COST EFFICIENT PREDICTIVE ROUTING FOR DISRUPTION TOLERANT NETWORKS

Transcription

1 COST EFFICIENT PREDICTIVE ROUTING FOR DISRUPTION TOLERANT NETWORKS A THESIS Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of the Ohio State University By Satyajeet Deshpande, B.Sc./M.Sc. Graduate Program in Computer Science and Engineering The Ohio State University 2011 Master s Examination Committee: Prof. Anish Arora, Advisor Prof. Ness Shroff

2 c Copyright by Satyajeet Deshpande 2011

3 ABSTRACT Disruption Tolerant Networks contain intermittent connectivity and high endto-end delays. Packets are transfered whenever a contact becomes available for a node. Examples of such networks vary from satellite networks to human networks to vehicular networks. Some of these these networks have a high constraint on energy and storage because of remoteness or inability to charge the nodes. Also some of these DTN networks exhibit semi-deterministic mobility characteristic showing periodicity with some variance. Extant research work in DTN have focused mainly on message reliability and message delay. Efficiency of routing algorithms in terms of cost and energy have not been explored enough. As DTN deployments become a reality energy efficiency of such networks become critical. In this thesis we propose Cost Efficient Predictive Routing in Disruption Tolerant Networks, which takes into account two important factors for messages of mobile nodes, i) their deadline and ii) cost which is defined as the number of transmissions required to transfer the packet to the destination. Our routing protocol calculates low cost paths and decides on which path to forward the message such that it not only optimizes cost but also delivers the message within the deadline. It uses a prediction algorithm that takes the inter contact times of nodes as input and gives the duration of next meeting times for a node. We compare our protocol with some past replication routing protocols and some forwarding protocols who use average metrics. We show that our protocol outperforms ii

4 the replication protocols almost three times in terms of cost and outperforms the forwarding protocols almost two times. We also show that it achieves equivalent throughput and delay compared to replication protocols who have the best delay and throughput and it has better throughput and delay compared to the forwarding protocols. We also show how our protocol can be refined to suit to different delay requirements of a network. iii

5 This document is dedicated to my parents, Mr. Sanjay and Mrs. Savita Deshpande. iv

6 ACKNOWLEDGMENTS Firstly, I would like to thank my Advisor, Prof. Anish Arora for giving his time and support for my Thesis and for his guidance, advice and help in this research work. His knowledge and experience in this field of study is exceptional along with his constant encouragement for his students. Because of him I was able to learn a lot and had a wonderful experience at the Dependable Distributed and Networked Systems research group at The Ohio State University. Also, I would like to thank my colleague, Mukundan Sridharan for his support and guidance throughout my research work. He was my constant help-line in many aspects of this thesis and I had a great time working with him in this interesting research area and I learnt a lot from him academically. Also I would like to thank Dr. Woods and Dr. Alex Morison for providing me with funding opportunity for two quarters of my program and it was a delightful experience working with them and learning interesting stuff of Cognitive Science. My parents, Mr. Sanjay Deshpande and Mrs. Savita Deshpande have always been the main pillar of support throughout my life and this Thesis along with my entire study here at Ohio State University would not have been possible without their numerous sacrifices for my happiness. I also would like to thank my cousins, Mr. Abhijeet Deshpande and Mrs. Aarti Deshpande for being such an amazing elder sister and brother for me and being my biggest support in every aspect of living a life in United States of America. I was extremely lucky to have them both very close to v

7 me which eased my student life in a new country tremendously and they never made me feel homesick or miss my family back in India irrespective of not going to meet my family for whole two years. During my two years of graduate program, I was lucky enough to get very supportive and understanding roommates in Neil Sawant, Swanand Phadke, Siddhesh Pai Raikar and Srinivas Hegde who have become my good friends with the passage of time and I thank them for having such a healthy home atmosphere during my study here. I also would like to specially thank Neil and Siddhesh for sharing with me so much about each other s personal problems and difficulties and standing by me all the time irrespective of my numerous flaws. Additionally, I would also like to thank my numerous friends in Badminton which was a big aspect of my life here at OSU. I thank Suryarghya Chakrabarti who was my mens doubles partner, my dearest friend and my training companion with whom I had a lot of fun time playing, training, improving and competing at a high level in various tournaments across Midwest. I would also like to thank my mixed doubles partner Miss. Deepti Reddy for being extremely supportive and playing with me at various Midwest and national level tournaments along with being one of my close friends and one of the nicest person to have met in USA. I would also like to thank Cindy Guan for helping me so many times and taking care of me like my mother in various tournaments. Also special thanks to my very good friend and well wisher Miss. Sadie Snyder for a wonderful experience and an enjoyable time during my first year in graduate school and ending up being one of my closest friends and one of the kindest person I have met in this world. Finally and most importantly I would like to thank God for his blessing and grace during my completion of Thesis and my graduate program at OSU. vi

