a disaster strikes. Recovery of communication infrastructure under natural disaster situations is often more complicated and prolonged due to extensive damage ...
An Approach for Short Message Resilience in Disaster-Stricken Areas Md. Nurul Huda, Farzana Yasmeen, Shigeki Yamada, and Noboru Sonehara National Institute of Informatics Tokyo, Japan Email: {huda, bonhomie, shigeki, sonehara}@nii.ac.jp Abstract—Large scale disasters may destroy essential network infrastructures and cause network-based service failure over a large area. Communication between two nodes in a resilient network should continue without any support of network infrastructure. Delay and Disruption Tolerant Network (DTN) is the choice for network resilience because it is not dependent on any infrastructure and supports flexibility in link delay and availability. Network resilience must consider the power limitations of the nodes and unusually very high traffic demand after a disaster hits. Existing DTN routing protocols’ performances are not satisfactory in terms of the two most important metrics of communication in a disaster-stricken area message delivery ratio and residual energy of the communicating devices. We propose a new message routing mechanism, called Location-aware Message Delivery (LMD), for communicating among wireless devices that uses greedy forwarding towards the intended destinations. Simulation results show the effectiveness of our proposed approach in the delivery ratio and energy saving of the devices. Overall efficiency of our scheme is superior to those of compared DTN routing protocols. Index Terms—Communication, DTN, routing protocol, residual energy.
I. I NTRODUCTION Network infrastructure is essential for reliable, convenient and quick communication among participating hosts. However, at any time these essential infrastructures may be rendered unavailable due to destruction by large scale disasters (hurricanes, earthquakes, tsunamis, floods), widespread power outages, or system failure due to sudden large overload after a disaster strikes. Recovery of communication infrastructure under natural disaster situations is often more complicated and prolonged due to extensive damage and lack of pre-conceived experience of the situation. The ability to provide and maintain an acceptable level of service in the face of various faults and challenges is termed as resilience [1]. Network resilience is gaining importance as a core concept to deal with various threats and attacks. When network infrastructure is not available in an area, communication must take place using infrastructureless decentralized mobile wireless networks such as wireless ad hoc networks, vehicular networks or delay and disruption tolerant networks (DTNs). Mobile ad hoc network (MANET) research has often assumed that a connected path exists between a sender and a receiver node at any point in time [2], [3]. This assumption might be unrealistic in sparse mobile wireless networks. In a disaster situation such as earthquake and tsunami, physi-
978-1-4673-0250-0/12/$31.00 ©2012 IEEE
cal transportation routes for vehicles might get destroyed or blocked. Therefore, network resilience cannot depend only on vehicular nodes and vehicular routing protocols [4] where node movement is essential for message forwarding. Delay and disruption tolerant network [5] has the ability to tolerate disruptions in connectivity among its components and can route messages even if connectivity among participating hosts is intermittent and an end-to-end path never exists. It can operate in environments where infrastructures are unavailable and cannot be installed (e.g., emergency operations, military grounds, protected environments). A subset of participating hosts perform routing, hop-by-hop, storing messages before successive forwarding opportunities become available. In order to support long delay, disruptions in connectivity and limited buffer space and energy, traditional routing algorithms for the Internet do not perform well in DTNs. Some solutions for DTN have been proposed starting from the very basic epidemic routing protocol [6]. DTN routing can be categorized as replication-based [6], [10]- [13] and knowledge-based [8]- [10]. Different replication-based protocols vary in the number of copies of a message and how they choose to make those copies. Different knowledge-based protocols vary in what information they use to make routing decisions, and also how they obtain the required information. Replication-based routing protocols use a large number of transmissions for each message and thus consume excessive energy. On the other hand, most of the knowledge-based protocols’ assumptions become invalid or the specific conditions needed for those protocols to perform well no longer hold after a large scale disaster strikes which changes social behavior. Existing routing protocols for DTNs do not perform well for disaster-stricken areas as the movement model, energy model and network load situation in a disaster-stricken area were not considered in designing those protocols. In this paper, we propose Location-aware Message Delivery (LMD) to provide short message communication services among family members, relatives, friends and coworkers in a disaster-stricken area. Our analysis deeply considers the environments and the two most important design goals for disaster-stricken scenarios- power saving of the limited battery-powered nodes and message delivery ratio. Simulation results show that LMD performs the best as compared to existing well-known routing protocols for DTNs.
