Lectures 5: Mobile Ad Hoc and Sensor Networks (II)

Lectures 5: Mobile Ad Hoc and

Sensor Networks (II)

Ing-Ray Chen

CS 6204 Mobile Computing

Virginia Tech

Courtesy of G.G. Richard III for providing some of

the slides 1

Fault Tolerance and Reliability

• Sensor nodes are more susceptible to failure because of direct exposure to the environment and energy depletion

• Failure and fault recovery are basic assumptions: incorporate redundancy to cope with failure

• Performing consensus in a cluster for high reliability of measurement – Clustering based on sensing responsibility

– Static vs. dynamic grouping • Dynamic grouping does not need to maintain state

information and is more accurate (near the event) but incurs overhead in forming the group and reaching consensus

2

Searching for Agreement: Static

Grouping

3

Searching for Agreement: Dynamic

Grouping

4

MAC Layer Protocols

• IEEE 802.11 scheduling protocols are not suitable for wireless sensor networks because: – With RTS/CTS, collision can still occur because of

hidden/expose terminal problems

– Listening to traffic to avoid collision requires the nodes to stay on

• TDMA is more suitable (requiring clock synchronization) – A number of reservation mini-slots can be used to reserve each

of the transmission slots

– Sensors can indicate whether or not they wish to transmit a message during the scheduling time segment

– Nodes that are not planning to send or receive a packet need to stay on only during the reservation time slot to see if other sensors are sending a packet to them

– Collisions are avoided, except for small reservation packets

5

Tradeoff between Energy Efficiency

and Reliability/Performance

• An important design issue

• Improved reliability vs. energy consumption

• Aggregating sensor readings vs. loss of information

• Energy-efficient protocols often involve increased delay, loss of accuracy, reduced reliability and/or other performance penalty – Direct sensor-BS transmission vs. sensor-CH-BS

– Sensor readings with vs. without redundancy

• Achieving application requirements while prolonging lifetime is a major challenge

6

Fault Tolerant Data Propagation

• Reference: [13] listed at the end

• Use path redundancy to cope with sensor “reading” faults – One path (no redundancy)

– Multiple paths to return sensor readings and a majority voting of the first three readings returned is performed to cope with faults

– For example, use Time To Live (TTL) to indicate how many hops a sensor reading message is to be propagated, thereby creating multiple paths to propagate the sensor reading message from source to sink

7

Fault Tolerant Data Propagation

• Source: node A

• Sink: node I

• When TTL = 3

hops, there are

7 paths from I to

A

• When TTL=4

hops, there are

21 paths source

sink

H G I

D E

F

B A C

8

Fault Tolerant Data Propagation An example

Source: node E (assume infallible)

Sink: node I (assume infallible)

p: link fault probability (causing reading error)

q: node fault probability (causing reading error)

TTL=1: Reliability is 1-p

TTL=2: what is the reliability? – Three possible paths: E->I, E->H-

>I, E->F->I, with fault probability of p, A, A

– System fails when at least 2 out of 3 paths fail (because of voting), so reliability = 1-pA2-2pA(1-A)-(1-p)A2 where A=1-(1-q)(1-p)2 =2p+q-2pq-p2+p2q

More path redundancy improves the reliability at the expense of more energy consumption

source

sink

H G I

D E

F

B A C

9

Energy Efficiency

• Metric: Mean Time to Failure (MTTF)

– Time till the first node dies (not useful)

– Time half of the sensor nodes die (too arbitrary)

– Time when the sensor network can no longer perform its intended function (yeah!)

• Difficult to define precisely

• Designing protocols so that – All the sensors die at roughly the same time

– Sensors die in random locations instead of in specific locations

10

Balancing Energy Consumption

• Clustering – is it always good?

– Triangular routing: sensors -> cluster head ->

base station

– Overhead in selecting and rotating among

sensors to be cluster heads

– Good if message aggregation is feasible;

otherwise directly sending sensing readings to

the base station may end up saving energy

more

11

Energy-Efficient Clustering


• Two key parameters:

– p: probability of a sensor becoming a cluster

head

– k: number of hops covered by a cluster

• Find optimal (p, k) that would minimize the

energy consumed

12

Energy-Efficient Clustering:

Formulation • Sensors are distributed following a

homogeneous spatial Poisson process with intensity l in a square area of size 4a2

• Per-hop distance is r

• Energy model: each sensor uses 1 unit of energy to transmit or receive 1 unit of data

• The information processing center is in the middle of the area

• Idea: Define a function for the energy used for all sensors to send their sensor readings to their CH and then to the processing center (PC), and find (p, k) that would minimize the energy used 13


Modeling (1) • Let a cluster head (CH) be uniformly and randomly

located at (Xi, Yi) in the square sensor area with –a ≤ Xi ≤ a and –a ≤ Yi ≤ a and let the PC be located in the center of the area at coordinate (0, 0).

