The previous two research threads led to new insights, models, and algorithms, in terms of both resource allocation in DTNs, as well as more accurate analytical models that where inline with recent mobility trace insights. Nevertheless, the majority of these works were still mostly based on simple “random” forwarding protocols like epidemic routing, spray and wait, 2-hop routing, and variants. These protocols, which could be arguably referred to as “1st generation” DTN routing, where based on simple forward-ing decisions: when a node with a message copy encounters another node without a copy, it can: (i) create a new copy (epidemic), (ii) create one with some probability
(probabilistic routing), (iii) create one only if the copy budget is not depleted (spray and wait), and other variabts. In other words, the decision to give a copy to a newly encountered node did not really depend on properties of the relay or the destination (e.g., whether the two might meet soon). This justifies the term “random”, used earlier.
The natural progress in this direction was to amend these protocols to make “smarter”
decisions that can assess the usefulness orutilityof a given relay node. For example, (i) the utility might capture the ability of that node to delivery messages to a given
destination (e.g., if the two nodes rely in the same “community” or location area and tend to meet often)
(ii) a node might also have high utility foranydestination (e.g., a node that tends to explore larger areas of the network compared to average nodes).
Utility-basedrouting protocols for DTNs had already been explored early on.Prophet was a popular early variant of epidemic forwarding. There, a node A encountering another node B would give the latter a copy of a message for destination X, only if the utility of B (related toX) was higher than that ofA. This utility in turn was based on a mechanism that considered the recency of encounter between the two nodes. It could also consider some transitivity in utilities, e.g., B could have a high utility for X, not because it sawXrecently, but because it sees other nodes that seeX recently.
Similarly, the early “single-copy” study of [10] considered such simple utility metrics but assuming a message is not copied, but rather forwarded. Single-copy DTN for-warding had the advantage of significantly limiting the overhead per message, but did not benefit from the path diversity that multiple-copy schemes exploit [25].
Finally, utility metrics were used to improve specific aspects of existing protocols.
For example, Spray and Focus [12] did not create any new copies after there was a total ofLin the network, in order to limit the amount of overhead per message (just as in Spray and Wait), but did allow each such copy to be handed over to a relay with higher utility for the destination, if one was encountered. Similarly,smart sprayingmethods were proposed to not randomly handover theLcopies, but to do so according to some meeting-related utility metric [16, 17, 18].
Nevertheless, the majority of these utility-based methods were simple heuristics, that sometimes improved protocol performance but not always. More importantly, util-ities were pairwise metrics, based on the meetings characteristics between the relay in question and the destination. A breakthrough came by the seminar works of [35, 36].
Motivated by the intricate structure between node contacts, revealed in the recently studied mobility traces, the authors suggested that contacts between mobile devices are subject to the samesocialrelations that the users carrying these devices are subject to.
Hence, it is only reasonable to utilize the new science ofComplex NetworksorSocial Network Analysisto answer questions related to which node might be a better relay for another (these terms, including the termNetwork Scienceare often used interchange-ably, to mean the same thing).
This gave rise to Social Network based opportunistic networking protocols. The main idea behind these first schemes was simple:
• First, collect knowledge about past contacts into a graph structure called asocial or contact graph; a link in this graph could mean, for example, that the two
endpoints of the link have met recently, or have been observed to meet frequently enough in the past.
• Then, use appropriate social network tools or metrics to make forwarding deci-sions.
For example, BubbleRap [36] uses Community Detection algorithms to split the network into communities. Then, epidemic routing was modified using communities anddegree centrality[30]. The node with the highest degree centrality would be the node that has met, for example, the largest number of other nodes within some time interval. If the message has not yet reached the destination community, a message copy in BubbleRap is given to an encountered node only if the latter has higherdegree centrality. When the message has reached a relay in the destination community, then local degree centralitywould be used instead (i.e., a node would become a new relay only if it was better connectedin that community).
SimBet [35] uses insteadnode betweeness(a different measure of node centrality) to traverse the network. It then “locks” to the destination usingnode similarity. Node similarity captures the percentage of neighbors in common with the destination. Due to the high clustering coefficient of social networks, if two nodes share many neighbors then they belong to the same community with high probability. Hence, trying to find a node with high similarity with the destination in SimBet is equivalent to trying to find a node in the destination community (just as BubbleRap does, in the first phase).
As expected, these two protocols outperformed traditional random and utility-based ones, by intelligently exploiting not justpairwise structurein node contacts, but rather macroscopic structuresuch as communities, betweeness, etc., that depend on the in-teractions of multiple nodes. Nevertheless, this new line of work also raised some important questions:
1. How should one optimally built the contact graph on which the social metrics will be calculated?
2. Do all opportunistic networks exhibit such “social” characteristics, and if so what are the most prominent ones? Do state-of-the-art mobility models capture these?
3. Can one still hope to do useful performance analysis when forwarding protocols become that complex and interdependent with the underlying mobility process?
