25-06-2012, 12:05 PM
Jamming-Aware Traffic Allocation for Multiple-Path Routing Using Portfolio Selection
[attachment=25373]
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.
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).
[attachment=25373]
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.
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).