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This paper proposes that the flocking behavior of birds can guide the design of a robust, scalable and self-adaptive congestion control protocol in the context of wireless sensor networks (WSNs). The proposed approach adopts a swarm intelligence paradigm inspired by the collective behavior of bird flocks. The main idea is to 'guide' packets (birds) to form flocks and flow towards the sink (global attractor), whilst trying to avoid congestion regions (obstacles). The direction of motion of a packet flock is influenced by repulsion and attraction forces between packets, as well as the field of view and the artificial magnetic field in the direction of the artificial magnetic pole (sink). The proposed approach is simple to implement at the individual node, involving minimal information exchange. In addition, it displays global self-* properties and emergent behavior, achieved collectively without explicitly programming these properties into individual packets. Performance evaluations show the effectiveness of the proposed Flock-based Congestion Control (Flock-CC) mechanism in dynamically balancing the offered load by effectively exploiting available network resources and moving packets to the sink. Furthermore, Flock-CC provides graceful performance degradation in terms of packet delivery ratio, packet loss, delay and energy tax under low, high and extreme traffic loads. In addition, the proposed approach achieves robustness against failing nodes, scalability in different network sizes and outperforms typical conventional approaches.

Over the last few years, the analysis and modeling of networks as well as the analysis and modeling of networked dynamical systems, has attracted considerable interdisciplinary interest, especially using the complex systems theory. These efforts are driven by the fact that systems, as diverse as genetic networks or the Internet can be effectively described as complex networks. Contrary, despite the unprecedented evolution of technology, basic issues and fundamental principles related to the structural and evolutionary properties of communication networks still remain largely unaddressed. The situation is even more complicated when we attempt to model the mobile communication networks and especially the 5th generation (5G) and eventually the forthcoming 6th generation (6G). In this work, we attempt to review basic models of complex networks from a communication networks perspective, focusing on their structural and evolutionary properties. Based on this review we aim to reveal the models of complex networks, that may apply when modeling the 5G and 6G mobile communication networks. Furthermore, we expect to encourage the collaboration between complex systems and networking theorists toward meeting the challenging demands of 5G networks and beyond. INDEX TERMS Complex systems, complex networks, networked complex system, 5G, 6G, wireless communications, wireless networks, mobile communication networks, modeling.

This paper proposes that the flocking behavior of birds can guide the design of a robust, scalable and self-adaptive congestion control protocol in the context of wireless sensor networks (WSNs). The proposed approach adopts a swarm intelligence paradigm inspired by the collective behavior of bird flocks. The main idea is to 'guide' packets (birds) to form flocks and flow towards the sink (global attractor), whilst trying to avoid congestion regions (obstacles). The direction of motion of a packet flock is influenced by repulsion and attraction forces between packets, as well as the field of view and the artificial magnetic field in the direction of the artificial magnetic pole (sink). The proposed approach is simple to implement at the individual node, involving minimal information exchange. In addition, it displays global self-* properties and emergent behavior, achieved collectively without explicitly programming these properties into individual packets. Performance evaluations show the effectiveness of the proposed Flock-based Congestion Control (Flock-CC) mechanism in dynamically balancing the offered load by effectively exploiting available network resources and moving packets to the sink. Furthermore, Flock-CC provides graceful performance degradation in terms of packet delivery ratio, packet loss, delay and energy tax under low, high and extreme traffic loads. In addition, the proposed approach achieves robustness against failing nodes, scalability in different network sizes and outperforms typical conventional approaches.

No abstract