8 VITA Born in Mumbai, India B.Sc. in Computer Science, University of Mumbai M.Sc. in Computer Science, University of Mumbai 2008-Present Graduate Research/Teaching Associate, Department of Computer Science and Engineering, The Ohio State University FIELDS OF STUDY Major Field: Computer Science and Engineering Specialization: Networking vii

9 TABLE OF CONTENTS Abstract Dedication Acknowledgments Vita ii iii v vii List of Figures x CHAPTER PAGE 1 Introduction Disruption Tolerant Networks Applications and Scenarios in DTN Motivation for this Project Problem Definition Informal Statement Definition Proposed Solution Contributions of this thesis Organization of the Thesis Related Work Routing Work in DTN Introduction Forwarding Algorithms Replication Algorithms Probabilistic Algorithms Other Approaches to Routing in DTN Mobility Models for DTN Analysis of Mobility Characteristics Various Mobility Models viii

10 3 Semi-Deterministic Mobility and Mobility Prediction Algorithm Overview Assumptions Prediction Problem Prediction Scheme Predicting Contact Delay The Routing Algorithm Introduction The Routing Algorithm Definitions Compute Available Path Vector Calculating the Best Path Validation of the Proposed Algorithm Simulation Scenarios Synthetic Traces Routing Algorithms Compared Replication Algorithms Forwarding Algorithms Important Metrics to be analyzed Number of Forwards or Cost Protocol Efficiency Throughput Average Delay Validation and Results Conclusion and Future Work Conclusion Future Work Bibliography ix

11 LIST OF FIGURES FIGURE PAGE 3.1 A complex frame of length one week, with seven sub-frames of two types A meeting probability frame between a pair of nodes Pseudocode of calculating the path vector exchanged between nodes Pseudocode of best m path to satisfy a delivery threshold rpd OSU Campus Environment COTA Bus on OSU Campus Environment Campus Network: Efficiency vs Rate Campus Network: Cost vs Rate Campus Network: Throughput vs Rate Campus Network: Average Delay vs Rate Campus Network Different Deadlines: Efficiency vs Rate Campus Network Different Deadlines: Forwards vs Rate Campus Network Different Deadlines: Throughput vs Rate Campus Network Different Deadlines: Average Delay vs Rate Bus Network: Efficiency vs Rate Bus Network: Forwards vs Rate Bus Network: Throughput vs Rate Bus Network: Average Delay vs Rate Bus Network Different Deadlines: Efficiency vs Rate x

12 5.16 Bus Network Different Deadlines: Cost vs Rate Bus Network Different Deadlines: Throughput vs Rate Bus Network Different Deadlines: Average Delay vs Rate Bus Network High Buffer: Efficiency vs Rate Bus Network High Buffer: Cost vs Rate Bus Network High Buffer: Throughput vs Rate Bus Network High Buffer: Average Delay vs Rate xi

13 CHAPTER 1 INTRODUCTION 1.1 Disruption Tolerant Networks Even after a rapid increase in Internet and a huge development in communication networks in the world, there still exists many environments where there is not enough network connectivity or end to end paths for the nodes. These areas include extra terrestrial environments, planned networks in space or remote areas in villages, deserts or forests. The entrance of wireless communications provided a major change in the development of many mobile networks in such areas. Nonetheless there still exists certain characteristics like intermittent connectivity between mobile nodes, low bandwidth and channel capacity, high end to end delay, sparsity of mobile nodes, low radio ranges etc. The networks that exhibit these characteristics came to be known as Challenged Environments. In 2002 Fall et al [11] in a seminal work of Inter Planetary Internet[IPN] initiated the concept of Delay Tolerant Networks which later also came to be known as Disruption Tolerant Networks (called DTN in rest of the thesis). A DTN is a network of nodes that has the ability of dealing with intermittent connections and also dealing with high end to end delays. They work with a mechanism called Store and Forward where nodes store messages or packets in its local buffer and when a connection activates with other nodes, they transfer the messages 1

