Multi-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities

Wireless Sensor Network (WSNs) provides promising solutions for monitoring in several domains including industrial monitoring and control, home automation and smart cities, etc. There are numerous restrictions on the current development of target detection and tracking algorithms which makes them unsuitable or effective for indoor use. Such constraints include changes in the direction and changing target speeds, missing a target, and target dynamics. These issues come with difficulty in detection and tracking multiple targets. Moreover, the majority of the target tracking algorithms were presented on the conditions that the target is typically smooth with no unexpected changes that are difficult absolutely. Moreover, sensing coverage considers the crucial issue in a wireless sensor network. This paper implies an algorithm for detection and tracking of moving targets (intruders) for an indoor environment based on the probabilistic model utilizing WSN for safety and security. A mathematical model is presented to determine the optimum number of sensor nodes needed. The findings of the simulation showed that the MTDT algorithm provides a low missing target rate of less than 0.7 % for worst-case and can be utilized for different kinds of environment scenarios.

[1]  Ammar Zakaria,et al.  A real-time greenhouse monitoring system for mango with Wireless Sensor Network (WSN) , 2014, 2014 2nd International Conference on Electronic Design (ICED).

[2]  L. M. Kamarudin,et al.  Internet of things: Sensor to sensor communication , 2015, 2015 IEEE SENSORS.

[3]  L. M. Kamarudin,et al.  Modeling and simulation of near-earth wireless sensor networks for agriculture based application using OMNeT++ , 2010, 2010 International Conference on Computer Applications and Industrial Electronics.

[4]  Mingyan Liu,et al.  Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[5]  Almir Davis,et al.  A SURVEY OF WIRELESS SENSOR NETWORK ARCHITECTURES , 2012 .

[6]  Joe-Air Jiang,et al.  Efficient Coverage and Connectivity Preservation With Load Balance for Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[7]  Fan Ya-qin,et al.  OPNET-based Network of MANET Routing Protocols DSR Computer Simulation , 2010, 2010 WASE International Conference on Information Engineering.

[9]  Winston Khoon Guan Seah,et al.  Coverage preservation in energy harvesting wireless sensor networks for rare events , 2015, 2015 IEEE 40th Conference on Local Computer Networks (LCN).

[10]  L. M. Kamarudin,et al.  Modelling indoor propagation for WSN deployment in smart building , 2014, 2014 2nd International Conference on Electronic Design (ICED).

[11]  T. Senthil Murugan,et al.  Cluster head selection for energy efficient and delay-less routing in wireless sensor network , 2017, Wireless Networks.

[12]  K. K. Pattanaik,et al.  Contextual outlier detection for wireless sensor networks , 2020, J. Ambient Intell. Humaniz. Comput..

[13]  Zeyad Q. H. Al-Zaydi,et al.  An adaptive people counting system with dynamic features selection and occlusion handling , 2016, J. Vis. Commun. Image Represent..

[14]  Khalid A. Darabkh,et al.  Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks , 2012, J. Netw. Comput. Appl..

[15]  Amit Grover,et al.  Routing Techniques in Wireless Sensor Networks , 2014 .

[16]  Delano M. Beder,et al.  Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application , 2017, Int. J. Distributed Sens. Networks.

[17]  Rabie A. Ramadan,et al.  Smart Environmental Monitoring Using Wireless Sensor Networks , 2013 .

[18]  Latifah Munirah Kamarudin,et al.  Device free localization technology for human detection and counting with RF sensor networks: A review , 2017, J. Netw. Comput. Appl..

[19]  Khairul Anwar,et al.  Development of real-time patient health (jaundice) monitoring using wireless sensor network , 2016, 2016 3rd International Conference on Electronic Design (ICED).

[20]  Martin Drobczyk,et al.  Deployment of a wireless sensor network in assembly, integration and test activities , 2016, 2016 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE).