Graphs - Traversals Traversal of Graphs Depth First Traversals (or search) – DFS Traversal

every other node that receives them from the neighbors of S, until they arrive at the destination. When reactive routing protocols such as DSR or

keep track of its state.If the first node fails, the second node takes up the operations from where the

The CSC's first step consists on creating all the possible direct edges by employing the maximum available transmission power $P_{MAX}$. The algorithm for this step takes as input: the node set $V = {v_1, v_2, ..., v_n}$ with location information; the maximum transmission power $P_{MAX}$. As output, a direct graph $\overline{G}$ is produced. Figure \ref{top_nos} shows an input graph which consists of 500$\times$500 area with $n = 70$ nodes and no edges. Figure \ref{top_original} show the resulting graph when nodes create edges based on its maximum transmission power. In this examples, the maximum transmission power is $P_{MAX} = 4900$ and the maximum transmission range is $R_{MAX}$ = 70 meters. Note that the resulting graph is not necessarily connected.

/* The add back node function adds link at back of list. In this function a new node n is used and data is taken into data part of n. Condition for whether

Now consider node S that needs to determine a route to node D. The proposed LAR algorithms use flooding with one modification. Node S defines (implicitly or explicitly) a request zone for the route request A node forwards a route request only if it belongs to tie request zone. To increase the probability that the route request Will reach node D, the request zone should include the expected zone (described above). Additional, the request zone may also include other regions around the request zone. There are two reasons for this:-

Exercise 2.3.5: It would take 2 messages using a hub, but only 1 if using a switch. Adding node E will not have an effect on the network.

With values 0 and 1, each node has at most 2 branches in a binary trie. As per the bits of the destination address, the search begins at the root level and goes to the right or to the left. The prefix is recollected as the long prefix checked so far every time we check a node marked as prefix while the trie is traversed. Once the best equivalent prefix is the last prefix remembered and there are no branches identical to the next bit in the destination address, the search stops.

maximization of network lifetime [8]. This protocol is also divided into two phase: 1. Clustering and 2. Routing of aggregated data. In clustering phase, a fixed topological arrangement is done by sensor nodes. In the data aggregation phase, heuristic is proposed. The advantage is that it provides energy efficiency and network lifetime also be increased.

The attacker counts the number of packet transmissions executed by each node in a region for a period of time. If the nodes are sending to a common destination node e.g., the sink node, then the latter should be located in a region of higher packet transmissions or

In this example, here node A wants to send data packets to node D and starts to find the shortest path for its destination, so if node D is a malicious node then it will show that it has active route to the specified destination. It will then send the response In the example, data packets transfer in a hierarchic data center network. The link capacity is 1000 kb/s. The number on each is the traffic load. The distribution of traffic is based on equal cost multi-path (ECMP). In figure 8, we can see that the 3). Congestions

Step 1: Construct a network diagram for the project. (NOTE: EF for activity H should be 19)

Provide an output that shows the routing table for your node after each iteration. Add a second table with two columns. One that shows the destination from your node and the second column indicating the number of hops to reach that node.

2. Construct a table containing the project activities using the letter assigned to each activity, the time estimates, and the precedence relationships from which you will assemble the network diagram.

#include <iostream> #include <fstream> #include <sstream> #include <cstdlib> #include <string> #include <cstring> #include <list> #include <queue> #include <climits> #include <functional> #include <vector> #include <cmath> #define MAX_N 150 #define INFINITE 1000 using namespace std; //Structure of router struct router { int router_id; //int inp_q[]; //int out_q[]; int table; float dist; int prev; }; struct router* routerinfo; //Structure for Link elements struct link { int link_id; int node1; int node2; float bandwidth; float prop_delay; }; struct link* linkinfo; //Structure of Packet that is created struct packet { int num; double size; int src; int dest; }; struct packet* packetinfo; //To implement Depth first Search class Network { int N; list<int> *adj; void DFSUtil(int n, bool visited[]); public: Network(int N); void addlink(int n, int w); void DFS(); }; Network :: Network(int N) { this->N = N; adj = new list<int>[N]; } void Network :: addlink(int n, int w) { adj[n].push_back(w); } void Network :: DFSUtil(int n, bool visited[]) { visited[n] = true; cout << n <<" "; list< int>::iterator i; for( i = adj[n].begin(); i!= adj[n].end(); ++i) if(!visited[*i]) DFSUtil(*i, visited); } void Network :: DFS() { bool *visited = new bool[N]; for (int i=0; i < N; i++) visited[i] = false; for(int i=0; i < N; i++) if(visited[i]==false)

Different approaches were proposed in (B. Chen et al., 2014; H. Chen, 2014; Li et al., 2014; Sandhya & Devi, 2013) for answering top-k queries. The safe interval which used for getting the top-k data points computed only in (Sandhya & Devi, 2013). This safe interval speeds up the query response time because it used for getting the top-k data points without the examination of all points for inclusion in TOPK. Also, there is no need to build the Voronoi Diagram (VD) as in (Li et al., 2014) or to build SWTP-tree as (H. Chen, 2014). In addition, updating the safe intervals is easier than updating nodes in (H. Chen, 2014; Li et al., 2014). In (Li et al., 2014) it is important to build the Voronoi Diagram (VD) to answer the bi-chromatic reverse k-nearest neighbor (BRkNN) query without the need to compute the BRkNN answer set for each data point. Li et al. (2014) address the k nearest neighbors’ answers which not addressed in the rest. Thus algorithms which proposed in (Li et al., 2014) outperform the rest in location-based applications. Quantile filter-based algorithm in (B. Chen et al., 2014) is very suitable for the wireless sensor network; it is