Simulation studies in Java Access QR Code ISO/IEC18004 in Java Simulation studies

17.6 Simulation studies using barcode implementation for java control to generate, create denso qr bar code image in java applications. Android report to the centraliz Java qr codes ed detector. However, since the size of each report is very small compared with the size of the data packet, the extra communication overhead is negligible. For example, if the average packet size is 1000 bytes, and the report size is 20 bytes, then the overall increase in traf c is only 2%.

If the memory resource is more precious than the communication resource, the modi ed detection scheme should be preferred. Until now we have assumed that each good node will keep listening to all the packet transmissions in its neighborhood. Next we show how to further decrease the overhead by letting nodes selectively listen to packet transmissions, with negligible degradation of the detection performance.

Speci cally, each node can selectively listen to its neighbors transmissions with a certain probability p, which we call probabilistic monitoring. That is, when a packet-transmission event happens in a good node s neighborhood, there is a probability p that this node will monitor this transmission and report the observation to the centralized detector. Now, when an attacker has injected n packets with the same sequence number via n node-disjoint routes (where n > 1), this attacker can avoid detection with probability no more than p(n) = (1 p)n + p(1 p)n 1 .

Furthermore, after the attacker has injected k packets, the probability that it will not be detected will have decreased to p(n)k , which goes to 0 with increasing k. By applying probabilistic monitoring, the communication overhead can be further decreased by 1 p, while the detection performance suffers only negligible degradation. One possible drawback of such a centralized detection mechanism is that the detector itself can also become the attackers target.

Besides increasing the protection level, one can also increase the number of centralized detectors. For example, if there are two detectors in the network, then, even if one of them has been compromised, the other should still work well. In this case, each node can either submit reports to both detectors, or each time randomly pick one to which to submit reports, where the latter case is equivalent to halving p.

. Simulation studies In our simulations, nod qr bidimensional barcode for Java es are randomly deployed inside a rectangular area, and each node moves according to the modi ed random waypoint model in [489], where a node starts at a random position, waits for a duration called the pause time that is modeled as a random variable with an exponential distribution, and then randomly chooses a new location and moves toward the new location with a velocity uniformly chosen between vmin and vmax . The physical layer assumes that two nodes can directly communicate with each other successfully only if they are within each other s transmission range. The MAC-layer protocol simulates the IEEE 802.

11 distributed coordination function (DCF) with a four-way handshaking mechanism [197]. Some of the simulation parameters are listed in Table 17.1.

In the simulations, 50 good nodes are selected as the packet generators, and each will randomly pick a good node to which to send packets, therefore the total number of source destination pairs is 50. Each malicious node will also randomly pick another malicious node as the destination to which to inject packets. All source destination.

Defense against traf c-injection attacks Table 17.1. Simulation Java QR Code 2d barcode parameters Number of good nodes Number of malicious nodes Minimum velocity (vmin ) Maximum velocity (vmax ) Average pause time Dimensions of space Maximum transmission range Average packet inter-arrival time Data-packet size Link bandwidth 100 0 50 2 m/s 10 m/s 300 s 1500 m 1500 m 300 m 1s 1024 bytes 1 Mbps.

pairs (either good or m alicious) are set to be legitimate, and, for each pair, packets are generated according to a Poisson process with a pre-speci ed traf c rate known by all nodes, such that the average packet inter-arrival time is 1 s. We set f s,d (t) to be t + 3 for any source destination pair (s, d). Malicious nodes that launch traf c-injection attacks will increase the average packet-injection rate by a factor of 10.

Also, all data packets are of the same size, and on average each route request packet is of size 100 bytes. In our simulations, each con guration has been run for 20 independent rounds using different random seeds, and the results are averaged over all 20 rounds. For each round, the simulation time is set to be 5000 s.

We use the average energy ef ciency and endto-end throughput as metrics to measure the network performance. Here the average energy ef ciency is de ned as the total number of good nodes successfully delivered packets over the total amount of energy spent by all good nodes, and the end-to-end throughput is de ned as the total number of good nodes successfully delivered packets over the total number of good nodes packets that needs to be sent. When we calculate the energy ef ciency, only transmission energy consumption has been considered.

One reason is that transmission energy consumption plays a major role in overall energy consumption, and another reason is that receiving energy consumption may vary dramatically among communication systems due to their different implementations. We assume that the transmission energy needed per data packet is normalized to be 1. We rst investigate the tradeoff between limiting the route request rate and system performance, although the performance also depends on other factors such as the mobility pattern, the number of nodes in the network, and the average number of hops per route.

To better illustrate the tradeoff between limiting the route-request rate and system performance, the other parameters are set xed. However, similar results can also be obtained with variation of these parameters. Figure 17.

6 illustrates the tradeoff between limiting the route-request rate and network performance. In this set of simulations, all malicious nodes will inject only route-request packets and will not inject any data packets or launch routing-disruption attacks. We assume that all good nodes have the same minimum route-request forwarding interval denoted by Tmin , but all malicious nodes will set their route request rate to.

Copyright © . All rights reserved.