Firefly-Aquila optimized Deep Q network for handoff management in context aware video streaming-based heterogeneous wireless networks

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Uttam P. Waghmode, U. Kolekar
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引用次数: 0

Abstract

Handoff management is the method in which the mobile node maintains its connection active when it shifts from location to other. The devastating success of mobile devices as well as wireless communications is emphasizing the requirement for the expansion of mobility-aware facilities. Moreover, the mobility of devices requires services adapting their behavior to abrupt context variations and being conscious of handoffs, which make an intermittent discontinuities and unpredictable delays. Thus, the heterogeneity of wireless network devices confuses the situation, since a dissimilar treatment of handoffs and context-awareness is essential for every solution. Hence, this paper introduced the Deep Q network-based Firefly Aquila Optimizer (DQN-FAO) for performing the handoff management. In order to establish the handoff management, the process of selecting network is very important. Here, the network is selected based on the devised FAO algorithm, which is the consolidation of Aquila Optimizer (AO) and Firefly algorithm (FA) that considers the metrics, such as Jitter, Handoff latency, and Received Signal Strength Indicator (RSSI) as fitness function. Moreover, the handover decision is taken by the DQN, where the hyper-parameters are tuned by the devised FAO algorithm. According to the hand over decision taken, the context aware video streaming is happened by adjusting the bit rate of the videos using network bandwidth. Besides, the devised scheme attained the superior performance based on the call drop, energy consumption, handover delay, throughput, handoff latency, and PSNR of 0.5122, 7.086 J, 10.54 ms, 13.17 Mbps, 93.80 ms and 46.89 dB.
萤火虫- aquila优化了基于上下文感知视频流的异构无线网络中的Deep Q网络切换管理
切换管理是移动节点在从一个位置转移到另一个位置时保持其连接活动的方法。移动设备和无线通信的巨大成功强调了扩展移动感知设施的需求。此外,设备的移动性要求服务调整其行为以适应突然的上下文变化,并意识到切换,这会导致间歇性的不连续和不可预测的延迟。因此,无线网络设备的异构性混淆了这种情况,因为对切换和上下文感知的不同处理对于每个解决方案都是必不可少的。因此,本文引入了基于Deep Q网络的萤火虫Aquila优化器(DQN-FAO)来执行切换管理。为了建立交接管理,网络的选择过程是非常重要的。在这里,网络的选择是基于设计的FAO算法,该算法是Aquila Optimizer (AO)和Firefly算法(FA)的整合,该算法将抖动、切换延迟和接收信号强度指标(RSSI)等指标作为适应度函数。此外,切换决策由DQN做出,其中超参数由设计的FAO算法进行调整。根据所做出的移交决策,利用网络带宽调整视频的比特率,实现上下文感知的视频流。此外,该方案在通话掉线、能耗、切换延迟、吞吐量、切换延迟、PSNR分别为0.5122、7.086 J、10.54 ms、13.17 Mbps、93.80 ms和46.89 dB等方面均取得了较好的性能。
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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