14 across the network towards the destination. These networks were also termed as Opportunistic Networks since the communication between nodes is opportunistic and not continuous. They make use of any possible connection may it be radio frequency communication or WiFi communication whenever they become available to transfer data between nodes. Since then DTN has grown to become one of the most intriguing research areas and there have been many challenges at the Routing layer, Mac layer, Application layer etc. which have been studied. 1.2 Applications and Scenarios in DTN The Interplanetary Internet [1] created by IPN SIG has a goal to have communication between earth and extra terrestrial regions or between spacecrafts in transit, satellites etc. Because of infeasibility of many networks for this project, Disruption Tolerant Networks play a big role. ZebraNet [4] is one more network where novel studies of animal migrations and inter-species interactions are studied and also position-aware computation from a power-efficient perspective is explored. Additionally, a number of DTN applications in the areas of Health-care, education and wild life have been deployed in developing nations. These projects and applications are undertaken by research groups like TIER [3] to build infrastructure and simplify communication in emerging regions. There also exists vehicular networks which are used for navigation, traffic updates and other data communication needs while in transit. In all the above mentioned environments and applications, there exists intermittent connectivity and high end to end delays and thus DTN research plays a significant role. 2

15 1.3 Motivation for this Project Many of the above mentioned challenged environments have sparse connectivity and have many intermittent connections to exchange messages which is why DTN s are built in which messages are delivered via other mobile nodes whenever an opportunistic contact is possible. But still within some of these regions like remote areas in emerging regions or wild life scenarios there exists hot spots like schools or hospitals where Internet connectivity exists and is the only way for other mobile nodes within the remote area to connect to the Internet. But instead of going to the hot spot or Base Station, it would be more efficient and helpful for the remote nodes to somehow send their information and queries via mobile nodes that visit the base stations constantly and thereby get the responses from them. Further it is conceivable that the messages in such a network might be of different priorities and might consequently have different delay/deadline requirements. For example, a villager trying to find out where the doctor is will have much higher delay requirements than another villager trying to find out what are favorable conditions for sowing?. The information could be data collection about a particular sensor environment in the village that collects data about crops or about inventory or it could be a query request about availability of a doctor. It might be even more important to provide message priorities and quality-of-service in a DTN network than in the regular MANET, because of the nature of such applications. Another important aspect in most of these regions may it be remote areas or wild life regions is the availability of energy. Many mobile nodes in these networks are assumed to run on low power energy resources and are expected to last for a long duration of time. The amount of energy spent in sending and receiving a message across a network channel is extremely high compared to local energy spent in computation. Many of the above mentioned applications rely heavily on energy and thereby have 3

16 a huge impact on number of times a packet is forwarded or sent across the network channel. Also it is very obvious that there is very low resource availability in such networks and applications and hence more copies and replications of a packet add significantly towards the energy and resource utilization is these networks. Also in many situations users have a particular deadline under which they need to communicate since the messages can be as urgent as 300 seconds or as unimportant as 1 to 2 days. Most DTN routing algorithms concentrate on reducing the end to end delay and improving the throughput. They do not emphasize on reducing the number of forwards or reducing the energy required for the mobile nodes in DTN. They also do not take into account deadline of a message. Hence it is essential and very useful to have a routing protocol in DTN that emphasizes on reducing the cost which is the amount of message transfers in a DTN, and yet achieve a good throughput and a respectable delay. 1.4 Problem Definition Informal Statement Design a routing protocol to optimize the overall cost of converge-cast, under delay and buffer constraints for a disruption tolerant network Definition Given a set of N nodes, a special Base Station (BS) node, a buffer capacity of B at each node, set of past contacts for every node met C ij, find a path from each node i, to the BS, such that, the total cost of the system is minimal and the total end-to-end delay of each path is less than a deadline D. 4

17 1.4.3 Proposed Solution In this thesis a routing protocol for DTN s is proposed which takes into account two important factors for messages of mobile nodes, deadline and cost. Our routing protocol bases its decision on the deadline for a message of a node and optimizes on cost along with achieving high reliability of messages for these nodes. With the prediction of node mobility, the routing algorithm tries to minimize on the number of packet sends on the network channel and yet complying with the delay requirements of the packet and buffer constraints on the network. The protocol uses a prediction algorithm which is created to exploit the semideterministic nature of the mobility model in the DTN environments. 1.5 Contributions of this thesis We make the following contributions in this thesis: 1. A prediction algorithm that learns the semi-determinism and periodicity of the mobility of nodes in DTN s and helps in making routing decisions for routing protocols. 2. A routing protocol that is reliable and reduces the energy of the nodes in the DTN and also meet the deadline of packets. 3. Analysis of many other existing routing protocols and validation of our protocol and its performance with the existing work. 1.6 Organization of the Thesis The rest of the thesis is organized as follows. 5

18 Chapter 2 describes the past work related to routing in DTN s, the protocols and also the mobility work that impacts routing in DTN s. Chapter 3 describes a prediction algorithm which provides important metrics that are used as an input to the routing algorithm. Chapter 4 illustrates in detail the core routing algorithm and its working. It also tries to show the optimality of the algorithm and outlines what information is required to be exchanged which is used by the routing algorithm. Chapter 5 first describes the simulation set up of the synthetic traces used to validate the routing algorithm. It then describes briefly the other routing algorithms used to compare with this study. Finally it shows the simulation results and the validation of the proposed routing algorithm and how the idea proposed outperforms other routing algorithms and achieves its claim described earlier. Finally, Chapter 6 concludes with a brief outline of how the problem definition was achieved and also gives areas directed for future work in this study. 6

