22-11-2012, 05:24 PM
Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection
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Abstract
Multiple-path source routing protocols allow a data
source node to distribute the total traffic among available paths.
In this paper, 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 multisource 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
J AMMING 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 cross-layer 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]. Hence, more
sophisticated antijamming methods and defensive measures
must be incorporated into higher layer protocols, for example
channel surfing [8] or routing around jammed regions of the
network [6].
PERFORMANCE EVALUATION
In this section, we simulate various aspects of the proposed
techniques for estimation of jamming impact and
jamming-aware 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 and for a
single link . 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 Fig. 2 while varying network and protocol parameters
in order to observe performance trends.
Computational Complexity
We note that both the centralized optimization problem in
(12) and the local optimization step in the distributed algorithm
are quadratic programming optimization problems with linear
constraints [13]. The computational time required for solving
these problems using numerical methods for quadratic programming
is a polynomial function of the number of optimization
variables and the number of constraints.
In the centralized problem, there are optimization
variables corresponding to the number of paths available
to each of the sources. The number of constraints in the centralized
problem is equal to the total number of links ,
corresponding to the number of link capacity constraints. In the
distributed algorithm, each source iteratively solves a local optimization
problem, leading to decoupled optimization problems.
Each of these problems has optimization variables
and constraints. Hence, as the number of sources in the network
increases, the distributed algorithm may be advantageous
in terms of total computation time. In what follows, we provide
a detailed performance evaluation of the methods proposed in
this paper.
CONCLUSION
In this paper, 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
multisource 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).We presented simulation results to illustrate the impact
of jamming dynamics and mobility on network throughput and
to demonstrate the efficacy of our traffic allocation algorithm.