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ISSN: 2278 –
1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Wireless Sensor Networks for Disaster Management 1 Harminder Kaur, 2Ravinder Singh Sawhney, 1Navita Komal 1 Students, 2Professor Abstract- World is a happening ground for the disasters almost daily. These incidents of mass destruction irrespective of the whether natural calamities or man-made catastrophes cause a huge loss of money, property and lives due to non-planning on the part of the governments and the management agencies. Therefore steps are required to be taken towards the prevention of these situations by pre determining the causes of these disasters and providing quick rescue measures once the disaster occurs. Wireless Ad hoc sensor networks are playing a vital role in wireless data transmission infrastructure and can be very helpful in these situations. Wireless sensor networks utilize the technologies which can cause an alert for the immediate rescue operation to begin, whenever this disaster is struck. Through this paper our aim is to review technological solutions for managing disaster using wireless sensor networks (WSN) via disaster detection and alerting system, and search and rescue operations. We have first discussed the basic architecture of WSNs that can be helpful in disaster management and the wireless sensor network models that can be employed for the different disaster situations. Finally, we propose how these networks can be effective in Indian scenario which lags behind the developed world in basic infrastructure amenities. Keywords: Wireless Sensor Networks, cluster, Sink Node, GRNN, Intelligent Drought Decision System. I. INTRODUCTION Global climate change is increasing the occurrence of extreme climate phenomenon with increasing severity, both in terms of human casualty as well as economic losses. Authorities need to be better equipped to face these global truths. An efficient disaster detection and alerting system could reduce the loss of life and properties. In the event of disaster, another important Fig.1.Wireless Sensor Network Architecture for disaster survivor detection issue is a good search and rescue system with high level of precision, timeliness and safety for both the victims and the sensor field. Each cluster with in a network is directly rescuers. Recently, Wireless Sensor Networks (WSNs) have interfaced with Sink node that is responsible for maintaining become mature enough to go beyond being simple fine- communication path between head nodes within each cluster grained continuous monitoring platforms and become one of towards base station. The base station uses UMTS based or the enabling technologies for disaster early-warning systems. Wimax based communication system that is responsible to Event detection functionality of WSNs can be of great help transfer disaster affected information from cluster based and importance for (near) real-time detection of, for example, network architecture towards emergency response Centre [2]. meteorological natural hazards and wild and residential fires. The Emergency response authority at the emergency Figure1 shows a wireless sensor network, where each cluster operations centre dispatch Emergency rescue teams towards in network architecture consists of four ad-hoc relay the point of need with in a limited time span for mission stations[1]. The sensor nodes within each cluster are critical applications. The Emergency response data base centre surrounded by ARS and in middle the head node is further route this information via satellite broadcast Antenna responsible for communicating with all the nodes within the towards Satellite Station. The Satellite communication infrastructure is responsible for transferring the disaster related information towards Mobile Ambulance and Hospital which will be responsible to 129 All Rights Reserved © 2012 IJARCET
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ISSN: 2278 –
1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 provide urgent Medical needs including medical first aid centers. Fig.2 shows a pictorial view of the deployment of services using this Telemedicine based infrastructure [3]. sensor nodes and data aggregation [5]. Level two deals with the setup of local base stations as well II. DIFFERENT WSN ARCHITECTURES as with data communication at district level. Level three could be involved with the central monitoring system at the A. Wireless Sensor Network for Flood and Water Level headquarters to process acquired data. Data analysis then Monitoring System takes place either at headquarter or at outside research centers that particularly do high-risk flood analysis. Fig.3 shows Each year floods cause loss of thousands of lives and billions various phases used for monitoring system. worth of property in india. Last year, major loss of human lives, cattle as well as billions worth of establishments was reported in the floods in Bihar and West Bengal. Each year both Ganga and Yamuna break their boundaries and cause numerous losses. Although all these losses cannot be eradicated fully but the losses to lives and property can be reduced to barest minimum level, if the protective measures can be taken before the disaster has struck in the form of flash floods. This can be made possible with the help of communication technology employed on top of wireless sensor networks. The system development involves the various phases and of course, all phases are equally important. Starting with the first phase of data collection, level one is to