The Pennsylvania State University. The Graduate School. The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering

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1 The Pennsylvania State University The Graduate School The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering DECISION MAKING ON ROUTING AND QUEUE MANAGEMENT WITH NODE INDEPENDENT MULTIPATH ROUTING IN MOBILE AD-HOC NETWORKS A Thesis in Industrial Engineering and Operations Research by Melike Oz Pasaogullari 2006 Melike Oz Pasaogullari Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2006

2 The thesis of Melike Oz Pasaogullari was reviewed and approved* by the following: Catherine M. Harmonosky Associate Professor of Industrial and Manufacturing Engineering Thesis Co-Advisor Co-Chair of Committee Sanjay Joshi Professor of Industrial and Manufacturing Engineering Thesis Co-Advisor Co-Chair of Committee Natarajan Gautam Associate Professor of Industrial and Manufacturing Engineering George Kesidis Associate Professor of Electrical Engineering and Computer Science and Engineering Richard J. Koubek Professor of Industrial and Manufacturing Engineering Head of the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering *Signatures are on file in the Graduate School

3 ABSTRACT ii A mobile ad hoc network (MANET) is a wireless network without a fixed infrastructure. Due to the mobile nature of the nodes, connectivity between the nodes is not fixed and routing is an important problem for these types of networks and more complex compared to their counterpart in wired networks. Multipath routing is a concept in which more than one path is used to transfer packets of data between sourcedestination pairs. Multiple paths can be used either as alternate (backup) paths in case of failures or simultaneously by splitting the traffic over multiple paths to balance the network load. The main goal of this research is to develop a solution methodology to the routing and scheduling problems in MANETs via distributed algorithms to select traffic flow to be transmitted and the type of routing --unipath or multipath-- to be used at each decision point. In this work, multipath routing in MANETs is analyzed, and the decisions for selecting the paths to be used among the available paths and determining the allocations to different paths are made. A centralized routing and scheduling problem has been formulated and sample cases have been solved using the General Algebraic Modeling System (GAMS) to help propose a heuristic for the distributed problem. A node-independent multipath routing (NIMR) algorithm which allows decision making at intermediate nodes is proposed. The node-independent multipath routing algorithm is compared with a path delay based source-based unipath (SBU) routing algorithm. The NIMR algorithm performs up to 7.12% better than the SBU routing algorithm on the average. However, the spread of the data shows that the SBU routing algorithm still performs better than the NIMR algorithm in 15% to 40% of the replications. Based on

4 iii these results, a hybrid algorithm which switches between the unipath and multipath routing algorithms based on the local information of the nodes is proposed. The NIMR, SBU and hybrid algorithms are compared in terms of average packet delay via hypothesis tests. When the NIMR and hybrid algorithms are used, an improvement ratio with respect to the SBU algorithm which differs from zero statistically is obtained in most of the cases for networks with more than 15 nodes. Also, for networks with more than 20 nodes, a negative improvement ratio which differs from zero statistically is obtained when the hybrid algorithm is compared with the NIMR algorithm indicating that the NIMR algorithm performs better than the hybrid algorithm. The NIMR algorithm is also compared with five routing algorithms adapted from the literature for different size networks and mobility scenarios in terms of end-to-end delay, throughput, and overhead of the routing algorithm. In general, the NIMR algorithm performs up to % better than the unipath routing algorithms and % better than the source-based multipath algorithms in terms of average packet delay. Based on the results of the simulation experiments, a source-based unipath routing methodology based on hop count is recommended for small size networks with 8 or 10 nodes, whereas the NIMR algorithm is recommended for larger networks. The effect of four different scheduling rules on the NIMR algorithm is also analyzed. The results show that there is not enough evidence to conclude that there is a significant different between the scheduling rules, however, the first in first out rule is recommended since it gives lower average end-to-end delay values.

