Post Disaster Management using Delay Tolerant Network

Post Disaster Management using Delay Tolerant Network Sujoy Saha1 , Sushovan2 , Anirudh Sheldekar2, Rijo Joseph C1, Amartya Mukherjee 2,Subrata Nandi2 1

2

Department Of Computer Application , Department of Computer Science and Engg National Institute of Technology, Durgapur, Durgapur-713209, India {sujoy.ju,bubususpatra,anisheld, mail2rjc,mamartyacse1,subrata.nandi}@gmail.com

Abstract — Delay-tolerant Networking (DTN) is an attempt to extend the reach of traditional networking methods where nodes are intermittently connected and an end-to-end path from source to destination does not exist all the time. Real networks like military, various sensors, post disaster management, deep space communication, Vehicular ad-hoc (VANETs) networks, are some examples of DTN. Our work mainly concentrates on the applicability of different flooding based routing scheme of DTN in post disaster scenarios. Cluster mobility model which maps human mobility more realistically rather than any other mobility in the context of disaster scenario has been considered. Further we have customized cluster mobility model according to the disaster like scenario and performed the simulation for delivery probability with respect to various constraints like buffer-size, transmission range, speed and density of nodes in ONE SIMULATOR. We also analyze the effect heterogeneous nodes in delivery probability. Keywords: Disaster Management, Cluster Mobility Model, Heterogeneous Network, Delivery Probability, Overhead Ratio, Average Latency.

1 Introduction In disaster affected areas the existing communication infrastructures like WLL, GSM or PSTN may get disrupted or destroyed. Thus, there exists a strong need for rapid deployment of communication networks that would provide much needed connectivity and communication capabilities for rescue-workers and survivors of a disaster affected zone to restore normalcy through properly co-ordinate resource management. For managing a post disaster situation, the prime requirement is to establish communication among disaster management groups or agencies. There will be different teams working together for managing the distribution of necessary commodities for the affected population in disaster-affected regions [1][2]. Information must be relayed and understood in the shortest amount of time possible in order to co-ordinate and carry out the required activities. Disaster response network is one kind of delay tolerant network [3]. In a disaster scenario like a fire burst or a natural calamity, the communication between the nodes no longer remains organized in the original infra-structural setting. The original assembly of nodes changes with the nodes moving in groups of people helping for the cause. In these kinds of networks traditional Ad-Hoc routing protocols fail to transfer messages from source to destination. A delay tolerant network differs from Ad-Hoc network due to the simple fact that message would be transferred to the destination node even if the source has no end-to-end connectivity with the destination at the time when that message is sent. So delay tolerant routing strategies are employed for efficient packet delivery among the nodes of such networks. A disaster environment could be modeled as a specialized mobility model, since disaster management always takes place amongst groups of people. Consider some disaster scenarios like Cyclone in some specific area, earth-quake, burst of fire etc. Let the place of the accident be termed as Activity Point. Now there would be various groups of people moving around the activity point like Medical Staff, Police, people, etc. Thus, a groupbased movement model would be a good choice for such a scenario where the mobile nodes exists in groups and communication takes place within the group as well as between the groups. Now, in such scenarios, the movement of node groups will be restrained to fixed local sites like Hospital, Police station, Activity point, etc. Thus, we can consider the scenario as one with different clusters of nodes that restrain to particular sites. Vehicles that move across these sites like police jeeps, ambulances and other relief vehicles can be carrier nodes between the clusters. Thus, the movement of nodes for such a scenario could be modeled using the basic idea of Cluster Mobility Model [4]. In the next section of this paper, we summarize some other mobility models such as Random Waypoint, Random Walk, Shortest Path Map Based and Working Day movement models in order to justify our choice of Cluster Mobility Model as the movement model for the scenario. In Section III we summarize about the different routing strategies that exists for DTN. The most challenging issue in the post disaster environment is the rate of transmission of critical information.

