Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Abstract

Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization

“…a) Metric for Evaluation: Several metrics are commonly used to evaluate the performance of swarm intelligence algorithms, including convergence speed, solution quality, robustness, scalability, and computational complexity [8]. Convergence speed measures the rate at which an algorithm converges to an optimal or near-optimal solution, while solution quality assesses the closeness of the obtained solution to the global optimum [33]. Robustness refers to the ability of an algorithm to maintain performance in the presence of noise or uncertainty, while scalability measures its ability to handle large-scale problems efficiently [35].…”

A Survey on the Optimization of Artificial Neural Networks Using Swarm Intelligence Algorithms

“…Hence, they have numerous applications in a variety of domains. For example, they can be used in water resources engineering [8], in wireless networks [9], in cloud-based Internet of Things [10], in optical systems [11], in recommender systems [12], in anomaly detection systems [13], and in supply chain management [14]. They can also be used for clustering [15], for feature selection [16] and for solving the traveling salesman problem [17], [18].…”

An enhanced distance and residual energy‐based congestion aware ant colony optimization routing for vehicular ad hoc networks

“…In OPBR, the priority of each candidate or neighbor vehicle is identified for data transmission. In Pham et al, 26 ACO relies on distance and pheromone metrics to find the shortest route. The indirect communication depends on the pheromone deposited on the ground towards the food source.…”