• Then the expected distance D between this CH and the PC is:

• Since there are on average n ᵡ p CHs, the total length from all these CHs to the PC is E[D] ᵡ np.

• Hence, the total expected energy by CHs to communicate the aggregated information with the processing center is E[D] ᵡ np/r.

adYdXaa

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a

a

a

a

ii765.0)

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14


Modeling (2) • Each non-CH sensor distributed with density (1-p)l joins

a closest CH distributed with density pl such that each cluster area becomes a Voronoi cell with the expected number of sensors in each cell given by (1-p)/p. It can be shown [14] that the expected total distance Lv of all the sensors within a cluster to their CH is given by:

• Since there are on average n ᵡ p CHs, the expected total length from all the sensors in the system to their CHs is E[Lv] ᵡ np

• Hence the total expected energy for all the sensors in the system to communicate 1 unit of information to their respective CHs is E[Lv] ᵡ np/r

2/32/1 )(2

)1(

)(2

11][

l

l

l p

p

pp

pLE v

15


Modeling (3)

• The expected total energy spent, C, by the system

is the sum of energy spent and is given by:

• Setting the derivative of E[C] with respect to p to

zero yields the optimal p value that minimizes E[C].

)]/(765.0)/()(2

)1([4

)]/(765.0)/()(2

)1([][

2/3

2

2/3

rparpp

pa

rparpp

pnCE

l

ll

l

l

16


Modeling (4)

• Once optimal p is determined, optimal k can be

determined by the property of a sensor node in a

Voronoi cell such that the probability of a sensor

node not joining a k-hop cluster is less than a:

l

a

prk

)7/ln(917.01

17

Simulation Experiment of Optimal

(p,k) for Energy-Efficient Clustering

18

On Optimal Path and Source

Redundancy in Sensor Networks


• Analyze the effect of redundancy on the mean time to system failure (MTTF) and determine the optimal source and path redundancy to maximize MTTF while satisfying QoS requirements in WSNs.

• Develop a hop-by-hop data delivery mechanism utilizing source and path redundancy with the goal to satisfy QoS requirements while maximizing the lifetime of the sensor system

• Query: must return a sensor reading to the PC within the real-time deadline. 19

Cluster based WSN architecture

Feature Region

Sensor node

Cluster head

Processing Center

20

Hop-by-Hop Data Delivery

Protocol

Cluster head Processing center1

m

2

21

Hop-by-hop Data Delivery

Protocol • Based on localized geographic routing

• Path redundancy: Form m paths from a source

CH to the PC:

• Select mp SNs in hop one to relay data

• Select only one SN in each of the subsequent hops

• Source redundancy: ms SNs to communicate with

the source CH each through a distinct path:

• only one SN relays the data in each of the

subsequent hops in each path

22

Probability Model

• System MTTF - Total number of queries the system can answer before it fails due to energy depletion, sensor faults, or channel error (related to WSN lifetime)

• Rq - Reliability of a query as a result of applying the hop-by-hop data delivery mechanism with m paths for path redundancy and ms sensors for source redundancy

• Design Goal: Find best redundancy represented by m

and ms such that MTTF is maximized when given a set

of system parameters characterizing the application

and network conditions. 23

Probability Model Cont. • dinter - a random variable denoting the distance

between a source CH and the processing center

• dintra - a random variable denoting the distance between a SN to the CH

r

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er

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24

Probability Model Cont.

• Average number of hops to forward sensor data from a

sensor to its cluster head:

• Probability of a SN on the next hop failing to receive data:

– q is the per-node failure probability

– e is the per-link failure probability

2/1int)(2

1

lprN h

ra

)]1)(1[(1 eqQr

25


• Treq – the deadline requirement of a query

• The minimum per-hop speed:

• When a SN forwards data, it will know if a neighbor SN

satisfies the speed requirement based on delay

information collected.

