© University of Alabama1 Chapter 1: Identifying the Intertwined Links between Mobility and Routing in Opportunistic Networks Xiaoyan Hong Bo Gu University of Alabama ROUTING IN OPPORTUNISTIC NETWORKS

© University of Alabama2 Outline  Introduction  Mobility models  Mobility characteristics  Routing protocols  Future directions  Summary

© University of Alabama3 MOTIVATION  Mobility intertwines with routing protocols to play a vital role in opportunistic networks  Mobility properties are utilized by routing protocols to improve performance  Study on mobility models, analytical results on motion characteristics and routing strategies will help developing novel integrated mobility and message dissemination solutions for opportunistic networks

© University of Alabama4 INTRODUCTION  Present a survey over mobility models, analytical results on motion characteristics and routing strategies  Mobility models are the evaluation tools for routing protocols and the sources for movement pattern analysis  Analytical results contribute to new mobility models with increased flexibility in reproducing desired network scenarios  Routing protocols can make use of underlying mobile topological structures from results of mobility analysis

© University of Alabama5 Intertwined Three Components Spatial properties Temporal properties Graph properties Motion Characteristics Proactive routing Reactive routing -contact based -community based -auxiliary node based Routing Protocols no map, no intention w map, no intention no map, w intention Mobility Models w map, w intention New model Analysis Assist routing Evaluation

© University of Alabama6 Outline: Mobility Models Spatial properties Temporal properties Graph properties Motion Characteristics Proactive routing Reactive routing -contact based -community based -auxiliary node based Routing Protocols no map, no intention w map, no intention no map, w intention Mobility Models w map, w intention New model Analysis Assist routing Evaluation

© University of Alabama7 MOBILITY MODELS  Movements are most likely the explicit or implicit results of their social or personal activities.  Physical locations  Social intentions  Classifications  Non-Map Without-Intention Models  Map Without-Intention Models  Non-Map With-Intention Models  Map With-Intention Models

© University of Alabama8 Non-Map Without-Intention Models  Attributes:  no restrictions on paths nor intention of movement  Basic model  Random Walk Model [8]: Memoryless  Random Waypoint Model [28]: Delay factor to simulate pauses  Random Direction Mobility Model[43]: Additionally deal with the movements when hitting simulation boundary  Realistic model  Gauss-Markov Mobility Model [30]: Simulate the acceleration and deceleration  Heterogeneous Random Walk[40]: Simulate the clustered network

© University of Alabama9 Map Without-Intention Models  Attributes:  movements are restricted to physical world paths.  Freeway model[1]: Vertical and horizontal tracks of freeway  City block[14]: Street grid  Street Random Waypoint mobility model[11]: Considering the intra-segment mobility and inter-segment mobility on street grid  Vehicular network model[44]: Stop signs, timed traffic lights and control on next road

© University of Alabama10 Non-Map With-Intention Models  Attributes:  No path restriction  With individual or shared movement intentions  Group based model  Reference Point Group Mobility Model (RPGM)[22]: paths of nodes in the same group following the movement of the group leader  Interaction-based mobility model[34]: characterizes the formation and disaggregation of hot spots at random times and locations  Community based model  Community based mobility model[35]: Captures the feature that a number of hosts are grouped together  Community model with cyclic pattern[54]: defines the repeating time period to model re-visits to the same locations

© University of Alabama11 Map With-Intention Models  Attributes  Realistic features such as moving along paths and with intentions  Trace based model  Bus traces[2], GPS trace[9], WLAN trace[51], Trace in campus [23]  Agenda Driven Mobility model[59]: use National Household Travel Survey (NHTS) data to synthesize each node’s agenda, which derives its mobility of when, where and what (pause time)  Graph-based model  Area Graph based mobility model[4]: A directed and weighted graph to model locations and paths between locations  Levy walk based model  Heavy-tail distribution[41]: movement increment is distributed according to a heavy-tail distribution

© University of Alabama12 Summary of the Models  Trend of mobility modeling has moved towards more realistic by taking considerations of both social intentions and geographical features  Artificially consider social interaction and attraction  Analyzing real world traces  WLAN associations give hits on mobility  Impact  Effective evaluation tools  Play an important role for message forwarding in opportunistic networks

© University of Alabama13 Outline: Mobility Characteristics Spatial properties Graph properties Motion Characteristics Proactive routing Reactive routing -contact based -community based -auxiliary node based Routing Protocols no map, no intention w map, no intention no map, w intention Mobility Models w map, w intention New model Analysis Assist routing Evaluation Temporal properties

© University of Alabama14 MOBILITY CHARACTERISTICS  Contribute to performance evaluation, simulation calibration, routings protocol design  Classifications  Characteristics of Flight  Locality Distribution  Temporal Characteristics  Joint Spatial and Temporal Analysis  Graph Characteristics

© University of Alabama15 Characteristics of Flight  Flight: the longest straight line trip from one location to another  Flight length distribution can be heavy-tail, or exponential  Flight reflects the diffusivity of mobility  Models with different diffusivity  Random Waypoint model, Brownian Motion, Levy Walk model  Impact  Diffusive nodes are helpful for relaying messages to larger areas

© University of Alabama16 Locality Distribution  Different movement patterns lead to various spatial locality distributions  Distributions can be uniform or heterogeneous  Discussed models:  Brownian-motion, Random Waypoint Model, Heterogeneous Random Walk  Impact  Cluster based routing is suitable in networks with heterogeneous distribution