5 TABLE OF CONTENTS iv LIST OF FIGURES...vii LIST OF TABLES...ix NOMENCLATURE...xi ACKNOWLEDGEMENTS...xiii Chapter 1 INTRODUCTION Background Problem Statement Research Objective Impact of the Research Organization of the Thesis...8 Chapter 2 LITERATURE REVIEW Routing Single-Path Routing Multipath Routing Backup Paths Traffic Splitting Scheduling Routing and Scheduling Summary...28 Chapter 3 METHODOLOGY: NODE-INDEPENDENT MULTIPATH ROUTING ALGORITHM Centralized Model Preliminary Results for the Centralized Model Node-Independent Multipath Routing Algorithm Route Discovery Route Maintenance Packet Forwarding Decisions Assumptions Preliminary Studies of the NIMR Algorithm Steady State Analysis Simulation Environment Arrival Pattern Analysis Source-Based Unipath Routing Algorithm Preliminary Results...61

6 3.5 Summary...67 v Chapter 4 METHODOLOGY: HYBRID ROUTING ALGORITHM Hybrid Routing Algorithm Design of Experiments and Best Parameter Selection for Hybrid Algorithm Hypothesis Tests and Comparison of the Algorithms Comparison with Other Routing Algorithms Algorithm 1: Source-Based Unipath Routing Based on Hop Lengths Algorithm 2: Hop-by-hop Unipath Routing Algorithm 3: Source-Based Multipath Routing Algorithm-I Algorithm 4: Source-Based Multipath Routing Algorithm-II Algorithm 5: Source-Based Multipath Routing Algorithm-III Hypothesis Tests and Comparisons Analysis of Other Performance Measures Discussions and Conclusions on the Routing Methodology Summary Chapter 5 ANALYSIS OF SCHEDULING RULES Scheduling Rules Significance Tests for Scheduling Rules Simulation of Large Networks Summary Chapter 6 CONCLUSIONS Relationship to the Existing Research Conclusions Impact of the Research Future Work Experimentation with Decision Rules Used in the Hybrid Algorithm Performance of the Hybrid Algorithm Congestion Level Updating Expected Path Delays Cross-Layer Design Energy Consumption Mobility Models Bibliography Appendix A Interval Plots for 95% Confidence Intervals...145

7 Appendix B Numerical Values of 95% Confidence Intervals vi

8 LIST OF FIGURES vii Figure 1-1: A small mobile ad hoc network...2 Figure 1-2: Schematics of MANETs and scope of present work...8 Figure 1-3: Overview of the research...10 Figure 3-1: Layout used for verification...36 Figure 3-2: Route discovery process...41 Figure 3-3: Cache clearing process...43 Figure 3-4: Organization of node queues...44 Figure 3-5: Average node utilization for different mode values of the Pareto distribution...53 Figure 3-6: Average packet delay for different mode values of the Pareto distribution...54 Figure 3-7: Packet throughput for different mode values of the Pareto distribution..56 Figure 3-8: Number of packets dropped due to full buffer for different mode values of the Pareto distribution...57 Figure 3-9: SBU algorithm with path delay vs. SBU algorithm with hop length...60 Figure 3-10: Histogram of average packet delay improvement ratio for 10 node network...64 Figure 3-11: Histogram of average packet delay improvement ratio for 15 node network...65 Figure 3-12: Histogram of average packet delay improvement ratio for 20 node network...66 Figure 4-1: Hybrid algorithm: average packet delay-10 node network...77 Figure 4-2: Hybrid algorithm: average packet delay-15 node network...78 Figure 4-3: Hybrid algorithm: average packet delay-20 node network...79 Figure 4-4: 95% CI for the average packet delay improvement ratio (%)...82 Figure 4-5: Comparison of the SBU, NIMR and hybrid algorithms...84