deal with the physical deployment of sensing devices in the riverbanks and implementation of an effective localization scheme depending on the situation and environment. The flow Fig.3 Smart System aided with Wireless Sensor Network path of the river, past records of water flow and future prediction of the route of the river, influence the placements of the wireless sensors. These sensors form clusters to B. Wireless Sensor Network for Forest Fire Detection communicate with the local base stations. The local base stations are powerful enough to communicate with one Forest house millions of rare species of animals, birds and another directly using wireless communications. insects. Forest fires cause not only the loss of their shelters but also cause huge loss to flora and fauna. Forest fires are common in the middle ranges of Himalayan region of India due to Lightening , excessive heat or carelessness on the part of local natives. There has been numerous occasions when the forest fires broke out due to the negligence of the natives in Uttarakhand, Chhattisgarh, and north-eastern states. Last year, southern Australia bore the brunt of ravaging forest fires, but the losses to the human lives were minimized on account of adequate evacuation steps taken well in time. Although it is almost impossible to put off raging fires, but the calamity can be averted provided the information about the site of the fire can be immediately sent to the nearest control centre and adequate measures be taken to control it, before it engulfs everything. A large number of sensor nodes are densely deployed in the forest. These sensor nodes are organized into clusters so that each node has a corresponding cluster header. Sensor nodes can measure environment temperature, relative Fig.2 Data collection and aggregation in wireless sensor network humidity and smoke. They are also assumed to know their location information by equipment’s such as Global The data sent from the sensors are aggregated in the local Positioning System GPS. Every sensor node sends base stations to provide as inputs to the data processing measurement data, as well as the location information, to the 130 All Rights Reserved © 2012 IJARCET
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ISSN: 2278 –
1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 corresponding cluster head. The cluster header calculates the weather index using a neural network method and sends the weather index to the manager node via sink. The sink is connected to a manager node via a wired network. A few wind sensor nodes are manually deployed over the forest and connected to the sink via wired networks to detect wind speed [6]. The manager node provides two types of information to users: (1) Emergency report for abnormal event (e.g. smoke or extremely high temperature is detected); (2) real-time forest fire danger rate for each cluster based on the weather indexes from the cluster header and other forest fire factors. Fig.5 System Architecture White blocks are the components at a sensor; shadowed blocks are the components at the base station; solid line represents data flow; dotted line represents control flow Various studies indicate that the frequency-based detector has better detection performance when the sensor receives higher signal energy [8]. Therefore, it is suggested that, the base station first selects a minimal subset of informative sensors based on the signal energies received by sensors while satisfying system sensing quality requirements. The selected sensors then compute seismic frequency spectrum using Fast Fourier Transform (FFT) and make local detection decisions which are then transmitted to the base station for fusion. In addition to the detection of earthquake occurrences, node-level earthquake onset time is critical for localizing earthquake source. In this approach, the Fig.4 A wireless sensor network for real-time forest fire detection base station first identifies an individual earthquake and estimates a coarse onset time [9]. C. Wireless Sensor Network for Earthquake Sensing D. Wireless Sensor Network for Tsunami Detection Mass destruction of human lives and property happens when The wounds are still fresh onto the hearts of all those earthquake strikes. The most ferocious earthquake that survivors who lost their near and dear ones in 2004 Tsunami happened in India was on 26th January, 2001 that rattled not which ravaged the complete east coast of India. It is estimated only India, but the other neighbouring countries like Pakistan, that tens of thousands of people died in that event only in Afghanistan and Iran also couldn’t escape its fury. Lakhs lost India and lakhs in other countries of Asia. lives, limbs and cattle and loss to the property was Even recently, almost 32 countries throughout the world unaccountable. Though loss to the property cannot be ruled were put on alert, when an earthquake of 8.9 measure on out, but many precious human lives can be saved by timely Richter Scale struck in Indian Ocean. Thanks to the effective action. System architecture for our approach has been shown monitoring system, an alert was sounded and more coastal in Fig.5 each sensor detects earthquake event every sampling areas were evacuated. Though Tsunami is not a common sight period based on seismic frequency spectrum. To handle the in India when compared with Japan, Indonesia, Vietnam and earthquake dynamics such as highly dynamical magnitude and Thailand, yet it is wiser to be alert rather than repenting after variable source location, each