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.
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