In this paper, we present new models and algorithms for control and optimization of a class of next generation communication networks: Hierarchical Heterogeneous Wireless Networks (HHWNs), under real-world physical constraints. Two biology-inspired techniques, a Flocking Algorithm (FA) and a Particle Swarm Optimizer (PSO), are investigated in this context. Our model is based on the control framework at the physical layer presented previously by the authors. We first develop a nonconvex mathematical model for HHWNs. Second, we propose a new FA for self-organization and control of the backbone nodes in an HHWN by collecting local information from end users. Third, we employ PSO, a widely used artificial intelligence algorithm, to directly optimize the HHWN by collecting global information from the entire system. A comprehensive evaluation measurement during the optimization process is developed. In addition, the relationship between HHWN and FA and the comparison of FA and PSO are discussed, respectively. Our novel framework is examined in various dynamic scenarios. Experimental results demonstrate that FA and PSO both outperform current algorithms for the self-organization and optimization of HHWNs while showing different characteristics with respect to convergence speed and quality of solutions.

We introduce a challenging problem in establishing and initially configuring or bootstrapping a Free Space Optical (FSO) network. In such networks, it is assumed that each communication node is a base station, including a router and wireless optical communications hardware, and its number of transceivers is limited. In addition, the FSO networks are characterized by narrow beam, directional links (e.g., operating at 1550 nm) and support up to Gbps data rates. The problem of initially configuring the transceivers to form a connected topology is NP-complete because of the transceiver limitation. What makes this problem even more challenging is the need to configure the transceiver in a "distributed" fashion, because a node can have only direct knowledge of its neighbors. We have developed a fully distributed approximation algorithm, which constructs a spanning tree with maximal node degree at most one larger than that in the optimal solution. Due to its distributed nature, this algorithm outperforms known serial algorithms. For a graph with 200 nodes generated in some randomized model, speed-ups greater than 6 have been demonstrated.

Abstract The performance and scalability of mobile, base‐station‐oriented wireless networks with respect to routing and mobility models is addressed in this paper. Using systems‐oriented, large‐scale discrete event simulation, both performance scalability and complexity scalability are analyzed. Performance scalability addresses the effect of network size on system performance. Complexity scalability, in this case, consists of communication complexity (i.e. amount of control information exchanged to create and maintain network connectivity). Results confirmed the scalability of base‐station‐oriented networks using the routing, and mobility models, using over 2000 fully dynamic hosts and routers. Copyright © 2004 John Wiley & Sons, Ltd.