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Manshaei, Mohammad Hossein; Lacage, Mathieu; Hoffmann, Ceilidh; Turletti, Thierry

doi:10.1002/wcm.v9:7

Cited by 10 publications

(1 citation statement)

References 244 publications

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“…However, GAs may have the problem of slow con-vergence and may be stuck in a local maximum. In order to improve the performances of GAs, in our prior work, [10] we invoked particle swarm optimization (PSO) as an adaptation method for dealing with the cognitive radios. Performance gains over basic GAs have been observed in terms of convergence speed and converged fitness values.…”

Section: Introductionmentioning

confidence: 99%

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Zheng

Yang

et al. 2016

Chinese Phys. B

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Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications.

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Section: Introductionmentioning

confidence: 99%

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Zheng

Yang

et al. 2016

Chinese Phys. B

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Power Management and Rate Control Mechanism for Wi-Fi Infrastructure Networks

Bouras

Kapoulas

Stamos

et al. 2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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Localized Energy-Aware Fault Management in Relay Based Sensor Networks

Bari

Bandyopadhyay

Jaekel

2011

2011 31st International Conference on Distributed Computing Systems Workshops

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In a relay based two-tier sensor network the individual sensor nodes, constituting the lower tier of the network, are partitioned into clusters and transmit their data directly to their respective cluster heads. Higher-powered relay nodes act as cluster heads and form the upper tier of the sensor network, that communicates received data to the base station. In this model, if a relay node becomes faulty, all data from the cluster of the faulty node, as well as data from other clusters that is routed through the faulty node will be lost. To avoid this potentially large data loss, it is critical that, after a failure is detected, data is rerouted as quickly as possible to avoid the failed relay node. This is best achieved by allowing each individual relay node to take corrective action immediately after discovering that a relay node is faulty, without any directive from the base station. Furthermore, it is also important to ensure that the energy dissipation of the relay nodes, using the new route, is as low as possible. In this paper, we propose a novel approach, where only the nodes originally transmitting to the failed node has to change their routing information, and all other nodes continue to operate as before. This greatly simplifies the coordination and synchronization required to establish a new routing scheme, resulting in a faster response time and reduced data loss. We have proposed an integer linear program (ILP) formulation that determines a routing scheme using this approach. We also present a simple but effective heuristic, capable of handling much larger networks. Our simulation results clearly demonstrate that the proposed approach can lead to significant improvements in the lifetime of a two tier network, compared to existing techniques.

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Untitled

Cited by 10 publications

References 244 publications

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

Power Management and Rate Control Mechanism for Wi-Fi Infrastructure Networks

Localized Energy-Aware Fault Management in Relay Based Sensor Networks

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