Wireless Based Network Dynamic Handover Decision Analysis Using Convolution Enhanced Evolving Attention Network

Dynamic handover decisions are among the most important in such a way that the transmission should be seamless with the preservation of quality of service (QoS) in wireless networks. Traditional handover mechanisms have various limitations, like high latency with increased packet loss, resulting in network congestion and inefficient resource distribution. These challenges become even more difficult because modern networks are extremely complex. To address these challenges, this paper proposes a dynamic handover decision analysis using convolution enhanced evolving attention network for improved QoS in wireless networks (CEEAN-DHOD-QoS-WN). This methodology incorporates a piranha foraging optimization algorithm (PFOA) to enhance the route selection based on some parameters, such as delay, bit error rate, jitter, energy consumption, average bit rate, and received signal strength. Then, an optimal decision is carried out to manage vertical handoff using convolution enhanced evolving attention network (CEEAN). The experimental results demonstrate significant improvements in throughput, latency, and overall network efficiency compared with the existing methods. The simulation outputs prove that the CEEAN-DHOD-QoS-WN technique attains 0.69, 0.65, and 0.61 ns lower delay and 20.12, 17.10, and 13.11 J lower energy consumption compared with existing methods: QOS aware vertical handover procedure in heterogeneous wireless network (QoS-VHO-HWN), a novel handover scheme for millimeter wave network: a method of integrating reinforcement learning and optimization (HOS-MWN-RLA), and user preference-dependent heterogeneous network management scheme for vertical handover (UP-HNMS-VH), respectively.