Density-Aware Routing in Highly Dynamic DTNs: The RollerNet Case

Transcription

1 Density-Aware Routing in Highly Dynamic DTNs: The RollerNet Case Pierre-Ugo Tournoux, Student Member, IEEE, Je re mie Leguay, Farid Benbadis, John Whitbeck, Student Member, IEEE, Vania Conan, and Marcelo Dias de Amorim, Member, IEEE Presentation by Dan Pechulis

2 Outline Background DTN Routing Challenges Routing Solutions The RollerNet Experiment Scenario Data Collection Metrics Routing Solutions and Analysis

3 Disruption Tolerant Network (DTN) Also referred to as Delay Tolerant Networks Foundations come from traditional network architecture, but DTNs have evolved into their own definition in the past decade Characterized by lack of continuous, reliable connections between nodes Require fundamentally different algorithms and analysis compared to more stable, reliable wired network or MANET Examples: Terrestrial mobile networks Satellite or deep space networks Military networks

4 DTN Definition Network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources [1] Optionally-reliable - messages and commands will not always succeed Asynchronous message forwarding nodes do not wait for success before proceeding Limited expectations no assumptions about current state of network

5 DTN Routing Challenges Basic assumptions often made do not hold End-to-end path might not exist Time to traverse path between 2 nodes might be long Packet drop probability may be significant Often, failure of these conditions means routing failure or non-delivery of data under many algorithms

6 DTN Routing Solutions Cannot assume end-to-end communication yet cannot simply fail if none exists Solutions generally incrementally move data towards the destination Involve multiple copies of data to account for instability and packet loss Flooding or epidemic routing are simple examples

7 Spray and Wait Routing Spray: L copies of a message are sent to distinct nodes on the network Wait: Nodes receiving message wait to encounter destination node directly Limits number of messages sent to the network Variant: Binary Spray and Wait Source sends half its current number of copies to each node it encounters that does not have any copies Each node receiving messages does the same

8 The RollerNet Experiment Collected a dataset based on location information obtained from a rollerblading tour in Paris Analyzed location information and applied existing routing algorithms to the data Proposed a variant of the Spray and Wait protocol named Density Aware Spray and Wait (DA-SW) Potential uses for such a network include status updates or safety and weather alerts

9 Dataset Details Tour consisted of about 2,500 people on Sunday afternoon Lasted 3 hours and covers 20 miles 20 minute pause midway through course

10 Data Collection Bluetooth contact logging via Intel imotes mobile devices (~ 10 m range) Periodic polls for nearby devices (5 second duration every 15 seconds) Logs time and id of any discovered device (other imotes as well as Bluetooth enabled mobile phones)

11 Device Distribution 62 total imotes devices 25 devices to 6 staff member groups with relatively static positioning 37 devices to non-staff participants All participants asked to activate Bluetooth on mobile devices so they are visible to imotes

12 Data Processing and Result Size Processing Single MAC occurrences discarded to eliminate corrupted addresses Time synchronized amongst imotes imotes carrier identification logged Results 5,000 seconds (first half of tour) 638 Bluetooth contacts 39,305 contacts between imotes 50,193 contacts between imotes and other Bluetooth devices

13 Comparison to Similar Studies

14 Accordion Phenomenon Well defined accordion pattern to the data Participants stop and start throughout tour Nodes are spread out while moving and closer together while stopped Similar to expected traffic patterns for other transportation methods Clear correlated effect on calculated metrics

15 Overall Metrics Average node degree average number of contacts for each node within each interval Connected components number of connected components and size of largest connected set for each interval Average delay time required for a packet to travel between any 2 nodes every 30 seconds

16 Head/Tail Pipeline Metrics Head and Tail nodes denoted by single imotes device held by staff member at front and rear of group Can determine delay and number of hops for a packet to travel between head and tail Allows the entire network to be viewed as a pipeline

17 Head/Tail Group Metrics First and last thirds of the group found by either inferred mobility (a) or message delay to head node (b) Increase in node degree in head group precedes similar increase in tail group Tail node degree sometimes remains high while head fluctuates, explained by difference in skill levels of participants

18 Head/Tail Group Metrics Second phenomenon shown in more detailed window D deceleration A acceleration C constant speed (l and h describe node density)

19 DTN Justification - Disconnection Various levels of connectivity as seen in graphs Figures show connectivity at various times between 1450 and 2050 seconds

20 DTN Justification Topology Change On average, 11 links go up or down every second Proactive protocols do not immediately register changes Reactive protocols may have routes invalidated by the time route is determined and data is ready to be sent Traditional MANET protocols cannot keep up with speed of topology change too many routing errors or new route requests

21 Epidemic Routing Mathematically examine average delay for epidemic routing for RollerNet dataset between 3 and 10 degrees Model contacts as independent Poisson Processes to imply exponential distribution and produce a random neighborhood graph First encounter: Probability of finding destination:

22 Epidemic Routing Total delay for m-1 steps: Disjoint members:

23 Epidemic Routing Low density: High density: Conclusions: limiting factor is node degree regardless of the size of the network Low density - time required to meet all nodes High density- time to propagate to all nodes

24 Oracle-Based Spray and Wait Run Binary Spray and Wait with a variety of message counts and choose the best one More packets = lower delay but higher overhead In this case, aiming for average delay around 70 seconds Overhead mirrors fluctuations in node degree How to define oracle in real time?

25 Density-Aware Spray and Wait Idea: use local information to determine optimal number of copies to send to maintain average delay Parameters: targeted delay value, current node degree Run simulations to build lookup abaci Figure shows results from simulation run on collected data at various SW values

26 DA-SW: Results Simulation achieved average delay of 68.1 seconds with standard deviation of 13.9 and overhead of 19.9 Oracle-SW: 70.8 average, 3.7 standard deviation, 19.1 overhead

27 DA-SW: Delay Need to account for topology change since DA-SW lags behind O-SW Anticipate state of network during transmission, rather than just looking at current state Solution: use separate abaci for acceleration, deceleration, low density constant speed (moving), high density constant speed (stopped) average, 10.3 standard deviation

28 DA-SW: Message Sizes Larger messages stress network bandwidth and exacerbate overhead More defined when network is of lower density, as more messages need to be sent

29 Conclusions and Contributions Produced a large DTN dataset exhibiting clear and correlated accordion effect Established basis for the use of node density analysis for more efficient DTN routing Proposed Density-Aware Spray and Wait algorithm to integrate node density into routing protocol

30 Critiques Dataset size- how to go about constructing and collecting a larger data set? What about the second half of the dataset? What about networks that do not stay in close proximity? How could abaci have used more predictive formulation? How specific is DA-SW to this particular scenario?

31 Sources A Delay-Tolerant Network Architecture for Challenged Internets. K. Fall. SIGCOMM, August Density-Aware Routing in Highly Dynamic DTNs: The RollerNet Case. P. Tournoux, J. Leguay, F. Benbadis, et al. IEEE Transactions on Mobile Computing, v 10 n 12, December 2011.