University of California Santa Cruz Efficient Routing In

UNIVERSITY OF CALIFORNIA SANTA CRUZ EFFICIENT ROUTING IN WIRELESS NETWORKS AND INFORMATION-CENTRIC NETWORKS A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in COMPUTER ENGINEERING by Ehsan Hemmati December 2018 The Dissertation of Ehsan Hemmati is approved: Prof. J.J. Garcia-Luna-Aceves, Chair Prof. Martine Schlag Prof. Brad R. Smith Lori Kletzer Vice Provost and Dean of Graduate Studies Copyright © by Ehsan Hemmati 2018 Table of Contents List of Figures v List of Tables vii Abstract viii Dedication x Acknowledgments xi 1 Introduction 1 2 Routing in Communication Networks 6 2.1 Routing in ICN . .6 2.2 Elements of NLSR and DCR Operation . .9 2.2.1 NLSR . .9 2.2.2 DCR . 12 2.2.3 Performance Comparison . 13 2.3 Routing in Mobile Ad-Hoc Networks . 20 2.3.1 On-Demand Routing . 23 2.3.2 Hybrid routing . 28 3 Link State Content routing 31 3.1 LSCR Operation . 31 3.1.1 Messages and Data Structures . 32 3.1.2 Routing to Nearest Replicas . 35 3.2 correctness . 41 3.3 Naming . 46 3.4 Routing Complexity . 46 3.4.1 Traditional Link-State Routing (LSR) . 47 3.4.2 Loop-free Distance-Vector Routing (LDVR) . 47 3.4.3 Link-State Content Routing (LSCR) . 48 iii 3.5 Simulation . 49 3.6 Summary . 52 4 Diffusion based Content Routing 53 4.1 DNRP operation . 53 4.1.1 Messages and Data Structures . 54 4.1.2 Sufficient Conditions for Loop Freedom . 56 4.1.3 DNRP Operation . 57 4.1.4 Example of DNRP Operation . 64 4.1.5 Routing to all instances of a prefix . 65 4.2 Correctness of DNRP . 65 4.3 Performance Analysis . 74 4.4 Summary . 76 5 Ordered Distance Vector Routing Protocol 77 5.1 ODVR Operation . 78 5.1.1 Information Exchanged . 79 5.1.2 Information Stored . 80 5.1.3 Maintaining Routing State On Demand . 82 5.2 Examples of ODVR Operation . 89 5.3 Correctness of ODVR . 91 5.3.1 Protocol Complexity . 95 5.4 Performance Comparison . 98 5.4.1 Simulation Results . 99 5.4.2 Effect of Mobility . 100 5.4.3 Effect of Number of Flows . 102 5.5 Summary . 103 6 Adaptive Approach to Routing in Ad-Hoc Networks 104 6.1 ADRP Operation . 105 6.1.1 Information Exchanged . 107 6.1.2 Information Stored . 108 6.1.3 Maintaining Routing State . 110 6.2 Loop Freedom in ADRP . 116 6.3 Performance Comparison . 120 6.3.1 Simulation Results . 120 6.3.2 Effect of Mobility . 121 6.3.3 Effect of Number of Flows . 123 6.4 Summary . 123 7 Conclusion 126 Bibliography 129 iv List of Figures 2.1 AT&T Network Topology, adapted from [42] . 15 2.2 Initialization: average number of messages sent per node, average number of operations done per node, average number of events per node.................................. 16 2.3 Adding a prefix: Average number of messages sent, operations done, and events processed per node . 18 2.4 Deleting a prefix: Average number of messages sent, operations done, and events processed per node . 18 2.5 Link failure: Average number of messages sent, operations done, and events processed per node . 19 2.6 Link recovery: (Average number of messages sent, operations done, and events processed per node . 19 2.7 Route requests are flooded because nodes must satisfy global con- straints . 25 2.8 Routing-table looping in AODV resulting from deleted routing state 26 3.1 Sample Network . 36 3.2 Valid next hops to destinations: (a) destination p, (b) destination q, (c) destination r .......................... 37 3.3 Valid next hops to prefix j ...................... 41 v 3.4 LSCR Performance: (a) average number of LSAs packets sent, (b) average number of operations, (c) average number of discovered anchor and number of anchors selected for routing per prefix per node.................................. 49 3.5 Impact of adding a new prefix: (a) average number of LSAs packets sent, (b) average number of operations . 50 3.6 Computation overhead of LSCR: (a) after prefix deletion, (b) link failure, (c) link recovery . 51 4.1 DNRP Operation Example . 63 4.2 Simulation results showing average number of messages and average number of operations vs number of replicas . 72 5.1 No routing-table loop occurs with ODVR even when nodes lose routing state and messages are unreliable . 90 5.2 Nodes receive fewer requests when request aggregation is used . 91 5.3 Nodes n, a and b are sources of data for d ............. 96 5.4 Simulation parameters . 99 5.5 Performance comparison as a function of Node Speed. 100 5.6 Performance comparison as a function of Pause Time. 100 5.7 Performance comparison as a function Number of Sources. 102 6.1 Performance comparison as a function of Node Speed. 122 6.2 Performance comparison as a function of Pause Time. 123 6.3 Performance comparison as a function Number of Sources. 124 vi List of Tables 4.1 State transit in DNRP . 