Active overload prevention based adaptive MAP selection in HMIPv6 networks

Tao, Ming; Yuan, Haitao; Wei, Wenhong

doi:10.1007/s11276-013-0603-z

Cited by 6 publications

(8 citation statements)

References 17 publications

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“…Besides, a tradeoff emerges when selecting anchoring nodes in the single‐anchoring approach: selecting a farther anchor may worsen the route sub‐optimality between MN and CN, while selecting a nearer anchor may introduce high signaling overhead because of frequent switching between different anchors when the MN is roaming around, as is shown by existing studies on Hierarchical Mobile IPv6 (HMIPv6) . This tradeoff is also demonstrated by the evaluations in this paper.…”

Section: Introductionmentioning

confidence: 60%

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A multi‐anchoring approach in mobile IP networks

Wang

Jiang

2016

Wireless Communications

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Many recent mobility solutions, including derivatives of the well-known Mobile IP as well as emerging protocols employed by future Internet architectures, propose to realize mobility management by distributing anchoring nodes (Home Agents or other indirection agents) over the Internet. One of their main goals is to address triangle routing by optimizing routes between mobile nodes and correspondent nodes. Thus, a key component of such proposals is the algorithm to select proper mobility anchoring nodes for mobile nodes. However, most current solutions adopt a single-anchoring approach, which means each mobile node attaches to a sole mobility anchor at one time. In this paper, "we argue that the singleanchoring approach has drawbacks when facing various mobility scenarios. Then, we offer a novel multi-anchoring approach that allows each mobile node to select an independent mobility anchor for each correspondent node. We show that in most cases our proposal gains more performance benefits with an acceptable additional cost by evaluation based on real network topologies. For the cases that lead to potential high cost, we also provide a lightweight version of our solution which aims to preserve most performance benefits while keeping a lower cost. At last, we demonstrate how our proposal can be integrated into current Mobile IP networks.

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Section: Introductionmentioning

confidence: 60%

“…Chung et al propose to make a distinction between MNs holding real‐time sessions and non‐real‐time sessions and let them select different MAPs to optimize performance. The work from Tao et al concerns more on the load of MAP. They make MAP periodically evaluate its load and report to access routers.…”

Section: Related Workmentioning

confidence: 99%

A multi‐anchoring approach in mobile IP networks

Wang

Jiang

2016

Wireless Communications

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“…Additionally, this paper only works on wired network traffic without considering the wireless mobile networks (Ikeda et al 2013). With the imperative evolution from IPv4 to IPv6, the applications in mobile IPv6 networks (Tao et al 2012(Tao et al , 2014) experience a rapid growth, we will facilitate the network traffic classification problem on the mobile traffic traces collected from IPv6 network as our future work.…”

Section: Discussionmentioning

confidence: 99%

SmoteAdaNL: a learning method for network traffic classification

Liu

Wang

Tao

2015

J Ambient Intell Human Comput

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Machine learning based network traffic classification is a critical technique for network management, and has attracted much attention. Recently, most of the researchers focus on achieving high flow classification accuracy (FCA). However the amount of ''mice'' flows is more than that of ''elephant'' flows in the Internet, these classifiers hence are more suitable for ''mice'' flows, but have low byte classification accuracy (BCA). To address this issue, the notion of byte misclassification is firstly explored. According to the exploration that most misclassified bytes belong to the minority class, a novel method of network traffic classification is proposed by combining the data re-sampling and ensemble learning algorithms. To enhance the classification accuracy of the minority class, the data re-sampling algorithm is employed to increase the number of minority class flows. The data re-sampling however will change the data distribution and degrade the generalization of a classifier. A boosting-style ensemble learning algorithm with the consideration of ensemble diversity hence is employed to improve the generalization. The experiments conducted on the real-world traffic datasets show that the proposed method achieves over 90 % BCA and 96 % FCA on average, and improves about 7.15 % BCA by comparing with the existing methods.

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“…MNs equipped with multiple wireless interfaces can access these networks on the move. Because each node must follow a specified process to perform binding updates [9], this hierarchical architecture causes additional signal and packet processing overhead. Therefore, it is necessary to find an optimal hierarchy to minimize the overall cost.…”

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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Active overload prevention based adaptive MAP selection in HMIPv6 networks

Cited by 6 publications

References 17 publications

A multi‐anchoring approach in mobile IP networks

A multi‐anchoring approach in mobile IP networks

SmoteAdaNL: a learning method for network traffic classification

Multiobjective Network Opportunistic Access for Group Mobility in Mobile Internet

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