Robustness in wireless sensor networks

Robustness in wireless sensor networks
David SIMPLOT-RYL
Centre de recherche INRIA Lille – Nord Europe
IRCICA/LIFL, CNRS 8022, Université de Lille 1
http://www.lifl.fr/~simplot
simplot@lifl.fr
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage
problem
III. Key distribution in wireless
sensor networks
IV. Routing with guaranteed
delivery
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage
problem
III. Key distribution in wireless
sensor networks
IV. Routing with guaranteed
delivery
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
RFID Tags
  Smart labels
  Radio Frequency Identification Tag
  By opposition to bar code which use optical
principles
  A stronly limited component:
  500 times smaller than a classical
microprocessor
Intel Pentium 4
RFID Tag
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Robustness in WSN
Chip with a size of some mm²
EAS Application
  Electronic Article Surveillance
  Once powered, the tag emits
  The reader listen channel and activate alarm
as early as transmission is detected
  During checkout, the tag is burned out
  Problem: power and hear the tag whatever
the tag orientation
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David SIMPLOT-RYL
Robustness in WSN
Applications
  Batch identification
  It is the capability to collect information from a set of tags
  In opposition to optical identification
Marathon
Automatic clocking in
Automatic luggage
sorting
Automatic inventory
50 items in less than one second
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David SIMPLOT-RYL
Robustness in WSN
More POPS, smaller
POPS…
100µm
Courtesy, Alien Technology
POPS = Portable Objects Proved to be Safe
POPS = Petits Objets Portables et Sécurisés
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David SIMPLOT-RYL
Robustness in WSN
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Courtesy, Auto-ID Center
The MIT Auto-ID Center Vision of “the
internet of things”
Networking the
physical world
Savant
Control
System
RF Tag
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David SIMPLOT-RYL
Robustness in WSN
Networked
Tag Readers
Auto-ID Center
classification
Class V tags
Readers. Can power other Class I, II and III tags;
Communicate with Classes IV and V.
Class IV tags:
Active tags with
broad-band peer-to-peer communication
Class III tags:
semi-passive RFID tags
Class II tags:
passive tags with additional
functionality
Class 0/Class I:
read-only passive tags
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Benefits of class IV
tags
  Decentralized
behavior
  The request is
broadcasted in the
whole network by
using multi-hop
method
  Similar to sensor
networks
Reader
Complete population
Monitoring station
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David SIMPLOT-RYL
Robustness in WSN
Sensor Nets for
Search and Rescue
Inactive Sensor
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Robustness in WSN
Sensor Nets for
Search and Rescue
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Robustness in WSN
Sensor Nets for
Search and Rescue
Active Sensor
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Robustness in WSN
Application
  In the UC Berkeley Botanical Garden, 50 “micromotes”
sensors are dangled like earrings from the branches of 3
redwood trees to monitor their growth.
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David SIMPLOT-RYL
Robustness in WSN
Event-driven model
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Robustness in WSN
On-demand model
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David SIMPLOT-RYL
Robustness in WSN
WASP Project
Wirelessly Accessible
Sensor Populations
Philips Research Eindhoven, Philips
Forschung Laboratorium, IMEC, CSEM, TU/
e, Microsoft Aachen, Health Telematic
Network, Fraunhofer IIS, Fokus, IGD,
Wageningen UR, Imperial College London,
STMicroelectronics, INRIA, Ecole
Polytechnique Federale Lausanne, Cefriel,
Centro Ricerce Fiat, Malaerdalen
University, RWTH Aachen, SAP, Univ of
Paderborn
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
SVP Project
Surveiller et Prévenir
CEA, INRIA, Institut
Maupertuis, Aphycare, LIP6,
M2S, Thales, ANACT
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and
coverage problem
III. Key distribution in wireless
sensor networks
IV. Routing with guaranteed
delivery
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Sensor Networks
=> Nodes periodically turn into sleep or active mode
=> Need for activity scheduling and self-organization
=> Connectivity and area coverage must be preserved
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David SIMPLOT-RYL
Robustness in WSN
Sensor Coverage
  How well do the sensors observe the physical space
 
 
 
 
 
