PAFF: predictive analytics on forest fire using compressed sensing based localized Ad Hoc wireless sensor networks

Early detection of a forest fire can save our flora and fauna. Ad Hoc Wireless Sensor Networks (WSN) plays an important role in detecting forest fire. This article proposes a model for early detection of forest fire through predictive analytics. In this approach, the forest area is divided into different zones. Status of a zone, i.e., High Active (HA), Medium Active (MA), and Low Active (LA), is predicted by applying the semi-supervised classification technique. Each zone has static sensors, mobile sensors, and an Initiator node. Initiator nodes of LA and MA zone transfer their mobile nodes (MN) to the nearer HA zone for the quick prediction of forest fire by using the Random trajectory generation (RTG) technique. This technique generates the intermediate points between LA/MA to HA zone to create the movement path of MN. Compressed sensing based Gradient descent (GD) localization technique is used to track the movement of MN by the anchor nodes. This technique reduces the energy consumption of MN that causes an increase in network lifetime. The analysis of the localization error of MN during its traveling towards the HA zone increases the accuracy of its path detection. Thus the increase of sensor nodes in the HA zone results in transferring a huge amount of data from HA zone to base station for quick prediction of a forest fire.

[1]  Zainul Abdin Jaffery,et al.  Maximization of wireless sensor network lifetime using solar energy harvesting for smart agriculture monitoring , 2019, Ad Hoc Networks.

[2]  Shehryar Khan,et al.  Continuous objects detection and tracking in wireless sensor networks , 2016, J. Ambient Intell. Humaniz. Comput..

[3]  Raffaele D'Errico,et al.  On-body TOA-based ranging error model for motion capture applications within wearable UWB networks , 2015, J. Ambient Intell. Humaniz. Comput..

[4]  Ian J. Wassell,et al.  Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework , 2012, IET Wirel. Sens. Syst..

[5]  Minh Tuan Nguyen,et al.  Compressive sensing based random walk routing in wireless sensor networks , 2017, Ad Hoc Networks.

[6]  Ruay-Shiung Chang,et al.  An innovative scheme for increasing connectivity and life of ZigBee networks , 2011, The Journal of Supercomputing.

[7]  Jack Xin,et al.  Computational Aspects of Constrained L 1-L 2 Minimization for Compressive Sensing , 2015, MCO.

[8]  Jia Wang,et al.  Forest fire spread model based on the grey system theory , 2018, The Journal of Supercomputing.

[9]  Jorge A. Atempa,et al.  Wireless Sensor Networks and Fusion Information Methods for Forest Fire Detection , 2012 .

[10]  S. Hamed Javadi,et al.  Fire detection by fusing correlated measurements , 2019, J. Ambient Intell. Humaniz. Comput..

[11]  Linqing Gui,et al.  RSS-based indoor localisation using MDCF , 2017, IET Wirel. Sens. Syst..

[12]  Zahir Tari,et al.  By-Passing Infected Areas in Wireless Sensor Networks Using BPR , 2015, IEEE Transactions on Computers.

[13]  Mohammad S. Obaidat,et al.  Extracting mobility pattern from target trajectory in wireless sensor networks , 2015, Int. J. Commun. Syst..

[14]  Mohammed Abo-Zahhad,et al.  Modeling and minimization of energy consumption in wireless sensor networks , 2015, 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS).

[15]  Ping Zhang,et al.  A secure data collection scheme based on compressive sensing in wireless sensor networks , 2018, Ad Hoc Networks.

[16]  Shilpa Verma,et al.  Ad-Hoc Network and Microcontroller Remote for EarlyWarning System in Forest Fire Control , 2013 .

[17]  Alaa Shakir Mahmood,et al.  Distributed Gradient Descent Localization in Wireless Sensor Networks , 2015 .

[18]  Harkiran Kaur,et al.  Fog-assisted IoT-enabled scalable network infrastructure for wildfire surveillance , 2019, J. Netw. Comput. Appl..

[19]  Ditipriya Sinha,et al.  Semisupervised Classification Based Clustering Approach in WSN for Forest Fire Detection , 2019, Wireless Personal Communications.