19 CHAPTER 2 RELATED WORK 2.1 Routing Work in DTN Introduction DTN has been an active area of research for a number of years now and there has been many DTN routing algorithms that have been proposed. Most of these algorithms can be classified into various categories which are forwarding versus replication. no network knowledge versus local knowledge versus entire global time state graph. source routing versus per-contact routing. We first classify the algorithms based on forwarding or replication and in that further provide details about the other classifications Forwarding Algorithms In early times, the first work in DTN routing were forwarding algorithms in which regular wired network forwarding and routing concepts were remodeled and modified to fit into DTN architecture. Jain et al [17] presented five different forwarding algorithms each in increasing order of knowledge oracles available at each node. Being 7

20 forwarding algorithms, they were most efficient in terms of number of messages in the network and usage of resources. First Contact (FC) [17] was trivial where node forwards packet to first node available and hence was completely inefficient and delivers least throughput and high delays. Next one, Minimum Expected Delay (MED) [17] was a time invariant algorithm where cost of an edge is the average waiting time. Although MED does not use huge information but only aggregate contacts to determine and optimize average waiting time which is practically possible and required in most DTN s, it also resulted in high delays and low throughput since it uses the same path for all messages with same source destination pair. Thus it was a static algorithm which is extremely incompatible for DTN architectures. Then came time varying modified versions of Dijkstra algorithm - ED [17], EDLQ [17], EDAQ [17] which had short delays and effective path planning for a packet but assumed having knowledge of the entire time state graph at every node. For most DTN s it is not a realistic assumption to have that much amount of information. Moreover EDLQ and EDAQ assumed to have entire queuing information available which is also not a realistic assumption for DTN s. Also all these algorithm are source based routing and hence are static routing strategies which is totally against DTN s idea of having hop based approaches. A slight advancement to the above technique came a paper called Practical Routing in DTN [18] which was an epidemic link state protocol where routing is done at every contact choosing the next best hop. Although it has per contact routing which makes it more dynamic as decisions are made at every new contact and not at source and also minimizes delay, it has a assumption that nodes assume to have a global view of the system between nodes at any time. Also a lot of information is assumed to be exchanged and available at nodes and each node is assumed to have the entire topology in its routing table which is large and difficult to obtain for 8

21 DTN s. The Optimal Probabilistic Forwarding protocol [24] uses an optimal probabilistic forwarding metric which is derived by modeling each forwarding as an optimal stopping rule problem. Although it considers regularity between mobility of nodes and also considers direct and indirect predictability, but has the same flaws which are that it assumes centralized global information updates and also makes forwarding decision when packet is created at source instead of per-contact routing. Most DTN architectures consist of nodes that do not have any information about the network before hand or do not have any global view of the system or the network state. Hence the need for algorithms, that do not assume a-priori knowledge or any information about the network and either have a mechanism that deals with it or learns small local information about the network gradually Replication Algorithms So with the assumption of having no a-priori information, came the birth of a lot of replication algorithms like Epidemic [28], MaxProp [7], Spray and Wait [27], RAPID [6]. These algorithms have a basic notion of flooding the network with copies of the packets, in order to achieve minimum delay and all these algorithms are per-contact routing algorithms. The simplest of them was Epidemic [28] which sends a copy to every new contact that does not have a copy thereby hoping that at least one copy will reach the destination and hence achieving minimum delay. As it is easy to see that this algorithm would lead to a number of problems. i) There exists many copies of packets in network thereby having a huge wastage of resources. ii) Contact durations may not be long enough to transmit all the packets in one contact. To minimize the number of packets, algorithms like Spray and Wait [27] were put forward where each node only forwards L copies in the network. There were many 9

