27-12-2011, 09:17 PM
Check out the attachment
29-02-2012, 10:05 AM
jhfuff "jamming aware traffic allocation for multiple path routing using portfolio selection ppt" ? Then
Ask Here with your need/request , We will collect and show specific information of jamming aware traffic allocation for multiple path routing using portfolio selection ppt's within short time...So hurry to Ask now (No Registration , No fees ...its a free service from our side)...Our experts are ready to help you..
04-03-2012, 10:38 AM
Respected Sir/Madam
I need notes and coding for ns2 pls send it to my mail arulsuju[at]yahoo.co.in
25-03-2012, 03:36 PM
:heart::blush::@:angel::shy:
23-04-2012, 10:31 AM
please provide me full detailed information about jamming aware.
14-02-2013, 03:13 PM
Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection 1Jamming-Aware.pdf (Size: 649.13 KB / Downloads: 92) Abstract Multiple-path source routing protocols allow a data source node to distribute the total traffic among available paths. In this article, we consider the problem of jamming-aware source routing in which the source node performs traffic allocation based on empirical jamming statistics at individual network nodes. We formulate this traffic allocation as a lossy network flow optimization problem using portfolio selection theory from financial statistics. We show that in multi-source networks, this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). We demonstrate the network’s ability to estimate the impact of jamming and incorporate these estimates into the traffic allocation problem. Finally, we simulate the achievable throughput using our proposed traffic allocation method in several scenarios. INTRODUCTION Jamming point-to-point transmissions in a wireless mesh network [1] or underwater acoustic network [2] can have debilitating effects on data transport through the network. The effects of jamming at the physical layer resonate through the protocol stack, providing an effective denial-of-service (DoS) attack [3] on end-to-end data communication. The simplest methods to defend a network against jamming attacks comprise physical layer solutions such as spread-spectrum or beamforming, forcing the jammers to expend a greater resource to reach the same goal. However, recent work has demonstrated that intelligent jammers can incorporate crosslayer protocol information into jamming attacks, reducing resource expenditure by several orders of magnitude by targeting certain link layer and MAC implementations [4]–[6] as well as link layer error detection and correction protocols [7]. CHARACTERIZING THE IMPACT OF JAMMING In this section, we propose techniques for the network nodes to estimate and characterize the impact of jamming and for a source node to incorporate these estimates into its traffic allocation. In order for a source node s to incorporate the jamming impact in the traffic allocation problem, the effect of jamming on transmissions over each link (i, j) ! Es must be estimated and relayed to s. However, to capture the jammer mobility and the dynamic effects of the jamming attack, the local estimates need to be continually updated. We begin with an example to illustrate the possible effects of jammer mobility on the traffic allocation problem and motivate the use of continually updated local estimates. Estimating Local Packet Success Rates We let xij (t) denote the packet success rate over link (i, j) ! E at time t, noting that xij (t) can be computed analytically as a function of the transmitted signal power of node i, the signal power of the jammers, their relative distances from node j, and the path loss behavior of the wireless medium. In reality, however, the locations of mobile jammers are often unknown, and, hence, the use of such an analytical model is not applicable. Due to the uncertainty in the jamming impact, we model the packet success rate xij (t) as a random process and allow the network nodes to collect empirical data in order to characterize the process. We suppose that each node j maintains an estimate μij (t) of the packet success rate xij (t) as well as a variance parameter !2 ij (t) to characterize the estimate uncertainty and process variability4. OPTIMAL JAMMING-AWARE TRAFFIC ALLOCATION In this section, we present an optimization framework for jamming-aware traffic allocation to multiple routing paths in Ps for each source node s ! S. We develop a set of constraints imposed on traffic allocation solutions and then formulate a utility function for optimal traffic allocation by mapping the problem to that of portfolio selection in finance. Letting 's! denote the traffic rate allocated to path ps! by the source node s, the problemof interest is thus for each source s to determine the optimal Ls×1 rate allocation vector "s subject to network flow capacity constraints using the available statistics !s and !s of the end-to-end packet success rates under jamming. PERFORMANCE EVALUATION In this section, we simulate various aspects of the proposed techniques for estimation of jamming impact and jammingaware traffic allocation. We first describe the simulation setup, including descriptions of the assumed models for routing path construction, jammer mobility, packet success rates, and estimate updates. We then simulate the process of computing the estimation statistics μij (t) and !2 ij (t) for a single link (i, j). Next, we illustrate the effects of the estimation process on the throughput optimization, both in terms of optimization objective functions and the resulting simulated throughput. Finally, we simulate a small-scale network similar to that in Figure 2 while varying network and protocol parameters in order to observe performance trends. CONCLUSION In this article, we studied the problem of traffic allocation in multiple-path routing algorithms in the presence of jammers whose effect can only be characterized statistically. We have presented methods for each network node to probabilistically characterize the local impact of a dynamic jamming attack and for data sources to incorporate this information into the routing algorithm. We formulated multiple-path traffic allocation in multi-source networks as a lossy network flow optimization problem using an objective function based on portfolio selection theory from finance. We showed that this centralized optimization problem can be solved using a distributed algorithm based on decomposition in network utility maximization (NUM). |
|