ABSTRACT

In heterogeneous networks, Vertical Handover (VH) plays a vital consequence of customer mobility as they have a high impact on networking performance like delay, throughput and call block probability. Even in the presence of various existing works, VH management exhibit certain drawbacks like method complexity, difficult network modelling and inaccurate handover. Thus, a hybrid methodology is proposed in this work which can provide accurate VH and considerable reduction in working complexity. Here, a combination of Deep Residual Neural (DRN) and Wind-Driven Water Wave Optimisation (WDWWO) is introduced to perform VH. Accurate prediction of Received Signal Strength (RSS) is a difficult task for mobile users while moving from one network to another. In order to tackle this situation, DRN is used with WDWWO based weight optimisation, hence the name Optimised DRN (ODRN). Almost every networking parameters like bandwidth, delay, throughput, velocity, BER, SNR, energy consumption, monetary cost and data traffic are included in ODRN modelling. The proposed work is implemented in NS2 platform and resultant performances like energy consumption, RSS, throughput, packet delivery ratio, packet loss, handover failure rate, algorithm convergence and latency are compared with conventional methods of D-TOPSIS, FIS-ENN and F-AHP.

摘要

在异构网络中，垂直切换 (VH) 对客户移动性起着至关重要的作用，因为它们对延迟、吞吐量和呼叫阻塞概率等网络性能有很大影响。即使在存在各种现有工作的情况下，VH管理也表现出方法复杂、网络建模困难和切换不准确等缺点。因此，在这项工作中提出了一种混合方法，它可以提供准确的 VH 并显着降低工作复杂性。在这里，引入了深度残差神经 (DRN) 和风驱动水波优化 (WDWWO) 的组合来执行 VH。当移动用户从一个网络移动到另一个网络时，准确预测接收信号强度 (RSS) 是一项艰巨的任务。为了解决这种情况，DRN 与基于 WDWWO 的权重优化一起使用，因此命名为优化的 DRN (ODRN)。ODRN 建模中包含几乎所有网络参数，如带宽、延迟、吞吐量、速度、BER、SNR、能耗、货币成本和数据流量。所提出的工作是在 NS2 平台上实现的，并且与传统的 D-TOPSIS、FIS-ENN 和 F-层次分析法。