SYNCHRONIZATIONAND LOCALIZATION

WIRELESS INFIDELITYMURIUM IQBAL

SHEKHAR SUMAN

PANKTI MAJMUDAR

SIDDHANTH DESHPANDE

AGENDA Time synchronization

Wireless sensor network synchronizationschemes

Secure synchronization

Secure Localization in Wireless Networks

TOA-based secure localization scheme

Mobility assisted secure localizationframework

Time synchronization

Wireless sensor network synchronizationschemes

Secure synchronization

Secure Localization in Wireless Networks

TOA-based secure localization scheme

Mobility assisted secure localizationframework

RESEARCH WE WILL COVER Time Synchronization and Localization in Sensor

Networkshttp://www.vs.inf.ethz.ch/res/papers/roemer-phd-2005.pdf

Secure Time Synchronization in Sensor Networkshttp://www.syssec.ethz.ch/research/TISSEC08.pdf

Secure Localization in Wireless Sensor Networkshttp://wins.lab.asu.edu/files/milcom05.pdf

Time Synchronization and Localization in SensorNetworks

http://www.vs.inf.ethz.ch/res/papers/roemer-phd-2005.pdf

Secure Time Synchronization in Sensor Networkshttp://www.syssec.ethz.ch/research/TISSEC08.pdf

Secure Localization in Wireless Sensor Networkshttp://wins.lab.asu.edu/files/milcom05.pdf

TIME SYNCHRONIZATION

What is Time-Synchronization

A method which allows individual entities in agroup to synchronize their clocks w.r.t eachother or to some coordinated universal time

What is Time-Synchronization

A method which allows individual entities in agroup to synchronize their clocks w.r.t eachother or to some coordinated universal time

The Myth of Simultaneity: “Event 1 and event 2 at same time”

Event 1 Event 2

Observer A:Event 2 is earlier than Event 1Observer A:Event 2 is earlier than Event 1

Observer B:Event 2 is simultaneous to Event 1

Observer C:Event 1 is earlier than Event 2

TIME SYNCHRONIZATION

Why do we need time-synchronization

Ordering Events

Coordinated Action

Data Logging

Notion of Global time

Performance Measurement

Why do we need time-synchronization

Ordering Events

Coordinated Action

Data Logging

Notion of Global time

Performance Measurement

TIME SYNCHRONIZATION INWIRELESS NETWORK critical middleware service in Wireless networks

allows engineers to design simpler and elegant algorithms

measuring the time of events detected by the sensors

measuring the time-of-flight of sound

recognizing duplicate detections of the same event

ordered logging of events during system debugging,

integrating multi-sensor data, or

coordinating on future action

critical middleware service in Wireless networks

allows engineers to design simpler and elegant algorithms

measuring the time of events detected by the sensors

measuring the time-of-flight of sound

recognizing duplicate detections of the same event

ordered logging of events during system debugging,

integrating multi-sensor data, or

coordinating on future action

CURRENT SYNCHRONIZATIONSCHEMESTSPNRBSTiny-SyncMini-SyncLTSFTSPNone of these protocols were designed tooperate in adversarial settings

TSPNRBSTiny-SyncMini-SyncLTSFTSPNone of these protocols were designed tooperate in adversarial settings

TIMING-SYNC PROTOCOLFOR SENSOR NETWORKSTraditional sender-receiver synchronizationThe logic is split into two phases

Level Discovery Phase A root node is selected from within the network Floods the network with level discovery packets

Synchronization phase Sender-receiver synchronization Node A sends sync pulse to Node B, Node B responds, Node

A syncs with Node B

Traditional sender-receiver synchronizationThe logic is split into two phases

Level Discovery Phase A root node is selected from within the network Floods the network with level discovery packets

Synchronization phase Sender-receiver synchronization Node A sends sync pulse to Node B, Node B responds, Node

A syncs with Node B

TIMING-SYNC PROTOCOLFOR SENSOR NETWORKSThis process continues through the network until all nodesare syncedClaimed to be more precise than receiver to receiversynchronizationDesigned for multi-hop networksHas four associated delays: send time, access time,propagation time, and receive timeAttacks against this scheme

Corrupt non-root node with wrong time Malicious node lies about it’s level Countermeasure would be to use redundancy

This process continues through the network until all nodesare syncedClaimed to be more precise than receiver to receiversynchronizationDesigned for multi-hop networksHas four associated delays: send time, access time,propagation time, and receive timeAttacks against this scheme

Corrupt non-root node with wrong time Malicious node lies about it’s level Countermeasure would be to use redundancy

REFERENCE BROADCASTSYNCHRONIZATIONReceiver to receiver synchronization methodSome nodes selected as beacons to send out syncmessagesClients can exchange reception times to calculate mutualconstraints such as drift and offset