• Ex: If the progressive speed is uniformly distributed

within a range [a, b], then the probability that a next-hop

SN fails to satisfy the speed requirement is given by:

req

setT

pA

XE2/1)(2

13825.0

][l

ab

aXExExcdfQ set

sett

][])[(

26


• The probability of a next SN fails to relay a broadcast packet

because of either channel/node failures or speed violation is

given by:

• nk - the average number of one-hop neighbors, nk = λ * πr2

• The probability that at least one next-hop SN (among nk) is

able to receive the broadcast message and satisfy the speed

requirement :

• Path success probability: The probability that a path from the

source CH to the PC is formed is given by:

)]1)(1[(1 trrt QQQ

kn

rtQ1

1int herN

27


The source cluster will fail to deliver data to the processing center if one of the following happens:

• None of the SNs in the first hop receives the message

• In the first hop, j (1≤ j <m) SNs (less than m) receive the message, but all j paths fail to deliver the message because the subsequent hops fail to receive the broadcast message

• In the first hop, at least m SNs receive the broadcast message from the source CH, but all m paths fail to deliver the message because the subsequent hops fail to receive the broadcast message

28


• The probability of the source cluster failing to deliver

data to the processing center:

k

erkk

erkk

n

mj

mNjrt

jn

rt

n

j

m

j

jNjrt

jn

rt

n

jmfp

QQC

QQCQ

)1()1()(

)1()1()()1(

1

1

1

1

int

int

29


• The probability that all ms SNs within a cluster fail to return sensor readings to the source CH:

• The probability of a source cluster not being able to return a correct response by the deadline, because of either path or source failure, or both:

• The query success probability:

sh

ras mNm

fsQ )1( 1int

)1)(1(1 sm

fs

m

fpfQQQ

fqQR 1

30

• The total energy required to forward data from ms SNs to the CH :

• The total energy consumed by the WSN to transmit sensor data from the source CH to the PC:

• Total energy consumption of the system to answer a query:

Eq = Ech + Es

Energy Consumption

])()[( 2int RTh

rass ErENmE l

])()[1(

)(2

int

2

RTh

er

RTch

ErENm

ErEE

l

l

31

Probability Model (Cont.)

• Average number of queries system is able to respond

to before energy depletion:

• MTTF of the system – the expected number of queries

the system can answer without experiencing a failure

(with the upper bound Nq)

initial thresholdq

q

E EN

E

1

1

(1 )q

q

NNi

q q q qi

MTTF iR R N R

32

Numeric Results WSN with model parameters: • n = 4000 [Total number of nodes]

• r = 1 unit [One hop radio range of sensor]

• l = 10 nodes/sq. unit [Sensor density]

• A= 20 units [A side of square area]

• nb = 50 bytes [Data packet length]

• Eelec = 50 nJ/bit [Energy used for communication]

• Eamp = 10 pJ/bit/m2 [Energy used by the transceiver amplifier]

• Eo = 2J [Initial energy of each sensor node]

• Ethreshold = 0 [If energy falls below this, the system fails]

• ns = 300 nodes [Number of sensors in a cluster]

• q = 10-6 [sensor node failure probability]

• e = 0.3 – 0.6 [channel transmission failure probability]

• (a, b) = (10, 100) [progressive speed distribution]

• Treq = 0.2 – 0.5 s [Required deadline of a query]

33

Optimal (m, ms)

• Optimal (m, ms) that maximize MTTF:

e = 0.35 0.4 0.45 0.5 0.55

Treq= 0.25sec 2,3 2,3 3,3 4,7 4,7

0.30 sec 1,1 2,3 2,3 3,3 4,7

0.35 sec 1,1 1,1 2,3 2,3 3,3

0.40 sec 1,1 1,1 2,1 2,3 2,3

0.45 sec 1,1 1,1 1,1 2,3 2,3

34

MTTF vs. (m, ms)

• Inadequate or

excessive

redundancy is

detrimental to

the MTTF of

the sensor

system

Treq= 0.4 sec, e = [0.45-0.5]

Optimal (m, ms): Top 3-D - (2,1), bottom 3-D – (2,3)

1

3

5

7

12

34

0

2000000.0

4000000.0

6000000.0

8000000.0

10,000,000

msm

MT

TF

e = 0.45

e = 0.5

35

Rq vs. (m, ms)

• Either m or ms

increases, Rq

increases

• Rq is more

sensitive to m

because path

redundancy

greatly improves

Rq compared

with source

redundancy

Treq= 0.4 sec, e = [0.45-0.5]

1

3

5

7

1

2

3

4

0.999998

0.999999

1

ms

m

Rq

e = 0.45

e = 0.5

36

Eq vs. (m, ms) • Eq is monotonically increasing as either m or ms increases

• Tradeoff between Rq and Eq: If more redundancy is used to answer a query, MTTF would increase due to a higher Rq, but on the other hand MTTF would decrease due to a high Eq. Hence, an optimal (m, ms) exists Treq= 0.3 - 0.4 sec