 
Sensor deployment: random vs. deterministic
Sensor coverage: point vs. area
Coverage algorithms: centralized, distributed, or localized
Sensing & communication range
Additional requirements: energy-efficiency and connectivity
Objective: maximum network lifetime or minimum number of sensors
  Area (point)-dominating set
  A small subset of sensor nodes that covers the monitored area (targets)
  Nodes not belonging to this set do not participate in the monitoring –
they sleep
  Localized solutions
  With and without neighborhood information
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David SIMPLOT-RYL
Robustness in WSN
Sensor coverage (2)
  Avoid blind points
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David SIMPLOT-RYL
Robustness in WSN
Sensor coverage (3)
  Avoid lost of connectivity
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David SIMPLOT-RYL
Robustness in WSN
Area-dominating set
  With neighborhood info [Tian and Geoganas, 2002]
  Each node knows all its neighbors’ positions.
  Each node selects a random timeout interval.
  At timeout, if a node sees that neighbors who have not yet sent any messages
together cover its area, it transmits a “withdrawal” and goes to sleep
  Otherwise, the node remains active but does not transmit any message
  With neighborhood info based on Dai and Wu’s Rule k [Carle and
Simplot-Ryl, 2004]
  Each node knows either 2- or 3-hop neighborhood topology information
  A node u is fully covered by a subset S of its neighbors iff three conditions hold
  The subset S is connected.
  Any neighbor of u is a neighbor of S.
  All nodes in S have higher priority than u.
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Wu & Dai dominating sets
  (Simplified version [Carle and
Simplot-Ryl 2004])
  Each node has a priority (e.g.
its ID)
  Variations:
<degree, ID>
<battery, degree, ID>
<random, battery, degree, ID>
…
  A node is not dominant iff
  The set of neighbors with higher
priority is connected and covers
the neighborhood
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Connected area
dominating sets
[Carle, Simplot-Ryl 2004][Gallais et al. 2006]
  We change notion of coverage
  A node u is covered by a subset A of its neighborhood if the monitoring area of u
is covered by nodes of A
  Remaining battery is used as priority
  Timeout is used to inform transmit priority
  Examples:
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David SIMPLOT-RYL
Robustness in WSN
Connected area
dominating sets (2)
  We obtain a Connected Area Dominating Set
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David SIMPLOT-RYL
Robustness in WSN
Coverage without
neighborhood info
  PEAS: probabilistic approach [F. Ye et al, 2003]
  A node sleeps for a while (the period is adjustable) and decides to be
active iff there are no active nodes closer than r’.
  When a node is active, it remain active until it fails or runs out of battery.
  The probability of full coverage is close to 1 if
where r is the sensing (transmission) range
  No guarantee of coverage or connectivity!
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Ranges
Communication Range, noted as CR
Sensing Range, noted as SR
SR2SR
SR
< CR
=≤CR
<
2SR
CR
CR
SRSR SR
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David SIMPLOT-RYL
Robustness in WSN
Monitored Area,
noted as S(u)
Overview TGJD
Neighbor
Discovery phase
Evaluating
phase
Sensing phase
One round
Time
  Neighbor discovery phase
  Evaluating phase
  Back-off scheme: avoid blind point (no neighboring nodes can
simultaneously decide to withdraw)
  A node decides to sleep: OFF message sent to one-hop neighbours
  Receiving OFF messages during the timeout: corresponding neighbors
are removed from the neighbor table
⇒ At the end of the timeout, decision is made considering
only non-turned-off neighbors
  Connectivity is ensured as long as 2SR ≤ CR
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David SIMPLOT-RYL
Robustness in WSN
Carle, Gallais, Simplot-Ryl
and Stojmenovic, 2005
(CGSS)
Neighbor
Discovery phase
Evaluating
phase
Sensing phase
One round
Time
  Evaluating phase
  Each node waits during a random timeout
  Once timeout ends, a node evaluates its coverage and decides
  Advertisement is then sent to one-hop neighbors
  It only contains the position (x,y)
  Any message received during the timeout stands for a new neighbor to be
considered
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
CGSS: Connectivity and
Advertisements
  Connectivity is ensured for any SR/CR ratio since full
coverage must be provided by a connected set
(cf. Dai and Wu)
SR=CR
  Which information must be broadcasted?
 Positive-only, PO: u broadcasts its position iff it remains active
 Positive and negative, PN: u emits whatever the decision is.
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
CGSS: Positive-only
(SR=CR)
1?
empty
5 ?
active
1
2 ?
1
7
3 4
2
active
6
6
1 2 3
active
3 ?
?
3
4
2
5
active
4 5
sleep
7 ?
1 2
4 ?
active
1
active
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
CGSS: Positive and
Negative
(SR=CR)
1
empty
5
active
1
2
7
3 4
2
active
I’m OFF
1
6
6
1 2 3
active
3
4
3
?
2
5
active
4 5
sleep
7 ?
1 2 6
4
sleep
1
active
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
CGSS / TGJD: Overview
  Main results:
  Similar number of active nodes
  Both provide full area coverage
  CGSS:
  provides connectivity for nodes with any SR/CR ratio
  Uses a quicker, more simple and more flexible coverage evaluation
scheme
  CGSS: Much lower communication overhead
  Impacts network lifetime once communication costs are considered
  CGSS: Knowledge comes with activity
  No impact on area coverage once messages get lost
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David SIMPLOT-RYL
Robustness in WSN
Experimental results
impact on protocol performances once messages get lost
=> Loss of messages induce coverage
Density 70loss for TGJD
100
David SIMPLOT-RYL
area
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90Robustness in WSN
80
Work in progress
Layer i
  Extend CGSS to k-coverage
  Insert a parameter (layer k) in activity
messages
  While i<k and covered at layer i,
evaluate coverage at layer i+1
  Each node looks at its k-coverage
  Decides to be active or not
  Non-ideal physical layer for sensing
 
