delay/fault-tolerant mobile sensor network (dft-msn): a new...

Introduction Related Work Two Basic Approaches RED FAD Simulation Results Conclusion

Delay/Fault-Tolerant Mobile Sensor

Network (DFT-MSN): A New

Paradigm for Pervasive Information

Gathering

Authors: Yu Wang, Hongyi Wu

Presenter: Chia-Shen Lee

October 16, 2007Authors: Yu Wang, Hongyi Wu Presenter: Chia-Shen Lee Delay/Fault-Tolerant Mobile Sensor Network


OutlineIntroduction

Pervasive Information Gathering

Delay/Fault-Tolerant Mobile Sensor Network

Related Work

Delay-Tolerant Network (DTN)

Two Basic Approaches

Direct Transmission

Flooding

Observation from the Two Basic Approaches

RED

Data Delivery

Message Management

FAD

FAD Parameter: Message Fault Tolerance

FAD Data Delivery Scheme

Simulation Results

Simulation Results

Conclusion

Conclusion

Authors: Yu Wang, Hongyi Wu Presenter: Chia-Shen Lee Delay/Fault-Tolerant Mobile Sensor Network




Characteristics

I Data gathering is human-oriented

I Delay and faults are usually tolerable

I Transparent




Information Gathering

I Relies on sensorsI Many small, portable, inexpensive sensor

I Low power, short range radio to form a connected

wireless network

I May not work effectively because the

connectivity between the mobile sensors is poor




Delay/Fault-Tolerant Mobile Sensor

Network

I Consists of two types of nodesI Wearable sensor nodes

I High-end sink nodes




DFT-MSN Overview




DFT-MSN Unique Characteristics

I Nodal mobility

I Sparse connectivity

I Delay tolerability

I Fault tolerability

I Limited buffer




Sensor Networks Common Characteristics

I Short radio transmission range

I Low computing capability

I Limited battery power




DFT-MSN

I Opportunistic network

I Communications exist with certain probabilities

I Replication is necessary to achieve a certain

success ratio

I Trade-off between data delivery ratio/delay and

overhead




DFT-MSN

I Two basic approachesI Direct transmission

I Flooding

I Simple and effective DFT-MSN delivery schemesI Replication-Based Efficient Data Delivery Scheme

(RED)

I Message Fault Tolerant-Based Adaptive Data Delivery

Scheme (FAD)




DFT-MSN

I Replication-Based Efficient Data DeliveryScheme (RED)

I Use Erasure coding to minimizes overhead

I Data transmission

I Message management

I Message Fault Tolerant-Based Adaptive DataDelivery Scheme (FAD)

I Message fault tolerance




Delay-Tolerant Network Property

I Occasionally connected networkI May suffer from frequent partitions

I May be composed of more than one divergent set of

protocols

I Originally aimed to provide communications forInterplanetary Internet

I Deep space communication in high-delay environments

I Interoperability between different networks in extreme

environments lacking continuous connectivity




Pertinent Work

I Network with static sensors

I Network with managed mobile nodes

I Network with mobile sensors



Network architecture

I N sensors, and n sink nodes

I Uniformly distributed in 1× 1 area

I Radio coverage a (a¿ 1)

I Define the service area of a sink node to be its

radio coverage area

I Total service area of all sink nodes A (A < 1)I A = 1− (1− a)n



Direct Transmission

Direct Transmission Scheme

I Sensor transmits directly to sink nodes only

I Generated data message is inserted to FCFS

queue

I Sensor does not receive or transmit any data

messages of other sensors



Direct Transmission


I Sensor are activated and deactivated periodically

I Assume sensor’s activation period be an

exponentially distributed random variable with a

mean of T , i.e. f(x) = 1T e

− xT

I Message length is equal to constant L

I Message arrival is Poisson processI average arrival rate λ = 1/T



Direct Transmission


I Service rate µ depends on available bandwidth w

I Probability p that sensor can communicate with

sink

I Probability that sensor within the coverage of atleast one sink is determined by total service areaof all sink nodes

I p = A = 1− (1− a)n ≈ na



Direct Transmission

Service Time

LemmaGiven a constant message length of L, a fixed

channel bandwidth of w (per time slot), and a

service probability of p, the service time of the

message is a random variable with Pascal

distribution.



Direct Transmission

Service Time

Proof.