1

3

5

7

1

2

3

4

0

0.001

0.002

ms

m

Eq

Treq

= 0.3

Treq

= 0.4

37

MTTF vs. (m, ms)

• As Treq increases (less stringent real-time deadline constraints), MTTF increases and the system would select less redundancy to maximize the MTTF

e= 0.45, Treq = [0.3-0.4] sec

Optimal (m, ms): Top 3-D - (2,1), bottom 3-D – (2,3)

1

35

7

12

34

0

2000000.0

4000000.0

6000000.0

8000000.0

10000000.0

ms

m

MT

TF

Treq

= 0.3

Treq

= 0.4

38

Encounter-based Routing in Delay

Tolerant Networks [16]

• Delay tolerant networks (DTNs) is a special case of

MANETs characterized by high end-to-end latency, frequent

disconnection, and opportunistic communication over

unreliable wireless links.

• Routing in DTNs are encounter-based by which a packet is

transmitted to the next carrier upon encountering.

• Who to select for data forwarding is the central issue in DTN

routing

• Social network analysis is used to exploit the underlying

social structure in order to provide information flow from

source to destination.

Social Networks For

Information Flow

• Social network analysis is the study of relationships between entities and on the patterns and implications of these relationships.

• Dynamic network graphs with time-varying links may be used to represent the relational structure of social networks.

• Each node is represented by a vertex.

• Relationships between nodes may be represented as edges.

Centrality in Graph Theory

• Centrality is the quantification of the relative

importance of a vertex within the graph.

• Central node has a stronger capability of

connecting other network members.

• Three widely used centrality measures are

degree, closeness, and betweenness measures

Centrality Measures

Degree centrality: the number of direct links that involve a

given node

Closeness centrality: the mean shortest path between a

node and all other reachable nodes.

Betweenness centrality: the extent to which a node lies on

the geodesic paths linking other nodes. It can be regarded

as how much a node can facilitate communication to other

nodes in the network

a(pi,pk)=1 if a direct link exists

between pi and pk

d(pi,pk)=shortest geodesic

distance between pi and pk;

N = # of nodes reachable by pi

gjk is the total number of geodesic paths

linking pj and pk, and gjk(pi) is the number

of those geodesic paths that include pi

Ego Networks

• Centrality metrics become difficult to evaluate in a

network with large node populations.

• This is the reason for the proposal of ego networks.

• Ego networks can be defined as a network consisting of

a single actor (ego) together with the actors they are

connected to (alters) and all the links among those

alters.

• Ego network analysis can be performed locally by

individual nodes without complete knowledge of the

entire network.

Ego Networks vs. Sociocentric

Networks

• Degree centrality can easily be measured for an ego network where it is a simple count of the number of contacts.

• Closeness centrality is uninformative in an ego network, since by definition an ego network only considers nodes directly related to the ego node.

• Betweeness centrality in ego networks has shown to be quite a good measure when compared to that of the sociocentric measure.

Betweenness Centrality in Ego-

Networks

In the figure below, the rankings of the betweeness are

identical for Sociocentric and Egocentric.

Tie Strength for Information

Flow

• Tie strength is a quantifiable property that

characterizes the link between two nodes.

• Strong ties are more likely to be activated for

information flow when compared to weak ties.

• Tie strength can be used for information flow to

determine which contact has the strongest

social relationship to a destination.

Tie Strength Indicators

• Frequency

• Closeness

• Long Period of time (Longevity)

• Reciprocity

• Recency

• Multiple Social Contexts (e.g., social communities)

• Mutual-Confiding Trust

Similarity

Here „score(x,y)‟ is the score for a future collaboration between nodes

x and y. N(x) and N(y) are neighbors of x and y respectively.

Two nodes being connected by a link in a dynamic social graph is

higher when the nodes in question have common neighbors:

SimBetTS: Social-metric

based Routing

• The combination of centrality, tie

strength, and similarity are highly useful

in routing based on local information

when the underlying network exhibits a

social structure.

• The combined metric is known as

SimBetTS utility (based on Similarity,

Betweeness, and Tie Strength)

Sim-Bet-TS utility

• Sim(Similarity)Bet(Betweenness)TS(Tie-Strength)

• When two nodes meet, they exchange a list of encountered nodes (with the encounter history certified by encountered nodes).