 
 
 
Unit disk may not be always valid ;-)
Would it really impact existing protocols?
Coverage evaluation scheme would be modified
Or shorter sensing radii could be announced
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Layer i+1
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage
problem
III. Key distribution in
wireless sensor networks
IV. Routing with guaranteed
delivery
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Key pre-distribution in WSN
  Security issues in wireless sensor networks:
  Fault insertion and pervasive listening
  No collaboration problem
  Cryptography for communication between valid nodes
  Public key mechanisms require too much computation power
  Use of symmetrical schemes
  Nodes can be captured
  A single shared secret can be compromised by the capture of a single
node (Nodes can be captured at anytime! Even during bootstrap!)
  Each node contains a subset of a key space
 Let K be a set keys. Each node has a randomly chosen subset of K
of size n
 Two nodes can communicate if they share a key
 Remark: if 2n>k, two nodes can always communicate
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David SIMPLOT-RYL
Robustness in WSN
Illustration
 Determine the key spool (here K=12)







 



 Key pre-distribution: each node receives n keys (here n=3)

S2
S1
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
David SIMPLOT-RYL
Robustness in WSN
Illustration (2)
 Random deployment
 Initialization phase: neighbour discovery + key establishment
With ID
Hello,
I am Bob
S2
S1
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Hello,
I am Alice
David SIMPLOT-RYL
Robustness in WSN
Illustration (2)
 Random deployment
 Initialization phase: neighbour discovery + key establishment
Without ID
Hello,
I am Bob
S2
S1
SETOP 2008
Hello,
me too
David SIMPLOT-RYL
Robustness in WSN
Illustration (3)
 Initialization phase (continued)

