© University of St Andrews, UK1 Chapter 14: Incentive-aware opportunistic network routing Greg Bigwood and Tristan Henderson University of St Andrews Routing in Opportunistic Networks

© University of St Andrews, UK2 Problem:  Opportunistic networking relies on cooperation between nodes to perform efficiently  Opportunistic routing protocols depend on nodes forwarding messages  Otherwise nodes must delivery directly to recipient  Cooperative forwarding incurs a cost to forwarding nodes  Energy  Storage  Self-interested nodes avoid forwarding cost:  Refuse to pass messages on for other nodes

© University of St Andrews, UK3 Outline  We discuss attack on opportunistic routing  With a focus on selfishness  We discuss incentive mechanisms for opportunistic routing  Conclude with discussions of outstanding challenges in the area

© University of St Andrews, UK4 Opportunistic Network Routing  Frequent disconnections  Mobile nodes coming into and out of range  Non-static forwarding paths  A varied set of nodes in range over time  No predictable interaction schedule  Nodes are most likely carried by users with diverse and variable mobility patterns  Nodes must opportunistically use any available nodes for forwarding

© University of St Andrews, UK5 Cooperation  Opportunistic networking necessarily relies on cooperation to perform efficiently  If all nodes participate, we find the shortest paths  If nodes do not participate in forwarding, we must pass message directly to destination  High latency  Low delivery ratio  Cooperative forwarding involves cost to intermediaries:  Storage of ferried messages  Energy cost of forwarding

© University of St Andrews, UK6 Selfishness  Selfishness: refusing to forward other nodes messages  Reduces cost for intermediary  Still expect their own messages to be forwarded by others  Harms performance of network

© University of St Andrews, UK7 Reality Mining Selfishness Simulation  As proportion of selfishness nodes increases, Delivery ratio decreases. Selfishness harms the network.

© University of St Andrews, UK8 Attacks on Opportunistic Routing  Manipulation of routes  Nodes may alter the delivery path  Selective maliciousness  Nodes may be malicious only under certain circumstances  Selfishness  Nodes messages may not reach destination  Users’ economically rational desire to preserve battery affects their selfishness

© University of St Andrews, UK9 Selfishness  Opportunistic routing protocols, in particular Epidemic routing and Spray-and-wait routing are vulnerable to selfishness (Panagakis et al).  Once 30% of the nodes in the network are selfish, performance degrades (Keränen et al).  Is there an acceptable amount of selfishness?  What if selfish nodes only forward to the destination, but not other intermediaries?  Is this acceptable?

© University of St Andrews, UK10 Incentivising Routing Participation  Many approaches in traditional networks  Bartering  Swap messages 1-for-1  Currency  Purchase credits to give to other nodes in return for their forwarding service  Asynchronous bilateral trading  Nodes perform actions that benefit each other, but not necessarily simultaneously  Watchdog mechanisms  Nodes monitor each others communication to ensure compliance

© University of St Andrews, UK11 Which are appropriate for Opp Nets?  Bartering   Not all nodes have equal number of messages to exchange  Currency   No out of band oracle to administer currency  Watchdog mechanisms   Not many encounters will be observed by a third party  Asynchronous bilateral trading   Nodes perform actions that benefit each other, but not necessarily simultaneously

© University of St Andrews, UK12 What information do we have?  We must rely only on encounters between nodes  Nodes can collect opinion data based on their interactions  Nodes can use collated opinion data to make decisions about the trustworthiness of individual nodes  Encounter tickets  Use PKI to generate provable encounter tickets  Used to prove messages were exchanges and encounters took place

© University of St Andrews, UK13 How to bootstrap the mechanism?  The incentive mechanism must throughout the entire lifetime of the network  We need a mechanism to generate initial trust opinion data  We can use Self-Reported Social Networks (SRSNs)  Use online social network data or similar out of band data to provide information available before network startup  These SRSN data may correlate with trustworthyness

© University of St Andrews, UK14 Attack against incentive mechanisms  Exploiting friendship mechansisms  Do not incentivise nodes to add as many other nodes as “friends”.  Increasing trust through epidemic behaviour  Malicious nodes may ignore routing protocols to gain credits/currency/inflated ranking  Tailgating  Generating large numbers of encounter tickets by following nodes  Manipulation of control traffic  Withholding ranking information  Offer non-existent routes

© University of St Andrews, UK15 Attack against incentive mechanisms 2  Defamation  Creating false reputation claims to damage other nods  Exploiting detection algorithms  Exploiting grace periods or allowances made for genuine device limitations such as battery failure  Do not encourage nodes to drop old messages (this may be acceptable)  Collusion  Sybil attacks  How do we know a user cannot easily create a new identity

© University of St Andrews, UK16 IRONMAN: Addressing these concerns  IRONMAN Incentives and Reputation for Opportunistic routiNg in Mobile and Ad hoc Networks (Bigwood et al)  Use SRSN information to bootstrap network  Increase personal ranking of nodes considered friends  Use encounter histories to detect selfishness  No oracles, watchdogs, infrastructure networks nor flooded delivery receipts required

© University of St Andrews, UK17 IRONMAN Detection Mechanism

© University of St Andrews, UK18 Incentive Mechanism Performance  Detection Time  The time it takes a mechanism to correctly detect selfish behaviour  Detection Accuracy  The proportion of selfish nodes that were correctly detected as selfish by a mechanism  Selfishness Cost  The proportion of forwarded messages that were generated as a result of a node creating a message while selfish

© University of St Andrews, UK19 Performance Comparison  Simulation of several popular incentive mechanisms  Epidemic routing over the Reality Mining Trace  We compare the selfishness cost when two proportions of nodes behave selfishly  Nodes have finite buffer, energy and message TTL  IRONMAN greatly outperforms other mechanisms

© University of St Andrews, UK20 Incentive Summary  By bootstrapping the trust mechanism using SRSNS, and using Encounter histories IRONMAN outperforms existing mechanisms  IRONMAN suited to particular networking constraints in Opportunistic Networks  This demonstrates that Incentive mechanisms designed for opportunistic routing and useful, and motivates future work in this area

© University of St Andrews, UK21 Challenges For Incentive Aware Routing  User behaviour  Some nodes may behave altruistically except under specific circumstances. Is this acceptable?  How can nodes corroborate information? Exact timings difficult in opportunistic network  Using social-network information  SRSN information has shown to be useful. Can we perhaps classify users based on social network information?  Are opportunistic routing patterns similar to social network communication patterns?  May lead to cross disciplinary research

© University of St Andrews, UK22 Challenges For Incentive Aware Routing  Cross-layer information use  Many Opportunistic Routing applications might themselves involve social networks. E.g. crowdsourcing and mobile social networks.  Can we use information from the application at the routing layer or (vice versa)? E.g., spammers have their messages dropped?  Modeling social network behaviour  Advanced simulation  Allows for comparison of social networks and network communication networks  Predictive user location may improve routing performance

© University of St Andrews, UK23 Challenges For Incentive Aware Routing  Academic challenges  Collecting datasets is costly  A lack of datasets is harming research  Datasets are not shared among researches effectively  Metrics for analysing incentive mechanism  No consensus on how best to compare and analyse the incentive mechanisms for opportunistic networks.  What constitutes a fair distribution of forwarding?

© University of St Andrews, UK24 Conclusions  Incentive mechanisms will be vital for any opportunistic networking deployment  Existing incentive mechanisms from MANETs and DTNs are innapropriate for opportunistic networks  Using SRSN information provides incentive mechanisms with a method of bootstrapping their protocols  There a many challenges left for opportimostic routing, many of which are cross-discipline problems