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Coordinated Sensor Deployment for Improving Secure Communications and Sensing CoverageYinian Mao, Min WuSecurity of ad hoc and Sensor Networks,Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks, November 07 - 07, 2005
Paper Review
Jun Sup LeeDependable Software LAB at KAISTNov. 21th 2006
Agenda
Introduction
Problem Statement
Contribution
Contents
Background
Static Sensor deployment
Location Adjustment
Conclusion
Q&A
Introduction| Sensor Network and Security
Sensor Network – Great Potential in application
Habitat monitoring
Wildlife tracking
Building surveillance
Military combat
Important design Issue
Efficient sensing coverage
Node-to-node or node-to-base-station communications
Security in information gathering and relay by the sensors
This paper shows that the system performance on these aspects
How the sensors are deployed in the field
How the sensor locations can be adjusted after the initial deployment
Introduction | Sensor Network and Security | characteristic
Sensing and Communication on Sensor Node Limitation of sensing range and the communication range
because of physical characteristics
Placement of sensor nodes will have great impacts on sensing coverage
communication connectivity
Rely on Wireless Transmission malicious adversaries could intercept the communications
Modify the data packets, or inject falsified packets.
Message authentication code with cryptographic
Symmetric key cryptography lower computational complexity
Preferred in practice
Key Pre-distribution
Introduction | Contribution
Analyze Impact on secure connectivity and sensing coverage
Static Sensor deployment
Hexagon lattice topology
Square lattice topology
Location adjustment after deployment
VFSec algorithm
Weighted Centroid algorithm
A new framework for coordinated updates of sensor locations.
Jointly optimize sensing coverage and secure connectivity
Current Work
Optimize the sensing coverage
Do not concern secure sensor communication
Background | Sensing Coverage and Sensing Capability
Sensing Coverage
Rs : Sensing radius
d(*,*) : Euclidean distance
S = 1 : sensor has the capability to sense
S = 0 : otherwise
Sensing Capability
Rc : Communication radius
d(*,*) : Euclidean distance
T = 1 : link exists
T = 0 : otherwise
Background | Efficient Sensing in Static Deployment
Static Deployment
Sensing efficiency ratio – (Circle covering problem : covering density, covering thickness)
, ,
Acol : actual covered area by all the sensor nodes
Aseq : sum of the area covered by each individual sensor
Lowerbound : (by hexagon lattice)
Nomalize distance D1
D1 : distance to its horizontal/vertical neighbor in Square lattice deployment
D2 : distance to its diagonal neighbor in Square lattice deployment
D3 : distance from a node to its six neighbors in hexagon lattice deployment
Background | Key Pre-distribution for Sensor Networks
Key pre-distribution in WSNs
Loading Keys into sensor nodes prior to deployment
Two nodes find a common key between them after deployment
Challenges
Memory/Energy efficiency
Security: nodes can be compromised
Scalability: new nodes might be added later
Each noderandomly selects R keys (Key Ring)
N1N2 …
Key Pool P
N4N3• When |P| = 1000, R=20 / 30
p (two nodes have a common key) = 0.335 / 0.605
Lattice-Structured Deployment | Fundamental Relations Between Deployment Lattices
Expected number of secure links versus communication radius
Square lattice and Hexagon lattice, key-pre distribution
: Key sharing probability
Lattice-Structured Deployment | Secure Connectivity Under Perturbed Deployment Lattice
Expected number of secure links per node versus communication radius.
Actual deployment location :
r : zero-mean distribution with Gaussian
Probability that a designed neighbor in the hexagon lattice can establish a secure link with the center node :
expected number of secure links for the center node (hexagon):
expected number of secure links for the center node (square):
A : horizontal/vertical neighbors
B : diagonal neighbors
Lattice-Structured Deployment | Secure Connectivity Under Perturbed Deployment Lattice
Expected number of secure links per node versus communication radius.
Key ring 100 / Key pool : 1200
Location Adjustment : Virtual Force | Effect on Secure Connectivity by the Existing Approach
Virtual Force algorithm
Maximize total sensing coverage
: Unit-length pointing from the location of ni to nj.
Move Node ni
Direction :
Magnitude :
Location Adjustment : Virtual Force | Effect on Secure Connectivity by the Existing Approach
Impact of location adjustment to the establishment of secure links using VFA
Half of the nodes are no longer connected with the largest connected group, which reduces the capability of secure communications between the sensor nodes.
Location Adjustment : VFSec | VFSec
VFSec Algorithm
Performance metric : ( : total sensing coverage, : secure link per node)
While average number of secure links per node is around 3
W1 = 1
W2 = 1/3
VFSec
Location Adjustment : VFSec | Simulation Results
Comparison of VFA and VFSec with Uniform random initial deployment
Location Adjustment : VFSec | Simulation Results
Comparison of VFA and VFSec using square deployment lattice under Gaussian deployment deviation.
Comparison of deployment lattice using VFSec under Gaussian deployment deviation.
Location Adjustment : WTC | Weighted Centroid Algorithm
Weighted Centroid Algorithm
1. Compute Voronoi cell V
2. Generate uniform grid points
3. Assign weight using assignment procedure
4. Compute location :
5. Compute the movement vector
Location Adjustment : WTC | Simulation Results
Comparison of the WTC and minmax algorithm, small Gaussian deployment deviation, hexagon lattice - key pre-distribution.
Comparison of the WTC and minmax algorithm, large Gaussian deployment deviation, hexagon lattice - key pre-distribution.
Location Adjustment : WTC | Simulation Results
Comparison of the weighted centroid and minmax algorithm, uniform random deployment with basic key pre-distribution.
Conclusion| Conclusions and outlook of this paper
Static sensor deployment
Square / Hexagon lattice
two lattice topology exhibits range-dependent performance
there is no all-time winner in the context of secure connectivity
Location Adjustment
VFSec / WTC
WTC algorithm outperforms under moderate to abundant node density
VFSec algorithm outperforms than the existing virtual force based algorithms
WTC is more suitable to be performed by individual sensors than VF
Performing WTC generally requires more computation than performing schemes based on virtual force