Routing in Intermittently Connected Wireless Networks Routing in Intermittently Connected Wireless Networks - a brief survey on recent works Joy Ghosh LANDER

Overview A brief introduction to DTN, ICMAN, etc. Pocket Switched Networks DTN Routing in Mobility Pattern Space Practical routing in DTN Spray and Wait: Routing for ICMAN Inter Planetary Networks

What are DTN, ICMAN ? Delay Tolerant Networks (DTN) No contemporaneous end-to-end paths Store-n-forward routing methodology Links/contacts are often subject to long delays Packet losses due to buffer overflows E.g., busses in fixed routes, satellites in orbits Delay tolerant networking research group (http://www.dtnrg.org) Intermittently Connected Mobile Ad hoc Networks (ICMAN) In the realm of DTN and MANET Focuses more on partially deterministic mobility

Pocket Switched Networks and Human Mobility in Conference Environments [1] Research Issues Internet connectivity islands – realms of DTN Dependence on managed infrastructure (DNS, DHCP, centralized servers, etc.) Effective use of human mobility as well as local/global connectivity Experimentation 54 participants of IEEE Infocom 2005 carried bluetooth devices to record connectivity statistics Implications on PSN forwarding algorithms

Pocket Switched Networks Human mobility – double edged sword (+) increases bandwidth as users store/carry data (-) unstable forwarding paths, varying reach-ability Opportunistic networking Contemporaneous path is restrictive Per-hop routing Make use of local connectivity and node mobility E.g., Data muling, Store-and-haul forwarding Locally forwarding to nodes with global connectivity Return path is tricky – Open Problem Use of both local and global connections makes it robust

Personal Devices Always-On, Always Carried NOTE: is Always-ON a practical assumption? Support owner’s task before others If resources can be spared Security and Privacy Uncontrolled and potentially malicious neighbors

Human mobility measurements Setup with 54 attendees of Infocom 2005 Intel iMotes with ARM processor, Bluetooth radio, Flash RAM, CR2 battery, packed in a floss box Bluetooth base band layer “inquiry” mode for 5 secs Sleep for ~120 seconds, responding to only “inquiry” Maintain “in-contact” list; when nodes don’t respond, write a tuple {MAC, start time, end time} to flash RAM (64K for data)

Contact Visibility Influence of time of day

Contact Duration - I Contacts between specific pairs of nodes

Contact Duration - II Contacts with any node in a group

Inferences Combination of local wireless and human mobility battles the absence of global connectivity When forwarding to any of a group of nodes, the power law coefficient increases significantly All nodes are not equal Some nodes are more active Some pairs of nodes see each other more often Difference in frequencies of connection opportunities within groups Temporal influence on contact patterns

DTN Routing in a Mobility Pattern Space [2] MobySpace Formalism of DTN by a high-dimensional Euclidean space on node’s mobility patterns Each dimension denotes the probability of finding a node in a specific location Node connections arise and dissolve dynamically as a function of node mobility in physical space Routing in DTN reduces to routing in the virtual space

Routing Concept Each node’s mobility pattern is denoted by a MobyPoint in the MobySpace Goal Opportunistically forward a “bundle” (messages in DTN) to a node with a mobility pattern matching more and more to the destination Action Forward bundle to nodes with MobyPoint closer and closer to the MobyPoint of the destination

MobySpace Characterization Contact based dimensions Each axis is a possible contact with a node The distance along that axis is the contact probability Nodes with similar sets of contacts AND similar frequencies will be close in such a space Location based dimensions Each axis represents a specific location The distance along that axis is the probability of finding the node in that location Nodes visiting similar locations with similar frequency will be closer in such a space

Possible limitations and Issues Not too effective if nodes change habits too often Even under well defined mobility patterns, bundle may reach local maximum In the location based case, 2 nodes may go to same locations with same frequency (MobyPoints coincide) but at different times

Case Study Nodes move around N locations (dimensions) following a power-law distribution P(i) is the probability for a node being at location i P(i) = K * (1/d )^ni , where: ni is the preference index of location i d is the exponent of the power-law based mobility K is a constant K = (1 – 1/d )/(1 – 1/d N) Higher d  smaller subset of preferred locations Lower d  wider choice of locations for nodes

MobyPoint matching functions Euclidean distance: Canberra distance: Cosine angle separation: Matching distance: Raw number of matching (within delta) location probabilities on an axis

