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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. Hand

Duke University

Adapted from:

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

Pradeep Kyasanur (Google)

Romit Roy Choudhury (UIUC)

Indranil Gupta (UIUC)

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

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

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

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

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

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

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” ?

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

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

Fails

Say computed p = 0.85

will not reach

these nodes

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

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

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

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

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

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 …

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

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 NeighborSet

2. If Y is not in NeighborSet, add X to ParentSet

3. If Y is in ParentSet, add X to SiblingSet

4. If Y is in SiblingSet, add X to ChildSet

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

SA

SA

Parent = {A}

Child = {A}

Parent = {A}

Deducing Relationships- Assume gossip sent by node i to node j
- If parent (i) Neighbor (j)
- Parent ( j ) i

S

A

B

C

E

AB

AB

Parent = {A}

Parent = {B}

Child = {A}

Child = {B}

Parent = {A}

Sibling = {B}

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

AE

AE

Parent = {A}

Parent = {B}

Child = {A}

Child = {B}

Parent = {A}

Sibling = {B}

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

Parent = {B,E}

Child = {A}

Child = {B,E}

Parent = {A}

Sibling = {B}

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

Sibling = {E}

S

A

B

C

E

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

Parent={B,E}

E

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

p = 1.0

p = 1.0

p = 0.9

S

A

B

C

p = 0.9

p = 1.0

E

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

p = 1.0

p = 1.0

p = 0.9

p = 0

S

A

B

C

E

p = 0.9

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

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

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

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

Topology-Aware Static Gossip Results

- Topology-Unaware Gossip:
- Must choose p ~ 1 in order to satisfy reliability requirements for all topologies

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

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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