I Service time: random variable X

I The number of time slots transmitted: s = Lw

I In each time slot, a node has the probability p to

be within the service area

I Distribution of X

FX(x) =x−s∑i=0

(s+ i+ 1

s− 1

)ps(1− p)i



Direct Transmission

Infinite Buffer Space

I Assume sensor has inifinite buffer space

I Poisson arrival rate and Pascal service time

I Model data generation and transmission as

M/G/1 queue with λ = 1T and µ = p

s = AwL

I To arrive at steady rate, λ < µ

I Minimun service area A > LT×w



Direct Transmission

Infinite Buffer Space

I Given λ and µ, we can derive the average

number of messages at a sensor

q = ρ+ρ2 + λ2 × ρ2

2× (1− ρ)

where ρ = λµ , average message delivery delay w = q

λ



Direct Transmission

Finite Buffer Space

I Assume sensor has finite buffer spaceI Keep maximum K messages in its queue

I Model data generation and transmission asM/G/1/K queue

I λ and µ are calculated in the same way

I What is the steady state probability of this

M/G/1/K queue?



Direct Transmission

Finite Buffer SpaceI Let kn denote the probability of n arrivals during

the period

kn =∞∑t=s

e−λt(λt)n

n!×

(t− 1

s− 1

)ps(1− p)t−s

I Let πi denote the probability that the system size

is i

πi =

{π0ki +

∑i+1j=1 πjki−j+1, (i = 0, 1, . . . , K − 2)

1−∑K−2j=0 πj, (i = K − 1)



Direct Transmission

Finite Buffer Space

I Solve previous equations, we can obtain

{πi|0 ≤ i ≤ K − 1}I The average number of messages at a sensor is

q =K−1∑i=0

iπi

I Denote q′i to be the probability that an arriving

message finds a system with i messages



Direct Transmission

Finite Buffer Space

I q′K =ρ−1+ π0

π0+ρ

ρ is the message dropping

probability, where ρ = λµ

I Effective message arrival rate λe = λ(1− q′K)

I Average message delivery delay equals w = qλe



Direct Transmission

Futher Discussion

I Bandwidth w is not a constant if a is large

I Consider average service time only

I Total available bandwidth W

I Average data transmission rate of a sensor is

w = WL × 1

1+(N−1)aλµ

, where λµ is the probability

that a sensor has data messages in its queue, and

1 + (N − 1)aλµ is the average number of active

sensors that transmit to the sink.



Direct Transmission

Futher Discussion

µ =wp

L=p

L× W

1 + (N − 1)aλµ

=pW

L− (N − 1)aλ



Direct Transmission

Numeric Results



Flooding

Flooding

I Simple flooding

I Optimized flooding



Flooding

Simple Flooding

I Sensor always broadcasts the data message in itsqueue to nearby sensors, which receive the datamessages, keep them in queue, and rebroadcastthem

I Lower data delivery delay

I More traffic overhead and energy consumption

I Generates and receives dataI Higher λ

I Higher service rate



Flooding

Simple Flooding Assumptions

I Infinite buffer space

I Activation period T , synchronized

I Bandwidth is high enough

I Mobility is high enough

I p is the probability that a sensor cancommunicate with with at least one sink node

I p = A = 1− (1− a)n ≈ na



Flooding

Simple Flooding

I Let pj to be the probability that message is not

delivered to the sink in the jth period

I Let Nj denote the number of sensors that have a

copy of the message in the jth period if the

message has not been delivered to the sink



Flooding

Simple Flooding

I Nj is calculated as follows:

Nj =

{(N − 1)a+ 1, j = 1

(N −Nj−1)(1− (1− a)Nj−1) +Nj−1, j > 1

I Thus, pj is derived below:

pj =

{p, j = 1

(1− (1− p)Nj−1)(1−∑j−1i=1 pi), j > 1



Flooding

Simple Flooding

I Average delay of delivering the data message

w = T∞∑j=1

j × pj

I When N1 = N2 = · · · = 1, it becomes direct

transmission



Flooding

Optimized Flooding

I Simple Flooding CharacteristicsI The lowest delivery delay

I High overhead

I High energy consumption

I Optimized FloodingI Estimate the message delivery proability

I Stop futher propogation of a message if its delivery

probability is high enough

I Reduce transmission overhead



Flooding

Optimized Flooding

I The message’s propagation is terminated after

period d

I Goal: to minimize d such that message delivery

probability in total D (D ≥ d) periods is higher

than a given threshold, i.e., pD ≥ γ



Flooding

Optimized Flooding

Nj =

(N − 1)a+ 1, j = 1

(N −Nj−1)(1− (1− a)Nj−1) +Nj−1, d ≥ j > 1

Nd, j > d

pj = [1− (1− p)Nj−1 ](1−j−1∑i=1

pi), p1 = p

For a given threshold γ, one can derive the minimun

d⇒ pD ≥ γ

w = T

∞∑j=1

j × pj



Flooding

Optimized Flooding

I After determining the optimal d, we can estimate

the average number of message copies made

during the d periods, Md

I Note Nj is the number of copies in the jth

period, given that the message has not been

delivered to the sink in the first j − 1 periods

I Nd is not equivalent to Md



Flooding

Optimized Flooding

I The number of copies reaches its maximun after

the dth period

I Let Uj denote the number of nodes which have a

copy of message but have not transmitted to the

sink nodes at the jth period

I Let Vj denote the number of copies that have

been sent to the sink

I Then we have . . .