Receiver to receiver synchronization methodSome nodes selected as beacons to send out syncmessagesClients can exchange reception times to calculate mutualconstraints such as drift and offset

REFERENCE BROADCASTSYNCHRONIZATIONEliminates uncertainty in sync path by removing sender

Can be extended for multi-hop networks Network separated into clusters Gateway nodes established which fall into multiple clusters Gateway nodes transform timing data to go from one cluster

to another

Eliminates uncertainty in sync path by removing sender

Can be extended for multi-hop networks Network separated into clusters Gateway nodes established which fall into multiple clusters Gateway nodes transform timing data to go from one cluster

to another

REFERENCE BROADCASTSYNCHRONIZATION

An attack against this synchronization schemewould involve compromising a node to give it anincorrect timeSince the synchronization is done from receiverto receiver, a malicious node within the networkcould send an incorrect time stamp duringsynchronizationTo avoid this attack an authentication processcan be implemented (using private keys) betweensending and receiving node pairs

An attack against this synchronization schemewould involve compromising a node to give it anincorrect timeSince the synchronization is done from receiverto receiver, a malicious node within the networkcould send an incorrect time stamp duringsynchronizationTo avoid this attack an authentication processcan be implemented (using private keys) betweensending and receiving node pairs

TINY-SYNC ANDMINI-SYNCMakes the assumption that each clock can be estimated bya fixed-frequency oscillatorTwo clocks can be associated with the following equation:

C1(t) = a12 • C2(t) + b12

Uses two-may messaging scheme to solve for a and b Node 1 sends message to Node 2 at timestamp to Node 2 receives message at tb and immediately sends reply Node 1 receives reply at tr

Makes the assumption that each clock can be estimated bya fixed-frequency oscillatorTwo clocks can be associated with the following equation:

C1(t) = a12 • C2(t) + b12

Uses two-may messaging scheme to solve for a and b Node 1 sends message to Node 2 at timestamp to Node 2 receives message at tb and immediately sends reply Node 1 receives reply at tr

TINY-SYNC ANDMINI-SYNCThe three time stamps together are one data pointMultiple exchanges are made to collect multiple data pointsA line is then fitted to these data pointsUpper and lower bounds for unknown values aregenerated:

The three time stamps together are one data pointMultiple exchanges are made to collect multiple data pointsA line is then fitted to these data pointsUpper and lower bounds for unknown values aregenerated:

TINY-SYNC ANDMINI-SYNC

TINY-SYNC ANDMINI-SYNCTiny-sync

Keeps only 4 data point constraints: those which yield thebest bounds on estimates among all the data points.

This may ignore important information by ignoring a currentdata point, that could be coupled with a future data point toyield better results

Mini-sync Also eliminates data points, but uses an algorithm to

determine if they are truly useless first Data points that may be useful in the future for obtaining

tighter bounds are stored

Tiny-sync Keeps only 4 data point constraints: those which yield the

best bounds on estimates among all the data points. This may ignore important information by ignoring a current

data point, that could be coupled with a future data point toyield better results

Mini-sync Also eliminates data points, but uses an algorithm to

determine if they are truly useless first Data points that may be useful in the future for obtaining

tighter bounds are stored

LIGHTWEIGHT TREE-BASEDSYNCHRONIZATIONHas two algorithms for synchronization in multi-hopnetworksThe first algorithm is centralized and requires a spanningtree to be formedSynchronization is done pair-wise across edges (similar toTSPN)The root node is given the entire trees depth and sizeRoot node calculates precision error at leaf nodes, entiretree’s synchronization time, and frequency ofresynchronization

Has two algorithms for synchronization in multi-hopnetworksThe first algorithm is centralized and requires a spanningtree to be formedSynchronization is done pair-wise across edges (similar toTSPN)The root node is given the entire trees depth and sizeRoot node calculates precision error at leaf nodes, entiretree’s synchronization time, and frequency ofresynchronization

LIGHTWEIGHT TREE-BASEDSYNCHRONIZATION

Reference nodes spread throughout networkEach node independently decides to synchronizeto nearest reference node as neededAll nodes along path to nearest reference nodesynchronize pair-wiseIf a neighbor of this node has already made async request, this node can tack onto that requestStops nodes from unnecessarily synchronizingentire tree

Reference nodes spread throughout networkEach node independently decides to synchronizeto nearest reference node as neededAll nodes along path to nearest reference nodesynchronize pair-wiseIf a neighbor of this node has already made async request, this node can tack onto that requestStops nodes from unnecessarily synchronizingentire tree

FLOODING TIME-SYNCHRONIZATIONPROTOCOL

Similar to TPSNAnchor node selected at randomBroadcasts time to entire networkperiodicallyNodes use delays across multiple broadcaststo calculate constraints Offset Drift

Anchor reselected periodically

Similar to TPSNAnchor node selected at randomBroadcasts time to entire networkperiodicallyNodes use delays across multiple broadcaststo calculate constraints Offset Drift