Find common key


S2
S1
  Nodes can proceed to mutual authentication by using this key or
establish a pairwise key
  But since the enemy can capture nodes and listen even during
initialization phase, a link which uses a key derived of “  ” is
considered as corrupted
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David SIMPLOT-RYL
Robustness in WSN
K=10 000
K=100
% of key space size
Probability of key
establishment
Number of keys
  What matters?
  n, the number of embedded keys
  p, the probability of key establishment
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
K, the size of key space is
derived from n and p
Key predistribution in
WSN (continued)
  Robustness of the network
  Percentage of compromised links (i.e. links that can be listened) vs
number of captured nodes
  Common strategy = increase the size K
 It diminishes the probability of communication establishment
 The number of nodes has to be increased in order to preserve
connectivity probability
 Probability of connectivity of the network if evaluate via random
network model
 This model does not take physical proximity for link establishment
  The metric has to be modified:
 Percentage of compromise links vs percentage of captured
nodes
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David SIMPLOT-RYL
Robustness in WSN
Examples of
randomly
generated graphs
according to
different
probabilities p and
different densities
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Robustness in WSN
Probability of connectivity
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Compromised nodes
Reducing p does not improve robustness
(because of connectivity constrainsts)
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Robustness in WSN
Summary
Offline: generation of key spool and distribution of keys
Deployment
Sensing phase
t
Initialization phase
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Robustness in WSN
Key-predistribution and
activity scheduling
Deployment
Sensing phase
Sensing phase
Sensing phase
t
1 round
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Robustness in WSN
Multi-deployment scheme
  Multi-deployment
  Set of nodes are sequentially deployed with disjoint key spaces
  The number of deployed sensor nodes is linear with number of rounds
  Multi-deployment with re-use of already deployed nodes
  Introduction of “bridge” nodes which can connect nodes of different
deployments
  These nodes are supposed to be stronger than usual nodes. It is not
clear that capture of bridge nodes are considered
  Evaluation = simple repetition of basic scheme
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Robustness in WSN
Evaluation
Deployment
Deployment
Sensing phase
Deployment
Sensing phase
Sensing phase
t
1 round
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David SIMPLOT-RYL
Robustness in WSN
Conclusion (1)
  Augmentation of number of keys does not improve
robustness of the networks
  Network lifetime enhancement:
  Multi-deployments schemes are efficient but what is the cost of
redeployments?
  Activity scheduling requires that all nodes are present at the beginning
and leads to low robustness
  Future works:
  Combination of activity scheduling and re-deployment (re-use of still
active nodes by using not completely disjoint key spaces)
  Topology control: there are no need to establish pairwise key with all
neighbors (e.g. RNG)
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Conclusion (2)
  Other solutions seem promising:
  Define unique session key for each couple of nodes with a probability of
100%
  Key spool = symmetric matrix K such K=AxB
  In each node we embed a random row and a random column
  Key establishment = exchange of rows and multiplication by own
column
  To do: evaluation of amount of data needed for key establishment
compared to key-predistribution scheme
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage
problem
III. Key distribution in wireless
sensor networks
IV. Routing with guaranteed
delivery
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Basic principles
  Nodes know their own
geographical position and
positions of their neighbors
   localized
  When a node has a packet for
a given destination (knowing
its position), it decides which
neighbor is the next hop
S
A
D
   memory-less
B
  Example:
  S forwards to neighbor B closest
to D
  [Finn 1987]
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David SIMPLOT-RYL
Robustness in WSN
Some variations
  Nearest forward progress
[Hou]
   small steps
  More forward progress
[Kleinrock]
   big steps
  Random progress [Nelson,
Kleinrock]
B
   ???
  …
  Most close to SD with
progress [Elhafsi, SimplotRyl]
  Variation of DIR [Basagni et al.]
which is not loop free
[Stojmenovic]
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David SIMPLOT-RYL
Robustness in WSN
E
A
C
S
A
’
F
D
DIR is not loop-free !
E
H
D
F
Transmission radius
G
•  Greedy and MFR are loop free
•  If we include constraints on positive progress, the protocol is loopfree
•  What if there is no neighbor with (strictly) positive progress?
•   Dead-end  Recovery process
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David SIMPLOT-RYL
Robustness in WSN
Face routing –
guaranteed delivery
S
P
C
R
A
L
B
R
E
U
L
D
F
[Bose et al. 1999]
  Construct planar subgraph
  Route in planar subgraph:
  SABCEBFED
  SC…ABFD…W…VP
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David SIMPLOT-RYL
Robustness in WSN
V
Right
hand rule
W
Planarization
GG
 
 
 
 
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RNG
GG = Gabriel Graph
RNG = Relative Neighborhood Graph
GG contains RNG
They are both planar (if the initial graph is the UDG)
David SIMPLOT-RYL
Robustness in WSN
Example of GG
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David SIMPLOT-RYL
Robustness in WSN
FACE is not energy
efficient
  For two reasons:
  The path itself can be long
  Because of GG, edges are short  can be inefficient
  Two counter-measures:
  Stop the recovery as soon as possible
 When in dead-end: note the distance to the destination
 Apply FACE until reaching a node which is closer than this distance
 Frey and Stojmenovic have shown that only one face is used for
each recovery
 This scheme is called GFG Greedy-FACE-Greedy [Data et al.]
where greedy part can be energy efficient (see later)
  Apply Shortest path over FACE
  inefficient since edges maintained in GG are the shortest ones
 Other solutions exist (see later)
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David SIMPLOT-RYL
Robustness in WSN
Cost-over-progress
scheme
  [Data, Stojmenovic]
  The principle is to consider each neighbor with positive
progress and to evaluate the cost of the complete path
  Current node = U
  Considered neighbor = V
  Destination = D
V
D
U
  Cost of one step = cost(U,V)
  Number of steps = |UD| / progress
 Progress = |UD|-|VD|
  Evaluation of the cost of the path under homogeneous hypothesis:
cost(U,V)*|UD|/progress
  When minimizing this total cost |UD| is constant, so it is
equivalent to minimize cost/progress
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David SIMPLOT-RYL
Robustness in WSN
Introducing shortest path
in greedy part
  After choosing the next neighbor, SP can be apply
SETOP 2008
 
 
 