9 Figure 4-6: Average packet delay improvement ratios for the NIMR algorithm...94 Figure 4-7: Average traffic delay improvement ratio (%)...98 Figure 4-8: Packet throughput improvement ratio (%) Figure 4-9: Traffic throughput improvement ratio (%) Figure 4-10: Overhead improvement ratio (%) Figure 5-1: 95% confidence intervals for average packet delay with different scheduling rules: NIMR algorithm Figure 5-2: 100 node network: average packet delay improvement ratio (%) Figure 5-3: 100 node network: packet throughput improvement ratio (%) Figure 5-4: 100 node network: overhead improvement ratio (%) Figure 6-1: Overview of the research Figure A-1: 95% CI for the average packet delay improvement ratio (%): NIMR algorithm vs. other algorithms viii

10 LIST OF TABLES ix Table 2-1: Literature review on routing in ad hoc networks-unipath and backup paths...22 Table 2-2: Literature review on routing in ad hoc networks-traffic splitting...23 Table 2-3: Literature review on scheduling in ad hoc networks...27 Table 3-1: Traffic information...36 Table 3-2: Packet allocation to paths for Case 1, 2 packets per traffic flow...37 Table 3-3: Packet allocation to paths for Case 2, 10 packets per traffic flow...37 Table 3-4: GAMS results for average traffic delay...38 Table 3-5: Simulation Parameters...50 Table 3-6: 95% confidence interval for the average packet delay improvement ratio (%)...63 Table 4-1: Factors and their levels...73 Table 4-2: Hybrid algorithm: parameter set at each design point...76 Table 4-3: Analysis of average packet delay improvement ratio (%) with respect to network size: Hybrid algorithm vs. SBU algorithm...85 Table 4-4: Analysis of average packet delay improvement ratio (%) with respect to network size: NIMR algorithm vs. SBU algorithm...86 Table 4-5: Results of the hypothesis tests for the NIMR algorithm...92 Table 5-1: Average packet delay improvement ratios (%) Table 5-2: Packet throughput improvement ratios (%) Table 5-3: Overhead improvement ratios (%) Table B-1: 95% confidence intervals on average packet delay improvement ratio (%): hybrid algorithm vs. SBU algorithm comparisons Table B-2: 95% confidence intervals on average packet delay improvement ratio (%): hybrid algorithm vs. NIMR algorithm comparisons...147

11 Table B-3: 95% confidence intervals on average packet delay improvement ratio (%): NIMR algorithm vs. SBU algorithm comparisons Table B-4: 95% confidence intervals on average packet delay improvement ratio (%): NIMR algorithm vs. Algorithm 1 comparisons Table B-5: 95% confidence intervals on average packet delay improvement ratio (%): NIMR algorithm vs. Algorithm 2 comparisons Table B-6: 95% confidence intervals on average packet delay improvement ratio (%): NIMR algorithm vs. Algorithm 3 comparisons Table B-7: 95% confidence intervals on average packet delay improvement ratio (%): NIMR algorithm vs. Algorithm 4 comparisons Table B-8: 95% confidence intervals on average packet delay improvement ratio (%): NIMR algorithm vs. Algorithm 5 comparisons Table B-9: 95% confidence intervals on average packet delay (ms): scheduling rule comparison using NIMR algorithm x

12 NOMENCLATURE xi AODV: Ad-hoc on demand distance vector AOMDV: Ad-hoc on demand multipath distance vector APR: Alternate path routing CHAMP: Caching and multipath CRN: Common Random Number D-MPR: Disjoint multipath routing DSR: Dynamic source routing FIFO: First in first out GAMS: General algebraic modeling system GPSR: Greedy perimeter stateless routing MANET: Mobile ad hoc network Mbps: Megabit per second MDAR: Multipath directional antenna ad-hoc routing MIP: Mixed integer programming M-MPR: Meshed multipath routing MP-AOMDV: Mobility prediction ad-hoc on demand multipath distance vector MRP-LB: Multi-path routing protocol with load balance policy MSR: Multipath source routing NIMR: Node-independent multipath routing OSI: Open systems interconnection QoS: Quality of service

13 RERR: Route error xii RREP: Route reply RREQ: Route request SBU: Source-based unipath SMR: Split multipath routing TDMA: Time division multiple access

14 ACKNOWLEDGEMENTS xiii This thesis would not be completed without the valuable support from my advisors, Dr. Harmonosky and Dr. Joshi. I would like to thank them for their support and advice during my graduate study at Penn State. Their guidance helped me to focus on the subject and investigate the important details. I would also like to thank my committee members, Dr. Gautam and Dr. Kesidis, for all the feedback and help they have provided throughout my study. I am also grateful to my husband, my parents, my sister, and all the other members of my family for their continuous support and for believing in me. For that, I dedicate this thesis to my family.