Anchor reselected periodically

SECURE TIMESYNCHRONIZATIONTime Synchronization is important henceprime target of Malicious Adversary

detrimental effect on functionality faulty estimates about the location of other nodes packets will be lost if the sleep-wakeup schedules

of nodes do not intersect trivial for adversaries to perform replay attacks in

security protocols that use time-stamping

Time Synchronization is important henceprime target of Malicious Adversary

detrimental effect on functionality faulty estimates about the location of other nodes packets will be lost if the sleep-wakeup schedules

of nodes do not intersect trivial for adversaries to perform replay attacks in

security protocols that use time-stamping

SECURE TIMESYNCHRONIZATIONSecure Time Synchronization in SensorNetworks - Ganeriwal et. al.

in-depth security analysis of sender-receiver synchronization protocols

proposes a protocol for secure pair-wisetime synchronization in sensor networks

proposes a protocol for secure groupsynchronization

Secure Time Synchronization in SensorNetworks - Ganeriwal et. al.

in-depth security analysis of sender-receiver synchronization protocols

proposes a protocol for secure pair-wisetime synchronization in sensor networks

proposes a protocol for secure groupsynchronization

TIME SYNCHRONIZATION INSENSOR NETWORKS

Two type of synchronization

sender-receiver synchronization

Receiver-receiver synchronization

Two type of synchronization

sender-receiver synchronization

Receiver-receiver synchronization

NETWORK-WIDE CLOCKSYNCHRONIZATION

Relies on above two protocols Establish a path among all nodes

like MST (spanning tree) Synchronize nodes pair-wise

Relies on above two protocols Establish a path among all nodes

like MST (spanning tree) Synchronize nodes pair-wise

SENSOR NODE CLOCK Each node maintains its own clock Only notion of time it has Essentially a timer that counts the

oscillations of a quartz crystal running ata particular frequency

Difference in the clocks of two sensornodes is referred as the offset error

Each node maintains its own clock Only notion of time it has Essentially a timer that counts the

oscillations of a quartz crystal running ata particular frequency

Difference in the clocks of two sensornodes is referred as the offset error

WHY DIFFERENT CLOCKTIME

Three reasons for the nodes to be representingdifferent times in their respective clocks

started at different time [Offset] running at different frequency [Skew] frequency of clock changes over time [Drift]

Three reasons for the nodes to be representingdifferent times in their respective clocks

started at different time [Offset] running at different frequency [Skew] frequency of clock changes over time [Drift]

WHY DIFFERENT CLOCKTIMELet clock for node A is represented by CA

This paper only addresses instantaneous timesynchronization which is Offset Error.

no fix for Skew or Drift

SENDER-RECEIVERSYNCHRONIZATION

SENDER-RECEIVERSYNCHRONIZATION

ARecv at T4

Send at T1 Recv at T2

T2 = T1 + DELAY + OFFSET

BClient Peer

Send at T3Recv at T4

T4 = T3 + DELAY- OFFSET

OFFSET = {(T2-T1)-(T4-T3)}/2

DELAY = {(T2-T1)+(T4-T3)}/2

ATTACKS ON TIMESYNCHRONIZATIONSome attacks on sender-receiver protocols

System Model Set of sensor nodes communicate over radio

transmissions Radio links are bi-directional Pair of nodes holds a shared secret key

Some attacks on sender-receiver protocols

System Model Set of sensor nodes communicate over radio

transmissions Radio links are bi-directional Pair of nodes holds a shared secret key

ATTACKS ON TIMESYNCHRONIZATIONAttacker Model Omnipresent but computationally bounded

adversary Constraints of the used cryptographic methods Able to eavesdrop, insert, modify, and block

arbitrary messages

Two Types External Attacker Internal Attacker

Attacker Model Omnipresent but computationally bounded

adversary Constraints of the used cryptographic methods Able to eavesdrop, insert, modify, and block

arbitrary messages

Two Types External Attacker Internal Attacker

ATTACKS ON TIMESYNCHRONIZATIONExternal Attack cannot authenticate herself as an honest network

node Attacker manipulates the communication

between pairs of mutually trusted nodes Cause them to de-synchronize attack is successful if faulty offset value is

calculated at node

External Attack cannot authenticate herself as an honest network

node Attacker manipulates the communication

between pairs of mutually trusted nodes Cause them to de-synchronize attack is successful if faulty offset value is

calculated at node

ATTACKS ON TIMESYNCHRONIZATIONThree possible attacks

Modify value of T1 and T3 Message forging and replay Delaying the transmission of

message thus increasing T2 and T4

Three possible attacks

Modify value of T1 and T3 Message forging and replay Delaying the transmission of

message thus increasing T2 and T4

ATTACKS ON TIMESYNCHRONIZATION First two can be fixed by traditional

security primitives Encryption, Hash and signature

Third attack pulse-delay attack Challenging to detect Jam the initial pulse and replay it at some

arbitrary time in the future Create a wormhole and then schedule the

packets between the nodes at will Sensor platforms are vulnerable to broadband

jamming

First two can be fixed by traditionalsecurity primitives Encryption, Hash and signature