120
ICOIN 2012
II. R ELATED W ORKS Mobile ad hoc networks (MANETs) [3] can be deployed with no pre-existing communication infrastructure. However, MANET protocols require establishment of end-to-end paths between source destination pairs a-prior to packet forwarding, which might not be possible in sparse networks. In DTNs, on the other hand, routing is hop-by-hop and does not require establishment of end-to-end paths. DTN protocols use storecarry-forward routing which utilizes opportunistic contacts among mobile hosts. A class of DTN routing protocols [6], [10]- [13] flood the network with multiple copies of the messages in order to improve network performances. In Epidemic [6] routing, each node maintains a summary vector, with a list of all pending messages. Whenever nodes encounter each other, they exchange summary vector information and thus come to know of which messages the other does not have, and in turn exchange such ’unseen’ messages. It is possible that a node may eventually carry a copy of all the messages generated in the network. Epidemic routing tends to find optimal paths despite disconnects and returns lower average end-to-end delays in ideal conditions. However, it is not a suitable protocol for disaster-stricken areas because of its high power drainage due to forwarding large number of messages when handling large traffic demand in disaster-stricken areas. PRoPHET [10] routing protocol reduces the overhead of flooding by forwarding copies probabilistically with limited information of node encounter patterns. Each node holds a delivery predictability table with the probabilities of successful delivery towards each known node in the network. The probability to deliver a message to a certain destination node increases upon encounters, and decreases over time in case no meeting occurs. PRoPHET can achieve a better performance in power saving over epidemic only if the nodes encounter patterns do not change sharply. It does not perform well under suddenly changed social communication patterns (such as disaster-stricken area) because the probability of meeting two specific nodes (calculated based on their history) does not apply anymore in a suddenly changed environment. To perform significantly fewer transmissions than epidemic and other flooding-based routing schemes, Spray and Wait [11] routing was proposed, consisting of two phases: (i) spray and (ii) wait. In the spraying phase, the source of a message initially starts with L copies. Any node a that has L > 1 message copies (source or relay), and encounters another node b (with no copies), hands over ⌈L/2⌉ copies to b and keeps ⌊L/2⌋ copies for itself. When a ’spraying’ node is left with only one copy, it switches to the wait phase and performs direct transmission to the intended destination when met. The performance of spray and wait depends largely on the number of initial copies, L, at the source. A large value of L consumes more energy due to multi-copy transmission but improves delivery performance. When L becomes equal to the number of nodes (N ) in the network, the routing protocol behaves like the epidemic protocol. However, when L 0. Hence, the transfer is done.
Most people spend a large portion of a day at one or two locations like their home, office, school etc. American Time Use Survey [15] shows that, people’s stay location is either unimodal or bimodal- most people spend most part of a day at home; or at home and the workplace or school. In a disaster situation, people usually send messages to their family members, relatives, friends and co-workers. Therefore, the devices need to exchange the information of only one or two locations (including stay time) where they spend the longest part of the days. Those two locations are the most probable locations of a user during the respective time. The exchange of statistical location information and respective time of the day takes place automatically among authorized personnel like family members, friends etc. when they meet each other. Thus, one can find the probable location information of his friend/family member by looking into the respective time of the day. In our simulation it is assumed that the location of the destination node is known to the source node. B. Routing protocol The Location-aware Message Delivery (LMD) system uses a single copy of the message and takes a greedy strategy, where the forwarding node makes locally optimal decisions to select next-hop neighbors based on the reduction of the current physical distance of the message towards the destination. Selecting the next hop by the minimum distance gives an inherently loop-free forwarding rule. The forwarding benefit to a neighboring node is defined by the amount of reduction of distance of the message from the destination caused by the forwarding. A node chooses the neighbor within its transmission range that has the most forwarding