[20]  W. Li,et al.  Node localization algorithm for wireless sensor networks using compressive sensing theory , 2016, Personal and Ubiquitous Computing.

[21]  Jiming Chen,et al.  Energy-Efficient Target Tracking by Mobile Sensors With Limited Sensing Range , 2016, IEEE Transactions on Industrial Electronics.

[22]  Fagui Liu,et al.  Greedy discrete particle swarm optimization based routing protocol for cluster-based wireless sensor networks , 2017 .

[23]  Hyochoong Bang,et al.  Real-time path planning to dispatch a mobile sensor into an operational area , 2019, Inf. Fusion.

[24]  Amit Sharma,et al.  An insight to forest fire detection techniques using wireless sensor networks , 2017, 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC).

[25]  P. Cortez,et al.  A data mining approach to predict forest fires using meteorological data , 2007 .

[26]  Federico Viani,et al.  An accurate prediction method for moving target localization and tracking in wireless sensor networks , 2018, Ad Hoc Networks.

[27]  Tomàs Margalef,et al.  Enhancing wildland fire prediction on cluster systems applying evolutionary optimization techniques , 2005, Future Gener. Comput. Syst..

[28]  Federica Verdini,et al.  Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons , 2017, Journal of Ambient Intelligence and Humanized Computing.

[29]  Shibo He,et al.  iLoc: A Low-Cost Low-Power Outdoor Localization System for Internet of Things , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[30]  Jiming Chen,et al.  TOC: Localizing wireless rechargeable sensors with time of charge , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[31]  Carlo Ratti,et al.  Analysis of visitors' mobility patterns through random walk in the Louvre museum , 2018, ArXiv.

[32]  Robert Weibel,et al.  From A to B, randomly: a point-to-point random trajectory generator for animal movement , 2015, Int. J. Geogr. Inf. Sci..

[33]  Honge Ren,et al.  Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation , 2018 .

[34]  Ji Huang,et al.  Intelligent Smoke Alarm System with Wireless Sensor Network Using ZigBee , 2018, Wirel. Commun. Mob. Comput..

[35]  Noureddine Moussa,et al.  A novel approach of WSN routing protocols comparison for forest fire detection , 2018, Wirel. Networks.

[36]  Lothar Thiele,et al.  Route selection for mobile sensor nodes on public transport networks , 2014, J. Ambient Intell. Humaniz. Comput..

[37]  Harkiran Kaur,et al.  Adaptive Neuro Fuzzy Inference System (ANFIS) based wildfire risk assessment , 2019, J. Exp. Theor. Artif. Intell..

[38]  Yanmin Zhu,et al.  Compressive detection and localization of multiple heterogeneous events in sensor networks , 2017, Ad Hoc Networks.

[39]  Abdenour Bouzouane,et al.  An experimental comparative study of RSSI-based positioning algorithms for passive RFID localization in smart environments , 2018, J. Ambient Intell. Humaniz. Comput..

[40]  Vandana Mohindru,et al.  Detection of forest fires using machine learning technique: A perspective , 2015, 2015 Third International Conference on Image Information Processing (ICIIP).

[41]  Norman Dziengel,et al.  Deployment and evaluation of a fully applicable distributed event detection system in Wireless Sensor Networks , 2016, Ad Hoc Networks.

[42]  Alempije Veljovic,et al.  Evaluation of Classification Models in Machine Learning , 2017 .

[43]  Zahir M. Hussain,et al.  Compressive Sensing with Chaotic Sequences: An Application to Localization in Wireless Sensor Networks , 2019, Wirel. Pers. Commun..

[44]  T. Engin Tuncer,et al.  Path planning for mobile-anchor based wireless sensor network localization: Static and dynamic schemes , 2018, Ad Hoc Networks.

[45]  Zahir M. Hussain,et al.  Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control , 2018, IET Wirel. Sens. Syst..

[46]  Abdelilah Jilbab,et al.  Hybridized Model for Early Detection and Smart Monitoring of Forest Fire , 2017 .

[47]  Ditipriya Sinha,et al.  Localization of sensors in WSN during Emergency Services (LSWES) , 2018, 2018 Conference on Information and Communication Technology (CICT).