sensor maintains separate the tragedy. A system for tsunami detection and mitigation statistical models of frequency spectrum for different scales of using a wireless ad hoc sensor network defines three types of seismic signal energy received by sensor [7]. nodes: sensor, commander, and barrier. A relatively large number of sensor nodes collect underwater pressure readings 131 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 across a coastal area. This data is reported to commander suggested model is based on an intelligent system called nodes which analyses the pressure data and predict which, if Drought Forecast and Alert System (DFAS), which is a 4-tier any, barriers need to fire. Although it is impossible to system framework composed of Mobile Users (MUs)[13], completely stop a tsunami, we propose using a number of Ecology Monitoring Sensors (EMSs), Integrated Service barriers which may be engaged to lessen the impact of the Server (ISS), and Intelligent Drought Decision System (ID2S). wave. Fig.6 illustrates the architecture of a prototype system DFAS combines the wireless sensor networks, embedded that can be implemented as live-model up to a level of multimedia communications and neural network decision perfection and satisfaction. Fig.6 depicts a sensor network technologies to effectively achieve the forecast and alert of the consisting of 80 underwater sensors (Sensor1-Sensor80) that drought. DFAS analyzes the drought level of the coming 7th are connected to two commander nodes which are in turn day via the proposed drought forecast model derived from the connected to four barriers (Barrier1-Barrier4). Back-Propagation Network algorithm. An algorithm as suggested by K. Casey, A. Lim, and G. The drought inference factors are 30 day accommodated Dozier et.al. has been implemented [10] which uses a general rainfall, daily mean temperature, and the soil moisture to regression neural network (GRNN) [11] as prescribed by D. improve the accuracy of forecasting drought [14]. These Specht, to predict the path of the wave. The GRNN analyzes inference factors are detected, collected and transmitted in the pressure data from sensor nodes and predicts which real-time via the Mote sensors and mobile networks. Once a barriers should fire to most effectively impede the tsunami. It region with possible drought hazard is identified, DFAS sends also uses a real-time response mechanism for diffusion. This altering messages to users’ appliances. System protocol is inspired by RAP [12] predicted by C. Lu, B. Blum, implementation results reveal that DFAS provide the drought T. Abdelzaher, J. Stankovic, and T. He but does not require specialists and users with complete environment sensing data location information. and images. DFAS makes it possible for the relevant personnel to take preventive measures, e.g., the adjustment of agricultural water, for a reduced loss. Fig.6 Tsunami detection and response system architecture E. Wireless Sensor Network model for Drought Forecast More irrigation techniques have almost got over the problem of drought, but like many other developing and under- privileged countries, India too is dependent on rain gods for the seasonal rains to meet their requirements of water for irrigation purposes. A mechanism has been suggested by Hsu- yang kung, jing-shiuan hua and chaur-tzuhn chen to forecast Fig.7 Drought forecast and alert system and network architecture the drought by the means of wireless sensor networks. The 132 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 III. SAHANA: OVERVIEW OF A DISASTER but in India there is still a lot to be done in this field as all MANAGEMENT SYSTEM these protective networks need huge investments and manpower for these dreams to become reality in India. Sahana is a Free and Open Source Software (FOSS) application that aims to be a comprehensive solution for REFERENCES information management in relief operations, recovery and rehabilitation. The Sahana project has proved without a doubt [1] Naveed Ahmad, Naveed Riaz, Mureed Hussain.”Ad hoc wireless Sensor the viability of FOSS solutions for humanitarian applications. Network Architecture for Disaster Survivor Detection”, International Journal More significantly, a disaster typically requires the of Advanced Science and Technology Vol. 34, September, 2011. coordination of multiple clients; hence Sahana has also [2] Y.Chu and A.Ganz, “WISTA: “A Wireless Telemedicine System for Disaster Patient Care”, Springer Issue on Mobile Networks and Applications, provided a platform for inter-organizational data sharing July 26, 2007. during a disaster. There is much left to be for the technical [3] T.R.Sheltami and A.S.Mahmoud and M.H.Abu-Amara “An Ad hoc development of FOSS as much sought disaster management wireless sensor network for Telemedicine applications”, The Arabian Journal software as the primary functional requirement is the need to for Science and Engineering, Vol: 32, PP: 131-143, December 30, 2006. [4] http://www.krazytech.com/technical-papers/deploying-a-wireless-sensor- deal with heterogeneity of data types (text, semi structured, network-on-an active-volcano-2 (visited on 15th April, 2012). Web HTML and XML, GIS, tabular and DBMS), and [5] Al-Sakib Khan Pathan, Choong Seon Hong and Hyung-Woo Lee secondary functional requirement is to develop standards and “Smartening the Environment using Wireless Sensor Networks in a protocols for data sharing [15]. Policies for data security and Developing Country”, Feb. 20-22, 2006 ICACT2006. [6] Liyang Yu, Neng Wangand V “Real-time