22 flavors of spray and wait most favorable being the Binary Spray and Wait which forwards L/2 copies to the node it meets (called the spray phase) and this process goes on until a node has 1 copy in which case it will wait (called the wait phase) to forward it to destination. Although this gives small delay when it comes to delivering packets and reduces the number of copies in the network by some factor, it still is large enough to have unnecessary wastage of resources which can be avoided if intelligent path planning could be done. As far as a solution to the second disadvantage of short contact durations, a protocol called MaxProp [7] was put forward which orders packets based on a cost assigned to each packet which is calculated through local estimation of delivery probability for nodes. This started a new trend of estimating delivery probability to reduce the number of transfers. MaxProp uses only frequency of meeting a node to calculate the node s delivery estimate and then orders the packets accordingly. Even though this reduces the problem of short contact durations since packets are prioritized, this only could be effective in vehicular DTN s or other DTN s with short contact duration. For DTN s having considerable contact durations, this protocol becomes epidemic thereby having the same wastage of resources since it is not doing any path planning. Then came a more intelligent estimation technique called RAPID [6] which only replicates a packet that locally results in the highest increase in utility. This algorithm is designed to work on a DTN with finite storage and finite bandwidth which is usually the case in most DTN s. The algorithm s core idea is to take a routing metric like delay and create a utility function which is calculated at every node to optimize that routing metric. It has many theoretical and empirical analysis to show that it is an effective and efficient algorithm for DTN s and also enlightens a new idea of estimating a utility for a packet within a particular deadline as a new metric. Still a minor flaw to this approach was that it assumes large meta data transfer over an in-band control 10

23 channel to exchange history information including all possible number of replicas of a packet in network etc and such information may not be always feasible for some DTN s. Moreover RAPID degenerates to Epidemic if contact durations which is the bandwidth is large which is the case in human mobility scenarios. All these algorithms try to optimize delay by controlled replication of packets which may not be a harsh constraint on many packets as long as they are delivered within some time and hence there is no mechanism for predictive cost optimization or reducing the number of forwards which could be a critical energy metric in many DTN s Probabilistic Algorithms Because of the drawbacks of the above algorithms of extensive replication consuming many resources and no utilization of node mobility, gave the rise for a new set of algorithms called probabilistic algorithms like PRoPHET [22], Predict and Relay [29], DTN routing with Trajectory Planning [8] etc. The simplest of these was PRoPHET [22] which calculates a delivery predictability for every node via three formulas and the frequency of meeting that makes the decision on which node to forward the packet to. It has lot of advantages like it makes use of predictable movement of nodes and tries to reduce communication overhead by forwarding instead of replication and yet achieve high delivery ratio and low delay, decisions are made per contact and not at source and it doesn t require any global knowledge of the system. It still has a few drawbacks that the correctness of predictability relies heavily on its three fundamental probability constants alpha, beta and gamma and no particular good value for those constants is proved. Thus different values for the parameters result in a lot of transmissions and having high 11

24 number of loops in the path. Also it results in low throughput since it is a single copy algorithm and a single packet drop results in unsuccessful delivery. Other probability algorithms include Predict and Relay [29] and DTN Routing with Probabilistic Trajectory Prediction [8]. Both these algorithms use a time homogeneous Semi-Markov process model to predict the probability distribution of future contact time to determine the next hop. On a positive note, these algorithms learn the mobility of node and assumes semi-deterministic mobility. It also tries to achieve maximum achievable delay with trying to minimize on number of packets in network. A fundamental property for prediction in these algorithms is the existence of landmarks or communities which provide the base for the math behind the prediction. This fundamental property may not necessary exist in all DTN s and there might be a need of a routing algorithm that works without communities or landmarks. Also these algorithms do not take into account source node s probability of meeting the intermediate node but assumes all forwarding nodes are neighbors in contact. Moreover all these algorithms do not emphasize on reducing the number of transmissions but only reduce the number of copies Other Approaches to Routing in DTN There also have been many other approaches to routing in DTN. Novel Time-Interval routing protocol [25] chooses the node in transmission range with least time interval among neighbors as next hop. Thus it simply finds the inter contact time between nodes and routes to the node that has least inter contact time in the past. Thus the prediction mechanism does not take into account future contacts or node movement thus making it more possible to failure. Cache Based routing [13] uses principle of recurrence in caches to identify which neighbors to keep in nodes neighbor table which acts as a routing table. It is a simple shortest path Dijkstra algorithm where edge 12

25 costs are frequency of meeting. It has many impractical assumptions like broadcasting the routing tables over the network to get global view and also is a source routing algorithm instead of per hop contact routing and also does not use or predict future contacts. Routing in Cyclic Mobispace [23] derives expected minimum delay metric in a probabilistic state space graph to make decisions on which node to route packet to. It has a good advantage of using both forwarding and replication but is decided at source which to take instead of dynamic decision at each hop. Also it requires every node to know information of all the inter contact times past and future of every other node. Thus it assumes entire state-time graph is available to every node and updates are received periodically to notify changes of the system which is a unfeasible assumption in many DTN s. Social-Stratification Probabilistic Routing [5] uses concept of social meeting which is same as frequency of meeting to calculate predictability between node delivery. Evan et al [19] give a nice overall view on some of the above routing algorithms along with their classification and also give some necessary metrics required for DTN algorithms to consider. Most importantly all these algorithms are either forwarding or replication based algorithms who try to minimize delay but neglect number of transmissions required to send a packet which in many DTN s is a high energy critical aspect. Also in most of them there is no learning of mobility to make predictions and they do not try to exploit determinism and periodicity of node movement. 13