Third attack pulse-delay attack Challenging to detect Jam the initial pulse and replay it at some

arbitrary time in the future Create a wormhole and then schedule the

packets between the nodes at will Sensor platforms are vulnerable to broadband

jamming

PULSE-DELAY ATTACK

PULSE-DELAY ATTACK

δ is estimated offset in clocksd is end-to-end delay∆ is delay introduced by attacker

δ is estimated offset in clocksd is end-to-end delay∆ is delay introduced by attacker

PULSE-DELAY ATTACK

Important observation is that by performing apulse-delay attack, the attacker also changesthe computed end-to-end delay

Infeasible for the attacker to just change thecomputed clock offset δ without changing thecomputed end-to-end delay d

Important observation is that by performing apulse-delay attack, the attacker also changesthe computed end-to-end delay

Infeasible for the attacker to just change thecomputed clock offset δ without changing thecomputed end-to-end delay d

SECURE PAIR-WISE TIMESYNCHRONIZATION

Where d* is maximum end-to-end delay between pairof node

SECURE PAIR-WISE TIMESYNCHRONIZATION Message integrity and authenticity are ensured

using MAC hash and KAB secret key

Prevents external attackers from modifying values

Attacker cannot impersonate node B [without KAB]

Replay attacks are prevented by using a randomnonce NA

Pulse-delay attacks are detected if delay isexceeded maximum delay d*

Requires d* estimation accurately

Message integrity and authenticity are ensuredusing MAC hash and KAB secret key

Prevents external attackers from modifying values

Attacker cannot impersonate node B [without KAB]

Replay attacks are prevented by using a randomnonce NA

Pulse-delay attacks are detected if delay isexceeded maximum delay d*

Requires d* estimation accurately

END-TO-END DELAYESTIMATION Three major component

Medium access time [microseconds to minutes] Packet transmission time [microseconds]

deterministic in future Signal propagation time [nanoseconds]

Medium Access Time Introduces highest uncertainty time-stamp the packets below the MAC layer packets are time-stamped as they are about to be

transmitted at the physical layer Achieve an accuracy of few microseconds

Three major component Medium access time [microseconds to minutes] Packet transmission time [microseconds]

deterministic in future Signal propagation time [nanoseconds]

Medium Access Time Introduces highest uncertainty time-stamp the packets below the MAC layer packets are time-stamped as they are about to be

transmitted at the physical layer Achieve an accuracy of few microseconds

MEASUREMENT ON MICA2MOTES

Implemented SPS Used TPSN protocol for synchronization MAC layer time stamping to achieve accuracy

of approx. 10 μs TinySec, a symmetric cryptography library is

used to calculate MAC on the fly

Implemented SPS Used TPSN protocol for synchronization MAC layer time stamping to achieve accuracy

of approx. 10 μs TinySec, a symmetric cryptography library is

used to calculate MAC on the fly

MEASUREMENT ON MICA2MOTES

MEASUREMENT ON MICA2MOTES 14 different discrete values Limited cock granularity on Mica2 motes (about 0.25µs) Software delay happens at the granularity of the CPU

clock cycles

Very low std deviation Roughly 0.5% of the absolute value of davg

Implies that the end-to-end packet delay will be within arange of 3% of the average delay davg, with a probabilityof 99.9%

Allows us to choose an appropriate and stable value ofthe maximal expected delay d*

14 different discrete values Limited cock granularity on Mica2 motes (about 0.25µs) Software delay happens at the granularity of the CPU

clock cycles

Very low std deviation Roughly 0.5% of the absolute value of davg

Implies that the end-to-end packet delay will be within arange of 3% of the average delay davg, with a probabilityof 99.9%

Allows us to choose an appropriate and stable value ofthe maximal expected delay d*

PERFORMANCE EVALUATION Synchronization precision achieved in a non-malicious

setting

Follows Gaussian distribution ϵ ~ N(davg, σ/sqrt(2))

Maximum impact of a pulse-delay attack on the achievedsynchronization precision

Maximum end-to-end delay – d* = davg + 3* σ

Maximum pulse delay - ∆ = 12*σ 40 ms for Mica2 Mote

Synchronization precision achieved in a non-malicioussetting

Follows Gaussian distribution ϵ ~ N(davg, σ/sqrt(2))