Other methodologies Epidemic [6] Opportunistic Random Whenever nodes meet they exchange bundles Optimal path  minimum delay High buffer occupancy and bandwidth utilization Opportunistic Always wait for destination only One transmission per bundle Random When destination is not near, forward at random Prevention of local loop added

Simulation Results – I (full knowledge)

Simulation Results – II (partial knowledge)

Practical Routing in Delay-Tolerant Networks [3] Design Goals Routing must be self-configuring Devices deployed in remote regions Must recover from failure without manual intervention Acceptable performance over wide variety of connectivity patterns Efficient use of buffer and network resources  Scalability factor Network Model Undirected graph with bidirectional links (contacts) Unsuitable for unidirectional satellite networks Contacts are assumed to have constant link bandwidth and delay

Based on previous work by others [7] S. Jain, K. Fall, R. Patra, “Routing in a Delay Tolerant Network”, ACM SIGCOMM 2004 Knowledge Oracles Contacts Summary Oracle Time-invariant / aggregate characteristics of contacts Contacts Oracle Time-varying DTN multi-graph Queuing Oracle Buffer occupancy at any node at any time Traffic Demand Oracle Future traffic demand

Knowledge vs. Performance [7]

Partial Knowledge Routing [7] Assigns cost to edges Costs reflect estimated delay on edge Queuing time: time till contact available Transmission delay: time to inject into edge Propagation delay: time to travel on edge Computes minimum cost path Conveniently uses different oracles Computationally efficient distributed algorithms already exist for shortest-path based routing problems Finds however single path from source to destination – no optimal splitting

Partial Knowledge Routing [7] Cost function: ω (e, t) If message arrives at node ‘u’ at time ‘t’, and if edge ‘e’ (between ‘u’ & ‘v’) is chosen, message will reach node ‘v’ at time ‘t + ω (e, t)’ FIFO: if t1 < t2, t1 + ω (e, t1) <= t2 + ω (e, t2) Algorithm with Time-invariant Costs Use modified Dijkstra’s algorithm If L[v] > (L[u] + ω (e, L[u] + T)) then L[v]  (L[u] + ω (e, L[u] + T)) (T  start time) Minimum Expected Delay (MED) Oracles: Contacts Summary Edge cost = avg. wait time + prop delay + trx delay Proactive routing is used for time-invariant cost Fixed routes for all messages Minimizes avg. waiting time but doesn’t optimize path Improvement Dynamically make use of superior contacts per-hop Multiple disjoint paths to balance load

Minimum Estimated Expected Delay (MEED) Does not require Contacts Oracle Depends on observed mobility history Aggregated mobility prediction over large time window Time taken for message delivery in DTN Nodes record connection and disconnection times over sliding history window Tunable parameter for reaction time to changes Topology distribution via epidemic link state routing

Who makes routing decision? Source routing (NO) In a DTN, source does not have end-to-end information Per-hop routing (NO) Intermediate nodes may not be able to route efficiently if topology changes Per-contact routing (YES) Routing tables are recomputed each time a contact arrives  most updated information Pros Is able to use contacts with high MED when present Cons Uses more resource for frequent re-computation Adds computational delay to messages May enter link loops (topology may change at each hop)

Topology distribution Link state routing via Epidemic algorithm Pros Each node contains full topology New node can get full information with one exchange Cons More memory required at each node  not scalable Merging topology information amongst nodes is complex

Simulation Scenarios Mobility traces from Dartmouth WLAN  ad hoc DTN More than 2000 users and 500 APs over 2 years WLAN  ad hoc DTN 2 nodes are in contact if they are connected to same AP at the same time

Simulation parameters 30 nodes for one month Nodes that at least contact another node 10 times in the month are included Each node generates 6 messages (10,000 bytes) / 12 hrs Protocols compared Earliest Delivery (ED)  full contact schedule [7] MED, MED Per Contact [7] MEED Epidemic [6]

Simulation Results - I

Simulation Results - II

Spray and Wait: An Efficient Routing Scheme for Intermittently Connected Mobile Networks [4] Design Goals Fewer transmissions per successful delivery Low contention under high traffic loads delivery delay close to optimal Scalable w.r.t. network size or node density Require low network knowledge

Spray and Wait - Concept Spray phase Every message is forwarded by source to L distinct “relays” L is a number chosen to guarantee high delivery probability Wait phase Each of the L nodes wait for direct transmission only Like SOLAR [11] Nodes send/spray to subset of acquaintances only Unlike SOLAR Each acquaintance does both direct transmission and also relaying to further acquaintances We choose the subset of acquaintances to guarantee high delivery probability