Flooding

Optimized Flooding

Uj =

(N − 1)(1− (1− a)1−p) + 1− p, j = 1

(1− p)Uj−1 + (N − Uj−1 − Vj−1)

×(1− (1− a)(1−p)Uj−1), d ≥ j > 1

Vj =

{p, j = 1

Vj−1 + p× Uj−1, d ≥ j > 1

Md = Ud + Vd



Flooding

Numeric Results




Two Basic ApproachesI Direct transmission minimizes

I Transmission overhead

I Energy consumption

I But . . .I Low message delivery ratio

I High message dropping rate (with small buffer space)

I Flooding minimizesI Message delivery delay

I But . . .I Very high tranmission overhead

I Lots of energy consumptionAuthors: Yu Wang, Hongyi Wu Presenter: Chia-Shen Lee Delay/Fault-Tolerant Mobile Sensor Network



DFT-MSN Key Issues

I Three key issuesI When should data messages be transmitted?

I Which messages should be transmitted?

I Whcih messages should be dropped?

I With the above issues taken into consideration,

we will propose two schemes for DFT-MSN,

namely, RED and FAD



Two Key Components

I Data Delivery

I Message management



Data Delivery

Nodal Delivery Probability

I The decision on data transmission is made basedon the delivery probability

I Indicates the likelihood that a sensor can deliver data

messages to the sink

I Not simply the probability that a node meets a sink



Data Delivery


I Let ξi denote the delivery probability of a sensori

I Initialized with 0 and updated upon an event

I ∆: time interval

I Whenever sensor i transmits a data message to

another node k, ξi should be updated



Data Delivery


ξi is updated as follows:

ξi =

{(1− α)[ξi] + αξk, T ransmission

(1− α)[ξi], T imeout

where [ξi] is the delivery probability of sensor i

before it is updated and 0 ≤ α ≤ 1 is a constant

employed to keep partial memory of historic status.



Data Delivery

Data TransmissionI Data messages are maintained in a FIFO queue

I Sensor i has a message at the top of queue ready

for transmission and is moving into the

communication range of sensors

I Sensor i first learns their delivery probabilities

and available buffer spaces

I Sensor i transmits its message to the neighbor j,

which has the highest delivery probability

(ξj > ξi) and available buffersAuthors: Yu Wang, Hongyi Wu Presenter: Chia-Shen Lee Delay/Fault-Tolerant Mobile Sensor Network


Data Delivery

Further DiscussionI Mutual reference

I Node j with slightly higher delivery probability than

node i

I After a successful transmission, ξi increase

I In the worst case, node j’s delivery probability

decreased because of timeout

I ξj < ξiI Node j transmits messages back again!

I Unnecessary propagationI Always send messages even when it has large enough

probability to reach the sinkAuthors: Yu Wang, Hongyi Wu Presenter: Chia-Shen Lee Delay/Fault-Tolerant Mobile Sensor Network


Data Delivery

Mutual Reference

LemmaFor two nodes i and j with delivery probability ξi

and ξj (assume ξj ≥ ξi), ξi <2−2α2−α ξj is necessary

and sufficient condition to avoid the mutual

reference problem



Data Delivery

Mutual Reference

Proof.(⇒) There exists δ so that the mutual reference

problem can be avoided if ξj ≥ ξi + δ. Thus, after

node i transmits a message to node j, the

inequation must hold: ξ′j − ξ′i > −δ, where ξ′j and ξ′iare the delivery probability of nodes j and i updated

according the equation after the transmission.



Data Delivery

Mutual Reference

Proof.In the worst case, ξ′i = (1− α)ξi + αξj, and

ξ′j = (1− α)ξj. Then we have

(1− α)ξj − (1− α)ξi − αξj > −δ

Thus, we arrive at δ > α2−αξj ⇒ ξi <

2−2α2−α ξj

(⇐) It is similar to the above.