26 2.2 Mobility Models for DTN Analysis of Mobility Characteristics In the ancient work done in mobility, Grossglauser and Tse [12] showed that any twohop forwarding scheme can be successfully used to route packets in such networks since they have finite mean delay. All the previous work based these facts on the assumption that the inter contact time between nodes follow an exponential curve. Then came the work by Chaintreau et al [9] who studied experimental data sets and claimed that the inter contact times between mobile nodes in a DTN has a heavy tail and follows power law characteristics and this finding has a significant impact on designing routing algorithms. They also state that the random mobility models are not sufficient for DTN routing algorithms because of the above finding. They state that for any routing algorithm, with a power law slope lesser than 1, the mean expected delay is infinite. This fact is based on a major assumption that the later sharp exponential decay is a consequence of simulation period. Karagiannis et al [20], counter argued the above theory and claimed that there exists a dichotomy of power law with exponential decay for inter contact times and that because of the exponential decay the infinite mean does not hold true. Also they state that the power law part turns over to exponential part over a characteristic time approximately half a day which also shows periodicity in mobility of nodes over a particular period of time. Both the above papers consider aggregate inter contact time distribution and assume that the pairwise contact distributions are independent and that the dichotomy could be a part of aggregation. Conan et al [10] argue that pairwise inter-contact patterns are a more refined and efficient tool for characterizing DTN s and follow the log normal and exponential parts and it also investigates analytically in both the exponential and log-normal cases how the aggregation of pairwise 14

27 inter-contacts may lead to aggregate inter-contacts with power laws of various degree. Hence it was shown that there is a need for a mobility model that captures mobility in DTN s in a more accurate way complying with the above power law and exponential findings Various Mobility Models An early work done in the area of having mobility models that agree to regularity of nodes was by Tuduce and Gross [2]. They use WLAN traces to generate mobility parameters and thereby build a mobility model that can be used for network simulations. They have empirical ways to get spatial and temporal processes of node movement called Prevalence and Persistence and importantly it complies with power law distribution property. They agree about the existence of user regularity and not randomness and try to exploit it but they don t use periodicity of node movement. Also the drawback of the above mobility model is that mobility is restricted to WLAN scenarios only since it uses WLAN traces to generate mobility and hence it may not necessarily hold for many other DTN s. Also it only has three basic transitional probabilities - neighboring, non-neighboring and same node which may not be enough to model the DTN accurately. Hong et al proposed Group Mobility [14] where nodes are assigned to groups and groups move in paths while nodes move as the group moves and also within the group. Node movement in this model is not periodic and also not deterministic or semi-deterministic while groups are fundamental aspects of mobility for each node and hence nodes can not move independent of groups which is more suited for mobile or ad-hoc networks rather than DTN s. The Weighted Way Point Model [15] was a simple way point model but with weights assigned to each location deciding the node s movement instead of random mobility. Although it follows certain semi-determinism, 15

28 periodicity of nodes or time based regularity of nodes is not considered and uniform distribution is used for speed and waiting times. All Nodes move in similar fashion irrespective of time, thereby having no time periods and most importantly location is a fundamental property for mobility. Model T++ [21] was an Empirical Joint Space- Time Registration Model where time space correlations are generated via popularity of locations. Again although they try to exploit spatio-temporal correlation between nodes, they assume clusters which are same as regions or communities for other papers. Moreover they only capture WIFI traces but not any other DTN traces and also assume OFF as a state in the mobility model. Hsu, Spyropoulos, Psounis and Helmy came up with a model called Time Varying Community Model [16] in which the simulation region is divided into communities where each community is a state of a Markov Chain. It has a transition matrix containing probabilities that decides nodes movement from one community to another. Many algorithms comply with this kind of mobility and they have also compared and matched with real life traces. They not only consider nodes regularity and semideterminism but also provide theoretical tractable proofs for hitting and meeting times and prove that it captures spatial and temporal correlations of nodes. The only disadvantage for the above model is that communities are fundamental properties for mobility without which the mobility model does not hold and also it does not show how the probabilities for every community and time period are set but leaves it to application-specific scenarios to set them making it an effective general mobility framework. 16