Spraying Techniques Source Spray and Wait Binary Spray and Wait Spray to first L distinct nodes that come in contact Binary Spray and Wait Source of each message starts with L copies At runtime, a node may have N messages (source + relay) Upon contact with a node with NO copies (source or relay) a node with N messages hands over floor (N/2) and keeps ceil (N/2) One copy left  direct transmission

Delay Comparison Theorem 1 When all nodes move in an IID manner, Binary Spray and Wait routing is optimal, that is, has the minimum expected delay among all spray and wait routing algorithms

Protocol Comparison Epidemic routing [6] Randomized flooding with p = (0.02 – 1) [8] Utility based routing with Uth = (0.005 – 0.2) [9] Optimal Binary Spray and Wait with L copies Seek and Focus single copy routing [10] Oracle based Optimal Routing [7]

Simulation Results - I 100 nodes in 500 x 500 grid with reflective barriers Random waypoint mobility model Radio range is 10 grid units Message inter-arrival time is Uniform (1, Tmax) Tmax is varied from 10,000 to 2000 Average load of 200 to 1000 messages per unit time

Simulation Results - II

Simulation Results - III

IEEE Spectrum – August 2005 Issue

Extension of DTN in Space

References Pocket Switched Networks and Human Mobility in Conference Environments - Pan Hui, Augustin Chaintreau, James Scott, Richard Gass, Jon Crowcroft, Christophe Diot, WDTN, ACM SIGCOMM 2005 DTN Routing in a Mobility Pattern Space - Jrmie Leguay, Timur Friedman, Vania Conan, WDTN, ACM SIGCOMM 2005 Practical Routing in Delay-Tolerant Networks - Evan P. C. Jones, Lily Li, Paul A. S. Ward, WDTN, ACM SIGCOMM 2005 Spray and Wait: An Efficient Routing Scheme for Intermittently Connected Mobile Networks - Thrasyvoulos Spyropoulos, Konstantinos Psounis, Cauligi S. Raghavendra, WDTN, ACM SIGCOMM 2005 The Interplanetary Internet - J. Jackson, IEEE Spectrum, Volume: 42  Issue: 8   Date:Aug. 2005, Pgs:  30- 35 Epidemic routing for partially connected ad hoc networks - Amin Vahdat, David Becker, Technical Report CS-200006, Duke University, April 2000 Routing in a delay tolerant network - Sushant Jain, Kevin Fall, Rabin Patra, August 2004,  ACM SIGCOMM Computer Communication Review , Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications,  Volume 34 Issue 4 The broadcast storm problem in a mobile ad hoc network - Y.-C. Tseng, S.-Y. Ni, Y.-S. Chen, J.-P. Sheu., Wireless Networks, 8(2/3):153–167, 2002. Probabilistic routing in intermittently connected networks - A. Lindgren, A. Doria, and O. Schelen. SIGMOBILE Mobile Computing and Communications Review, 7(3):19–20, 2003. Single-copy routing in intermittently connected mobile networks - T. Spyropoulos, K. Psounis, and C. S. Raghavendra, In Proc. of IEEE Secon’04, 2004. Sociological Orbit aware Location Approximation and Routing in MANET – Joy Ghosh, Sumesh J. Philip, Chunming Qiao, IEEE Broadnets 2005

Partial Knowledge Routing I [7] Cost function: ω’ (e, t, m, s) Edge ‘e’, time ‘t’, message size ‘m’, node assigning cost ‘s’ ω’ (e, t, m, s) = t’ (e, t, m, s) – t + d (e, t’) where, c (e, t)  capacity of edge ‘e’ at time ‘t’ Q (e, t, s)  queue size at source of edge ‘e’, at time ‘t’ as predicted by node ‘s’ t’  earliest time queued data at ‘e’ and message can be injected into the edge Integral  volume of data through ‘e’ in interval [t, t’’] d (e, t’)  propagation delay seen by message

Partial Knowledge Routing II [7] Algorithms with Time-varying Costs Earliest Delivery (ED) Contacts Oracle Q (e, t, s) = 0 Source routed Buffer overflow  cascaded delay Earliest Delivery with Local Queuing (EDLQ) Q (e, t, s) = data queued for ‘e’ at ‘t’ if e = (s , *) = 0 otherwise Per-hop routed  path vector to avoid loops Earliest Delivery with All Queues (EDAQ) Contacts + Queuing Oracles Q (e, t, s) = data queued for ‘e’ at ‘t’ at node s Reservation of edge capacity along computed path