Scalable Routing in Delay Tolerant Mobile Networks Hao Wen 1 Jia Liu, Chuang Lin, Fengyuan Ren, Chuanpin Fu 1 Department of Computer Science, Tsinghua University

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 2/51 Outline Background Related work Region-based mobility pattern Protocol design Evaluation Conclusion

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 3/51 Background As a kind of challenged networks, Intermittently Connected Mobile Network (ICMN) is a Delay- Tolerant Mobile Network (DTMN) that is made up of mobile nodes Intermittent Connectivity Variable Delay Key metrics in DTMN: Packet Arrival Rate Delay Scalability (not considered by most previous work)

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 4/51 Typical cases of DTMN Princeton ZebraNet: Track and monitor Zebra in Africa UMass DieselNet buses: VANET … Background

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 5/51 Big challenges for routing protocols in DTMN Due to frequent network disruption, it is difficult to establish reliable end-to-end paths between mobile peers. Carry-and-forward is being widely used. However, it will bring much overhead compared with traditional routing. The constraints brought by mobility and resources make the routing problem much more challenging, especially for resource-constrained devices such as sensor nodes or Bluetooth devices. Background

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 6/51 Background In this paper, we take advantage of the spatial property and propose two scalable protocols based on regional movement, Since the long-term spatial property is relatively stable over time, our protocols avoid complicated computation for delivery probabilities and excessive storage for tracking encounter history.

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 7/51 Outline Background Related work Protocol design Evaluation Conclusion

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 8/51 Flooding routing is extremely wasteful of limited resources, such as wireless bandwidth and storage space. Distributing a bounded number of copies to reduce the overhead [Spray and Wait, WDTN 05] Replicating packets with a small probability [Wireless Network 2002] They do not make use of gaining knowledge about network conditions, so their performance is not satisfying under more realistic conditions. Related Work-Flooding Type

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 9/51 Based on encounter history, they estimate delivery probabilities between nodes. RAPID [Sigcomm 07] Translates the routing metric into per-packet utilities Calculate utility according to average meeting time Every node records meeting history of all nodes This type exploits past knowledge of encounters but faces a challenge of choosing the right time scale : Short scale: distance effect, the long-run trends are difficult to capture from a short-scale temporal way. Long scale: consuming much storage and computation Related Work- Utility Type

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 10/51 There are many recent works on WLAN measurements which reveal the important spatial properties of the real-world users. They imply that not only temporal but also spatial info can be taken into account during the design of practical routing protocols. Related Work

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 11/51 Outline Background Related work Protocol design Evaluation Conclusion

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 12/51 Our solution is motivated from a simple observation: location- preference and re-appearance are usually observed as typical features not only from human beings but also from other species: In a short time, node may move in somewhat random way or paused at some location In a long-time scale, node change position among areas that related to their lifestyle. From a social context, visited locations and people's encounters both have a strong connection with the affiliation and lifestyle. Different from encounter-based protocols, space- based pattern offers us a different view. Protocol design

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 13/51 The main idea of our space-based protocols is to find the popular regions for destination node and distribute copies of packets inside those popular regions. The concept of space is defined as a kind of logic converge We simply adopt a square coverage as a unit space and assume nodes are equipped with any localization method. Protocol design

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 14/51 Step 1: calculate distribution probability matrix R Step 2: choose candidate destination regions based on two methods Step 3: the source node will send one copy to every chosen region with the help of relay nodes Step 4: after the relay node reaches the destination regions, it will trigger a spray-and-search distribution Protocol design

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 15/51 Every unit time, every node will track the current region and record into space transition matrix B. Then we can get distribution probability matrix R and transition probability matrix P Protocol design-Step 1

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 16/51 Protocol design-Step 2 In terms of choosing candidate destination regions, we propose two space-based protocols according to probability or distance, respectively. SpacE-Probability (SEP) optimized protocol regions are simply chosen according to the distribution probability r j (y) of node y: Given the probability threshold P, L regions are chosen in decreasing order of r j (y)