29 CHAPTER 3 SEMI-DETERMINISTIC MOBILITY AND MOBILITY PREDICTION ALGORITHM 3.1 Overview The mobility of nodes in human mobility or vehicular mobility scenarios follow a dichotomy of power law and exponential behavior as was shown by Karagiannis et al [20]. This observation along with the observation of Conan et al [10] of utilizing pair wise inter contact time patterns which are more refined than aggregate inter contact behavior give a new direction on predicting the future contact times for DTN nodes. Also the applications and scenarios mentioned in Section 1.2 shows that the challenged environments of human mobility, animal mobility or vehicular mobility exhibit a more periodic behavior. This periodic behavior and semi-deterministic mobility if exploited by a prediction scheme can give better insights in making routing decisions. Most DTN routing focus on whether or not to make a copy when meeting a node and not on planning a path, because path planning requires either knowledge of all the paths or prediction of the paths. Path planning is a more efficient way of routing since one can reduce on cost and yet achieve the reliability. But for high performance of path planning algorithm, we need high quality prediction and such high quality prediction might not be possible in all DTN s, but in some of them which exhibit the semi-determinism this might be possible. Thus if a prediction scheme that can give 17

30 the delay for the next contact for all the neighbors and also how often the neighbors meet, then it can find a more energy efficient routing solution and can make a decision on whether to forward to current neighbor or wait for a future contact that might arrive. The performance of the routing algorithm then will depend on the accuracy of the prediction algorithm used. As explained above we are interested in predicting not only how often a given pair of nodes meet, but also the time at which they are likely to meet, given their past history of contacts. Thus we are interested in building a prediction methodology that has the following properties: 1. The prediction should work for both random and deterministic meeting patterns. 2. If there is determinism in a meeting pattern,should take advantage of it and give better prediction. 3. It should take into consideration, that the pattern observed could be a result of multiple periodic or random processes. 4. Should be simple enough to be implemented on resource constrained nodes. 3.2 Assumptions We assume that the mobility of nodes in a certain class of DTN s is somewhat predictable and this is reflected in the contact pattern of nodes. We also assume that pattern of contact is somewhat periodic. This periodicity of behavior is captured by what we call a frame, which is the length of time after which the pattern repeats itself. Hence predicting how the nodes behave relies on predicting their behavior in the frame. For a contact pattern to be deterministic means, the frame is finite. Hence, a 18

31 frame is the minimum (finite) amount of time after which the contact pattern repeats itself. As shown in Figure 3.1 the frame is of finite length 1 week divided into finite time slots of length 1 day. Figure 3.1: A complex frame of length one week, with seven sub-frames of two types 3.3 Prediction Problem The prediction problem can be stated as follows: Given a history of contact H i,j, which is an array size hl of tuples < t c, l c > (time of contact and length of contact) and time t, predict the delay before the next contact. 19

32 Variable H ij Meaning History of meeting between node i and j, an array of tuples < t c, l c > time of contact t c and length of contact l c F ij Frame length of meeting times between node i and j, that is the length of time after which the meeting pattern between i and j keep repeating S mn P c ij (S mn ) Time Slot n in sub-frame m Average number of contacts at time slot S mn between nodes i and j 3.4 Prediction Scheme We adopt the following prediction methodology developed by Mukundan Sridharan [26] : we divide time into frames of length fl and each frame into slots S n of fixed length sl, where n (1.. fl/sl). Next we find the average number of a contacts per slot P c i,j (S n ) for each slots in S n, given the history, which is simply the ratio of the number of contacts in time slot S n and the number of frames of contact history data available, i.e., hl/fl. P c i,j (S n ) is not a probability density function in the traditional sense, i.e., P c(s n ) 1 S n center 20

33 P c i,j (S n ) is basically a histogram as can be seen in Figure 3.2, normalized by the number of frames of data available. P ci,j (S n ) gives the average number of contacts per frame. Given the P c i,j (S n ), it is very easy to get a predication for having at-least one contact in future. Figure 3.2: A meeting probability frame between a pair of nodes Let us consider a simple example, where the frame length is one day and the slot length is one hour, and lets say we have contact history data for one week. Let us also assume that there are 3 contacts in between 9 am - 10 am in the history data on various days. In this case, we will have 24 slots in the frame, and the probability of contact for the time slot S 10 (9 am - 10 am) will be 3/7, since we have 3 contacts in that time slot and we have 7 frames of history data. 21

34 3.5 Predicting Contact Delay Given the normalized histogram of average contacts P c i,j and the frame length tf ( i, j), we calculate the maximum expected delay for the next contact for a probability threshold P c thresh (for example, say 0.95), by summing up the individual contact probabilities in each slot, so the the sum equals the threshold P c thresh and finding the corresponding delays for the contacts. A natural threshold in our slot average case is 1.0 or a number very close to 1.0, which means that as the cumulative sum crosses 1.0 you are assured of at-least one contact before or by that time slot. Thus, a node i, for each node j it meets, predicts the maximum contact delays < Cj 1, Cj 2,.., Cj n > with respect to a time frame tf ( i, j), based on the contact history between the two nodes. 22

