Emerging Paradigms · Study deck

DTN Social Routing: Contact and Utility Models

A delay-tolerant node must choose a carrier before an end-to-end path exists.

Blueprint Bina is your guide for this deck.

adhocsocialrouting

Blueprint Bina, the module guide, in a scene from this chapter.

After studying this chapter

Learning objectives

You will be able to:

Apply Context-Aware Routing: Use utility functions based on mobility, colocation, and resources
Design SocialCast Protocols: Implement social network-based forwarding with bounded replication
Calculate Utility Functions: Combine colocation probability, mobility scores, and resource levels
Optimize Forwarding Decisions: Select carriers based on delivery likelihood rather than flooding

Major section

Begin With a Better Carrier

A protocol means an agreed set of rules for passing data.

One phone has a report for the clinic.
Copying it to every phone may waste space and power.
A better first choice may be a person who often reaches the clinic or meets people who do.
A popular carrier can become busy or reveal private patterns.

Major section

Begin With a Better Carrier (continued)

Past contact can guide a choice, but people change shifts, routes, and habits.

Fewer copies save resources but may miss a rare path.
This simple story does not prove that social ties stay stable or that the score is fair.
Jo has more contacts but rarely goes near the clinic.
The report expires tomorrow.

Major section

Begin With a Better Carrier (continued)

For a social-contact forwarding rule, build a forwarding record and compare choices; check drift, utility maths, privacy, and resource bounds before accepting the result.

Lee may be the best next carrier.
Lee starts a new shift.
The old score may fail at once.
The field record should stay easy to read.

Context-aware DTN routing diagram showing a source comparing candidate carriers by encounter history, mobility, resource score, and total utility before forwarding to the highest-utility carrier for the destination.

Major section

Begin With a Better Carrier (continued)

It should say why one carrier won.

It should show the facts used that day.
It should show who did not get a fair chance.
It should show what happened after the copy left.
If the report arrives, count the cost as well as the win.

Major section

Start With the Node That Usually Meets the Right People

Social routing starts from an observation: in many mobile systems, contacts are not random.

Some devices, vehicles, people, or gateways regularly meet the communities that need the message.
For IoT deployments, that pattern can make forwarding more selective than flooding.
The routing question becomes whether past contact evidence is strong enough to trust the carrier with the next copy.

Major section

Smart Messenger Service

"Instead of telling EVERYONE my message like in epidemic routing," said Temperature Terry, "what if I only tell people who are LIKELY to meet the person I'm trying to reach?".

the microcontroller snapped his fingers. "Like if you need to send a message to the school librarian, don't give copies to random strangers -- give it to kids who go to that school every day!".
the LED added, "And the best part is: you only make 5-10 copies instead of hundreds.
The Squad’s illustrative math: Assume epidemic routing delivers 95% of messages with 1,000% overhead.

Major section

How to Use: Key Concepts

Homophily: Tendency for people to contact socially similar others; enables community-based routing to outperform random routing.

Social Centrality: Node's position in a social network graph; highly central nodes (hubs) interact with many others and make better message carriers.
SimBet Routing: Combines social similarity (common contacts) and betweenness centrality to select next-hop carriers.
Bubble Routing: Messages stay in source community until a node with higher global centrality can carry them to the destination community.

Major section

Checkpoint: CAR Utility

Epidemic routing makes about fifteen copies and reaches 95% delivery in this illustrative trace.

CAR sends only to a carrier with a higher score.
It makes about three copies, reaches 88%, uses less energy, and raises mean delay from 450 to 520 seconds.
Delivery ratio: epidemic reaches about 95%; CAR reaches about 88%.

Comparison of epidemic routing and context-aware routing showing epidemic routing with many message copies and high delivery, and CAR with selective forwarding, fewer copies, slightly lower delivery, and lower energy use — CAR versus epidemic routing trade-off for disconnected IoT networks

Deck summary

Key takeaways

A protocol means an agreed set of rules for passing data.

Past contact can guide a choice, but people change shifts, routes, and habits.
For a social-contact forwarding rule, build a forwarding record and compare choices; check drift, utility maths, privacy, and resource bounds before accepting the result.
It should say why one carrier won.
Social routing starts from an observation: in many mobile systems, contacts are not random.

Retrieval practice

Recall check 1 of 4

Blueprint Bina says: answer from memory, then check your reasoning.

Q1A social DTN plan sharply cuts replicas. Which review tests the trade-off?

AReport reduced replica count alone because fewer copies establish delivery after contact changes.

BUse earlier contact patterns because the same schedule predicts missed and delayed bundles.

CFlood all bundles because extra copies reduce loss more than they consume storage and transfer capacity.

DCompare delivery and delay with buffer and transfer load as contact patterns change.

Show answer

Answer: D The plan must balance delivery with delay and resource use.

Retrieval practice

Recall check 2 of 4

Blueprint Bina says: answer from memory, then check your reasoning.

Q2A DTN deployment forwards bundles through staff devices using contact-history scores. Delivery falls after staff schedules change. Which record should be reviewed first?

AOnly the payload encryption keys on the staff devices, because intact encryption shows the routing layer is still healthy.

BOnly the original epidemic-routing baseline captured during the pilot, because social routing is supposed to ignore day-to-day changes in staff contacts.

CNo record at all, because bounded replication is designed to deliver with the same probability whatever the mobility pattern.

DThe forwarding record covering history-decay window, utility weights, replica budget, buffer policy, and privacy-approved contact evidence.

Show answer

Answer: D Social-routing evidence can drift when mobility patterns change, so the scoring window and forwarding policy need review.

Retrieval practice

Recall check 3 of 4

Blueprint Bina says: answer from memory, then check your reasoning.

Q3In the CAR utility function U(X, D) = alpha * P_colocation + beta * Mobility + gamma * Resources, what does the colocation probability P_colocation(X, D) represent?

AThe physical distance between node X and destination D right now

BThe predicted likelihood that node X will encounter destination D

CThe number of hops between X and D in the current network topology

DThe battery level of node X relative to destination D

Show answer

Answer: B Colocation probability P_colocation(X, D) predicts how likely node X is to physically encounter destination D in the future, based on historical encounter patterns analyzed through Kalman filtering.

Retrieval practice

Recall check 4 of 4

Blueprint Bina says: answer from memory, then check your reasoning.

Q4A SocialCast carrier meets a peer with better contact history, but the message has reached its copy cap. What should it do?

AMake a new copy because the peer has a better score.

BIgnore the cap if the peer has more storage.

CErase every existing copy before the next contact.

DKeep the current copies and log why no new copy was made.

Show answer

Answer: D The bounded replication rule blocks another copy and records the reason.

Print reference

Answers

Answer key.

D · The plan must balance delivery with delay and resource use.
D · Social-routing evidence can drift when mobility patterns change, so the scoring window and forwarding policy need review.
B · Colocation probability P_colocation(X, D) predicts how likely node X is to physically encounter destination D in the future, based on historical encounter patterns analyzed through Kalman filtering.
D · The bounded replication rule blocks another copy and records the reason.