15 Chapter 1 INTRODUCTION 1.1 Background Computer communication networks have been receiving attention because of the need to provide flexible and effective service. Of the two types of networks, wired and wireless, wired networks have been actively researched in the past, and effective approaches have been proposed to different problems in this area, such as routing, admission control, and scheduling. However, for wireless networks, there are still open research areas, especially in mobile ad hoc networks. The main reason for this is that ad hoc networks do not have a stable infrastructure. A mobile ad hoc network (MANET) is a special type of wireless network where the network consists of n nodes, and the edges (e) of this graph represent the wireless connections between the nodes. The graph -G (n, e) - can be considered as a time-varying graph due to the mobility of the nodes. The network is subject to changes due to changes in node and link connectivity; therefore, the complexity of the problems for these types of networks is higher compared to the fixed networks. Figure 1-1 shows schematics of a small mobile ad-hoc network and illustrates the effect of mobility on the topology. Figure 1(a) shows the topology of the network before node 5 moves. The topology after node 5 moves is shown in Figure 1(b). As seen from the figure, the links (3,5) and (4,5) are no longer valid after node 5 moves,

16 2 since the distance between these nodes is larger than the transmission distance, i.e., the maximum distance to maintain a connection. However, after node 5 moves, a new link is introduced between node 5 and node a) Topology before node 5 moves b) Topology after node 5 moves Figure 1-1: A small mobile ad hoc network With the advances in technology, mobile ad hoc networks provide easier communication between mobile devices since they do not require a fixed infrastructure. As mentioned by Macker and Corson (1998), mobile communications have become more desirable since they provide location transparency. Also, Sun (2001) has pointed out that mobile ad hoc networking is considered as one of the most important and essential technologies for future computing environments. The main application areas of mobile ad hoc networks include military applications such as battlefields, establishing communication in case of an emergency, or sharing information among a group of users in a conference (Macker and Corson (1998), Sun (2001)). To improve this future network structure, different approaches have been proposed to provide solutions to different problems of MANETs. In MANETs, the

17 3 problems of interest include routing, scheduling, multicasting, security, energy consumption, and clustering. As stated in Sun (2001), routing strategy is the most important research problem among others in MANETs. Routing in mobile ad hoc networks is not a well defined problem due to the decentralized environment and mobility; and when the barriers such as link qualities, path loss, power consumption, and mobility are considered, routing in mobile ad hoc networks becomes a very complex problem (Sun (2001)). Wu and Harms (2001) have also mentioned the importance of routing in MANETs and stated that routing in MANETs is a non-trivial task because hosts movements cause frequent topology changes and require robust and flexible mechanisms to discover and maintain the routes. The routing problem in ad hoc networks searches for paths to be used to send the flow of data from the source node to the destination node. A general approach is to use a source-based routing algorithm where the source initiates the request to find a path from the source to the destination. In this approach, the complete path information is attached to the flow, and the flow is transferred sequentially to the next node in the path until it reaches the destination node. There are two main approaches used for routing the flow of data after finding the paths: single paths and multiple paths. In single-path routing, the path found is generally the shortest path based on hop length of the path. The primary path is found through the path-finding mechanism and is used as the only path. In case of a failure along the path, a new path search is initiated. Therefore, when a node/link failure occurs in this method, the flows will be subject to additional delays due to the time spent in finding a new path. In multipath routing, more than one path is determined during initial path finding.