benefit. For a message m carried by node a and destined for the node D, the transfer benefit to a neighboring node b is expressed by βm (a, b) = dist(a, D) − dist(b, D), where dist(a, D) is the Euclidian distance between node a and D. No forwarding is done when the forwarding benefit is negative. Finally, when the destination node of a message is found within the transmission range of the message-carrying node, the message is delivered to the destination irrespective of its other neighboring nodes and no further forwarding is done. Fig. 1 depicts a scenario where the node S is carrying a message m destined for node D. One candidate neighbor n1 comes within the transmission range of S. S requests the location information of the candidate neighbor n1 . With the location information of n1 , it calculates the distances of D from the two nodes S and n1 as d0 = dist(S, D) and d1 = dist(n1 , D) respectively. In this particular scenario, let d0 < d1 . Thus, the forwarding benefit from S to n1 is βm (S, n1 ) = (d0 − d1 ) < 0. Since the forwarding benefit is negative, S does not forward the message to n1 . In the scenario in Fig. 2 the distances of the node n2 from the node D is d2 = dist(n2 , D) and that of S from D is d0 = dist(S, D). In this scenario, let d2 < d0 . Hence, the forwarding benefit of the message (destined for D) from S to n2 , βm (S, n2 ) = (d0 −d2 ) is positive. Thus, node S forwards the message to n2 . When more than one candidate next-hops are within the transmission range of S (Fig. 3), it needs to choose one of them. With the location information of the neighbors, it calculates the distances of di from each of the candidate neighbors. In Fig. 3, n2 and n3 are the neighbors of S and d0 = dist(S, D), d2 = dist(n2 , D) and d3 = dist(n3 , D). In this particular scenario, let d0 > d3 > d2 . Thus, the forwarding benefit to n2 is βm (S, n2 ) = (d0 − d2 ) > 0 and to n3 is
122
Fig. 3. Forwarding scenario: Forwarding benefits from node S to nodes n2 and n3 for a message m destined for node D are βm (S, n2 ) and βm (S, n3 ) respectively. As, βm (S, n2 ) > βm (S, n3 ) the message is forwarded to node n2
βm (S, n3 ) = (d0 − d3 ) > 0. However, as forwarding to n2 is more beneficial, S forwards the message to n2 . IV. E VALUATION C RITERIA In order to compare routing protocols, we use the following four metrics for evaluating their performance. Delivery Ratio is defined as the fraction of generated messages that are successfully delivered to the final destination within a given time period. Latency is the time between when a message is generated and when it is received. Many applications also have some time window where the data is useful. Therefore, latency might be important for some DTN applications Residual Energy represents the remaining battery energy of a mobile node. Some routing strategies transmit/receive more messages than others because they use multiple copies of each message or make different decisions about the next hop. Since saving energy is one of the most important evaluation criteria for battery operated devices, we use this metric for routing protocols evaluation. Efficiency is the combined metric of residual energy and delivery ratio and calculated by (residual energy × delivery ratio). The two metrics can be combined together as they can be scaled to the same (0-1) scale. Maximum efficiency is 1 when both the residual energy and delivery ratio are 1. Though, theoretically achieving efficiency equal to 1 is impossible since message transmission reduces residual energy. When either of these two metrics reaches 0, efficiency becomes 0.
Node characteristics:- All nodes shared an equal buffers size of 1000MB. Wireless transmission range was taken as 100m and the transmission speed was 250Kbps. Random waypoint movement model was used with a node movement speed from 0.5 to 6 m/s. Initial energy was 2000 Joules. Power consumption :- Power consumption for a message transmission was 1.4 watts and that for a its reception was 0.9 watts while idle time (neither transmitting nor receiving) power consumption rate was 0.01Watts/sec. B. Analysis of LMD In this section we analyze LMD’s performance with respect to delivery ratio, residual energy, and delivery delay and observe the effects for varying input parameters. The graph in Fig. 4 shows the changing trends of message delivery ratio and residual energy of the nodes for varying node density (δ). We changed node density by changing the number of nodes in the same simulation area. From the graph, we see that in low node density conditions (δ < 50) a smaller increase in node density increases both the delivery ratio and residual energy more sharply than those in higher density conditions (δ > 50). Thus, it can be said that LMD performs well beyond certain node density conditions.
Fig. 4. Effects of node density on message delivery ratio and residual energy of the nodes.