To enhance the packet delivery ratio we require intelligent DTN routing strategies. In Section IV we have described and analyzed the simulation results of delivery probability that has been carried out for various routing algorithms on cluster mobility model for post disaster scenario with respect to buffer-size, transmission range, speed and density of nodes in the network. The effect of the heterogeneous nodes in delivery ratio in the context of DTN is also explored. The constraints are so chosen as to derive an optimal configuration for the nodes to be deployed for communication in post-disaster scenarios.

2. MOBILITY MODEL OVERVIEW Mobility model helps to emulate closely the real life scenario of mobile nodes. All mobility models are based on some basic parameters like starting location, ending point, velocity of mobile node, movement direction. Works have been carried out on mobility models seeking to increase their realism in simulations by gathering information on existing scenarios to provide insights of node mobility and how they affect the performance of routing algorithms. Significance: In scheduled or predictable contacts it is possible to predict the future in terms of the time for which contacts will be available and how long they will last. However, in disaster recovery networks, it is almost impossible to predict the future location of the nodes. Communication is to be enabled in such networks using those unpredictable contacts between the nodes which are also known as intermittent or opportunistic contacts. It is extremely important in DTN to understand the mobility of the relay nodes that carry messages from source to destination [4]. Mobility models establish relationship among individuals and help us to study their movements in real life. It is extremely important in DTN to understand the mobility of the relay nodes that carry messages from source to destination [4]. Even if few nodes in the network are mobile and others are static, then they might block the flow of data from source to destination. If majority of the nodes in the network are mobile, then the routing protocols will have more opportunities to deliver the message to the destination by exploring the mobility of the relay nodes. An example of this type of network is a vehicular network where the cars, trucks are all mobile nodes. Since real life experiments are not feasible, we resort to simulation experiments which give us real-like results. Mobility models establish relationship among individuals and help us to study their movements in real life. Mobility models can be broadly classified into Entity-Based mobility model and Group-based mobility models [10]. In the former model, the nodes move individually and their movement is not influenced by the other nodes whereas the in the latter the movement of nodes is influenced by that of the member nodes. Entity Based models generate results that are more non-human like. On the other hand, group mobility model provide results which are more real, as human mobility occurs mainly in groups. Random Waypoint [5][8 ] model is a very common Entity-Based mobility model in which each mobile node randomly selects one point as its destination and travels towards this destination with constant velocity chosen uniformly and randomly from [0, Vmax ]. Upon reaching the destination, the node stops for a duration defined by the ‘pause time’ parameter Tpause. After this duration, it again chooses another random destination and moves towards it. Random Walk [6] [8] is another Entity-Based movement model and can be considered as a type of Random Waypoint model with zero pause time. In Random Walk model, nodes change their speed and direction after a time interval. Each and every node randomly and uniformly chooses its new direction θ(t) from [0, 2π ] for every new interval t. Similarly, a new speed, v(t), is chosen from [0, Vmax] uniformly. Thus, during any time interval t, a node moves with the velocity vector (v(t).cosθ (t), v(t).sin θ (t)). If a node moves and touches the boundary of the simulation area, it gets bounced back to the simulation area with an angle of θ(t) or π − θ(t). Shortest Path Map Based [8] mobility model is a map based movement model that uses algorithms like Dijkstra's algorithm to find shortest path between two random map points. This model is also an Entity-Based movement model. Working day mobility [9] model is a Group-based movement model [10]. This model basically is the technical resultant of different sub-models of node mobility during the whole day. This model involves activities that are the most common and capture most of a working day for the majority of people. However, the activities of nodes differ from each other. These sub-models repeat every day, resulting in periodic repetitive movement. Cluster Mobility Model: As the name suggests this mobility model classifies the whole network in number of clusters. Depending upon the applicability and mobility, literature of cluster mobility model categorizes the nodes in two different ways. The nodes responsible for carrying data from one cluster to another or maintaining inter cluster relationship are known to be Carrier nodes. Other than Carrier nodes all the other nodes present inside the cluster are treated as internal nodes. Movement of the internal node is defined around a particular point within the cluster which is known as Cluster Center and move around this cluster center. Cluster mobility model falls under the umbrella of Group based mobility model which unlike Random mobility model try to establish a social relationship between nodes within the network based on their activities to define the cluster first.