18 4 Multipath routing can be used in three different ways. The first is to use the shortest path as the primary path, storing the other multiple paths as backup paths to reduce the time needed to search for another path in case of a failure in the primary path. A second approach to using multiple paths is to send the same flow through all available paths to improve robustness and fault tolerance at the cost of flow of information. Thirdly, the flow can be split among available paths and reorganized at the destination point. In this case, different packets of a traffic flow can follow different paths, which may result in out-of-order packets at the destination point. The idea behind traffic flow splitting is to minimize the delay and balance the network load. In some cases, such as non-congested networks, small traffic sizes, or close source-destination points, single path routing may be the best way of sending packets to their destinations. However, as the traffic size increases, using multipath routing may decrease end-to-end delay by balancing the network load. There are two types of routing protocols used for ad hoc networks: reactive and proactive. In proactive routing, the routing information to every destination point is stored at each node. In reactive protocols, routes are discovered as they are needed. The disadvantage of proactive routing is the resulting overhead associated with maintaining the paths all the time. In a mobile ad hoc network, the paths are subject to changes; thus proactive routing requires frequent re-computation of the paths. The disadvantage of reactive (on-demand) routing protocols is the additional route discovery delays, since the paths need to be computed as needed. One of the main performance measures used in routing is end-to-end delay, which is the time required to send a traffic flow from its source node to its destination node.

19 5 The types of delays that the data packets will face are transmission delays, queueing delays, and propagation delays. Transmission delay depends on the size of packet and bandwidth, whereas propagation delay depends on the distance between nodes and propagation speed, which is very fast. Queueing delays form a significant amount in endto-end delay and can be reduced by balancing the network load. 1.2 Problem Statement In this work, a distributed decision-making algorithm for mobile ad hoc networks, which decides the routing scheme and explores routes from source to destination points, is proposed to minimize end-to-end delay as the primary performance measure. In the proposed distributed decision making algorithm, the main focus is on the packet forwarding decisions. In this work, up-to-date information is used to improve the system performance by allowing decision making at each node. Therefore, the main decisions are made not only at the source node but also at the intermediate nodes, which is referred to as node-independent routing. By using node-independent routing, different routing decisions may be made for a specific traffic flow at each node. In this work, a traffic flow is defined as a set of data packets of equal length. At each node, whether to use single path (unipath) or multiple paths is determined based on the number of packets of the selected traffic flow ready to be transmitted. The paths that can be used among the available paths is decided based on the ratio of the delay on the paths to the delay on the shortest path. In order to develop a routing scheme which is

20 6 robust to different network structures, decision rules are used to combine the sourcebased unipath routing and the proposed node-independent routing. The second problem that is analyzed in this work is the queue management. Queue management (scheduling) decisions at each node involve selecting the traffic flow for which routing decisions are made. The effect of scheduling on the routing decisions is analyzed, and the scheduling rule that will be used to improve the efficiency of the routing algorithm is identified. In multipath routing, if the traffic flow is split, the problem of out-of-order packets at the destination point is not considered in this work; it is assumed that an existing sequencing algorithm is used to reorder the packets. Also, propagation delay is neglected, since it is small compared to the other delays, due to the short transmission range. In this work, it is assumed that the nodes can transmit at the same time even if they are within the transmission range of each other without any collision. 1.3 Research Objective The objective of this research is to develop a solution methodology for the routing and scheduling problems in mobile ad hoc networks that will minimize end-to-end delay as the primary objective. A multipath routing algorithm via traffic splitting is proposed in order minimize end-to-end delay by balancing the network load. The proposed scheduling algorithm is used for queue management at each node.

