TY - GEN
T1 - LBSNN
T2 - 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
AU - Mechta, Djamila
AU - Harous, Saad
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - In this paper, we focus on Neural Networks (NN) to be applied for moving sink in Wireless Sensor Networks (WSNs). The system under consideration is composed of a set of a large number of autonomous nodes equipped with limited batteries not rechargeable. Energy consumption has always been a problem for this kind of network, the mobility of the base station (BS) has been exploited in huge projects to solve this problem and extend the lifetime of WSN. There are a number of different models of swarm intelligence that can provide powerful inspiration for researchers in the WSN mobile domain, such as ant colonization optimization (ACO), optimization of swarms (AFSA), bacterial forage and others. The main objective of our work is to propose a new mobility scheme of BS based on neural networks. We have implemented our proposed protocol LBSNN and compared the results obtained with the LEACH protocol and found satisfactory results. Experiments demonstrate the efficiency of LBSNN is meaningfully better and that our proposed model leads to a clear increase in network lifetime.
AB - In this paper, we focus on Neural Networks (NN) to be applied for moving sink in Wireless Sensor Networks (WSNs). The system under consideration is composed of a set of a large number of autonomous nodes equipped with limited batteries not rechargeable. Energy consumption has always been a problem for this kind of network, the mobility of the base station (BS) has been exploited in huge projects to solve this problem and extend the lifetime of WSN. There are a number of different models of swarm intelligence that can provide powerful inspiration for researchers in the WSN mobile domain, such as ant colonization optimization (ACO), optimization of swarms (AFSA), bacterial forage and others. The main objective of our work is to propose a new mobility scheme of BS based on neural networks. We have implemented our proposed protocol LBSNN and compared the results obtained with the LEACH protocol and found satisfactory results. Experiments demonstrate the efficiency of LBSNN is meaningfully better and that our proposed model leads to a clear increase in network lifetime.
KW - Bio-inspired Methods
KW - Mobile BS
KW - Neural Networks.
KW - Optimization
KW - WSNs
UR - http://www.scopus.com/inward/record.url?scp=85071592965&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071592965&partnerID=8YFLogxK
U2 - 10.1109/UEMCON.2018.8796548
DO - 10.1109/UEMCON.2018.8796548
M3 - Conference contribution
AN - SCOPUS:85071592965
T3 - 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
SP - 475
EP - 481
BT - 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018
A2 - Chakrabarti, Satyajit
A2 - Saha, Himadri Nath
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 November 2018 through 10 November 2018
ER -