V. E XPERIMENTAL R ESULTS We used the opportunistic network (ONE) simulator [18] to obtain performance results of four routing algorithms: Epidemic [6], Direct Delivery [18], Spray and Wait [11] and our LMD. A. Simulation setup The simulation was run for 20K seconds with all nodes having similar characteristics. Average values of five runs were taken and plotted on the graphs. Network characteristics:- Messages were generated at intervals between 25 and 35 second during 1000 seconds to 15000 seconds of the simulation time. Message size was taken as 250KB. Number of hosts in the network was 50 to 600 with 50 as the step size. Message sources and destinations were chosen randomly from all of the participating nodes.
Fig. 5. Effects of node mobility on message delivery ratio and residual energy of the nodes.
The effects on delivery ratio and residual energy are insignificant for changing node mobility (Fig. 5). Residual energy of a node does not change much for the nodes’
123
Fig. 6. Effects of the transmission range of mobile nodes on message delivery ratio and residual energy of the nodes.
Fig. 8. Effects of node density, node mobility, transmission range and network load on message delivery latency.
Fig. 7. Effects of network load on message delivery ratio and residual energy of the nodes.
Fig. 9. Comparison of message delivery ratios among the four routing protocols for varying node density.
movement speeds used in our simulation. This is because the change in mobility does not affect the number of transmissions needed for delivering the messages. However, at very small movement speeds, the delivery ratio decreases because of less number of contact opportunities with neighbors. Similarly, nodes transmission range has insignificant effects on residual energy (Fig. 6). However, for lower transmission ranges, nodes find less number of neighbors and thus delivery ratio decreases significantly. Fig. 7 shows the effects of network load on delivery ratio and residual energy. The effect on delivery ratio was found to be insignificant. However, in high loads (i.e., low message interval) the residual energies of the nodes decrease sharply. More messages are created and a larger number of transmissions are needed in the network via intermediate nodes resulting in low residual energy. Fig. 8 shows the message delivery latencies for the messages that were delivered. This graph is represented to show interesting relative change in delivery ratios for different node density, mobility, transmission range and network load. The X axis does not show absolute values of the input parameters (node density, mobility, transmission range and network load) because different ranges of values were used for the four input parameters. However, the leftmost side of the X axis has the lowest respective value and the rightmost side has the highest value. From the graph, it can be seen that the transmission range has the most adverse effects (maximum slope) on the
Fig. 10. Comparison of residual energies among the four routing protocols for varying node density.
delay followed by node density and mobility. Network load has the least effects on delay. C. Comparative Performances Fig. 9 through 12 plot comparative performances of the four routing protocols: Epidemic [6], Direct Delivery [18], Spray and Wait [11] and our LMD and ”Epi”, ”DD”, ”snw” and ”LMD” are used as their legends respectively. Fig. 9 shows the delivery ratios for varying node densities. We see that epidemic and LMD perform the best in delivery followed by spray and wait, and direct delivery performs the worst. For low node density, both LMD’s and epidemic’s performances decrease. Fig. 10 compares the average residual energy of the nodes for varying node density. Direct delivery has the maximum
124
Fig. 11. Comparison of message delivery latencies among the four routing protocols for varying node density.
We have presented a message resilience approach by using a routing protocol, called Location-aware Message Delivery (LMD) for exchanging short messages in disaster-stricken areas among families and fiends. It can deliver messages to destinations without any network infrastructure and even if no end-to-end path between the source and the destination exists. Comparative performance studies shows that out protocol is very efficient in saving battery energy while the delivery ratio is very close to that of the flooding based epidemic protocol. LMD performs very well beyond certain node density levels and can be recommended over energy-consuming multi-copy strategies for communication in disaster-stricken areas. R EFERENCES
Fig. 12. Comparison of efficiencies among the four routing protocols for varying node density.
residual energy because it uses single hop delivery. Locationaware message delivery and spray and wait have similar residual energy values. Epidemic’s performance in saving energy is very poor because it generates a large number of message copies and floods them. Fig. 11 shows the latencies for the four protocols. Epidemic delivers the quickest followed by LMD. For low node densities, their performance decreases and reaches to a similar level to spray and wait. Spray and wait’s performance is mid-level in latency and direct delivery performs the worst. The graph in Fig. 12 compares the efficiencies of the four protocols. From the graph, it can be seen that the efficiency of Epidemic is the worst and that is mainly because of its high energy consumption for flooding. Our LMD was found to be the best in efficiency followed by spray and wait. Direct delivery lies in between spray and wait and epidemic. VI. C ONCLUSION Network resilience has drawn considerable attention after several unexpectedly large scale disasters (like the great east Japan earthquake 2011) have occured in several countries. Conventional network services fail when the damage is large and traffic demand is high. Though existing technologies cannot support full length network resilience, providing minimum resilience like short message resilience is very important.
