Prototyping a Scalable P-system-Inspired Dynamic Task Assignment Algorithm for a Centralized Heterogeneous Wireless Sensor Network

2020 ◽  
Vol 45 (12) ◽  
pp. 10353-10380
Author(s):  
Titus Issac ◽  
Salaja Silas ◽  
Elijah Blessing Rajsingh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Task Assignment ◽  
Wireless Sensor ◽  
P System ◽  
Dynamic Task ◽  
Assignment Algorithm ◽  
Heterogeneous Wireless Sensor Network ◽  
Dynamic Task Assignment

Modelling a Deep Learning-Based Wireless Sensor Network Task Assignment Algorithm

2020 ◽  
pp. 84-109
Author(s):  
Titus Issac ◽  
Salaja Silas ◽  
Elijah Blessing Rajsingh
Keyword(s):  
Deep Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Task Assignment ◽  
Human Interaction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Feed Forward Neural Network ◽  
Functional Capabilities ◽  
Assignment Algorithm

The 21st century is witnessing the emergence of a wide variety of wireless sensor network (WSN) applications ranging from simple environmental monitoring to complex satellite monitoring applications. The advent of complex WSN applications has led to a massive transition in the development, functioning, and capabilities of wireless sensor nodes. The contemporary nodes have multi-functional capabilities enabling the heterogeneous WSN applications. The future of WSN task assignment envisions WSN to be heterogeneous network with minimal human interaction. This led to the investigative model of a deep learning-based task assignment algorithm. The algorithm employs a multilayer feed forward neural network (MLFFNN) trained by particle swarm optimization (PSO) for solving task assignment problem in a dynamic centralized heterogeneous WSN. The analyses include the study of hidden layers and effectiveness of the task assignment algorithms. The chapter would be highly beneficial to a wide range of audiences employing the machine and deep learning in WSN.

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