21 1.4 Impact of the Research 7 In this work, the routing and scheduling problem in MANETs are studied. In the routing problem, the packet forwarding decisions are considered whereas in the scheduling problem, management of node queues is studied. Routing occurs in the network layer of the open systems interconnection (OSI) layer where packet forwarding decisions are made. In wireless networks, OSI consists of application, transport, network, link and physical layers. This work focuses on the routing and scheduling decisions in the network layer. This work presents a more intelligent routing algorithm that allows decision making at any step of the routing process. A node-independent multipath routing algorithm with splitting is proposed to improve the performance of the network in terms of end-to-end delay. In this work, the effect of the scheduling rules for queue management is also analyzed. This work focuses on congested networks and aims to minimize end-to-end delay by balancing the network load using multipath routing via traffic splitting. The main application areas of MANETs, e.g. military operations, personal communications, establishing communications in conferences and in case of emergencies, are among the applications areas of this work. More specifically, in this work, the solution methodologies focus on networks where large data is exchanged such as voice, video, and large data files in which load balancing can improve the network performance in terms of end-to-end delay. Schematics of MANETs and the scope of this work are presented in Figure 1-2.

22 8 Mobile device Mobile device Network Link Physical Network Link Physical packet Application Transport Network Link Physical source node intermediate node Network Link Physical intermediate node Network Link Physical intermediate node Application Transport Network Link Physical destination node Application Areas: Military operations Conferences Emergencies intermediate node Network Link Physical intermediate node Open Systems Interconnection (OSI) Layers of a Wireless Network: End user processes Application Flow control and error Transport recovery Routing Network Medium access control Link and error checking Bit level communication Physical Focus of this work Focused Environments: Congested systems Exchange of large files video voice large data Multihop networks Problem/Research Areas: Routing Scheduling before packet forwarding decisions priority setting for packets Scheduling in MAC layer Security Mobility models Power control Quality of Service (QoS) Figure 1-2: Schematics of MANETs and scope of present work 1.5 Organization of the Thesis The organization of this thesis is as follows: Chapter 2 presents a review of the literature on routing and scheduling. A review of single-path and multipath approaches to the routing problem in ad hoc networks is presented as well as a review of the approaches to the scheduling problem. In this work, a centralized mixed integer programming model (MIP) of the routing and scheduling problem is developed to gain insight into these problems. Based

23 9 on the results of the MIP model for the sample networks, a distributed multipath routing algorithm is proposed. The performance of the proposed node-independent multipath routing (NIMR) algorithm is analyzed under different network conditions and compared to a source based unipath (SBU) routing algorithm which is based on path delays. The centralized MIP model and the proposed NIMR algorithm are explained in Chapter 3. After the comparisons of the NIMR algorithm with the SBU algorithm, a hybrid routing algorithm that uses the proposed node-independent multipath routing and sourcebased unipath routing methodologies is investigated. Different decision rules are analyzed to decide the switching conditions between these two methodologies and to make splitting decisions when multipath routing algorithm is in use. The threshold values for the decision rules of the hybrid algorithm are explored via a full factorial experimental design. The methodology used in the proposed hybrid routing algorithm and the experimental analysis are given in Chapter 4. Also in Chapter 4, the performance of the NIMR algorithm is analyzed in terms of different output measures via comparisons to hybrid algorithm and different routing algorithms adapted from the literature. In this work, the queue management problem is also studied. The effect of scheduling rules to select the traffic flow from the node queue for which routing decisions will be made is investigated under different network conditions. In Chapter 5, the experimental analysis of different scheduling rules for the NIMR algorithm is presented. Also in Chapter 5, a large network is simulated in order to verify the results obtained through the experimental design performed in Chapter 4. Finally, in Chapter 6, the conclusions and future work are presented. The overview of the research is summarized in Figure 1-3.

24 10 Algorithms Developed/Analyzed Routing Methodology Centralized MIP model Performance Analysis Small networks are analyzed in terms of end-to-end delay NIMR algorithm SBU algorithm Decision rules for combining Hybrid algorithm Comparison of NIMR and SBU algorithms in terms of end-to-end delay Determine threshold values for decision rules to minimize end-to-end delay Comparison of NIMR, SBU and hybrid algorithms in terms of end-toend delay Routing algorithms adapted from literature: Source-based unipath Hop-by-hop unipath Source based multipath I-II-III Benchmarking: Comparison of NIMR with hybrid algorithm and five routing algorithms in terms of end-to-end delay, throughput, and overhead. Scheduling Methodology FIFO Maximum load Minimum hop Maximum hop Significance tests for scheduling rules in terms of end-to-end delay Figure 1-3: Overview of the research