[1] J. P. G. Sterbenz, D. Hutchison, E. K. Cetinkaya, A. Jabbar, J. P. Rohrer, M. Scholler, and P. Smith, “Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines, Computer Networks,” Vol. 5, pp.1245-1265, 2010. [2] C. E. Perkins, Ad Hoc Networking. Addison-Wesley, 2001. [3] E. M. Royer and C. K. Toh, “A Review of Current Routing Protocols for Ad-hoc Mobile Wireless Networks,” in IEEE Personal Communications, pp.46-55, 1999. [4] R. A. Santos, A. Edwards, R. Edwards and L. Seed, “Performance evaluation of routing protocols in vehicular ad hoc networks,” Int. Journal of Ad Hoc and Ubiquitous Computing, Vol.1, no.1, pp.80-91, 2005. [5] K. Fall, “A delay-tolerant network architecture for challenged internets,” in Proc. of SIGCOMM’03, August 2003. [6] A. Vahdat and D. Becker, “Epidemic Routing for Partially Connected Ad Hoc Networks,” Department of Computer Science, Duke University, Tech. Rep. CS-2000-06, 2000. [7] S. Jain, K. Fall and R. Patra, “Routing in a Delay Tolerant Networking,” in Proc. of ACM SIGCOMM, August/September 2004. [8] V. Zhao, M. Ammar, and E. Zegura, “A message ferrying approach for data delivery in sparse mobile ad hoc networks,” in Proc. of MobiHoc’04, May 2004. [9] M. P. N. Sarafijanovic-Djukic and M. Grossglauser, “Island Hopping: Efficient Mobility Assisted Forwarding in Partitioned Networks,” in Proc. of IEEE SECON’06, September 2006. [10] A. Lindgren, A. Doria, and O. Schelen, “Probabilistic routing in intermittently connected networks,” Mobile Computing and Communications Review, vol. 7, no. 3, July 2003. [11] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Spray and wait: an efficient routing scheme for intermittently connected mobile networks,” in Proc. of WDTN’05. New York, NY, USA: ACM Press, 2005, pp.252-259, 2005. [12] E. Kranakis, H. Singh, and J. Urrutia, “Compass routing on geometric networks,” in Proc. of the 11th Canadian Conf. on Computational Geometry, CCCG, 1999. [13] B. Burns, O. Brock, B. N. Levine, “MV routing and capacity building in disruption tolerant networks,” in Proc. of IEEE INFOCOM 2005. [14] M. M. Carvalho, C. B. Margi, K. Obraczka, J. J. Garcia-Luna-Aceves, “Modeling energy consumption in single-hop IEEE 802.11 ad hoc networks,” in Proc. of the 13th Int. Conf. on Computer Communications and Networks, pp.367-372, 2004. [15] http://www.bls.gov/tus/ (last accessed September 30th, 2011). [16] Y. Ye, Y. Zheng, Y. Chen, J. Feng, and X. Xie, “Mining individual life pattern based on location history,” in Proc. of the 10th Int. Conf. on Mobile Data Management: Systems, Services and Middleware MDM, 2009. [17] R. Montoliu and D. Gatica-Perez, “Discovering Human Places of Interest from Multimodal Mobile Phone Data,” in Proc. of the 9th Int. Conf. on Mobile and Ubiquitous Multimedia, MUM2010. December 1-3, 2010. [18] A. Keranen, J. Ott, and T. Karkkainen, “The ONE Simulator for DTN Protocol Evaluation,” in Proc. of the 2nd Int. Conf. on Simulation Tools and Techniques, SIMUTools, Rome, Mar. 2009.
125
