smartgossip: an adaptive broadcast service for wireless sensor networks

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SmartGossip: An Adaptive Broadcast Service for Wireless Sensor

Networks

Presented By Thomas H. HandDuke University

Adapted from:“SmartGossip: An Adaptive Broadcast Service for Wireless Sensor Networks”

Pradeep Kyasanur (Google)Romit Roy Choudhury (UIUC)

Indranil Gupta (UIUC)

romit

Thanks for the introduction, Ben, and thank you all for staying till the last talk for the day, especially in a city like Vancouver. I will try my best to make your stay worthwhile.Ok. Today I will talk to you a little bit about our paper titled "SmartGossip: ...." This is joint work with Pradeep Kyasanur and Indranil Gupta.

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Statement of the Problem

Sensor Network Broadcasting There are some sensor network applications

that rely heavily on network-wide broadcasts•E.g. Alarms, code-updates

Goal:•Deliver one copy of the broadcast packet to each

sensor in the network, while minimizing the number of transmissions

Task:•Create a protocol that will be able to efficiently

broadcast a message to all nodes in the network, while minimizing number of transmissions

romit

Network wide broadcast is an important service in sensor networks. It is widely used for stand alone applications like information dissemination, code updates, etc, It is also a service on which the network layer depends heavily to accomplish its own operations, such as routing, querying, etc.

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Deterministic and Probabilistic Approaches Deterministic Approach (Classical):

•Try to solve the problem by assigning some subset of the network forwarding responsibilities

•This leads to unfairness and unreliability•Unfairness – all of the work is placed on a few nodes•Unreliability – if some of these key nodes fail, then

many packets will be lost and overall throughput will decrease

Probabilistic Approach (Gossip)•All nodes in the network must forward messages•Each node assigned a gossiping probability, pgossip

•Choosing pgossip appropriately can lead to better network reliability and better load-balancing

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Static and Adaptive Gossiping Must choose pgossip correctly

•This depends on the network topology – e.g. number of nodes, node density, etc.

•Pre-assigning a value to pgossip leads to inefficiency

In static gossip, all nodes are given the same gossip probability

We need a protocol that can adaptively control pgossip to result in high efficiency and reliability

romit

Network wide broadcast is an important service in sensor networks. It is widely used for stand alone applications like information dissemination, code updates, etc, It is also a service on which the network layer depends heavily to accomplish its own operations, such as routing, querying, etc.

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Past Static Gossip Methods

Adaptive Neighbor Method Allow a node to choose its gossiping probability

inversely proportional to the number of neighbors it has (Haas, et al.)

Adaptive Overhead Method Allow node to choose its gossip probability

based on the number of duplicate messages it receives (Levis, et al.)

Large number of duplicate messages means that many nodes depend on it

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Smart Gossip Introduction

Aim is to achieve an efficient, fair, and reliable protocol In Smart Gossip, the importance of each node is

quantified using an algorithm that takes into account network topology

This allows for network adaptation Completely decentralized

Node Importance The dissemination of a gossip message will rely more

heavily on some nodes more than others Smart Gossip can assign different gossip probabilities to

different nodes based on the network conditions

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Smart Gossip Introduction Cont’d…

Promoting Fairness and Flexibility Instead of having a predetermined subset of

the network responsible for the broadcast, the load is shared by all nodes

The protocol can adapt to changing network conditions – gossip probability for each node is updated periodically

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How to Implement Smart Gossip …

Given some random network topology How do we choose a suitable value of “p” ?

Even if network topology is homogeneous It may change over time due to failure and

mobility

Finally, what if topology is not known a priori ? How can you choose “p” ?

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We Ask …

Given some topology deployment How do we choose a suitable value of “p” ?

Even if topology is homogeneous It may change over time due to failure and

mobility

Say computed p = 0.85

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We Ask …



mobility Fails


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We Ask …



mobility


15% of packetswill not reach these nodes

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The Main Idea Behind Smart Gossip

Concept

Identify which of YOUR friends get to know gossip earlier than you do•Request those friends to gossip more

Friends who get to know gossip later than you will request you to gossip more

You choose your gossip probability as:•MAX value of all requests from YOUR friends

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Simple Example …

When H spreads a gossip F gets gossip only from G F asks G to always gossip Thus, pG= 1.0

B receives gossip from A,C,D,E,F B also observes that A,C,D,E received gossip

from F• Indicates that B must depend only on F; A,C,D,E and B

are independent

B asks F to always gossip, thus pF = 1.0

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For Example …

B asks F to always gossip,thus pF = 1.0

B does not require A,C,D,E to gossip at all

Thus pA = 0, pC = 0, pD = 0, pE = 0

Observe that only 2 transmissions (from G and F) are sufficient for broadcast

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Average Reception and Forwarding Percentages

Reliability Evaluation: Average Reception Percentage:

•Reception Percentage = % messages received

•Average Recept. % = Recept. % averaged over all nodes

Overhead Evaluation: Average Forwarding Percentage

•Forwarding Percentage = % gossip messages forwarded

•Average Fwd. % = Fwd. % averaged over all nodes

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Protocol Details

For first gossip pkt, nodes transmit with p=1

Enables nodes to deduce neighbor dependences

Transmitters piggyback pkt with parent-id from which it received the pkt

Nodes record transmitter-id, and its parent-id, and deduce parent, child, sibling relationships …

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So What is the Parent I.D (pid)?

