Green Access Point Selection for Wireless Local Area Networks Enhanced by Cognitive Radio

Ge, Wendong; Chen, Shanzhi; Ji, Hong; Li, Xi; Leung, Victor C. M.

doi:10.1007/s11036-013-0437-z

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…By formulating ANS as a stochastic game with negative network externality and modeling the operation of achieving the optimal decision as a multidimensional Markov Decision Process, Yang et al [16] propose a modified value iteration algorithm to obtain an ANS decision. Ge et al [17] formulate ANS as a restless bandit problem and solve it using a first-order relaxation-based primal-dual index heuristic algorithm. By taking the efficient resource management of different networks as an optimization problem and transferring the problem into a good selection of weights to match their QoS parameters, Maria et al [18] develop several heuristic methods using simple rules to find the best available network.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Network Opportunistic Access for Group Mobility in Mobile Internet

Tao

Dong

Ota

et al. 2018

IEEE Systems Journal

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The integration of existing and emerging heterogeneous wireless networks in Mobile Internet is a combination of diverse but complementary wireless access technologies. Satisfying a set of imperative constrains and optimization objectives, access network selection (ANS) for mobile node (MN) is an inherent procedure in mobility management that needs to be solved in a reasonable manner for the whole system to operate in an optimal fashion. However, ANS remains a significant challenge. Because many MNs with distinctive call characteristics are likely to have correlated mobility and may need to perform mobility management at the same time, this paper, with the goal of investigating group mobility solutions, proposes a Network Opportunistic Access for Group Mobility (NOA-GM) scheme. By analyzing the directional patterns of moving MNs and introducing the idea of opportunistic access, this scheme first identifies underloaded access networks as candidates. Then, the candidates are evaluated using normalized models of objective and subjective metrics. On this basis, the ANS problem for group mobility can be conducted as a multi-objective combination optimization and then transferred to a signal-objective model by considering the optimization of the performance of the whole system as a global goal while still achieving each MN's performance request. Using an improved Genetic Algorithm with newly designed evolutionary operators to solve the signal-objective model, an optimal result option for ANS for group mobility is achieved. Simulations conducted on the NS-2 platform show that NOA-GM outperforms the compared schemes in several critical performance metrics.

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Section: Related Workmentioning

confidence: 99%

Multiobjective Network Opportunistic Access for Group Mobility in Mobile Internet

Tao

Dong

Ota

et al. 2018

IEEE Systems Journal

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“…Yet, di↵erently from (1), this is not weighted by a variable "radio" factor, but by a constant term (⇣). The power consumption of the radio frontend is added by (11), whereas (12) adds the tra c processing cost.…”

Section: Variants To the Power Model Of The Apmentioning

confidence: 99%

“…The energy saving issue in wireless networks is currently the focus of many research activities. For example, there is a plethora of works dealing with the analysis and reduction of the power consumption in cellular networks [1,2,3], wireless sensor networks [4,5], wireless mesh networks [6,7,8], and also wireless Local Area Networks (WLANs) [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

The impact of the access point power model on the energy-efficient management of infrastructured wireless LANs

et al. 2016

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“…Network-assisted techniques require the APs to take an active role in disseminating network availability and performance information. In [18], every available AP sends it state information to the nodes through the beacon frame. The state information includes the number of associated stations, measured interference at the AP, and channel gain between the AP and the node.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging on Neighbour Information for Optimizing Network Selection

Pedrasa

Seneviratne

2014

AEJ

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Current operating systems rely on Received Signal Strength (RSSI) to choose which network to connect. However, RSSI is not a good measure of actual network performance and requires the interfaces to be turned on, which can significantly drain the battery of the mobile node. This work revisits how network discovery is performed in a multi-radio, mobile environment. The current method of basing decisions on physical-layer characteristics cannot adequately capture network service parameters. Going beyond radio properties allows users to make better choices as to which is the "best'' network and Point of Attachment to connect to. In this work we extend MOBIX, a system for managing MOBility using Information eXchange, which allows nodes to exchange information in a peer-to-peer manner in addition to leveraging on a centralized server. We performed simulations to study the performance of MOBIX with and without the map server and show that our enhanced system strikes a balance of providing a scalable, updated network performance information in densely populated areas and relying on a network server in less urbanized areas with lower number of nodes.

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Green Access Point Selection for Wireless Local Area Networks Enhanced by Cognitive Radio

Cited by 6 publications

References 31 publications

Multiobjective Network Opportunistic Access for Group Mobility in Mobile Internet

Multiobjective Network Opportunistic Access for Group Mobility in Mobile Internet

The impact of the access point power model on the energy-efficient management of infrastructured wireless LANs

Leveraging on Neighbour Information for Optimizing Network Selection

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