Fig.1 – Snapshot of Cluster Mobility Model from ONE simulator Due to social status, relationship, profession, and friendship human does have a tendency to move in group. Secondly this mobility model certainly makes sense in disaster and defense activities. From the theoretical point of view cluster mobility model certainly outperforms other mobility models in the context of mapping the human mobility in disaster scenario where human moves in a group. That actually motivates our work to simulate routing strategies cluster mobility model and explore the future directions. A post-disaster scenario can be easily modeled in cluster mobility model. Groups of people could be considered as clusters and the node movements could be modeled as movement of these people within and across the clusters. For example, consider a point in a city where a disaster strikes. The fire-station that involves in the post-disaster management can be mapped as a cluster and the firemen with communicating devices could be matched to the nodes of that cluster. A hospital could be considered as another cluster with doctors, nurses and other supporting staff matched as nodes of that cluster. A police station could be another cluster of nodes with policemen matched to nodes. The point in the city where the disaster has struck or hospital would soon become a cluster of nodes with rescuers and relief-teams including firemen, policemen, doctors, nurses and others who would rush towards the spot for post-disaster activities, thus making those as Activity Points.

Fig.2 – Activity points as clusters in a sample city-like scenario The nodes involved in these rescue activities will start moving within the clusters as well as across them. It can be noted that at any point of time, majority of the nodes will be moving within some cluster with lower speeds and only a few nodes will be moving across the clusters and that too with higher speeds. Such a scenario basically resembles the Cluster Mobility Model rather than any other traditional mobility models.

3.ROUTING PROTOCOL OVERVIEW In DTN literature, routing protocols are broadly categorized as Forwarding based or Flooding based depending upon whether or not the protocol creates message-replicas (copies of the same message) or not. Routing Protocols that use only a single copy of the message are called as Forwarding Based routing protocols. On the

other hand routing protocols that do create more than one copy of the message are called as Flooding Based [10] protocols. Further, Flooding based routing algorithms [13] can be classified as Direct contact, Tree-based flooding, Exchange based flooding and Utility based flooding. Owing to the dynamicity of DTN one has to choose the suitable routing algorithm for message delivery. With the help of simulations, we attempt to study, analyze and discuss the performance of different routing schemes in cluster mobility model which maps human mobility in the best possible way in a post disaster perspective. Here, we will be considering only the flooding based routing protocols and we are only bothering about successful timely delivery of the message rather than concentrating on the overheads incurred. Flooding Families [16]: Routing protocols that belong to these families make use of replication technique. In our work we are taking the flooding algorithms like Epidemic Routing, PRoPHET, Spray & Wait, Spray & Focus and MaxProp. Epidemic routing [11], guarantees that through sufficient number of exchanges, all nodes will eventually receive the message. The nodes maintain a Summary Vector that will keep track of the messages they generate or receive during message delivery using unique message IDs. When two nodes meet they exchange their summary vectors and request the exchange of the messages they do not have. Extreme flooding in this routing technique leads to heavier resource consumption [2][11]. In PRoPHET [12] when two nodes meet, they exchange Summary Vectors which also contain the delivery predictability information stored at the nodes. Nodes make use of this information to update their internal delivery predictability vector. The information is also used to find which messages are to be requested from the other node. A node forwards a message to another node or multiple nodes, if the delivery predictability is higher than a fixed threshold value [4] [12]. MaxProp [13] routing algorithm is knowledge based flooding routing algorithm. It also works similar to Epidemic by trying to replicate and transfer message copies to whomever coming in contact. However, each node maintains a delivery likelihood vector, obtained by doing incremental averaging. When two nodes meet, these vectors are also exchanged. With the help of this vector each node can calculate the shortest path to the destination. Another specialty of MaxProp is its use of acknowledgments to remove the delivered messages from the buffers of all nodes thereby preserving resources for the use of undelivered messages. In MaxProp the nodes maintain a list of previous relays too in order to prevent data getting relayed for a second time to the same node. In Spray and Wait [14] the number of copies of a message in the network is limited in order to reduce the overhead of extensive flooding in message forwarding. It has two phases in routing: Spray Phase and Wait Phase. When a new message gets generated at the source and needs to be routed to a given destination, Spray and Wait algorithm first enters the “Spray phase” for this message. When a message is generated at the source it also creates L forwarding tokens for this message. Whenever two nodes encounter, they exchange those messages that the other node does not have based on number of forwarding tokens left for each message. Thus n copies of message m are spread to n distinct nodes in this phase. In Wait phase, each of n nodes carrying copy of message m waits for a chance to perform a direct delivery of message to the final destination.

