A Generalized Cellular Automata Approach to Modelling Contagion and Monitoring for Emergent Events in Sensor Networks In order to improve the invulnerability and adaptability in sensor networks, we propose a cellular automata (CA) based propagation control mechanism (CACM) to inhibit and monitor emergentevent contagion. The cellular evolving rules of CACM are figured in multidimension convolution operations and cell state transform, which can be utilized to model the complex behavior of sensor nodes by separating the intrinsic and extrinsic states for each network cell. Furthermore, inspired by burning pain for Wireworld based monitoring model, network entropy theory is introduced into layered states on CACM to construct particlebased information communication process by efficient distribution of eventrelated messages on network routers, thus an invulnerable and energyefficient diffusion and monitoring being achieved. Experiment results prove that CACM can outperform traditional propagation models in adaptive invulnerability and selfrecovery scalability on sensor networks for propagation control on malicious events.

Evolving Structures in Complex Systems In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for measuring the complexity growth can be defined. This includes approaches based on compression algorithms and artificial neural networks. We believe such a metric can be useful for designing systems that could exhibit openended evolution, which itself might be a prerequisite for development of general artificial intelligence. We conduct experiments on 1D and 2D grid worlds and demonstrate that using the proposed metric we can automatically construct computational models with emerging properties similar to those found in the Conwayu0027s Game of Life, as well as many other emergent phenomena. Interestingly, some of the patterns we observe resemble forms of artificial life. Our metric of structural complexity growth can be applied to a wide range of complex systems, as it is not limited to cellular automata.