Maximum impact of a pulse-delay attack on the achievedsynchronization precision

Maximum end-to-end delay – d* = davg + 3* σ

Maximum pulse delay - ∆ = 12*σ 40 ms for Mica2 Mote

WHY SPS WORKS Attacker requires sufficiently fast and sophisticated

hardware to carry out this pulse-delay attack So it should not increase delay by more than 40 ms

Infeasible for an external mote-class attacker radio speed of Mica2 motes is 78.5 kbps just receive and forward will cause 100ms

Even it is possible for faster hardware, delay is stillbounded

No distance-dependent delay, end-to-end delay is onlycaused by slow radio so no need to calculate d* at run time Different but fixed for different type of radios

Attacker requires sufficiently fast and sophisticatedhardware to carry out this pulse-delay attack So it should not increase delay by more than 40 ms

Infeasible for an external mote-class attacker radio speed of Mica2 motes is 78.5 kbps just receive and forward will cause 100ms

Even it is possible for faster hardware, delay is stillbounded

No distance-dependent delay, end-to-end delay is onlycaused by slow radio so no need to calculate d* at run time Different but fixed for different type of radios

RECOVERY can nodes detect attack, and calculate correct

offset ? only if only packet transmission was delayed not possible if attack is done on both packet

transmission Each packet transmission will introduce a new

unknown variable ∆ pulse delay

can nodes detect attack, and calculate correctoffset ? only if only packet transmission was delayed not possible if attack is done on both packet

transmission Each packet transmission will introduce a new

unknown variable ∆ pulse delay

VARIATION OF SPS FOR FASTHARDWARE on-the-fly MAC insertion is infeasible for fast

hardware MAC need to be generated after timestamp prediction-based, add delay of computing MAC

Enhanced SPS - Hardware independent send two different packets First message is refresh packet and does not

contain Timestamp value Second message send timestamps of first

message hence no on-the-fly MAC required

on-the-fly MAC insertion is infeasible for fasthardware MAC need to be generated after timestamp prediction-based, add delay of computing MAC

Enhanced SPS - Hardware independent send two different packets First message is refresh packet and does not

contain Timestamp value Second message send timestamps of first

message hence no on-the-fly MAC required

ENHANCED SPS

SECURE GROUPSYNCHRONIZATION System model

Each node can authenticate messages Can not impersonate and send valid message on

behalf of other

Lightweight SGS Notations

N – Number of nodes in group Ti - sending time of the packet at node i (Ci) Tij - time at which the packet broadcasted by node i

is received at j (Cj) δij - offset between local clocks at node I and j dij - delay for the packet transfer from node i to j

System model Each node can authenticate messages Can not impersonate and send valid message on

behalf of other

Lightweight SGS Notations

N – Number of nodes in group Ti - sending time of the packet at node i (Ci) Tij - time at which the packet broadcasted by node i

is received at j (Cj) δij - offset between local clocks at node I and j dij - delay for the packet transfer from node i to j

LIGHTWEIGHT SECUREGROUP SYNCHRONIZATION

LIGHTWEIGHT SECUREGROUP SYNCHRONIZATION Each node broadcast challenge packet After receiving Nmin, broadcast Response packet

contains triples [Tij, Nj, Gj] for all challenge packets contains N-1 MACs to authenticate each triplet sending time Ti‘

Each node Gi estimates local clock of other nodes Estimate clock offset for each node

Estimate group clock, take median of all local clocks

Each node broadcast challenge packet After receiving Nmin, broadcast Response packet

contains triples [Tij, Nj, Gj] for all challenge packets contains N-1 MACs to authenticate each triplet sending time Ti‘

Each node Gi estimates local clock of other nodes Estimate clock offset for each node

Estimate group clock, take median of all local clocks

LIGHTWEIGHT SECURE GROUPSYNCHRONIZATION Complexity

For each node, challenge and response packet 2N packet transmission only problem, N-1 on-the-fly MAC generation

can be delayed as E-SPS use public key infrastructure

Each node will sign packet with its privatekey

Complexity For each node, challenge and response packet 2N packet transmission only problem, N-1 on-the-fly MAC generation

can be delayed as E-SPS use public key infrastructure

Each node will sign packet with its privatekey

INTERNAL ATTACKERS Can introduce variable offset delay Synchronization fails because estimates are

different, not because wrong estimate local clock estimates should be consistent Can be solved using Byzantine agreement model of

consensus Calculate and broadcast pair-wise offset set Run Byzantine consensus algorithm at each node

Works only if number of compromised nodes areless than (N – 1)/3

Can introduce variable offset delay Synchronization fails because estimates are

different, not because wrong estimate local clock estimates should be consistent Can be solved using Byzantine agreement model of

consensus Calculate and broadcast pair-wise offset set Run Byzantine consensus algorithm at each node

Works only if number of compromised nodes areless than (N – 1)/3

SECURE TIMESYNCHRONIZATION Time Synchronization in Wireless Network

Pair-wise synchronization Network-wide synchronization

Different Attacks Internal/External Attack Pulse-delay Attack

Secure Pair-wise Time Synchronization End-to-end delay estimation Enhanced SPS for fast hardware