Spray and Focus [15] is an extension of Spray and Wait. Spray Phase in Spray and Focus algorithm is same as that in Spray and Wait Routing algorithm. When a relay has only one forwarding token for a given message, it switches to the “Focus phase”. Unlike Spray and Wait, where messages are routed using Direct Transmission [16][17] in the Wait phase, in the Focus phase of Spray and Focus a message can be forwarded to a different relay according to a given forwarding criterion

4.Simulation Result Simulation has been carried out in ONE simulator version 1.4.1. Five routing algorithms namely Epidemic, PRoPHET, Spray and Wait, MaxProp and Spray and Focus were simulated in the post-disaster scenario modeled on Cluster mobility. This section explains the environment modeling parameters and performance analysis metrics that were chosen and also analyses of the results of the simulations. 4.1 Environment Model Parameters of Simulation, Routing Algorithms and Mobility Model are specified in Table1, Table2 and Table3.

Parameter Simulation Time Update Interval No. of nodes

Table 1: Simulation Parameters considered for ONE Simulator Value 86400s = 24hrs 1s 120

Buffer size of nodes Cluster Nodes Speed Carrier Nodes Scan interval of nodes Cluster Nodes WaitTime Carrier Nodes Message TTL MAC Protocol Range Bluetooth Data rate Range Wi-Fi Data rate Message Creation Interval Message Size Simulation Area Size

Routing Algorithm Epidemic PRoPHET MaxProp Spray And Wait Spray And Focus

((25nodes × 4clusters) + 20carrier_nodes) 500MB 0.5mps – 1.5mps = 1.8kmph – 5.4kmph 5mps – 15mps = 18kmph – 54kmph 0s 0min – 2min 0min – 10min 240min = 4h 802.11, 802.15.1 10m 2Mbps 40m 18Mbps 25s – 120s 50KB – 1MB 15.3 sq.km (4.5km x 3.4km)

Table 2: Parameters of Routing Algorithms Parameter Value N/A Seconds In Time Unit ProbSet maximum size No. of Copies Binary Mode No. of Copies Binary Mode

Parameter No. of clusters Cluster Radius No. of nodes in a cluster No. of carrier nodes

N/A 30s 50 3 TRUE 3 TRUE

Table 3: Parameters of Mobility Model Value 4 800m 25 20

Simulations were run for 24hrs with an update interval of 1s. Nodes have a 500MB buffer. Since scan interval is taken as 0s, nodes continuously scan for neighbors. Speed of cluster nodes is kept as 1.8kmph – 5.4kmph (pedestrian speed) and wait-time as 0min – 2min in order to mimic the movement of rescuers in the scenario. Similarly, the carrier nodes have a speed of 18kmph – 54kmph and wait-time of 0min – 10min. Waittime is the time for which a node waits or pauses on reaching its destination. In all the simulations nodes uses Bluetooth interface with a range of 10m and data rate of 2Mbps, except in heterogeneous network scenario where some percent of nodes have Bluetooth interface and others have Wi-Fi interface with a range of 40m and data rate of 18Mbps. After every 25s – 120s any one node generates a message of size 50KB – 1MB, to be delivered to any other node in the network. In PRoPHET, if a pair of nodes does not encounter each other in a while, the delivery predictability values age. The aging equation is shown below:

where γ є [0, 1) is the aging constant, and k is the number of time units that have elapsed since the last time the metric was aged. In the simulations for PRoPHET 30s of simulation time makes one time unit, as given in Table2. In the simulations for MaxProp each node can estimate and maintain delivery likelihood values for a maximum of 50 neighbors, as given in Table2. Spray and Focus and Spray and Wait operates in binary mode and the number of copies of a message is

limited to 3, a near to optimal value considering the number of nodes in each cluster.