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 17/51 Protocol design-Step 2 SpacE-Distance (SED) optimized protocol The source node x makes decision based on the Euclidean distance d kj from the current region k to the destination region j. The problem is formulated to To reduce the complexity of this 0/1 Knapsack Problem we use a greedy algorithm: choose L regions in decreasing order of distance value per unit of probability weight, i.e.,

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 18/51 To forward copies to L chosen regions, relay nodes are chosen according to the distribution probability in SEP or the expected distance in SED. SpacE-Probability (SEP) optimized protocol When two nodes encounter in the region v (v!= j), the copy will be forwarded to the node that has a bigger r j. SpacE-Distance (SED) optimized protocol Nodes will exchange transition probability p vi and calculate expected distance from v to j. The copy will be forwarded to the node that has a smaller Dvj. Protocol design-Step 3

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 19/51 When relay node Z arrives at destination region j, choose w nodes to take copies using Spray and Search Spray phase: Z distribute data using binary forward to w nodes ( jump-start spreading in a quick manner ) Search phase: the copy will be forwarded to a better relay according to the policy in step 3. Protocol design-Step 4 Z Z Z

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 20/51 Outline Background Related work Region-based mobility pattern Protocol design Evaluation Conclusion

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 21/51 Simulator: Opportunistic Network Environment (ONE) [Jorg Ott, 2008] Epidemic: a greedy strategy Spray and Wait: a passive strategy Prophet: based on the encounter frequency Maxprop: based on the last encounter time The numbers of copies of SEP/SED and SNW are both restricted to 10% of all n nodes. The overhead is defined as Evaluation-Experiment setups

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 22/51 Bus Network Model [Jorg Ott, Mobility Model 2008] Based on the city area of Helsinki with × m 2. Total n buses are evenly distributed in 8 bus routes The buses move at 7-10m/s with a 10-30s waiting time at each bus stop. RENA generates 16 regions with equal unit size. Evaluation-Experiment setups

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 23/51 Epidemic and Prophet increase firstly and then slowly decrease to about 60% MaxProp achieves the best performance when n < 72 and then deteriorate rapidly due to the fast increase of computation and control packets SEP, SED and SNW gradually increase to stable values Evaluation-Bus Network

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 24/51 Due to the estimation of delivery probability based on the encounter frequency, Prophet spent less overhead than Epidemic. SNW consumes the least overhead as a result of passive waiting. By calculating global shortest path based on the last encounter time, MaxProp only behaves well when the number is small. Using limited computation and storage, our proposed protocols achieves not only better scalability but also high performance. Evaluation-Bus Network

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 25/51 Although our protocols are proposed based on the spatial property, they could achieve feasibility in most scenarios. In the extreme case without any location-preference property, such as random mobility models, our protocols could naturally transfer to SNW: when there are no candidate destination regions, they simply chooses its current region as destination and starts binary spray. So the delivery performance of SNW is the lower bound. Evaluation-Performance Bound

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 26/51 In this paper, we take advantage of the macro-level spatial information and propose two space-based probabilistic forwarding protocols. Different from temporal-based protocols using encounter history to calculate the delivery probability, our protocols make good use of regional movement pattern hidden in spatial property and need not expend much computation for calculation. Compared with several typical protocols in a bus network, the scalability of our protocols is well verified. Conclusion

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 27/51 Thank you for your attention. Q&A

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 28/51 Backup Parameters Connectivity: interpersonal communication (10m range, 2 Mbps) using Bluetooth devices. Every t interval time, one mobile user generates one packet of 1 KB to a random destination.

QoS Lab in Department of Computer Science & Technology, Tsinghua University Page 29/51 Backup-Region Size Different choices depend on the specific granularity of the movement in different applications. In addition, we should also consider the storage occupied by control packets. (the average storage per region consumed by control overhead is about 650 Bytes). In this paper, our empirical analysis suggests that it is better to keep control packets under 20% of the whole storage.