Swarm Intelligence-Based Uplink Power Control in Cognitive Internet of Things (CIoT) for Underlay Environment

Abstract

Internet of things (IoT) aims to shift intelligence to things and tends to increase the spectrum utilization efficiency. However, in doing so, it might generate high interference to the primary users (PUs) due to massive data flow into the networks. Cognitive radio smartly addresses this challenge by enabling different spectrum sharing modes while guaranteeing the quality of service. Motivated by this fact, the incorporation of cognitive abilities in IoT has given birth to a new sub-domain in IoT, known as Cognitive IoT (CIoT). This paper considers a single cell scenario in which multiple CIoT users (CUs) coexist with a PU in an underlay environment, and their communication performance has been optimized while adhering to the transmit power and interference constraints. Furthermore, two swarm intelligence-based implementations of the proposed algorithm have been provided, one based on Artificial Bee Colony (ABC) and the other based on Particle Swarm Optimization (PSO), and their effectiveness to solve the constrained power allocation problem for CIoT networks has been proved through simulations.

Optimization of Electric Automation Control Model Based on Artificial Intelligence Algorithm

“…3 Wireless Communications and Mobile Computing where allowed h describes the nodes that ant h can select next, ½ϑ ji ðtÞ 2 describe the importance of visibility factors, and ½ψ ji ðtÞ −1 describes the importance of pheromone locus intensity (5) When each ant finishes a node, equation ( 4) is adopted to refresh the local pheromone, and the local information volatility coefficient is adaptively transformed, as shown in…”

“…In particular, the use of artificial intelligence technology in dangerous areas can greatly reduce the damage to operators. In industrial and agricultural production, the introduction of artificial intelligence technology can reduce the unit cost and labor cost of production and improve the operation accuracy in the process of industrial and agricultural production; on the basis of minimizing manual operations, the safety of production activities is effectively improved, and production efficiency and enterprise benefits are improved [5]. Machine learning has expanded the capabilities and scope of electrical engineering optimization, resulting in major advances not just in terms of cost but also in terms of safety and real-time operation control [6].…”