4.2 Performance Metrics The metrics that are chosen to analyze the performance of the routing algorithms are Delivery probability, Overhead ratio and Average latency. Delivery probability is the ratio of number of delivered messages to that of created messages, making it a good metric to measure the efficiency of routing algorithms in delay tolerant scenarios.

Overhead ratio is calculated as the difference of relayed and delivered number of messages upon number of delivered messages. Overhead ratio thus gives a measure of the overhead incurred by the routing schemes in delivering messages.

Latency of a message delivery is the time elapsed from the creation of a message at source to its successful delivery at the destination. Thus Average latency is the average of latencies of all those successful message deliveries. 4.3 Results and Discussion Simulations were performed with varying constraints of buffer size, transmission range, Bluetooth interface density, Carrier node speed and Message size. Buffer size and transmission range were chosen in order to check the dependency of the routing algorithms on the factors that are device-dependent. Message size was chosen in order to study its effect on the bandwidth and buffer usage. Analysis on carrier node speed was done to find the effect of indirect delays in message delivery resulting from the speed variations of carrier nodes. Bluetooth interface density was chosen to study the effect of introducing heterogeneity in the scenario. 4.3.1 Delivery Probability and Overhead Ratio with respect to Buffer Size

Fig.3 – Performance of routing algorithms on varying Buffer size From the simulation results plotted in Fig. 3, it can be seen that Spray and Wait does not produce higher delivery probability although it manages to set a lower benchmark in overhead ratio than the other flooding schemes. Low overhead and less delivery probability of Spray and Wait is a resultant effect of Wait Phase mainly. On the other hand Spray and Focus put up effective delivery probability with less overhead ratio in smaller buffer size. But as the buffer size increases the number of message relayed in Spray and Focus also increases which boosts up the overhead ratio. Epidemic and PRoPHET, two basic flooding schemes, start with higher overhead ratio. PRoPHET manages to outperform Epidemic in both parameter and set up higher benchmark in delivery probability than all other flooding schemes due to restricted flooding as well as probability based message delivery. Even though MaxProp shows best performance at lower buffer sizes, PRoPHET outperforms it at higher buffer sizes. The performance of MaxProp owes to the dynamic delivery probability calculation, application of Dijkstra’s algorithm and other complimentary mechanism. Its starts with very high overhead ratio due to transformation of the entire message destined for neighbors, relays of routing information vector to other nodes in the network as well as generating acknowledgement for all delivered message. However, it can be seen that

above 60MB, the overhead incurred by MaxProp is slightly less than that of Epidemic itself. 4.3.2 Delivery Probability and Overhead Ratio with respect to Transmission range. In a post-disaster scenario, the constraint of transmission range of nodes can be a real barrier to achieve good delivery