Forest Fire Detection with privacy during the different phases of a disaster have to be Wireless Sensor Networks” IEEE paper 2005. developed to provide robust performance and finally be able [7] E. Endo and T. Murray, “Real-time seismic amplitude measurement to provide additional capabilities as the communications (RSAM): a volcano monitoring and pre diction tool,” Bulletin of Volcanology, infrastructure is restored. Real time response may be needed vol. 53, no. 7, 1991. [8] G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, for some modules such as mobile messaging and situation and M. Welsh, “Deploying a wireless sensor network on an active volcano,” control. IEEE Internet Computing, vol. 10, no. 2, 2006. [9] R. Sleeman and T. Van Eck, “Robust automatic P-phase picking: an on- IV. CONCLUSION line implementation in the analysis of broadband seismogram recordings,” Physics of the Earth and Planetary Interiors, vol. 113, 1999. [10] K. Casey, A. Lim, and G. Dozier, “Evolving general regression neural Wireless sensor networks (WSNs) have attracted significant networks for tsunami detection and response,” in Proceedings of the attention over the past few years. As technology emerges over International Congress on Evolutionary Computation(CEC). IEEE, July 2006. the decades, WSN has come to the spotlight for its unattained [11] D. Specht, “A general regression neural network,” IEEE Transactions on Neural Networks, 1991. potential and significance. We observe that these wireless [12] Chenyang Lu, Brian M. Blum, Tarek F. Abdelzaher, John A. Stankovic, sensor network architectures serves us a lot in predetermining and Tian He,” RAP: A Real-Time Communication Architecture for Large- the causes of the natural as well as man-made disasters and Scale Wireless Sensor Networks,” IEEE Real-Time and Embedded providing rescue and preventive measures if somehow any Technology and Applications Symposium (RTAS 2002) San Jose, CA, September 2002. area is struck by these disasters. Hence these structures help in [13] Hsu-yang kung, jing-shiuan hua and chaur-tzuhn chen “Drought forecast protecting many precious human as well as animal lives that model and framework using wireless sensor networks by” Journal of would have been destined to perish from the effects caused by information science and engineering 22, 751-769 (2006) . these disasters. [14] C. N. Hsi, “Study on integrating GIS techniques and MODIS image into drought monitoring,” Master dissertations, Department of Forestry, National WSNs not only contributes in saving human lives but also Pingtung University of Science and Technology, Taiwan, 2004, pp. 1-17. plays an important role in conserving our unique flora and [15] F.Naumann and L. Raschid, “Information integration and disaster data fauna consisting of many plants and biological micro- management (DISDM),” University of Maryland, Tech. Rep., 2006. organisms, which are important for the survival of humans, by AUTHORS PROFILE alerting us from the dangers of forest fires. Finally we conclude by stressing on the need to survey the state of the research and classified the different schemes. We developed Harminder Kaur is pursuing her M.TECH in taxonomy of relevant attributes and categorized the different Electronics Technology Department from Guru Nanak Dev University Amritsar. She obtained her schemes according to the objectives, the desired cluster Bachelor of Technology degree in Electronics and properties and clustering process. We highlighted the effect of communication engineering from Guru Nanak Dev the network model on the pursued approaches and engineering college Ludhiana (PTU). Her major summarized a number of schemes, stating their strength and areas of interest are fiber optic communication, Computer networks, wireless sensor networks and limitations. These technologies have produced very effective VLSI. She is going to be publishing papers in these results, once put to use in the deploy these wireless sensor areas and currently she is working on ant colony optimization and particle networks for mission critical applications. In this paper, we swarm optimization with wireless sensor networks. developed countries like USA, Germany, France and England 133 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Ravinder Singh Sawhney has been working in the Department of Electronics Technology, Guru Nanak Dev University, Amritsar for the last 15 Years. He has more than 50 publications in various international journals as well as international and national conference proceedings. His major areas of Interest are Wireless Local Area Networks, Mesh Networks, MANETS and Wireless Sensor Networks. Currently he is working in the field of electron transport at nanometer scale towards the fabrication of nano electronic devices. He has memberships of various international computer and engineering societies. He has been on advisory and review panel of various international journals. Navita Komal is pursuing her M.TECH in Electronics Technology Department from Guru Nanak Dev University Amritsar. She obtained her Bachelor of Technology degree in Electronics and communication engineering from Guru Nanak Dev University Amritsar. Her major areas of interest are Data communication, Computer networks, wireless sensor networks and Neural Networks. She is going to be publishing papers in these areas and currently she is working on ant colony optimization and particle swarm optimization with wireless sensor networks. 134 All Rights Reserved © 2012 IJARCET
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