• This list is used to locally calculate the betweenness utility, the similarity utility, and the tie strength utility.

• Each node then examines the messages it is carrying and computes the SimBetTS utility of each message destination.

• Messages are then exchanged where the message is forwarded to the node holding the highest SimBetTS utility for the message destination node.

Betweenness Centrality in

SimBetTS

The betweenness centrality is calculated by computing the

number of nodes that are indirectly connected through

the ego node (who is a carrier candidate):

The betweenness centrality of the ego node is the sum of the reciprocals of

the entries of Ai,j’, where Ai,j’ is equal to A2i,j [1 - Ai,j] and i, j are the row

and column adjacency matrix entries, respectively.

Ai,j is built by each individual node using the encounter history exchanged.

Similarity in SimBetTS

The adjacency matrix allows for the calculation of

similarity between the newly encountered node (n) and the

destination node (e). The number of common neighbors is

used for ranking known contacts but also for predicting

future contacts. The similarity metric is calculated by:

In the above similarity calculation, Nn and Ne are the sets of contacts

by nodes n and e, respectively.

Tie Strength in SimBetTS

Tie strength is calculated based on frequency, closeness

and recency as follows:

f(m) is the number of times node n encountered

node m (the destination node) and F(n) is the total

number of encounters node n has experienced

d(m) is the total amount of time node n has been

connected to node m and D(m) is the total amount of

time node n has been connected across all

encountered nodes (i.e., % of hangout time with m)

rec(m) is the length of time between node n

encountered node m and the time node n was

on the network and L(n) is the total amount of time

node n has been a part of the network

Node-Utility in SimBetTS

• The aim is to select the node that provides the

maximum utility for carrying the message. This is

achieved using a pairwise comparison matrix on the

normalized relative weights of the attributes

• The attributes here are ‘Similarity’, ‘Betweenness’ and

‘Tie-Strength’

Node-Utility in SimBetTS

U is {SimUtil, BetUtil, TSUtil}

The similarity utility SimUtiln, betweenness utility BetUtiln, and tie

strength utility TSUtiln of node n for delivering a message to the

destination node d compared to node m are given by:

SimBetTS Routing Protocol

Behavior

• The message is forwarded to nodes with a high

betweenness and social similarity, until a node with a

high tie strength for the destination node is found.

• In all cases, the tie strength utility for the final hop is the

highest contributing utility value.

• In all cases, the betweenness utility value is much

reduced in its influence of the forwarding decision as

the message is routed closer to the destination

SimBetTS vs.

Epidemic/PROPHET

• Epidemic: Flooding-based routing

• PROPHET: history-based routing

• uses past encounters to predict the

probability of future encounters by

exploiting the transitive nature of

encounters to predict the probability of

indirectly encountering the destination

node.

Performance of SimBetTS

vs. Epidemic/PROPHET

Performance of SimBetTS

vs. Epidemic/PROPHET

• Single-copy (R=1) or multicopy SimBetTS (R=4) has a

higher message delivery ratio compared with PROPHET.

• Multicopy SimBetTS routing achieves delivery performance

similar to epidemic with short path lengths and low end-to-

end delay.

• The use of replication (i.e., R>1) comes at the cost of more

forwarding messages and control data. However, when

compared to epidemic, the overhead of SimBetTS is still

relatively low.

References Chapters 8-11, F. Adelstein, Gupta, G.G. Richard III and L.

Schwiebert, Fundamentals of Mobile and Pervasive Computing, McGraw Hill, 2005.

13. J.Y. Chen, Y.S. Shue, H. Ogunleye and S. Bagchi, “A Comparative Study on Data Fault Tolerant Requirements for Data Propagation in Sensor Networks,” Department of Electrical and Computer engineering, Purdue University, 2003.

14. S. Bandyopadhyay, and E. Coyle, “An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks”, Proc. IEEE INFOCOM, April 2003, pp. 1713-1723.

15. I.R. Chen, A.P. Speer and M. Eltoweissy, “Adaptive Fault Tolerant QoS Control Algorithms for Maximizing System Lifetime of Query-Based Wireless Sensor Networks,” IEEE Transactions on Dependable and Secure Computing, Vol. 8, No. 2, 2011, pp. 161-176.

16. E.M. Daly and M. Haahr, “Social Network Analysis for Information Flow in Disconnected Delay-Tolerant MANETs,” IEEE Transactions on Mobile Computing, Vol. 8, No. 5, May 2009, pp. 606-621.

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Lectures 5: Mobile Ad Hoc and Sensor Networks (II)

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