QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems

The Internet of Things (IoT) technology with huge number of power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device; however, this could also degrade the quality of service (QoS) pr...

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Bibliographic Details
Published in:IEEE systems journal 2017-09, Vol.11 (3), p.1447-1455
Main Authors: Liumeng Song, Kok Keong Chai, Yue Chen, Schormans, John, Loo, Jonathan, Vinel, Alexey
Format: Article
Language:English
Subjects:
Algorithms
Classification
Cluster
Computer simulation
Cooperative communication
Data collection
Energy consumption
Energy efficiency
Energy management
Genetic algorithms
Industrial applications
industrial Internet of Things (IoT) system
Internet of Things
Multiple objective analysis
network lifetime
nondominated sorting genetic algorithm (NSGA-II)
Pareto optimum
Particle swarm optimization
Power demand
Power efficiency
Power management
Provisioning
Quality of service
quality of service (QoS)
Quality of service architectures
quantum particle swarm optimization (QPSO)
Sorting
Sorting algorithms
Tradeoffs
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Summary:The Internet of Things (IoT) technology with huge number of power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device; however, this could also degrade the quality of service (QoS) provisioning. In this paper, we study the problem of how to achieve the tradeoff between the QoS provisioning and the energy efficiency for the industrial IoT systems. We first formulate the multiobjective optimization problem to achieve the objective of balancing the outage performance and the network lifetime. Then, we propose to combine the quantum particle swarm optimization (QPSO) with the improved nondominated sorting genetic algorithm (NSGA-II) to obtain the Pareto-optimal front. In particular, NSGA-II is applied to solve the formulated multiobjective optimization problem, and the QPSO algorithm is used to obtain the optimum cooperative coalition. The simulation results suggest that the proposed algorithm can achieve the tradeoff between the energy efficiency and the QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance.
ISSN:1932-8184
1937-9234
1937-9234
DOI:10.1109/JSYST.2015.2465292