TY - GEN
T1 - Hybrid Energy Efficient and QoS Aware Algorithm to Prolong IoT Network Lifetime
AU - Srinidhi, N. N.
AU - Lakshmi, Jyothi
AU - Dilip Kumar, S. M.
N1 - Funding Information:
Acknowledgment. This research work has been funded by the Science and Engineering Research Board (SERB-DST) Project File No: EEQ/2017/000681. Authors sincerely thank SERB-DST for intellectual generosity and research support provided.
Publisher Copyright:
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2019
Y1 - 2019
N2 - The Internet of Things (IoT) consists of large amount of energy compel devices which are prefigured to progress the effective competence of several industrial applications. It is very much essential to bring down the energy utilization of every device deployed in IoT network without compromising the quality of service (QoS). Here, the difficulty of providing the operation between the QoS allocation and the energy competence for the industrial IoT application is deliberate. To achieve this objective, the multi-objective optimization problem to accomplish the aim of estimating the outage performance and the network lifetime is devised. Subsequently, proposed Hybrid Energy Efficient and QoS Aware (HEEQA) algorithm is a combination of quantum particle swarm optimization (QPSO) along with improved non dominated sorting genetic algorithm (NGSA) to achieve energy balance among the devices is proposed and later the MAC layer parameters are tuned to reduce the further energy consumption of the devices. NSGA is applied to solve the problem of multi-objective optimization and the QPSO algorithm is used to gain the finest cooperative combination. The simulation outcome has put forward that the HEEQA algorithm has attained better operation balance between the energy competence and the QoS provisioning by minimizing the energy consumption, delay, transmission overhead and maximizing network lifetime, throughput and delivery ratio.
AB - The Internet of Things (IoT) consists of large amount of energy compel devices which are prefigured to progress the effective competence of several industrial applications. It is very much essential to bring down the energy utilization of every device deployed in IoT network without compromising the quality of service (QoS). Here, the difficulty of providing the operation between the QoS allocation and the energy competence for the industrial IoT application is deliberate. To achieve this objective, the multi-objective optimization problem to accomplish the aim of estimating the outage performance and the network lifetime is devised. Subsequently, proposed Hybrid Energy Efficient and QoS Aware (HEEQA) algorithm is a combination of quantum particle swarm optimization (QPSO) along with improved non dominated sorting genetic algorithm (NGSA) to achieve energy balance among the devices is proposed and later the MAC layer parameters are tuned to reduce the further energy consumption of the devices. NSGA is applied to solve the problem of multi-objective optimization and the QPSO algorithm is used to gain the finest cooperative combination. The simulation outcome has put forward that the HEEQA algorithm has attained better operation balance between the energy competence and the QoS provisioning by minimizing the energy consumption, delay, transmission overhead and maximizing network lifetime, throughput and delivery ratio.
UR - http://www.scopus.com/inward/record.url?scp=85066118020&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066118020&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-20615-4_6
DO - 10.1007/978-3-030-20615-4_6
M3 - Conference contribution
AN - SCOPUS:85066118020
SN - 9783030206147
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 80
EP - 95
BT - Ubiquitous Communications and Network Computing - 2nd EAI International Conference, Proceedings, 2019
A2 - Venkatesha Prasad, R.
A2 - Kumar, Navin
PB - Springer Verlag
T2 - 2nd EAI International Conference on Ubiquitous Communications and Network Computing, UBICNET 2019
Y2 - 8 February 2019 through 10 February 2019
ER -