Lightweight Secure Group Synchronization Byzantine Consensus solution for internal attackers

Time Synchronization in Wireless Network Pair-wise synchronization Network-wide synchronization

Different Attacks Internal/External Attack Pulse-delay Attack

Secure Pair-wise Time Synchronization End-to-end delay estimation Enhanced SPS for fast hardware

Lightweight Secure Group Synchronization Byzantine Consensus solution for internal attackers

LOCALIZATIONSeveral applications and protocols in wireless sensornetworks require a near exact location estimation of sensornodes.Eg: tracking, precision navigation, surveillance

LOCALIZATIONProper functioning depends onaccurate position estimation ofnodes.

What if attackers in hostileenvironments take advantage ofvulnerabilities in the localizationalgorithm?Adversaries can subvert normalfunctionalities of location-dependentWSNs by exploiting weakness(es) inlocalization algorithm

Proper functioning depends onaccurate position estimation ofnodes.

What if attackers in hostileenvironments take advantage ofvulnerabilities in the localizationalgorithm?Adversaries can subvert normalfunctionalities of location-dependentWSNs by exploiting weakness(es) inlocalization algorithm

TYPES OF LOCALIZATIONALGORITHMS

Range-free (using coarse metrics to place bounds on candidatepositions), eg: SeRLoc N. Bulusu, J. Heidemann, and D. Estrin, Gps-less low costoutdoor localization for very small devices, IEEE PersonalCommun. Mag., vol. 7, no. 5, pp. 2834, Oct. 2000. T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. F.Abdelzaher, Range-free localization scheme in large scale sensornetworks, in ACM MOBICOM'03, San Diego, CA, Sep. 2003. D. Niculescu and B. Nath, DV based positioning in ad hocnetworks, Journal of Telecommunication Systems, 2003. L. Hu and D. Evans, Localization for mobile sensor networks,in ACM MOBICOM'04, Philadephia, PA, Sep/Oct 2004.

Range-free (using coarse metrics to place bounds on candidatepositions), eg: SeRLoc N. Bulusu, J. Heidemann, and D. Estrin, Gps-less low costoutdoor localization for very small devices, IEEE PersonalCommun. Mag., vol. 7, no. 5, pp. 2834, Oct. 2000. T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. F.Abdelzaher, Range-free localization scheme in large scale sensornetworks, in ACM MOBICOM'03, San Diego, CA, Sep. 2003. D. Niculescu and B. Nath, DV based positioning in ad hocnetworks, Journal of Telecommunication Systems, 2003. L. Hu and D. Evans, Localization for mobile sensor networks,in ACM MOBICOM'04, Philadephia, PA, Sep/Oct 2004.

TYPES OF LOCALIZATIONALGORITHMS

Range-based (estimating distances to landmarks based onvarious physical properties) eg: Distance Bounding Protocol L. Doherty, K. S. Pister, and L. E. Ghaoui, Convex optimizationmethods for sensor node estimation, in IEEE INFOCOM'01,Anchorage, Alaska, April 2001. A. Savvides, C. Han, and M. Srivastava, Dynamic fine-grainedlocalization in ad-hoc networks of sensors, in ACMMOBICOM'01, Rome, Italy, July 2001. D. Niculescu and B. Nath, Ad hoc positioning system (APS)using AoA, in IEEE INFOCOM'03, San Francisco, CA, April 2003. X. Cheng, A. Thaeler, G. Xue, and D. Chen, TPS: A time-basedpositioning scheme for outdoor wireless sensor networks, inIEEE INFOCOM'05, Miami, FL, March 2005.

Range-based (estimating distances to landmarks based onvarious physical properties) eg: Distance Bounding Protocol L. Doherty, K. S. Pister, and L. E. Ghaoui, Convex optimizationmethods for sensor node estimation, in IEEE INFOCOM'01,Anchorage, Alaska, April 2001. A. Savvides, C. Han, and M. Srivastava, Dynamic fine-grainedlocalization in ad-hoc networks of sensors, in ACMMOBICOM'01, Rome, Italy, July 2001. D. Niculescu and B. Nath, Ad hoc positioning system (APS)using AoA, in IEEE INFOCOM'03, San Francisco, CA, April 2003. X. Cheng, A. Thaeler, G. Xue, and D. Chen, TPS: A time-basedpositioning scheme for outdoor wireless sensor networks, inIEEE INFOCOM'05, Miami, FL, March 2005.