© University of Alabama17 Temporal Characteristics  Many properties have been analyzed:  encounter frequency, pause time, hitting time, meeting time, inter- contact time, filling time, scattering time  Impact  Encounter history matters for choosing next forwarder  Pause time, hitting time, meeting time, inter-contact time are useful in estimating message delay and delivery rate  Filling time and scattering time describe the dynamics of hot spots, can be useful for cluster-based routing

© University of Alabama18 Joint Spatial and Temporal Analysis  Time and space are closely related in mobility  Trajectory similarity: Compute similarity using a set of metrics including Euclidean distance, etc.  Discussed models: Vehicular model[29], Mobyspace [27], location based time-dependent link analysis[20][21]  Impact  Routing uses clusters or high similarity nodes  Help to identify popular locations in mobile networks and trajectory segments  Calculate communication latency

© University of Alabama19 Graph Characteristics  Using graph properties to identify mobility patterns  Centrality [17]  Degree centrality, closeness centrality, betweenness centrality  Social networks  k-clique community, network connectivity  Discussed models: Clique community[25], Continuum framework [10]  Impact  Node with higher centrality as forwarder, community helps to group mobile nodes, connectivity analysis

© University of Alabama20 Summary: Characteristic Analysis (I) CategoriesMobility CharacteristicsFeatures for Routing Flight LengthLongest straight line trip from one location to next location; node diffusivity Message forwarder adopts high diffusive nodes for fast dissemination Locality Distribution Distribution of node positions during moving process is either uniform or heterogeneous Cluster based routing is suitable in networks with heterogeneous distribution Temporal Characteristics Encounter frequency, pause time, hitting time, meeting time, inter- contact time, filling time, scattering time Encounter history for choosing next forwarder; Estimating message delay and delivery rate

© University of Alabama21 Summary: Characteristic Analysis(II) CategoriesMobility CharacteristicsFeatures for Routing Joint Spatial- Temporal Time and location relationships of groups, trajectory similarity Routing uses clusters or nodes with high similarity Graph Characteristics Degree centrality, closeness centrality, betweenness centrality, k-clique community Nodes with higher centrality as forwarder; community helps to group mobile nodes; connectivity analysis and evolution for performance

© University of Alabama22 Outline: Routing Protocols Spatial properties Temporal properties Graph properties Motion Characteristics Proactive routing Reactive routing -contact based -community based -auxiliary node based Routing Protocols no map, no intention w map, no intention no map, w intention Mobility Models w map, w intention New model Analysis Assist routing Evaluation

© University of Alabama23 ROUTING STRATEGIES  Routing principle: store-carry-forward  Classifications:  Proactive Routing: with centralized or off-line knowledge about network  Reactive Routing: without a global or predetermined knowledge Contact based routing: forward messages using the encounter history Community based routing: identify and rely on various clusters Auxiliary node based routing: introduce mobile or static message ferries

© University of Alabama24 Proactive Routing  knowledge such as contacts history, queuing length and traffic demands  Use a graph with time-varying delay and capacity  Discussed protocols:  Framework of DTN routing which takes different levels of network knowledge [26]  Treat routing as a resource allocation problem[2]  Link with contact probability calculated from cyclic movement pattern [32]  Routing assisted by static relay nodes deployed at critical locations for cyclic movement pattern[19]  Mobyspace with the assumption of full network knowledge [27]

© University of Alabama25 Reactive: Contact based routing  Forwarding decision is made when two nodes encounter each other  Discussed protocols  Epidemic routing [52]: forward to each contact  PROPHET: employ a probabilistic metric called delivery predictability [31]  Spray and Wait protocol: broadcasts only a fixed number of copies of message [49]  Seek and Focus protocol: hybrid protocol which includes utility- based routing and randomized routing [49]

© University of Alabama26 Reactive: Community based routing  Identify and use a special group of nodes  Better sociability  Frequent contacts with the destinations  Attached to a hot location  Discussed Protocols  Distributed method to identify central nodes[13]  Multicast routing [18]  Island Hopping [46]  Connected dominating set for VANET [33]

© University of Alabama27 Reactive: Auxiliary node based routing  Introduce nodes specially designed for message relay, either mobile or static  Discussed routing  Auxiliary node with Levy Walk pattern[47]  Levy Walk searching[53]  Mobile message ferry[57]  Static throw box[58]

© University of Alabama28 Summary: Routing Protocols  Relationships among routing strategies, mobility models and their characteristics  TABLE II and TABLE III summarize the following  Categories  Routing protocols  Main routing strategies  Mobility models and features  Applicable environments

© University of Alabama29 FUTURE DIRECTIONS  Social network related analysis and its connection to opportunistic networks  Movements within a real road system  Novel message dissemination schemes that explore new social network properties  Management of opportunistic networks, examples include extending coverage, capacity and traffic aggregation

© University of Alabama30 CHAPTER SUMMARY  This chapter presents a survey over mobility models, analytical results on motion characteristics, and routing strategies that largely rely on mobility in opportunistic networks  More important, it provides a systematical overview and identifies the intertwining connections among the three areas.

© University of Alabama31 Mobility Characteristics Movement Patterns CHAPTER SUMMARY Random walk, Random waypoint,… Manhattan Model, Freeway model, … Group Mobility model, community based model,… Trace based model, Graph based model,… Flight, locality, temporal characteristics, joint spatial-temporal, graph features Routing Schemes Proactive routing, reactive routing (contact based, community based, auxiliary node based))) Applications of Opportunistic Networks Gossipmule, content spreading in mobile social networks, opportunistic Internet access, rural area networks) Abstraction Mobility assistance Evaluation Comm. support

© University of Alabama32 Thanks for your attention!