Wireless Sensor Network is a self-organization network that consists of a distributed number of minor devices for performing the monitoring activities within the deployed environment. Many wildfires cause forest damages affecting a large number of living organisms. This research study discusses a system design approach to wireless sensor network (WSN) based on the monitoring of wildfire. The main goal of the proposed solution is to intelligently estimate the scale and intensity of the wildfire which is ignited in the forest. The energy efficient and priority based techniques have been implemented for the data communication between the wireless sensor networks. The dynamic routing paths can be created with the help of the proposed data communication solution. These dynamic paths are developed depending upon a number of parameters such as weight, energy, fire, weather index and security. For the sake of validation, the proposed solution designs and implements a prototype by using Microsoft Framework (tools and technologies) to accomplish an extensive number of simulation experiments. The results and evaluations clearly show the efficiency and dependability of our proposed approach based on the wireless sensor network.
Saeed Ullah Jan,
An Improved Forest Fire Alerting System Using Wireless Sensor Network, Advances in Networks.
Vol. 6, No. 1,
2018, pp. 21-39.
Alliance, Z. (2006). ZigBee specification. Document 053474r06. Retrieved from Document 053474r06, Version 1 (2006).
Bagheri, M. (2007). Efficient K-Coverage Algorithms for Wireless Sensor Networks and Their Applications to Early Detection of Forest Fires. Computing Science SIMON FRASER UNIVERSITY, MSc:75.
Bahrepour, M., Nirvana, M., & Havinga, P. J. (2008). Automatic fire detection: A survey from wireless sensor network perspective.
Breejen, & E., M. B. (1998). Autonomous forest fire detection. Third International Conference on Forest Fire Research and Fourteenth Conference.
Burgan, R. E. (1988). National Fire-Danger Rating System. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment, Res. Pap. SE-273.
Cano, A., Reig, C., Milan-Scheiding, C., & Lopez-Baeza. (2012). Automated Soil Moisture Monitoring Wireless Sensor Network for Long-Term CalVal Applications. Wireless Sensor Network, 4 (8).
Chae, M., Yoo, H., Kim, J., & Cho, M. (2006). Bridge Condition Monitoring System Using Wireless Network (CDMA and Zigbee). 23rd International Symposium on Automation and Robotics in Construction ISARC 2006, Tokyo, Japan, 3 – 5 Oct.
Chen, Z. (2013). A review of automated formal verification of ad hoc routing protocols for wireless sensor networks. arXiv preprint arXiv, 305.7410.
Chowdhury, N., Mahabub, H., & Samiul, I. (2013). IOT: Detection of Keys, Controlling Machines, and Wireless Sensing Via Mesh Networking through Internet. Global Journal of Researches In Engineering.
Cracknell, A. P. (1997). The Advanced Very High-Resolution Radiometer (AVHRR). CRC Press.
Dutta, D., Dilip, K., & Arindam, K. (2013). Analysis of IEEE 802.15. 4 MAC under low duty cycle. arXiv preprint arXiv:1301.6532. edition.cnn. (2013, 20 12). http://edition.cnn.com/2013/10/20/world/asia/australia-fires/. Retrieved from http://edition.cnn.com: http://edition.cnn.com/2013/10/20/world/asia/australia-fires/.
Flannigan, M. D. (1998). Future wildfire in circumboreal forests in relation to global warming. Journal of Vegetation Science, 469-476.
Fleming, G., & Robertson, R. (October 2003.). Fire Management Tech Tips: The Osborne Fire Finder. USA: Technical Report 0351 1311-SDTDC, USDA Forest Service.
Forestry Division Newsdesk, N. (2008). http://www.dfr.state.nc.us/news_pubs/. Retrieved 01 12, 2013, from http://www.dfr.state.nc.us/news_pubs/: http://www.dfr.state.nc.us/news_pubs/.
García, H., & Ana, B. (2008). WSN Application Scenarios. London: Springer.
Gat, E. (1998, Retrieved 2008-04-06). On three-layer architectures. Artificial Intelligence and Mobile Robots, 195–210.
Groot., W. J. (1998). Interpreting the Canadian Forest Fire Weather Index (FWI) System. In Proc. of the Fourth Central Region Fire Weather Committee Scientific and Technical Seminar, Edmonton, Canada.
