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David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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 [email protected]
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage problem
III. Key distribution in wireless sensor networks
IV. Routing with guaranteed delivery
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage problem
III. Key distribution in wireless sensor networks
IV. Routing with guaranteed delivery
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
Chip with a size of some mm² RFID Tag
Intel Pentium 4
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
More POPS, smaller POPS…
100µm
Courtesy, Alien Technology
POPS = Portable Objects Proved to be Safe POPS = Petits Objets Portables et Sécurisés
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
The MIT Auto-ID Center Vision of “the internet of things” Co
urte
sy, A
uto-
ID C
ente
r
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Networking the physical world
RF Tag
Networked Tag Readers
Savant Control System
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Benefits of class IV tags
Decentralized behavior The request is
broadcasted in the whole network by using multi-hop method
Similar to sensor networks Complete population
Reader
Monitoring station
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Sensor Nets for Search and Rescue
Inactive Sensor
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Sensor Nets for Search and Rescue
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Sensor Nets for Search and Rescue
Active Sensor
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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.
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Event-driven model
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
On-demand model
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
SVP Project
Surveiller et Prévenir
CEA, INRIA, Institut Maupertuis, Aphycare, LIP6, M2S, Thales, ANACT
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage problem
III. Key distribution in wireless sensor networks
IV. Routing with guaranteed delivery
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Sensor Networks
=> Nodes periodically turn into sleep or active mode => Need for activity scheduling and self-organization => Connectivity and area coverage must be preserved
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Sensor coverage (2)
Avoid blind points
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Sensor coverage (3)
Avoid lost of connectivity
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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.
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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:
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Connected area dominating sets (2)
We obtain a Connected Area Dominating Set
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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!
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Ranges
Monitored Area, noted as S(u)
SR < CR < 2SR Sensing Range, noted as SR
Communication Range, noted as CR
SR = CR 2SR ≤ CR
CR
SR SR SR
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
Overview TGJD
Time
Neighbor Discovery phase
Evaluating phase
Sensing phase
One round
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Carle, Gallais, Simplot-Ryl and Stojmenovic, 2005
(CGSS)
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
Neighbor Discovery phase
Evaluating phase
Sensing phase
One round
Time
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Connectivity is ensured for any SR/CR ratio since full coverage must be provided by a connected set (cf. Dai and Wu)
CGSS: Connectivity and Advertisements
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.
SR=CR
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
CGSS: Positive-only (SR=CR)
1 2
3 4
5
6
7
1
3
4
5
6
7
active
empty
?
? 1
1
1
2
active 1
2
2
2active
3
3
?
active
?
4
4?
active
sleep 5
?
?
active
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
CGSS: Positive and Negative
(SR=CR)
1 2
3 4
5
6
7
1
3
4
5
6
7
active
empty
1
1
1
2
active 1
2
2
2active
3
3
active
4
4
active
sleep 5
?
?
sleep
I’m OFF
6
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
=> Loss of messages induce coverage loss for TGJD
30
40
50
60
70
80
90
100
0 200 400 600 800 1000 1200
% o
f co
ve
red
are
a
time
Density 70
TGJDPOPNPR
PNR
Experimental results impact on protocol performances once messages get lost
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Work in progress
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
Layer i
Layer i+1
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage problem
III. Key distribution in wireless sensor networks
IV. Routing with guaranteed delivery
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Illustration
S1 S2
Key pre-distribution: each node receives n keys (here n=3)
Determine the key spool (here K=12)
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Illustration (2)
Random deployment
Initialization phase: neighbour discovery + key establishment
S1 S2
Hello, I am Bob
Hello, I am Alice
With ID
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Illustration (2)
Random deployment
Initialization phase: neighbour discovery + key establishment
S1 S2
Hello, I am Bob
Hello, me too
Without ID
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Illustration (3)
S1 S2
Initialization phase (continued)
Find common key
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Probability of key establishment
% of key space size Number of keys
K=10 000
What matters? n, the number of embedded keys p, the probability of key establishment
K, the size of key space is derived from n and p
K=100
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Examples of randomly
generated graphs according to
different probabilities p and different densities
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Probability of connectivity
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Compromised nodes
Reducing p does not improve robustness (because of connectivity constrainsts)
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Summary
Offline: generation of key spool and distribution of keys
Sensing phase
t
Deployment
Initialization phase
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Key-predistribution and activity scheduling
Sensing phase
Deployment
Sensing phase Sensing phase
t
1 round
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Evaluation
Sensing phase
Deployment
Sensing phase
Deployment
Sensing phase
Deployment
t
1 round
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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)
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Outlines
I. Wireless Sensor Networks
II. Activity scheduling and coverage problem
III. Key distribution in wireless sensor networks
IV. Routing with guaranteed delivery
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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 memory-less
Example: S forwards to neighbor B closest
to D [Finn 1987]
S DA
B
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Some variations
Nearest forward progress [Hou] small steps
More forward progress [Kleinrock] big steps
Random progress [Nelson, Kleinrock] ???
… Most close to SD with
progress [Elhafsi, Simplot-Ryl] Variation of DIR [Basagni et al.]
which is not loop free [Stojmenovic]
AB C D
E FS
A’
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Transmission radius
D
H
G
F
E DIR is not loop-free !
• Greedy and MFR are loop free
• If we include constraints on positive progress, the protocol is loop-free
• What if there is no neighbor with (strictly) positive progress? • Dead-end Recovery process
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
D F
E C
B A
Right hand rule
R L
L R
S
W
V
U
P
Face routing – guaranteed delivery
[Bose et al. 1999] Construct planar subgraph Route in planar subgraph:
SABCEBFED SC…ABFD…W…VP
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Planarization
GG = Gabriel Graph RNG = Relative Neighborhood Graph GG contains RNG They are both planar (if the initial graph is the UDG)
RNG GG
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Example of GG
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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)
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
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
U
V
D
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Introducing shortest path in greedy part
After choosing the next neighbor, SP can be apply 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
SETOP 2008
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
Summary: 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
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
Gre
edy
Rec
over
y
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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:
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
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
Gre
edy
Rec
over
y
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Performances
Cost/progres
SP after MFR
SPP after C/P
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
GG over CDS
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Performances
Cost/Progress
SP after MFR
without DS with DS
SP
P af
ter C
/P
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
What else?
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Multicasting
Same problem but the message as to be sent to several targets
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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:
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
If U is the current node, the progress when considering V is |MST(U,t1,…,tN)|-|MST(V,t1,…,tN)|
U
V
t2
t1
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Performances
MSTEAM
Ruiz et al. 2007
Centralized Algorithm
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Next?
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
Conclusion & perspectives
Best-known position-based routing (unicast and multicast) have been presented
With geographical information Basic algorithm: YES (MFR) Energy efficient (EE): YES (cost/progress, ……) Guaranteed delivery (GD): YES (FACE, GFG) EE+GD:
Without geographical information Basic algorithm: YES (VCap) Energy efficient (EE): Guaranteed delivery (GD): EE+GD:
YES (VCost…) YES (LTP) YES (HECTOR)
YES (ETE)
ETE’
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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
David SIMPLOT-RYL Robustness in WSN
SETOP 2008
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 [email protected]