25 Chapter 2 LITERATURE REVIEW There have been a number of studies on different types of routing algorithms in the literature. Reactive vs. proactive routing and single path vs. multipath routing algorithms have been proposed for ad hoc networks as well as scheduling algorithms for queue management. A summary of the literature on reactive routing and scheduling is given next. 2.1 Routing Reactive routing algorithms are used to discover routes from source to destination nodes on demand, i.e., when there is no prior route information. This type of routing algorithm is more suitable for mobile ad hoc networks due to their dynamic nature. In this section, existing reactive single-path routing and multipath routing approaches proposed for ad-hoc networks are reviewed Single-Path Routing Single-path routing algorithms find only one path to transfer data from source to destination. Two well-known algorithms, Dynamic Source Routing (DSR) and Ad-Hoc on Demand Distance Vector Routing (AODV), are reviewed in this section. Most of the routing algorithms proposed in the literature are extensions of DSR and AODV.

26 12 Johnson and Maltz (1996) proposed DSR, a source-based routing algorithm, to find a path between source and destination. In DSR, the path found at the end of the route discovery process is the shortest path from source to destination. For data forwarding, the path information is added to the header of the packet and used by the intermediate nodes to forward the packet until the packet reaches its destination. DSR also supports storing multiple paths to be used in case of failures. The simulation results show that the ratio of the lengths of the paths found by DSR to the optimal length of the paths is insignificant. The ratio is 1.09 when mobility frequency is high and decreases down to 1.01 as mobility frequency decreases. Ad-hoc On Demand Distance Vector Routing (AODV), proposed by Perkins and Royer (1999), is not a source-based algorithm. Instead of following the path given in the header file, route tables are used at each node to forward the packets. In case there is no route information stored at the source node, the source node initiates a new route discovery. In order to obtain information about their neighbor nodes, a HELLO message is sent by each node, and their routing table is updated if there is no response from a neighbor to the HELLO message. The performance of AODV was analyzed in terms of goodput ratio, bandwidth overhead ratio, route acquisition latency, average path length, and collision rate. The results show that as the number of nodes increases from 50 to 1000, goodput ratio decreases from 98.75% to 70.53%, bandwidth overhead ratio increases from 1.14 to 1.49, average route acquisition latency increases from 206 ms to 548 ms, average path length increases from 3.94 hops to hops, and collusion rate increases from 1.43% to %.

27 2.1.2 Multipath Routing 13 Multiple paths can be used as backup paths or simultaneously by splitting the traffic over multiple paths. The literature on these two approaches in multipath routing is reviewed in this section Backup Paths Zhang and Mouftaf (2004) proposed an algorithm that finds the least cost routing, subject to delay and bandwidth constraints by using a shortest path algorithm. The algorithm considers the cost term as the number of hop count, which is the number of links used from source to destination. In this approach, alternative paths are discovered if the shortest path fails the quality of service (QoS) requirements in terms of delay or bandwidth. In case of failures, the source initiates a new route discovery to find the alternative paths. Storing more than one path at the end of the initial route discovery process to shorten route discovery times in case of failures was also proposed. This algorithm was compared with the flooding algorithm, in which the nodes that receive a packet send it to all of their neighbors, and the shortest path algorithm. Although the flooding algorithm outperforms the shortest path algorithm and the proposed methodology in terms of the ratio of the total number of routed connection requests to the total number of connection requests, the proposed methodology performs better than the flooding method in terms of control message overhead and hop count of the paths. Lee and Gerla (2000) also used alternative paths in case of a failure, but the alternative paths that are found during the search of a primary path are stored in a mesh