Gungor, V. C., & Gerhard, P. (2013). Industrial Wireless Sensor Networks: Applications, Protocols, and Standards. CRC Press.
Hussain, A., & Niazi, M. (2010). A novel agent-based simulation framework for sensing in complex adaptive environments. IEEE Sensors Journal, 11 (0), doi:10.1109/JSEN.2010.2068044.
J. San-Miguel-Ayanz, J. C. (2003). Section 2: Current methods to assess fire danger potential. In Wildland Fire Danger Estimation and Mapping The Role of Remote Sensing Data. World Scientific Publishing Co. Pte Ltd.
Karali, A. (2013). Evaluation of the Canadian Fire Weather Index in Greece and Future Climate Projections. Advances in Meteorology, Climatology and Atmospheric Physics. Springer Berlin Heidelberg, 501-508.
Knuth, D. E. (1977). A generalization of Dijkstra's algorithm. In Information Processing Letters 6.1 (pp. 1-5).
Lee, & Tsung-Han. (2013). Modeling and Performance Analysis of Route-Over and Mesh-Under Routing Schemes in 6LoWPAN under Error-Prone Channel Condition. Journal of Applied Mathematics 2013.
Lee, S.-J., Gerla, M., & Toh, C.-K. (1999). A simulation study of table-driven and on-demand routing protocols for mobile ad hoc networks. Network, IEEE, 13 (4), 48-54.
Lim, Y.-s., & S. Lim, e. a. (2007). A Fire Detection and Rescue Support Framework with Wireless Sensor Networks. Convergence Information Technology.
Milke, J. A., & McAvoy, T. J. (1995). Analysis of signature patterns for discriminating.
Muller, H. C., & Fischer, A. (1995). A robust fire detection algorithm for temperature and optical smoke density using fuzzy logic. Security Technology, Sanderstead.
Niazi, M., & Hussain, A. (2009). Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad-hoc and other complex networks. IEEE Communications Magazine, 47 (3), 163 - 173.
Okayama, Y. (1991). A primitive study of a fire detection method controlled by the artificial neural net. Fire Safety Journal, 17 (6), 535-553.
PAK, M. (2012, 06 06). http://www.microsoft.com/en-pk/default.aspx. Retrieved from http://www.microsoft.com.
Pripužic, K. H. (2008). Early Forest Fire Detection with Sensor Networks: Sliding Windows Skylines Approach. Faculty of Electrical Engineering and Computing, Department of Telecommunication, White Paper.
Silberschatz, A., Galvin, P. B., & Gagne, G. (2010). Process Scheduling. (J. (Asia), Ed.) Operating System Concepts (8th ed.).
Son, B., Yong-sork, H., & J, K. (2008). A design and implementation of a forest-fires surveillance system based on wireless sensor networks for South Korea mountains. International Journal of Computer Science and Network Security (IJCSNS), 12 (6.9), 124-130.
Thuillard, M. (2000). Application of Fuzzy Wavelets and Wavelets in Soft Computing.
Tiwari, A., Ballal, P., & Frank, L. (2007). Lewis Energy-efficient wireless sensor network design and implementation for condition-based maintenance. ACM Transactions on Sensor Networks (TOSN) archive, Volume 3, Issue 1.
Tymstra, C., Bryce, R., & Wotton, B. (2009). Development and structure of Prometheus: the Canadian wildland fire growth simulation model. Nat. Resour. Can., can. For. Serv., North. For. Cent., Edmonton, AB. Inf. Rep. NOR-X-417.
Vescoukis, V., & T. Olma, e. a. (2007). Experience from a Pilot Implementation of an "InSitu Forest Temperature Measurement Network. Personal, Indoor and Mobile Radio Communications".
Viani, F., Rocca, P., Oliveri, G., & Massa, A. (2012). Pervasive remote sensing through WSNs. Antennas and Propagation (EUCAP), 2012 6th European Conference on, (pp. 49-50).
Yu, L., & Wang. (2005). Real-time forest fire detection with a wireless sensor.
Zhiping, L., & Q. Huibin, e. a. (2006). The Design of Wireless Sensor Networks for Forest Fire Monitoring System. School of Electronics and Information, Hangzhou Dianzi University.