An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things

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.

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach

“…The IoT nodes set their states between transmitter and receiver before the start of the transmission period. The total energy ( ) consumed to send bit of packet is twofold (a) energy exhaust in transceiver circuit to process the packet (b) energy consumption takes place to amplify the bit of packet over distance either in free-space energy ( ) or multipath energy ( ) corresponds to threshold distance similar to the first order radio model [ 3 ] as follows: …”

“…Wireless sensor networks (WSNs) are prominently used for the collection of data and communication over the fifth generation (5G) and beyond 5G IoT network envision as sixth generation (6G) technology [ 2 ]. Moreover, the combination of IoT networks with WSNs has many potentials in various applications, such as precision agriculture, intelligent transport systems, health care, smart cities, military, environment and habitat monitoring, environment anomalies and human intrusion detection [ 3 , 4 ]. However, with all these remarkable properties, the fallout in the unbalanced energy consumption and lower lifetime of battery-enabled IoT devices limits the seamless communication of intelligent devices over the IoT network.…”

Threat Analysis and Distributed Denial of Service (DDoS) Attack Recognition in the Internet of Things (IoT)

“…Sometimes, this system develops by combining the misuse and anomaly intrusion patterns to reduce the intermediate access. By considering these patterns, statistical analysis, evolutionary algorithm, protocol verification, rule-based, and machine learning techniques [24] are introduced to detect and prevent intrusion activities. The general description of these methods is illustrated in Table 1.…”