TYPES OF LOCALIZATIONALGORITHMSBoth range-free and range-based methods depend on theuse of anchors (nodes that know their own location) Anchors may know their own location through GPS or by

means of manual configuration

Range-free methods have less demand onsensor hardware, BUT localization isrelatively coarse Hence may beunsuitable for military applications

Comparatively, range-based methods canproduce much finer-grained solutions

[Note: This survey primarily covers range-based methods]

PROBLEMS WITH RANGE-BASED LOCALIZATIONALGORITHMSAlmost all previous range-based localization algorithmsdesigned for non-adversarial scenarios Unsuitable forhostile environmentsAdversaries can easily subvert normal functionalities byexploiting weaknesses of algoirthms make sensors obtainwrong locations away from true ones

DIRE IMPLICATIONS!Types of adversaries Internal adversaries External adversaries

Almost all previous range-based localization algorithmsdesigned for non-adversarial scenarios Unsuitable forhostile environmentsAdversaries can easily subvert normal functionalities byexploiting weaknesses of algoirthms make sensors obtainwrong locations away from true ones

DIRE IMPLICATIONS!Types of adversaries Internal adversaries External adversaries

ADVERSARY MODELSExternal Adversaries No access to authentic keying material of the network Can be isolated from network by effective authentication

schemes

Internal Adversaries Compromised nodes running malicious code or

adversaries steal authentic keying material from legitimatenodes

Can authenticate self to other legitimate nodes More difficult to defend against

External Adversaries No access to authentic keying material of the network Can be isolated from network by effective authentication

schemes

Internal Adversaries Compromised nodes running malicious code or

adversaries steal authentic keying material from legitimatenodes

Can authenticate self to other legitimate nodes More difficult to defend against

TIME OF ARRIVALBASED LOCALIZATIONAssume three anchors- A, B & C B transmits at time t1 to locatee S

and receives response at time t2

Hence dSA = (t2-t1)/c Similary, A calculates dSA and C

calculates dSC

Using any standard multilaterationtechnique, location estimate of Scan be obtained

A

BdSA

dSB

Assume three anchors- A, B & C B transmits at time t1 to locatee S

and receives response at time t2

Hence dSA = (t2-t1)/c Similary, A calculates dSA and C

calculates dSC

Using any standard multilaterationtechnique, location estimate of Scan be obtained C

Locatee dSB

dSC

ADVERSARY MODEL TOA-based schemes work in 2 phases:

Distance measurements between anchors and locatee Data fusion to estimate location

Distance enlargement attacks

Distance reduction attacks

TOA-based schemes work in 2 phases: Distance measurements between anchors and locatee Data fusion to estimate location

Distance enlargement attacks

Distance reduction attacks

SECURE LOCALIZATIONSCHEME (SLS) Few anchor nodes localize sensor nodes in the presence

of adversaries

A1 A2

A3Adversary

Locatee

NETWORK AND TRUST MODEL= Set of anchor nodes

S = Locatee= No of anchor nodes (>=3)

Anchors know their own location All anchors and S within transmission range of each other An effective MAC protocol exists Anchors share a pair-wise secret key Anchors are trusted (a reasonable assumption) = Pairwise secret key between S and

= Set of anchor nodesS = Locatee

= No of anchor nodes (>=3)

Anchors know their own location All anchors and S within transmission range of each other An effective MAC protocol exists Anchors share a pair-wise secret key Anchors are trusted (a reasonable assumption) = Pairwise secret key between S and

OVERVIEW Anchor leader broadcasts “start” message Each Ai runs K-Distance to estimate Dsi After measurements, run CalPos to estimate Run TestValid to test estimate Use , … cardinality subsets to

estimate if TestValid fails Report abnormality if no estimate passes

Anchor leader broadcasts “start” message Each Ai runs K-Distance to estimate Dsi After measurements, run CalPos to estimate Run TestValid to test estimate Use , … cardinality subsets to

estimate if TestValid fails Report abnormality if no estimate passes

K-DISTANCE

LocateeSAnchor

Ai

= 2l/b l = length of nonce Nj and Ns,j

K-DISTANCE Adversaries cannot reduce…. and

Message Authentication Code S can guess with probabilty

K Distance estimations protect against distance-enlargementattacks

Mitigate sporadic measurement errors K = tradeoff between algorithm overhead, measurement

errors and level of resilience

Adversaries cannot reduce…. and

Message Authentication Code S can guess with probabilty

K Distance estimations protect against distance-enlargementattacks

Mitigate sporadic measurement errors K = tradeoff between algorithm overhead, measurement

errors and level of resilience

USING K-DISTANCE Mean can be affected by just one adversarial attack Use median of K time estimatesTsi = Median(T) = T(r) r = (K+1)/2 For 1 enlarged estimate T(j’)

If T(j’), T(j) < T(r), no change in estimate Else Tsi = [ T(r-1), T(r+1) ]

For m enlarged estimates, possibly Tsi = [ T(r-m), T(r+m) ] Tolerates enlargement of about half estimates Works if K is large…. TestValid helps otherwise