Fig.4 – Performance of routing algorithms for varying Transmit range ratio. Higher transmission ranges trades for higher power consumption which cannot be much tolerated by mobile nodes, especially in this scenario. In cluster mobility model we can relate both of these two terminologies called: Transmission range and Node Density. Both of these are products of increment of number of nodes within the network. So increment of transmission range for each of the node will cause identification of larger number of neighbors. On the other hand, node density severely affects the sparse nature of the network. All the flooding schemes in our simulation produce much better delivery probability with the increment of transmission range. But over head ratio differs a lot depending upon the number of copies made by particular routing strategies in order to ensure successful delivery of the message. From the simulation results plotted in Fig. 4, it can be seen that Epidemic, PRoPHET and MaxProp performs quite well as number of identified neighbors in single scan is large which is technically equivalent to increasing the number of copies. But these flooding schemes have shown tendency to produce huge overhead ratio with the gradual increment of transmission range. Spray and Wait scheme achieves lowest over head ratio because it does not deliver the single copy of the message at Wait Phase until there is a direct contact with the destination. But this wait for direct contact makes Spray and Wait vulnerable in the context of delivery probability. Spray and Focus is challenged by the initial time it takes to calculate the utility function and difficulties it might face to explore the network due to sudden identification of huge number of nodes. MaxProp achieves the highest deliver probability at high transmit ranges. It has shown optimum result when transmission range was kept 2030 meters. It almost achieves .85 to .90 of delivery probability. But with the increment in transmission range it shows inclinations towards higher overhead ratio. As the number of internal nodes as well as carrier nodes does not increase generally, Spray and Focus also is a good enough routing algorithm to count on. 4.3.3 Delivery Probability and Overhead Ratio in Heterogeneous Network Structure Here one of the most realistic environments is chosen where we have varied the number of nodes with Bluetooth interface and gateway nodes which have both the interfaces of BT and Wi-Fi. Initially all the nodes are Wi-Fi interface enabled and we have increased this value until all the nodes are only having Bluetooth interface. Hence this scenario is much more practical than the previously discussed scenarios. As Wi-Fi interface does really mean increment of Transmission range and data rate, all the Flooding and Spraying Schemes achieves higher delivery probability when all the node are having Wi-Fi interface as can be seen in Fig.5. We have seen before that overhead ratio of Epidemic, PRoPHET and MaxProp are directly proportional with the transmission range. Here also, as Wi-Fi interface results in higher transmission range, overhead ratio increases for all of the above mentioned schemes. On the other hand overhead ratio is inversely proportional with the transmission range in case of both of the Spraying Schemes here it has shown exactly same result.

Fig.5 – Performance of routing algorithms for varying Bluetooth interface percentage 4.3.4 Delivery Probability and Overhead Ratio in Carrier Nodes Speed Mobility of nodes is exploited in DTNs for relaying the message from source to destination. Speed of the nodes has got a lot to do with the timely delivery of message to the destination, which is of extreme importance in post-disaster scenario. Node Speed is very important issue in time of Post Disaster Management. Here we take realistic human walking

Fig.6 – Performance of routing algorithms for varying carrier node speed of 1-5 Km/hr and varying the Carrier node speeds. The key thing to observe from the graphs in Fig.6 is that performance (delivery probability) differs substantially among the routing algorithms in cluster mobility model. Here we observed that the overhead ratio and the average latency decreased when we increased the carrier node speeds in comparison to other relative parameters like buffer size, transmit range etc, and it goes to constant except Spray and Focus routing Algorithm. Due to the high speed of carrier nodes, packets are brought in very short time to the adjacent cluster. However, carrier nodes pause for a wait-time when these nodes are inside a cluster. Atan optimal speed of 5-10 m/s all the routing algorithms gives very good delivery probability with lower Average latency and Overhead Ratio. 4.3.5 Delivery Probability and Overhead Ratio in Message Size Message size is a challenging issue in the context of Social Network Structures. Increment of the Message Size is functionally dependent on sparse nature of the network as well as scalability of the network.

Fig.7 – Performance of routing algorithms for varying message size As can be seen in the graph of Fig.7, the performance of all the routing strategies is severely challenged by the increment of message size above 500KB. The lower data rate (2Mbps) along with the reduced contact times of nodes can be a reason for this drop in performance. Since the messages has to be passed atomically in storeand-forward message switching, successful node-to-node transfer of large sized messages is much difficult to achieve within the constraints of reduced contact times and low data rates. Since TTL value of the messages is taken as 4hrs, the buffer size limit of 500MB will not affect the performance much at lower message sizes. But when the message sizes are sufficiently big, the limited buffer size can also contribute to the drop in performance. In order to accommodate newer messages into their buffers nodes may drop older ones, magnifying the effect of increased message sizes on the performance of routing schemes. One interesting fact that can be noted from the results is that the overhead ratio is higher for Spray and Focus in most of the cases. This can be a side-effect of the forwarding technique used by the algorithm to focus the message to the destination in the focus phase. From the simulations it was noted that messages are getting carried away through longer relay-transfers in Spray and Focus than any other routing algorithm and many messages were even relayed through cyclic paths, thereby increasing the number of relayed messages.