As mentioned previously, it is important for a node to establish neighbor dependences

Some nodes might completely rely on another node for the gossip, while other nodes might not

Header of each gossip message contains pid and required gossip probability field prequired

Each node maintains four sets: NeighborSet, ParentSet, SiblingSet, and ChildSet

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Establishing Neighbor Relationships

When a node received a gossip message, its relationship with the sender is established in the following way: Node A receives a message from X with pid = Y

1. Add X to NeighborSet2. If Y is not in NeighborSet, add X to ParentSet3. If Y is in ParentSet, add X to SiblingSet4. If Y is in SiblingSet, add X to ChildSet

•Nodes in NeighborSet also exist in only one of the other 3 sets

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Deducing Relationships

Assume gossip sent by node i to node j If parent (i) Neighbor (j)

•Parent ( j ) i

S A B C

E

SASASA Parent = {A}

Parent = {A}

Child = {A}

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Parent = {A}

Parent = {A}

Child = {A}



•Parent ( j ) i

If parent (i) Neighbor (j)• If parent (i) Parent (j), then Sibling ( j ) i

• If parent (i) Sibling (j), then Children ( j ) i

• If parent (i) Children (j), then Children ( j ) i

S A B C

E

ABABAB

Sibling = {B}

Child = {B} Parent = {B}

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Parent = {A}

Parent = {A}

Child = {A}



•Parent ( j ) i




S A B C

EAEAE

Sibling = {B}

Child = {B} Parent = {B}

AE

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Parent = {A}

Parent = {A}

Child = {A}



•Parent ( j ) i




S A B C

E

Sibling = {B}

Child = {B,E} Parent = {B,E}

Sibling = {E}

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Choosing Probabilities

Each node calculates number of parents ( k ) Assume 99% assurance necessary for gossip

Node suggests each parent to gossip using ‘p’:

0.99 = ( 1 – (1 - p)k )

Each node receives multiple requests of ‘p’ Uses Max { pi } as its own gossip probability

S A B C

E

Parent={B,E}

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0.99 = ( 1 – (1 - p)k )


S A B C

E

p = 0.9

p = 0.9p = 1.0

p = 1.0p = 1.0

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0.99 = ( 1 – (1 - p)k )


S A B C

E

p = 0

p = 0.9

p = 0.9p = 1.0p = 1.0

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Reliability

Node Failures Node failures affect broadcast Source node flags packet periodically (p=1) Allows for updating dependences

Link Losses Node requests upstream nodes to retransmit

•We require each node to buffer few packets

Children overhear this request Children do not request retransmissions

themselves

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Wireless Losses

Resilience toward wireless losses necessary If F does not get a packet, all its dependents will

also not get it

Smart Gossip: F requests its parents for missing pkt (seq # j) F piggybacks { j } in following gossip packets Nodes A,B,C,D,E do not request for packet j

•They know that F is trying to retrieve it

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Performance Evaluation

Qualnet Simulator, version 3.7

Metrics used Average Reception Percentage Average Forwarding Percentage Resilience to link/node failures

Network Information 100 randomly chosen topologies – 50 nodes

each Transmission range is 280 meters Nodes placed in a 1000m2 square, located

uniformly at random

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Performance Evaluation Continued

Smart Gossip Compared with Static Gossip Compared with Adaptive Overhead and

Adaptive Neighbor approaches

•Topology Aware – minimum pgossip that meets the reliability needs of the network will be used

•Topology Unaware – Uses one pgossip for ALL topologies tested

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Topology-Aware Static Gossip Results

Topology-Unaware Gossip:

Must choose p ~ 1 in order to satisfy reliability requirements for all topologies

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Average Forwarding Results For Static Gossip

Gossip overhead increases linearly with Gossip Probability

For some topologies, it may not be necessary to set p close to 1

This adds overhead and sparks the need for an adaptive protocol

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Average Reception Percentage Comparisons

Smart Gossip

Adaptive Overhead

Adaptive Neighbor

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Forwarding Overhead

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Adaptation to Node Failures

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Conclusion

Broadcast is an important problem Gossip is good – but not practical for sensor nets Need to adapt gossip based on topology / failures

Smart Gossip Form dependence graphs using distributed

protocol Dependence relations suggest suitable probability

Results Overheads are low, and yet good percolation Robust to node and link failures

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The End

smartgossip: an adaptive broadcast service for wireless sensor networks

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