Mean can be affected by just one adversarial attack Use median of K time estimatesTsi = Median(T) = T(r) r = (K+1)/2 For 1 enlarged estimate T(j’)

If T(j’), T(j) < T(r), no change in estimate Else Tsi = [ T(r-1), T(r+1) ]

For m enlarged estimates, possibly Tsi = [ T(r-m), T(r+m) ] Tolerates enlargement of about half estimates Works if K is large…. TestValid helps otherwise

CALPOS – CALCULATE ALOCATION ESTIMATE

Anchor leader calculates location estimate

(Xi, Yi) = Location of anchor i

Anchor leader calculates location estimate

(Xi, Yi) = Location of anchor i

TESTVALID – TEST VALIDITY OFLOCATION ESTIMATES

No enlargementattacks

TESTVALID – TEST VALIDITY OFLOCATION ESTIMATES Check if estimated position inside polygon

Adversaries must enlarge and minimize estimates .. But K- Distance prevents minimization

Small measurement errors 2-sided max allowable limit

Check

Check if estimated position inside polygon Adversaries must enlarge and minimize estimates .. But K- Distance prevents minimization

Small measurement errors 2-sided max allowable limit

Check

TESTVALID – TEST VALIDITYOF LOCATION ESTIMATESMeasurement errors or Minor adversarial impact

TESTVALID – TEST VALIDITYOF LOCATION ESTIMATESTest if estimate within polygon

SECURE LOCALIZATION INWIRELESS SENSOR NETWORKS Test SLS on heterogenous WSNs

Assumptions: Na (total number of) mobile anchorsAttributes: known locations; computationally morepowerful than standard sensors Na = ng (>= 1; no. of groups) x na (>=3; size of group) ng determines trade-off betweem n/w bootstrappingdelay and localization overhead

[Note: Also assume that mobile anchors have data mulecapabilities] possibly improves data-delivery performance

Test SLS on heterogenous WSNsAssumptions: Na (total number of) mobile anchorsAttributes: known locations; computationally morepowerful than standard sensors Na = ng (>= 1; no. of groups) x na (>=3; size of group) ng determines trade-off betweem n/w bootstrappingdelay and localization overhead

[Note: Also assume that mobile anchors have data mulecapabilities] possibly improves data-delivery performance

SECURE LOCALIZATION INWIRELESS SENSOR NETWORKSThree phases of implementation: Each anchor of an ng-member group obtains distance

estimate to locatee through two-way TOA ranging Anchors collaboratively run SLS to get valid location

estimate Location estimate determined is securely transmitted to

traget sensor

[As opposed to traditional sensor localization methods,individual distance measurement & multilateration notrequired proposed scheme shifts resource-hungry ranging& computation to relatively powerful anchors]

Three phases of implementation: Each anchor of an ng-member group obtains distance

estimate to locatee through two-way TOA ranging Anchors collaboratively run SLS to get valid location

estimate Location estimate determined is securely transmitted to

traget sensor

[As opposed to traditional sensor localization methods,individual distance measurement & multilateration notrequired proposed scheme shifts resource-hungry ranging& computation to relatively powerful anchors]

Benefits?

Reduction in sensor cost

Anchors are less common &can be augmented to be madetamper proof withouttremendous extra cost

MOBILITY ASSISTEDSENSOR LOCALIZATION

MOBILITY ASSISTEDSENSOR LOCALIZATIONLocalization information is securely transmitted from anchorgroup to individual sensors using pairwise shared secretkeys

Pairwise key establishment is very efficient each sensor eeds to memorize only it’s own ID-based key

and computational overhead is negligible master key Kma is known to network planner and tamper

proof anchor each sensor is loaded with individual key corresponding

to its unique ID, calculated by means of a hanshingfunction

Localization information is securely transmitted from anchorgroup to individual sensors using pairwise shared secretkeys

Pairwise key establishment is very efficient each sensor eeds to memorize only it’s own ID-based key

and computational overhead is negligible master key Kma is known to network planner and tamper

proof anchor each sensor is loaded with individual key corresponding

to its unique ID, calculated by means of a hanshingfunction

MOBILITY ASSISTEDSENSOR LOCALIZATIONWhenever pausing after one movement, the anchor leader A1announces group existence by broadcasting a helloLocationmessage All sensors hear helloLocation message but only those

that are not yet localized respond (out of these, mostsensors are inside the sensor polygon, determined by A1)

Possible MAC-layer collisions possible Process for localizing sensor inside anchor polygon

Whenever pausing after one movement, the anchor leader A1announces group existence by broadcasting a helloLocationmessage All sensors hear helloLocation message but only those

that are not yet localized respond (out of these, mostsensors are inside the sensor polygon, determined by A1)

Possible MAC-layer collisions possible Process for localizing sensor inside anchor polygon

QUESTIONS?