Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia.
Department of Computer Science, Islamia College Peshawar, Peshawar, Khyber Pakhtunkhwa 25000, Pakistan.
Sensors (Basel). 2021 Oct 26;21(21):7103. doi: 10.3390/s21217103.
In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.
近年来，许多应用程序都在探索网络边缘，以降低通信和管理成本。它们还与物联网（IoT）集成，以实现网络设计的可扩展性和多媒体应用的异构服务。许多提出的解决方案在开发健壮的协议和减少关键网络的响应时间方面发挥了重要作用。然而，它们大多数都无法支持具有约束带宽的动态特性下的高多媒体流量的转发过程。此外，它们增加了数据在不确定环境中的丢失率，并通过增加交付延迟来影响网络性能。因此，本文提出了一种使用物联网人工智能的多媒体传感器移动边缘优化模型，旨在以低资源消耗维持实时数据采集过程。此外，它通过集成软件定义网络（SDN）和移动边缘来提高网络通信的不可预测性。首先，它利用物联网人工智能（AIoT），形成多跳网络，并通过稳定资源管理为约束网络提供主要服务。其次，SDN 与移动边缘直接关联，以支持多媒体传感器的负载均衡和集中管理。最后，多媒体流量以不变的形式并通过共享子密钥进行协商而发送到应用程序。实验结果表明，该模型在传输率方面的平均提升了 35%，在处理延迟方面的平均提升了 29%，在网络开销方面的平均降低了 41%，在分组丢弃率方面的平均降低了 39%，在分组重传率方面的平均降低了 34%，与现有解决方案相比具有更好的性能。