Data Delivery

Unnecessary Propagation

I Results in extra transmission and energy

consumption

I To avoid this, each node maintains an additional

parameter, direct delivery probability ψ

I Indicates how likely this node can transmit the

messages directly to the sink

I If ψ is larger than a predefined threshold, the

node only transmit messages to the sink directly



Message Management

Message Management

I Replication is usually employed

I Erasure-coding efficiently addresses the trade-off

between delivery ratio/delay and overhead



Message Management

Message Management AssumptionsI b blocks of equal size

I S × b small messages, referred to block

messages, where S is the replication overhead

I Each block message has a constant delivery

probability of p

I Delivery probability of the original message

P =Sb∑

j=b

(Sb

j

)pj(1− p)Sb−j



Message Management

Replication Overhead

I Given p, determine the optimal b and S to meet

the desired message delivery probability H

I Find the minimum S for given b and p so that P

is no less than H

S(p, b) = min

{S

∣∣∣∣∣Sb∑

j=b

(Sb

j

)pj(1−p)Sb−j ≥ H

}



Message Management

Blocks

I b may vary from 1 to the length of the message

I Larger b, more overhead, less bandwidth

utilization

I Minimum block size m

I Maximum message size M

I Maximum value of b is bmax=Mm



Message Management

Determine optimal b to minimize S



RED Property

I AdvantagesI Simplify message manipulation and queue

management at intermediate nodes

I DisadvantagesI Inaccurate erase-coding parameter S and b

I Propagation may incur overhead and inefficiency

I How does FAD solve these problems?I Increase complexity in message and queue

management



Two Important Parameters

I Nodal delivery probabilityI It has been discussed in previous section

I Message fault toleranceI Indicates the amount of redundancy and the

importance of a message




FAD Parameter

I Sensor may keep a copy of message aftertransmission

I Multiple copies of message created and maintained by

different sensors

I Result in redundancy

I Fault tolerance introduced to represent

redundancy and importance of message

I Let F ji denote the fault tolerance of message j in

the queue of sensor i




FAD Parameter

I Fault tolerance ≡ the probability that at leastone copy of the message is delivered to the sinkby others

I Initialized to be 0

I Consider sensor i multicasts message j to Znearby sensors, denoted by Ξ = {ψz|1 ≤ z ≤ Z}

I Creates a total of Z + 1 copies

I Fault tolerance needs to be assgined




FAD ParameterI Message to sensor ψz is associated with F j

ψz′

F jψz

= 1− (1− [F ji ])(1− ξi)

Z∏

m=1,m 6=z(1− ξψm)

I Fault tolerance of message at sensor i is updated as

F ji = 1− (1− [F j

i ])Z∏

m=1

(1− ξψm)

I [F ji ] is the fault tolerance of message j at sensor i before

multicasting




Two Components

I Queue management

I Data transmission




Queue Management

I Data messages of a sensor come fromI After sensor acquires data from sensing unit

I When sensor receives messages from others

I After sensor sends out message to nonsink, it may

insert message into its queue again

I Queue ManagementI Sort messages in queue

I Determine which message to drop if queue is full




Queue Management

I Fault toleranceI Signifies how important the message are

I Smaller fault tolerance, more important, and higher

priority to transmit

I Sort messages in queue with increasing order of fault

tolerance




Queue Management

I SendI Message at top of queue is transmitted first (the

smallest fault tolerance)

I DropI Queue is full and the new message has the larger fault

tolerance than the end of queue

I Message fault tolerance is larger than the threshold γ

even if the queue is not full

I Special: the message has been transmitted to sink




Queue Management

I Assume sensor’s queue space at most K

messages

I kmi : number of messages with fault tolerance

level of m in queue of sensor i (0 ≤ m ≤ 1)

I Available buffer space at sensor i for new

messages with fault tolerance x:

Bi(x) = K −∑xm=0 k

mi

I Bi(x) = 0, drop




Data Transmission

Decisions are based on delivery probability

I Sensor i, message j, a set of Z ′ sensors

I Ξ′ = {ψz|1 ≤ z ≤ Z ′}I Φ: subset of Z ′ sensors. γ: threshold

I Bψz(F j

i ): number of available buffer slots at

node ψz with F ji




Data Transmission

Algorithm Identification of receiving sensors

Φ = ∅for z = 1 : Z ′ do

if ξi < ξψz AND Bψz(F ji ) > 0 then

Φ = Φ ∪ ψzend if

if 1− (1−F ji )

∏m∈Φ(1− ξm) > γ then

Break

end if

end for



Simulation Results

Default Simulation Parameters



Simulation Results

Simulation



Conclusion

ConclusionI DFT-MSN Unique Characteristics

I Sensor mobility, loose connectivity, fault tolerability,

delay tolerance, buffer limit

I Optimized Flooding: minimizes the transmission

overheadI Both of RED & FAD: high delivery message ratio

with acceptable delayI RED: lower complexity in message and queue

management

I FAD: lower message transmission overhead


delay/fault-tolerant mobile sensor network (dft-msn): a new...

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