Finding Mobility Pattern of Movable Target in Wireless Sensor Networks by Crowdsourcing Designed Mechanism

Target tracking in wireless sensor networks is one of the well-known applications of such networks. The use of sensor-based electronic devices is becoming widespread and can be used for target tracking method. The obvious feature of these networks based on crowdsourcing mechanism is that the sensor nodes can be mobile. This paper presents a target tracking in a wireless sensor network which is generated by a crowdsourcing mechanism. The path of the target tracking has been extracted through SIR particle filter and statistical analysis model. Because of knowing the direction of the target movement can be effective in predicting the pursuit nodes and reducing of energy consumption, the proposed target tracking algorithm is based on prediction. The simulation results of the proposed algorithm on a wireless sensor network has been concluded by NS2 package. More effective target tracking algorithms can be presented by means of achieved mobility pattern in this research.

[1]  Mingyan Liu,et al.  Building realistic mobility models from coarse-grained traces , 2006, MobiSys '06.

[2]  Christian Bonnet,et al.  Mobility models for vehicular ad hoc networks: a survey and taxonomy , 2009, IEEE Communications Surveys & Tutorials.

[3]  Cecilia Mascolo,et al.  Designing mobility models based on social network theory , 2007, MOCO.

[4]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[5]  Han-Chieh Chao,et al.  Jumping ant routing algorithm for sensor networks , 2007, Comput. Commun..

[6]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[7]  Zhen-Jiang Zhang,et al.  An Energy-Efficient Motion Strategy for Mobile Sensors in Mixed Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[8]  Tracy Camp,et al.  Trace-based mobility modeling for multi-hop wireless networks , 2011, Comput. Commun..

[9]  Mohammad S. Obaidat,et al.  Providing reliable and link stability-based geocasting model in underwater environment , 2012, Int. J. Commun. Syst..

[10]  Sudip Misra,et al.  Policy controlled self-configuration in unattended wireless sensor networks , 2011, J. Netw. Comput. Appl..

[11]  Pramod K. Varshney,et al.  Target Tracking via Crowdsourcing: A Mechanism Design Approach , 2014, IEEE Transactions on Signal Processing.

[12]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[13]  Thomas R. Gross,et al.  A mobility model based on WLAN traces and its validation , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[14]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

[15]  Kien A. Hua,et al.  Handling high mobility in next-generation wireless ad hoc networks , 2010 .

[16]  Kun Yang,et al.  An enhanced community-based mobility model for distributed mobile social networks , 2012, Journal of Ambient Intelligence and Humanized Computing.

[17]  Mohammad S. Obaidat,et al.  A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks , 2008 .

[18]  Yu-Chee Tseng,et al.  Energy-efficient network selection with mobility pattern awareness in an integrated WiMAX and WiFi network , 2010 .

[19]  B. John Oommen,et al.  Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Elmar Gerhards-Padilla,et al.  A survey on mobility models for performance analysis in tactical mobile networks , 2023, Journal of Telecommunications and Information Technology.

[21]  Jean-Yves Le Boudec,et al.  Perfect simulation and stationarity of a class of mobility models , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[22]  Daqing Zhang,et al.  Extracting social and community intelligence from digital footprints , 2014, J. Ambient Intell. Humaniz. Comput..

[23]  Christian Bettstetter,et al.  Mobility modeling in wireless networks: categorization, smooth movement, and border effects , 2001, MOCO.

[24]  X. Wang,et al.  Time-division secret key protocol for wireless sensor networking , 2011, IET Commun..

[25]  Rajesh K. Gupta,et al.  Path Planning of Data Mules in Sensor Networks , 2011, TOSN.

[26]  Zygmunt J. Haas,et al.  Predictive distance-based mobility management for PCS networks , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[27]  Ravi Jain,et al.  Model T: an empirical model for user registration patterns in a campus wireless LAN , 2005, MobiCom '05.

[28]  Ahmed Helmy,et al.  Weighted waypoint mobility model and its impact on ad hoc networks , 2005, MOCO.

[29]  Samee Ullah Khan,et al.  Clustering-based power-controlled routing for mobile wireless sensor networks , 2012, Int. J. Commun. Syst..

[30]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[31]  Ahmed Helmy,et al.  Modeling Time-Variant User Mobility in Wireless Mobile Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[32]  Zhenming Feng,et al.  SaMob: A Social Attributes Based Mobility Model for Ad Hoc Networks , 2011, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[33]  Ravi Jain,et al.  Model T++: an empirical joint space-time registration model , 2006, MobiHoc '06.

[34]  Sudip Misra,et al.  Localized policy-based target tracking using wireless sensor networks , 2012, TOSN.

[35]  Xiaofei Wang,et al.  Multiple mobile agents' itinerary planning in wireless sensor networks: survey and evaluation , 2011, IET Commun..

[36]  Louise E. Moser,et al.  An analysis of the optimum node density for ad hoc mobile networks , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

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

[38]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[39]  Ahmed Helmy,et al.  Gauging human mobility characteristics and its impact on mobile routing performance , 2012, Int. J. Sens. Networks.

[40]  Injong Rhee,et al.  SLAW: A New Mobility Model for Human Walks , 2009, IEEE INFOCOM 2009.

[41]  Chu-Sing Yang,et al.  An adaptive joining mechanism for improving the connection ratio of ZigBee wireless sensor networks , 2010 .

[42]  Kevin C. Almeroth,et al.  Real-world environment models for mobile network evaluation , 2005, IEEE Journal on Selected Areas in Communications.

[43]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[44]  Sandeep K. S. Gupta,et al.  Research challenges in wireless networks of biomedical sensors , 2001, MobiCom '01.

[45]  Sudip Misra,et al.  MobiL: A 3-dimensional localization scheme for Mobile Underwater Sensor Networks , 2013, 2013 National Conference on Communications (NCC).

[46]  Hwangnam Kim,et al.  An empirical framework for user mobility models: Refining and modeling user registration patterns , 2011, J. Comput. Syst. Sci..