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characteristics of the network in order to minimize overheads An intuitive approach for organizing into clusters is according to node mobility Clusters are not static but change as nodes move around As nodes move, they may drop out from their existing cluster and join a different cluster that is closer to them Clusters in some cases may join together or a single cluster may split into two or more clusters Typically a node might be elected in the cluster as a leader and such a node is typically called the clusterhead There are several algorithms that have been proposed in the literature for organizing nodes in clusters based on a variety of criteria, including how the clusters adapt to connectivity changes due to mobility, and how clusterheads for each cluster are elected (although certain cluster-based algorithms do not require the election of clusterheads) We will now discuss some representative clustering algorithms and will then consider how these concepts can be applied to intrusion detection We consider clustering both with and without the use of clusterheads 54311 Clustering with Clusterheads One of the heuristics for selecting clusterheads that has been proposed in [87] is the highest-degree algorithm In this algorithm, the nodes that have the largest number of direct neighbors become the clusterheads In this case, neighbors are those nodes that are within each other s transmission ranges The clusterhead selection can be implemented as follows Each node discovers through periodic hello messages its direct neighbors and the number of neighbors that each of them have Based on that information, a node selects itself as the clusterhead if it has more neighbors than its neighbors This is a very simple algorithm that has a low rate of clusterhead change Another potential algorithm is the Graphcluster algorithm [88] The rst part of this algorithm involves forming a spanning tree by using the breadth- rst-search (BFS) tree algorithm In the BFS algorithm, a node initiates the algorithm and becomes the root If multiple nodes initiate the algorithm, the one with the lowest ID wins The root starts transmitting periodic tree discovery beacons and ooding them in the network As each node receives a beacon it transmits its own beacon which includes its id, the root s id, the id of its parent (its neighbor that is closest to the root), and its distance from the root Nodes that receive such beacons can discover the shortest path to the root and they send updated tree building beacons Eventually the tree forms One such possible tree is shown with bold lines in Figure 57 In the Graphcluster algorithm, once the tree is formed through the BFS algorithm, clusters are then formed as follows Each node counts the number of its children (it can discover those through the BFS advertisements) This information is then included in the subsequent tree building beacons These cluster members then start to be aggregated from the leaf nodes towards the root Once a node discovers that it has more than k children it forms either one cluster (if it has ,2k children) or multiple clusters by partitioning its children s subtrees into clusters Figure 58 shows one example for the formation of clusters (assuming k 3) Once nodes become part of a cluster they are not counted by their parents in the cluster formation algorithm The algorithm terminates once the cluster formation is stable After that the algorithm tries to maintain the clusters as they exist by making small changes for nodes that join or leave clusters due to mobility 54312 Clustering Without Clusterheads Algorithms have also been proposed for creating clusters that do not involve the election of an actual leader in the cluster The advantage of these approaches is that they do not require that a node take on extra.

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responsibilities Such extra responsibilities could result in that node draining its power, and expending a lot of its CPU cycles and memory We next look at some such algorithms Krishna et al [89] have introduced a scheme for organizing nodes in clusters without a clusterhead In this case, clusters are formed in a way that members of each cluster are always one hop away from each other In other words, each member of the cluster has direct connectivity to every other member of the cluster This type of a cluster is called a clique (see Fig 59) Krishna et al [89] describe one approach for forming cliques In this approach, each node maintains a list of its direct neighbors, a list of all clusters, and a list of nodes connecting clusters together Whenever a new node appears on the network it gets from its

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