59 vii Abstract Efficient Routing in Wireless Networks and Information-Centric Networks by Ehsan Hemmati The current Internet architecture was designed at the 1960s and 70s, to ad- dress communication needs of that time: sharing limited, expensive, and static computer resources. Since then, the Internet usage pattern has been shifted from conventional host-centric model to a flexible content-oriented model, in which users and contents are distributed and mobile. Internet of Things is becoming a new paradigm to describe global access to services and information offered by billions of heterogeneous devices, "things", ranging from resource-constrained to powerful devices in an interoperable way. The first part of this dissertation studies the routing in Information-Centric Networks (ICN). ICN has been recently proposed and is inspiring the design of the future Internet architecture. The goal in these architectures is to provide a cost-efficient, scalable and mobile content distribution networking by adopting a content-based model of communication. ICN not only addresses the change in the Internet usage pattern but also matches the IoT applications, since they target data regardless of the identity of the object that stores or originates them. In this dissertation, Named-data network (NDN) and Content-Centric Net- working (CCNx) are presented and routing strategies for them are evaluated. A comprehensive performance evaluation is done through the simulation experi- ments. We enhanced the performance of link-state routing by introducing a new protocol called LSCR, link-state content routing protocol, a loop-free name-based routing algorithm that propagates link-state information selectively and provides viii multi-path routing to content that may be replicated in different locations. We also introduced the first content routing protocol based on the diffusing computation, DNRP. DNRP provides multiple loop-free routes to the nearest instances of a data using only distance information and without requiring periodic updates, knowledge of the network topology, or the exchange of path information. MANET paves the way for the development of brand new IoT communication platforms with a high potential for a wide range of applications in different domains. Each layer in the design model require redefinition or modifications to function efficiently in MANETs every is mobile and usually has limited resources on computation, storage, power, etc. Routing in Mobile Ad-Hoc Networks is studied in the second part of this dissertation. We introduce ODVR (Ordered Distance Vector Routing), that provides loop-free routes at every instant based solely on distances to destinations main- tained by nodes and reference distances included in route requests. Routing state is established on demand by means of route requests stating the reference distance and replies from nodes that satisfy this distance. To make the routing more efficient in an IoT environment with a base or gateway nodes, we introduce ADRP, a hybrid routing algorithm that takes advantage of the strengths of reactive routing algorithms as well as the benefits of proactive ones. ADRP uses the same signaling for both reactive and proactive routing. ix To my beloved wife Faeze, my extraordinary parents Masoumeh and Hossein, and amazing brothers Amir and Arash. x Acknowledgments First and foremost I want to thank my advisor, Professor J. J. Garcia-Luna- Aceves, for the patient guidance, encouragement and advice he has provided throughout my time as his student. He’s the funniest advisor and one of the smartest people I’ve ever met. J. J. has supported me not only by providing a research assistantship, but also academically and emotionally through the rough road to finish this thesis. I hope that one day I could be as lively, enthusiastic, and energetic as he is and to be able to command an audience as well as he can. I would also like to thank my committee members, professor Martine Schlag and professor Brad Smith. to serve in my committee despite their busy schedule and and for all their valuable feedback. I would like to extend my thanks to Emily Gregg and Tracie Tucker for all their great help through my PhD program. Finally, I would like to thank my awesome friends in CCRG (Computer Communication Research Group) Maziar M. Barijough, Ali Dabirmoghaddam, Turhan Karadeniz, James Mathewson, and Spencer Sevilla who were always there for me, in the hard and fun research and personal times. Last but not least, I would like to thank my mom and dad, Masi and Hossein to whom I owe the happy life that I have led so far. Thank you for your endless love, support, and encouragement.

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