica-tion and determine how many smartphones arerequired for the best context inference. Developautomatic techniques to prevent redundant infor-mation capture due to close proximity ofsmartphones.</p><p> Determination of the most suitable time-period ofmonitoring with respect to the application.</p><p> Context Modeling &amp; Inference Issues A distributed context aware inference formulating</p><p>framework is required. Since new contexts are continuously added in the</p><p>sensor network, the requirement of continuousadaptive learning algorithm is an issue that drawsattention.</p><p> Context inference is a complex task that requires agood mechanism for mapping simple captured con-text data to a higher level of data. This is not an easyad hoc network for mitigation of crowd disasters, Ad Hoc Netw.</p></li><li><p>task and requires using various context modelingtechniques. Research is still ongoing to nd the bestmethod that will disregard errors, automaticallyadapt the system to new types of data, learn auton-omously, and reason correctly.</p><p> Context-based Action Generation Issues Visualizing results and incorporating results on the</p><p>screens of hand-held devices. Overcoming low bandwidth in wireless networks by</p><p>introducing algorithms that minimize communica-tion as well as the number of hops.</p><p>can also be estimated. Accelerometers can be used formotion detection, position, and posture sensing.</p><p> Digital Compass: The digital compass provides twomeasures; the orientation and the ambient magneticeld. The values for orientation are in radians/secondand measure the rate of rotation around the X (roll), Y(pitch) and Z (yaw or Azimuth). The compass measuresthe ambient magnetic eld in the X, Y and Z axis, andthose values are in micro-Tesla (lT).</p><p> Gyroscope: Gyroscopes are used for measuring angularvelocity, angular rotation, and the rate of rotation</p><p>their daily personal information via SNS. Context aware</p><p>ent of</p><p>e applerformh is in</p><p>l</p><p>M.V. Ramesh et al. / Ad Hoc Networks xxx (2013) xxxxxx 3 Limited battery power. Minimizing the data-sampling frequency to save</p><p>energy and communication cost.</p><p>Based on the analysis of various context aware frame-works, shown in Table 1, the observations that can be ex-tended to our system under development for multipleapplication support include: the agent based architectureof CoBrA and the distributed middleware architecture ofSOCAM and Gaia.</p><p>The recent studies illustrated in [7] highlight the use ofsmartphones for context-aware applications because theyare relatively powerful in processing and they also supportvarious embedded sensors. Various smartphone platformsavailable are Android, iPhone, Symbian, RIM, Windowsphone and Linux. The two most promising platforms wereiPhone and Android because of their popularity, highusability, powerful CPUs, and available sensors.</p><p>The key features of Android includes support for ma-chine learning based programming in Java, access to morecore OS functionality, no requirement of any certicationor developer registration to deploy the software to thehardware, and the Android SDK is available on multipleplatforms [8]. The Android application framework hasbuilt-in context support. The main two parts are the rawcontext sources and context processing [7].</p><p>The raw context source support includes packages andclasses for Bluetooth, a camera, and a sensor manager forcollecting data from the embedded sensors on the Androiddevice (location data, time, etc.). Context sources providenecessary information about the entity it is associatedwith. Smart context sources supported by Android can becategorized as hard and soft sensors [8]. The hard sensorsinclude:</p><p> Accelerometer: A tri-axial accelerometer is a sensor thatreturns a real valued estimate of acceleration along thex, y and z axis from which velocity and displacement</p><p>Table 1Study of existing context aware frameworks.</p><p>Context awareframework</p><p>Specic features analyzed for the developm</p><p>CoBrA [1] Context Broker component spans multiplSOCAM [2] Distributed with centralized server and it pGaia project [3] Distributed middleware infrastructure whic</p><p>applicationsCenceme [4] Sharing of context information in web portaInContexto [5] Obtain context from a smartphone userPlease cite this article in press as: M.V. Ramesh et al., Context aware(2013), are progressing to utilize the user specicinformation collection from SNS.</p><p>The context processing enables the management of thisraw context data, from hard and soft sensors, into usefulcontextual data such as speech recognition, face recogni-tion, text-to-speech, and location proximity.</p><p>Using an analysis of existing frameworks, we developedthe new idea of integrating mobile phone sensing with thepotential of wireless multimedia sensor networks(WMSNs) to reduce the geographical restriction of moni-toring. To achieve reliable alert generation with real timevalues and ease of deployment and maintenance, our pro-posed crowd abnormality detection system utilizes thecapabilities of WMSN and the embedded smartphone sen-sors. Our research work also brings in multi-sensor modal-ities for activity recognition. Another key objective is...</p></li></ul>