Static, Dynamic and Adaptive Heterogeneity in Socio-Economic Distributed Smart Camera Networks
We study the self-organising behaviour of socio-economic distributed smart camera networks, those which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes which are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: non-adaptive mixed strategies, and adaptive strategies which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralised online learning can lead to more efficient outcomes than the homogeneous case, and in some cases, than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic and adaptive heterogeneity in decentralised systems, suggesting that all have a key role to play towards efficient self-organisation.
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