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.
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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
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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.
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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.
  • Those deeper routes test the carrier rule before it becomes field policy.
  • The report expires tomorrow.
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Major section

Begin With a Better Carrier (continued)

A stopped copy is not always a failure; it may be the rule protecting the rest of the service.

  • Jo has more contacts but rarely goes near the clinic.
  • Lee may be the best next carrier.
  • Lee starts a new shift.
  • The old score may fail at once.
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Major section

Begin With a Better Carrier (continued)

If the report arrives, count the cost as well as the win.

  • The field record should stay easy to read.
  • 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.
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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.
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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.
  • For battery-powered sensors, that savings means years more battery life!
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Major section

Key Concepts

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.
  • Contact History: Past encounter records used to predict future contacts; stored in encounter tables indexed by node ID and time.

Why it matters

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

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Major section

Checkpoint: CAR Utility

Energy per delivery: epidemic is high because many nodes carry copies; CAR is medium because it forwards selectively.

  • Delivery ratio: epidemic reaches about 95%; CAR reaches about 88%.
  • Average latency: epidemic is faster in this trace, about 450 seconds versus 520 seconds.

Numbers to remember

95%Delivery ratio: epidemic reaches about 95%
88%CAR reaches about 88%.
CAR versus epidemic routing trade-off for disconnected IoT networks
CAR versus epidemic routing trade-off for disconnected IoT networks
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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.
  • A stopped copy is not always a failure; it may be the rule protecting the rest of the service.
  • If the report arrives, count the cost as well as the win.
  • Social routing starts from an observation: in many mobile systems, contacts are not random.
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Retrieval practice

Recall check 1 of 2

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

Q1A DTN deployment forwards bundles through staff devices using contact-history scores. Delivery falls after staff schedules change. Which record should be reviewed first?

AThe forwarding record covering history-decay window, utility weights, replica budget, buffer policy, and privacy-approved contact evidence.
BOnly the payload encryption keys on the staff devices, because intact encryption shows the routing layer is still healthy.
COnly the original epidemic-routing baseline captured during the pilot, because social routing is supposed to ignore day-to-day changes in staff contacts.
DNo record at all, because bounded replication is designed to deliver with the same probability whatever the mobility pattern.
Show answer

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

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Retrieval practice

Recall check 2 of 2

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

Q2In 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.

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Print reference

Answers

Answer key.

  1. A · Social-routing evidence can drift when mobility patterns change, so the scoring window and forwarding policy need review.
  2. 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.
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