V. Conclusion In this paper we have addressed Delay Tolerant Networking for monitoring disaster strike areas where infrastructure-based as well as Ad-Hoc networks fail to communicate owing to the unavailability of end-to-end connectivity and fully connected network. We simulated flooding and spraying based DTN Routing Algorithms where PRoPHET and MaxProp outperformed all other routing algorithms in cluster mobility model. Our work seems to be the first time to include Cluster mobility model for use in real life application like post disaster management. As the dimension of human communications and mobility are getting dynamic day by day, there are greater scopes to explore and modify the mobility model mentioned here. Disaster scenarios of Cyclone and Earthquake prone zones, coastal areas where transport communication system is quite different from city like environment, offer new challenges to merge the usability of infrastructure based network and DTN. This is a new dimension of research which we have kept for future works. References [1]Prof. Chandan Mazumdar, Joysree Das, Sujoy Saha, Manash Upadhyay, Sanjoy Saha, “Rapidly Deployable Wireless data Communication Network (RDWCN) for Disaster Management- An Experiment", 20th Indian Engineering Congress, Kolkata, West Bengal, December 15-18, 2005. [2]Joysree Das, Sujoy Saha , Avijit Kundu, Manash Upadhyay , Kaustuv Chatterjee, Sanjoy Saha, “Rapidly Deployable Decentralized Disaster Management System and Information Network for Rural Areas" presented at 37th IETE Mid – Term Symposium on “Information Communication Technology – Initiative for Rural Development, (ICTIRD-06)” Kolkata, West Bengal, April [3]Sushovan Patra, Anerudh Balaji, Sujoy Saha,Amartya Mukherjee, Subrata Nandi ” A Qualitative Survey on Unicast Routing Algorithms in Delay Tolerant Networks in Proc of , AIM2011S ,2011, Nagpur [4]Md Yusuf S. Uddin,David M. Nicol,David and M. Nicol, “A POST-DISASTER MOBILITY MODEL FOR DELAY TOLERANT NETWORKING” Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, and R. G. Ingalls, eds. [4]Morteza Romoozi, Hamideh Babaei, Mahmood Fathy and Mojtaba Romoozi “A Cluster-Based Mobility Model for Intelligent Nodes at “Proceeding ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I ,2009

[5]J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, A performance comparison of multi-hop wireless ad hoc network routing protocols, in Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking(Mobicom98), ACM, October 1998. [6]T. Camp, J. Boleng, and V. Davies, A Survey of Mobility Models for Ad Hoc Network Research, in Wireless Communication and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483502, 2002 [7]A SURVEY OF MOBILITY MODELS in Wireless Adhoc Networks Fan Bai and Ahmed Helmy University of Southern California,U.S.A [8]Santosh Kumar, S. C. Sharma, Bhupendra Suman “Mobility Metrics Based Classification & Analysis of Mobility Model for Tactical Network"International Journal of Next-Generation Networks (IJNGN) Vol.2, No.3, September 2010 [9]Frans Ekman, Ari Keränen, Jouni Karvo, and Jörg Ott: Working Day Movement Model. 1st SIGMOBILE Workshop on Mobility Models for Networking Research, Hong Kong, May 2008 [10]T. Camp,J. Boleng,V. davies, “A Survey of Mobility Models for Ad Hoc Network Research”, Wireless Communication & Mobile Computing(WCMC):Special issue on Mobile Ad Hoc Networking:research.Trends and Applications,2002,2(5): 483-502. [11]Evan P.C. Jones Paul A.S. Ward, “Routing Strategies for Delay Tolerant Networks”, Submitted to Computer Communication Review 2008 [12]A. Lindgren, A. Doria, and O. Schelen, “Probabilistic Routing in intermittently connected networks”, volume 3126, pages 239–254. 2004. [13]John Burgess Brian Gallagher David Jensen Brian Neil Levine MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks [14]Thrasyvoulos Spyropoulos, Konstantinos Psounis, Cauligi S. Raghavendra “Proceedings of the ACM SIGCOMM workshop on Delaytolerant networking, 2005 [15]Thrasyvoulos Spyropoulos, Konstantinos Psounis, Cauligi S. Raghavendra “Spray and Focus: Efficient Mobility-Assisted Routing for Heterogeneous and Correlated Mobility” Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops ,2007