 
Ruiz et al. proposed to apply this after MFR
Problem: SP can include nodes which are farther to the destination
 the path has to be embedded in the message
 or at least the “truncated SP”
David SIMPLOT-RYL
Robustness in WSN
Removing memorization
of the SP
  In order to prevent the insertion of SP (or truncated SP) in
message, we can use positive path
  A path is said to be positive is the distance to the destination is strictly
decreasing
5.  Note the distance from the current node to the destination
6.  Apply FACE until reaching a node which is closer, then go to 1
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David SIMPLOT-RYL
Robustness in WSN
Recovery
1.  the current node choose the target neighbor (e.g. MFR)
If there is no target, go to 5 (recovery)
2.  It computes the shortest positive path to this node
3.  It sends the packet to the next node in this path
4.  Go to 1
Greedy
  Summary:
Selecting the target
neighbor
  Ruiz et al. proposed to apply MFR
  Cost-over-progress can also be applied
  More accurate: we can replace the cost by the cost of the
shortest positive path…
  Summary:
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David SIMPLOT-RYL
Robustness in WSN
Recovery
5.  Note the distance from the current node to the destination
6.  Apply FACE until reaching a node which is closer, then go to 1
Greedy
1.  the current node choose the target neighbor
which is the neighbor with strictly positive progress which
minimizes cost-over-progress where the cost is the cost of the
shortest positive path
If there is no target, go to 5 (recovery)
2.  It computes the shortest positive path to this node
3.  It sends the packet to the next node in this path
4.  Go to 1
Performances
Cost/progres
SP after MFR
SPP after C/P
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David SIMPLOT-RYL
Robustness in WSN
Energy-efficient recovery
  The main problem is length of the edges in GG
   idea:
  before applying GG:
  apply a connected dominating set algorithm which minimizes the
number of dominant nodes
  A set of nodes is said to be dominant iff each node of the
network is in this set of a neighbor of one node in this set
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Wu & Dai dominating sets
  (Simplified version [Carle and
Simplot-Ryl 2004])
  Each node has a priority (e.g.
its ID)
  Variations:
<degree, ID>
<battery, degree, ID>
<random, battery, degree, ID>
…
  A node is not dominant iff
  The set of neighbors with higher
priority is connected and covers
the neighborhood
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Energy-efficient recovery
  The main problem is length of the edges in GG
   idea:
  before applying GG:
  apply a connected dominating set algorithm which minimises the
number of dominant nodes
  A set of nodes is said to be dominant iff each node of the
network is in this set of a neighbor of one node in this set
  Then, apply FACE over the dominating set where routing
along an edge uses shortest positive path
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Robustness in WSN
GG over CDS
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Performances
Cost/Progress
without DS
with DS
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Robustness in WSN
SPP after C/P
SP after MFR
What else?
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Multicasting
  Same problem but the message as to be sent to several
targets
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Robustness in WSN
MST-based splitting
decision
  Ingelrest et al. have shown that MST is a good
approximation of the Steiner tree and propose to use it for
splitting decision:
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David SIMPLOT-RYL
Robustness in WSN
Greedy part
  For each group, apply a variation of cost-over-progress
  The cost is as usual (techniques presented in the first part apply)
  But what progress???
  Again, we use MST in order to estimate to distance
t1
V
  If U is the current node,
the progress when considering V is
|MST(U,t1,…,tN)|-|MST(V,t1,…,tN)|
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David SIMPLOT-RYL
Robustness in WSN
t2
U
Recovery part
  We can apply a variation of FACE:
  When a dead-end is detected we note the distance to the closest target/
destination and we apply FACE to this destination until we reach a node
for whom there exists a target closer than this distance.
  Other possibility: replace the line to the destination by the MST edge
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Robustness in WSN
Performances
Ruiz et al.
2007
MSTEAM
Centralized
Algorithm
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Robustness in WSN
Next?
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Robustness in WSN
Conclusion &
perspectives
  Best-known position-based routing (unicast and multicast)
have been presented
  With geographical information
 
 
 
 
Basic algorithm:
YES
Energy efficient (EE):
YES
Guaranteed delivery (GD): YES
EE+GD:
YES
(MFR)
(cost/progress, ETE’
……)
(FACE, GFG)
(ETE)
  Without geographical information
SETOP 2008
 
 
 
 
Basic algorithm:
YES
Energy efficient (EE):
YES
Guaranteed delivery (GD): YES
EE+GD:
YES
David SIMPLOT-RYL
Robustness in WSN
(VCap)
(VCost…)
(LTP)
(HECTOR)
Conclusion &
perspectives
  Are these protocols efficient in real world?
  Big problem: the guaranteed delivery part requires
planarization of the connection graph…
  Planarization of an arbitrary graph cannot be done in a
localized way
  Challenge: which properties are needed for the physical
layer in order to find a localized algorithm for the
planarization
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN
Robustness in wireless sensor networks
David SIMPLOT-RYL
Centre de recherche INRIA Lille – Nord Europe
IRCICA/LIFL, CNRS 8022, Université de Lille 1
http://www.lifl.fr/~simplot
simplot@lifl.fr
SETOP 2008
David SIMPLOT-RYL
Robustness in WSN