Design and performance evaluation of algorithms for wireless self-organizing systems

Abstract

The work done during the PhD course involves the study of the Self- Organization of wireless sensors, robots and UAV networks. In particular, this thesis investigates how each node composing the system can take advantage from the Self-Organization and from mobility, in a way to optimize some networks parameters as coverage and energy consumption. Self-Organization is a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of a system. The rules specifying interactions among the systems components are executed using only local information, without reference to the global pattern [1]. Mobility, although still for some types of systems is not considered a primitive of the network: in recent years has been the subject of many studies just as useful feature to achieve certain objectives, not least the energy consumption in transmission. The network issues has been addressed using different approaches from the theoretical studies aimed at finding the maximum achievable performance benchmarks, through the introduction of appropriate optimization models, the proposal of distributed heuristics and more realistic communication protocols, and the use of biology-inspired mechanisms, such as genetic algorithms (GA) and neural networks (NN). The purpose of this type of approach is to move in the direction of networks that are able to self-organize by adapting to different environmental conditions and dynamic as well as hard scenarios (i.e. environment disasters). The rest of the thesis is organized as follows: in Chapter 1 background on Self-Organizing Systems is given. In Chapter 2 we investigate on the impact of the Propagation Environment on Controlled Mobility Algorithms; distributed heuristics to Film Sport Events with Flying Robots in Chapter 3 and Bio- Inspired approaches in Chapter 4. Finally, a new communications protocol for WSN called Decentralized Time-Synchronized Channel Swapping is analyzed in Chapter 5.