C2, Networks, and Self-Synchronization

C2, Networks, and Self-Synchronization

A. H. Dekker (University of Ballarat, Australia)
DOI: 10.4018/978-1-4666-6058-8.ch009
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This chapter examines the connection between network theory and C2, particularly as it relates to self-synchronization, which requires a rich network structure. The richness of the network can be measured by the average degree, the average path length, and the average node connectivity. The chapter explores the connection between these measures and the speed of self-synchronization, together with other network properties, which can affect self-synchronization, resilience, and responsiveness. Two important network structures (random and scale-free) are described in the context of self-synchronization. Experimental data relating network topology to self-synchronization speed is also explored. In particular, the chapter notes the connection between average path length and self-synchronization speed, as well as the importance of good networking between sub-networks.
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Network Topology And Problem Type

Much of Command and Control (C2) consists of addressing challenging resource allocation problems – often under conditions of uncertainty and risk. It is true that there is a core part of C2 which is essentially creative, and involves outlining a conceptual framework or way of thinking about the problem at hand (Builder et al., 1999). However, a large part of C2 involves the allocation of people and platforms (on the one hand) to places and tasks (on the other). A good network topology will facilitate this process.

In studying C2-related resource allocation problems, we can divide them into three categories, which we will call “easy,” “difficult,” and “fiendish.” An example of an “easy” problem is finding the largest number in a set. The effort required to solve such an “easy” problem will be at most proportional to the size of the problem, since the problem can be solved by a single scan through the set. Technically, such problems are known as linear-time or sub-linear-time problems.

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