Abstract
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.
Highlights
A novel paradigm known as the internet of things (IoT) [1,2,3] emerged in the past decade due to the development of wireless technologies
The authors in [31] proposed a novel clustering method based on power demand, which assures the security of data information in industrial internet of things (IIoT)-based applications using machine learning
The experiments were conducted in OMNET++ [38,39], which is widely used by the reIn this section, we present the simulation environment and experiments discussion
Summary
A novel paradigm known as the internet of things (IoT) [1,2,3] emerged in the past decade due to the development of wireless technologies. The proposed model supports trustworthy data delivery to network applications without compromising the identities of devices and content It utilizes the artificial intelligence of things with mobile edges to offer multi-hop routing services and to attain low-cost communication overhead. The three main contributions of the proposed model are as follows: 2 It offers a learning approach, with a node prediction-based multimedia algorithm by exploring the mobile edges; it attains high delivery performance with efficient management of network bandwidth. It offers a low-cost computation algorithm for constraint resources, with the integration of SDN technology and boundary edges for reducing the response interval, and delays constraint multimedia applications.
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