35 CHAPTER 4 THE ROUTING ALGORITHM 4.1 Introduction As was explained in Chapter 3, if a prediction algorithm can predict future contact times for pair of nodes, then it is possible to have a routing algorithm that uses that prediction to make a more intelligent decision on which node to forward the message to. Many DTN nodes have a high constraint on energy as can be seen from Section 1.2 and hence we try to develop a routing algorithm that optimizes on cost which is the number of transmissions on the network. We also add delay constraints of packets as a metric to consider while optimizing on cost, such that the packet is delivered on the lowest cost path that satisfies a deadline as explained in the problem definition stated in Section 1.4 In this section we discuss the routing logic and the routing metrics maintained by the protocol. The routing algorithm presented in this chapter is in joint work with Mukundan Sridharan [26]. A source node generates a packet and hands it over to the routing layer. The routing layer, after receiving the packet, adds a routing header, and enqueues it in its data buffer. The logic of the routing consists of finding paths to the destination, its costs and its estimated delay. A number of paths might be available through each of the neighbor, within the next time frame, and all such paths and their corresponding 23

36 Neighbor ID Path Vector (Contact-time Delay Cost) Table 4.1: Path table for node i metrics are maintained. When two nodes meet, each of them calculate the list of paths available to them to the destination, that meet a deadline dl, through all their neighbors except the node they are currently meeting and send it to the other node. This list of paths through each neighbor is stored in a path table which contains the fields as shown in in Table 4.1. The nodes then search through their path table to find the best cost path that meets its deadline dl. 4.2 The Routing Algorithm When a node i meets node j, they go through the following steps: 1. Update history and contact predictions: In this step the contact history of the nodes are updated and the contact prediction algorithm explained in Chapter 3 is run to get the expected contact delays. 2. Compute the available paths vector: Next we compute the set of paths available from a node to the destination, through all of its available neighbors (excluding the current contact). Through each of the neighbors, multiple paths could be available, 3. Exchange available path information: In this step the nodes exchange the paths with each other. 4. Update the path table with new information In this step the node updates its routing table with the paths it received 24

37 5. Compute the best path for each packet Then it computes the lowest cost path that satisfies the deadline. 6. Forward packets: It forwards the data packets meant for (current contact) node j Definitions A path is a sequence of contacts between pairs of nodes, starting at node i and ending at the base station (BS). A path, say l, starting at i is quantified, by contact time P AT H i,l (ct) - the time at which the current node i is likely to meet the next node in the sequence, a delay P AT H i,l (d) - the expected delay for a packet to reach the destination, after it has been handed over to the next node in the sequence (please note that, this delay does not include the waiting time at node i), a cost P AT H i,l (c) - the cost of delivering a packet using the path from node i to BS. Recollect that the contact prediction algorithm, for a node i, for each node j it meets, predicts the number of contacts k in time frame tf ( i, j) and the maximum contact delays ct(j, k) with respect to a time frame tf ( i, j), based on the contact history between the two nodes. These delays are stored as vectors for each neighbor j in the contact time table Compute Available Path Vector Let P AT HV EC represent the set of path available to destination d (that is BS) from node i, that meet the packet deadline dl. Let P AT H j,l represent the l th path through node j. Each path consist of an estimated contact time, delay to destination, the cost of the path and the expected probability of delivery through the path and let P AT H j,l (ct), P AT H j,l (d), P AT H j,l (c) and P AT H j,l (p) represent them respectively. 25

38 In this step we will compute the set of available paths P V to the destination, from node i, excluding the paths through node j, which will be send to node j. To calculate P AT HV EC to be send to node j a node i considers each path through each of the neighbors j (except the current neighbor j, to avoid cycles) and find the contact between i and j that minimizes the waiting time at j and adds it to the delay of that path. The cost and probability of delivery are updated appropriately. The subset of paths that do not meet the deadline are eliminated from P AT HV EC Calculating the Best Path In this step, using the path vectors from all neighbors (including the current neighbor) a node calculates m paths. Given the current time curt ime at node i, and the deadline for the packets dl, for each neighbor j and for each path P AT H j,l published by the neighbor, a end-to-end delay is calculated, by the estimating the two-hop handover delay (lets call this T HD ( j, l)), i.e., the handover delay at node i to node j and at node j to the next contact in the path. We want to choose a contact between node i, j, from ct(j, k)) such that T HD(j, l) is minimized. The end-to-end cost of deliver for each of these paths are also calculated. For example, if the cost function used is a simple hop count, then the hop-count is incremented by one. Once the end-to-end metrics through the neighbors are calculated, paths that do not meet the deadline dl are eliminated. The path with the least cost among all neighbors is chosen. If this minimum cost path is through node j, then the packets are forwarded to node j. 26