Tracking of Moving Target in Wireless Sensor Network with Improved Network Life Time Using PSO

2021 ◽  
Author(s):  
Dhiren P. Bhagat
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Life Time ◽  
Wireless Sensor ◽  
Moving Target ◽  
Network Life Time

scholarly journals A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network

2019 ◽  
Vol 15 (6) ◽  
pp. 155014771985823 ◽  
Author(s):  
Mohammad Z Masoud ◽  
Yousef Jaradat ◽  
Ismael Jannoud ◽  
Mustafa A Al Sibahee
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Life Time ◽  
Wireless Sensor ◽  
Sink Node ◽  
Hybrid Clustering ◽  
Detection Process ◽  
Network Life Time ◽  
Hole Detection

In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.

WSN Management Self-Silence Design and Data Analysis for Neural Network Based Infrastructure

2020 ◽  
pp. 1440-1458
Author(s):  
Nilayam Kumar Kamila ◽  
Sunil Dhal
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Energy Conservation ◽  
Sensor Network ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Infrastructure ◽  
Network Life Time

In recent Wireless Sensor Network environment, battery energy conservation is one of the most important focus of research. The non-maintainable wireless sensor nodes need modern innovative ideas to save energy in order to extend the network life time. Different strategy in wireless sensor routing mechanism has been implemented to establish the energy conservation phenomenon. In earlier days, the nodes are dissipating maximum energy to communicate with each other(flooding) to establish the route to destination. In the next evolution of this research area, a clustering mechanism introduced which confirms the energy saving over the flooding mechanism. Neural Network is an advanced approach for self-clustering mechanism and when applied on wireless sensor network infrastructure, it reduces the energy consumption required for clustering. Neural network is a powerful concept with complex algorithms and capable to provide clustering solutions based on the wireless sensor network nodes properties. With the implementation of Neural Network on Wireless Sensor Network resolves the issues of high energy consumption required for network clustering. The authors propose a self-silence wireless sensor network model where sensor nodes change the sensing and transmitting mechanism by making self-silent in order to conserve the energy. This concept is simulated in neural network based wireless sensor network infrastructure of routing methodology and the authors observe that it extends the network life time. The mathematical analysis and simulation study shows the improved performance over the existing related neural network based wireless sensor routing protocols. Furthermore, the performance & related model parameters data set analysis provides the respective dependent relation information.

Sign in / Sign up

Export Citation Format

Share Document