Post on 07-Feb-2016
description
Algorithms for Wireless Sensor Networks
Marcela Boboila, George Iordache
Computer Science Department
Stony Brook University
Presented paper
[Li05] Zang Li, Wade Trappe, Yanyong Zhang, Badri Nath, Robust statistical methods for securing wireless localization in sensor networks, IPSN’05
The problem
Location based services are exposed to malicious attacks
=> design localization algorithm that are robust to corrupted measurements
Not concerned with accidental anomalies (i.e. open a door, someone passing by), but with intelligent, coordinated attacks
Attacks in wireless localization
Attacks in wireless localization
Approach
Mitigate the vulnerabilities instead of introducing countermeasures for every possible attack
“Live with bad nodes instead of eliminating all possible bad nodes”
Proposed solutions
Triangulation-based localization: Solution: switch from least squares (LS)
estimation to least median squares (LMS) when attacked
RF-based fingerprinting localization: Solution: use a median-based distance metric
Triangulation – Least Squares (LS) Method Gather a collection of (x, y, d)
d = the distance from the wireless device to an anchor (x, y)
Map values on a parabolic surface:
minimum is the wireless device location
Resolve an overdetermined system, for which we know and we determine
Triangulation - attack
An intruder can perturb the distance d (i.e. alters hop count)
A single perturbation can alter the result
General formulation of the LS
N = total number of samples
θ = the parameter to estimate (location)
corresponds to
corresponds to position (xi, yi) of the anchors
Solution: Least Median of Squares (LMS)
The LMS estimator [P. J. Rousseeuw ’84] is among the most widely used robust linear statistical estimators
The residue from LS:
Minimize the medium of the residue squares:
LMS algorithm
• Choose a number of M subsets of size n from the N samples
• Applying LS, find the estimate , j=1,...,M for each subset
• Based on the median residual error assign a weight for each (i.e. weight=1 if the error is less than a threshold, or 0 otherwise)
• Compute weighted estimated
LMS algorithm
• LS – no attack:
• LMS – attack:
How choose n and M for LMS?
Idea: at least one subset is “good” (no contamination) with probability:
ε = contamination ratio => εN samples are outliers
n=4 (3 would be minimum to decide a location)
M=20 (depends on computational capabilities)
P>=0.99
ε <=30%
How to get a location estimate from samples efficiently?
Nonlinear LS: Linear LS:
How to get a location estimate from samples efficiently?
Use linear LS – reduces computational complexity
Simulations
The strength of the attack:
N = 30 anchor nodes, 500 x 500 m2 region
Simulations
LMS: the error increases to a maximum, then decreases slightly and then stabilizes
At low attacking strength, LS performs better than LMS With high contamination ratios, the system performs poorly
Simulations
Why LS performs better than LMS at low attacking strength? linear regression: LMS detects well only when
outlier and inlier are well separated
Simulations
The variance indicates the distance between inliers and the outliers
Establish threshold T If the variation (variance expansion due to
outliers) > T, then apply LMS, else apply LS
Proposed solutions
Triangulation-based localization: Solution: switch from least squares (LS)
estimation to least median squares (LMS) when attacked
RF-based fingerprinting localization: Solution: use a median-based distance metric
RF-based fingerprinting RADAR system – in buildings
How it works: Setup phase: form a radio map with signal strengths
(fingerprints) a mobile host broadcasts to base stations records are written in radio map on central base station
and they have the format described below:
(x, y) – mobile position - received signal strength at the ith base station
Localization phase: nearest neighbor in signal space (NNSS)
RF-based fingerprinting - attack
Corrupted signal strength at one base station (i.e. insert an absorbing barrier between mobile host and base station)
Solution: use the median distance “nearest” neighbor:
minimize
Observations
What the paper does: Logical, well-structured paper, strengthened by graphical
results Makes a classification of possible attacks in wireless
sensor networks Employs previously developed statistical methods to
minimize the effect of adversaries in the localization process, instead of eliminating it
Proposes a lower-computational method (LMS), in comparison with a previous, related one (LS). The reduction in computational demands suggests that this method can be better integrated in sensor networks
Observations
What the paper does not: It doesn’t study the effect over the whole system when the
method is applied: computational complexity, energy consumption, feasibility,
time for algorithm completion Doesn’t study a broader range of undesired interferences:
arbitrary interferences with the signal information (weather conditions, etc.)
accidental or malicious movement of sensors in places out of the scope of the application
Not original - it adapts a method (LMS) which has already been applied in different areas (security, etc.) (see references)
Observations
How to strengthen the paper: Comparison with other methods used to secure
the localization process in sensor networks Results showing how well the global localization
algorithm (more nodes, not only one, need to determine their position ) performs
Results indicating overall energy consumption, computation, time costs, etc.
Instead of simulation, employ a real situation