The goal of this chapter is to present our contributions towards answering these three questions.
Chapter Contributions
The begining of the chapter is concerned with the first question, which we attempted to answer in the following two works [37, 38]:
• T. Hossmann, F. Legendre, and T. Spyropoulos, “From Contacts to Graphs:
Pitfalls in Using Complex Network Analysis for DTN Routing,” in Proc. of IEEE International Workshop on Network Science For Communication Net-works (NetSciCom 09), co-located with INFOCOM 2009.
• T. Hossmann, T. Spyropoulos, and F. Legendre,“Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing,”in Proc. of IEEE INFOCOM 2010.
Contribution 4.1We studied different “aggregation methods”, i.e., methods to convert the history of past contacts into a social graph, and showed that the performance of Social Network based protocols is very sensitive to the contact graph creation method;
simple empirical tuning or rules of thumb will likely fail to unleash the potential of such protocols.
Contribution 4.2We proposed a distributed and online algorithm, based on spectral graph theory. It enables each node to estimate the correct social graph in practice, as-sessing the role of new nodes in the graph without any training, in a manner reminiscent of unsupervised learning. This algorithm is generic, and could be applied as the first step ofanyopportunistic networking protocol that uses social network metrics.
Regarding the second question, some initial insights about community structure had been observed in [36]. However, we embarked on a systematic study towards answering that question in [39, 40]:
• T. Hossmann, T. Spyropoulos and F. Legendre,“A Complex Network Analysis of Human Mobility,” in Proc. of IEEE NetSciCom 11, co-located with IEEE Infocom 2011.
• T. Hossmann, T. Spyropoulos, and F. Legendre,“Putting Contacts into Context:
Mobility Modeling beyond Inter-Contact Times,” in Proc. of ACM MobiHoc 2011. (best paper award runner-up)
Our contributions in these papers can be summarized as follows:
Contribution 4.3 We performed a large study of contact traces coming from state-of-the-art mobility models, existing real traces, and traces collected by us (from geo-social networks). We convert each of them into a geo-social graph, using some of the earlier methods from this chapter.
Contribution 4.4We investigated whether these graphs exhibit well-known properties of social networks: (i) high clustering and community structure, (ii) small-world con-nectivity, and (iii) power-law degree distributions. Indeed, we found that the majority of mobility traces exhibit the first two, but not always the third property .
Contribution 4.5 We showed that state-of-the-art synthetic mobility models, while able to recreate some of these social properties well, are unable to model bridging nodes, which seem to be common in real traces. To this end, we suggested a modi-fication of such models usingmulti-graphs, which can be applied as an “add-on” to different mobility models, without interfering with other desirable properties of these models.
These developments were quite positive, corroborating the initial evidence about the strength of social network analysis for opportunistic D2D networking, as well as demonstrating how to properly tap into this potential. Nevertheless, the complexity of protocols based on social graph metrics came on top of the existing challenges of performance analysis for non-IID mobility models, mentioned earlier. The intricate dependence of forwarding decisions on past contacts between many different nodes
creates state history for the Markovian analysis model that is hard to keep track of.
What is more, by that time, a large number of utility-based routing protocols had been proposed, making it difficult to come up with a proprietary analytical model (e.g., a specific Markov chain) for each different protocol. To this end, in the following two works, we proposed a unified analytical performance prediction model, coined DTN-Meteo[41, 42].
• A. Picu and T. Spyropoulos, “Forecasting DTN Performance under Heteroge-neous Mobility: The Case of Limited Replication,” in Proc. of IEEE SECON 2012.
• A. Picu, and T. Spyropoulos,“DTN-Meteo: Forecasting the Performance of DTN Protocols Under Heterogeneous Mobility,”in IEEE/ACM Transactions on Net-working, 23(2): 587-602, 2015.
Our main contributions, can be summarized as follows:
Contribution 4.6We attempted to generalize the performance prediction models for heteregenous mobility of Chapter 3, in order to allow deriving perfomance metrics for generic utility-based algorithms. Our framework combined the theory of Markov Chain Monte Carlo (MCMC) based optimization and the theory of Absorbing Markov chains as follows:
• Each state of a Markov chain corresponds to a possibleconfigurationorstateof the network (e.g., which nodes have which message(s)) at a time.
• Themobility processgoverns the possibletransitionsand the respective transi-tion rates between states.
• Thealgorithm(e.g., forwarding) governs theacceptance probabilitiesof a po-tential transition, a probability that may depend on the utility of the previous and the potential new state.
• Absorbingstates correspond to desired final states of the protocol (e.g. message delivered to its destination, or to all destination in case of multicast, etc.). Perfor-mance metrics can be expressed in terms of absorption times and probabilities.
Contribution 4.7Using this framework, we managed to successfully model the state-of-the-art protocols mentioned earlier, namely SimBet and BubbleRap, running on top of generic, trace-based mobility models. We also showed that this framework applies to various delivery semantics, beyond end-to-end message delivery, such as multicast, anycast, etc.