28 14 structure in which the primary path is connected to the alternative paths. This algorithm performs better than AODV in terms of throughput, but provides longer end-to-end delays due to longer alternative paths used. The results show that using alternative paths as backup paths performs worse than AODV under heavy traffic. As in Zhang and Mouftaf s (2004) methodology, alternative paths are used only in case of a failure. Nasipuri et al. (2001) extended DSR to calculate multiple paths from source to destination. The primary path is the shortest path that DSR returns. Then, analysis to determine alternative paths is done based on two scenarios. In the first scenario, only the source node has alternative paths to the destination, whereas in the second scenario, a disjoint alternative path is also available at the intermediate nodes. In the first scenario, in case of a failure on the primary path, the source node sends the packets by using the next shortest alternative path. In the second scenario, if there is a link failure, an intermediate node sends the packets by using its alternative route to the destination. In case all the alternative routes fail, a new route discovery is initiated in both scenarios. The results show that multipath routing improves the system performance compared to single path routing up to around 25%, based on the total number of routing packets transmitted. Marina and Das (2001) presented Ad hoc On-demand Multipath Distance Vector Routing (AOMDV) protocol as a multipath extension of AODV. A route discovery algorithm was presented to find multiple link-disjoint paths which are used in case of failures. AOMDV and AODV were compared based on packet delivery ratio, end-to-end delay, route discovery frequency, and normalized routing load under different mobility and traffic scenarios. The results show that, in terms of packet delivery ratio, AOMDV and AODV give similar results in the static case, and AOMDV performs better than

29 15 AODV in case of high mobility. AOMDV also improves the performance in terms of end-to-end delay, route discovery frequency, and normalized routing load. Sambasivam et al. (2004) presented a mobility prediction based adaptive multipath routing algorithm, MP-AOMDV, which is based on AODV. In this algorithm, multiple paths are discovered during the route discovery process. The stability of the paths in terms of signal strength is used to select the primary path among the multiple paths available and the path with the highest signal strength is used in order to prevent using invalid or stale paths. Periodic update messages are used to keep track of the stability of the paths. Node-disjoint and link-disjoint versions of the algorithm were compared with AOMDV and DSR in terms of packet delivery ratio, end-to-end delay, and overhead. The results show that MP-AOMDV, especially the node-disjoint version, performs better than both AOMDV and DSR in terms of end-to-end delay and packet delivery ratio with lower control overhead. In ad hoc networks, the nodes within the transmission range of each other transmit data packets one by one. In such cases, directional antennas can be used in order to increase the channel capacity by allowing more than one node to transmit at a time. Li et al. (2004) used directional antennas in their multipath routing algorithm and analyze the effect on the routing algorithm. In the proposed algorithm, Multipath Directional Antenna Ad Hoc Routing (MDAR), multipath information is stored in the routing tables of each node and used in case of a failure to retransmit the packets. The results show that MDAR works better than DSR in terms of end-to-end delay and delivery ratio. MDAR provides shorter end-to-end delay and a higher delivery ratio.

30 16 There are also studies in sensor networks on multipath routing where multipath information is used as backup in case of a failure. De et al. (2003) proposed a multipath routing algorithm where the multipath information is used in case of a failure for wireless sensor networks. A meshed structure is used, where nodes have more than one next hop information to the destination, in their multipath algorithm. In the proposed algorithm, meshed multipath algorithm (M-MPR), one link is selected to forward the packets at each node. In case of a failure, instead of retransmitting from the source node, the intermediate node selects another path to retransmit the packet. The algorithm was analyzed in terms of throughput and power consumption and compared with disjoint multipath routing (D-MPR), in which the paths used do not have intermediate nodes in common. Another difference between D-MPR and M-MPR is that D-MPR is a source-based algorithm in which routing decisions are made at the source node. According to the results, M-MPR provides higher throughput than D-MPR and has the same power consumption Traffic Splitting As mentioned in Chapter 1, another routing approach is to use multiple paths simultaneously via splitting of the traffic to provide a load balanced network by assigning the packets of a single flow to different paths. Lee and Gerla (2001) proposed an on-demand routing algorithm for ad hoc networks, Split Multipath Routing (SMR), which splits the traffic into two maximally disjoint paths between source and destination. Two different versions of the algorithm were compared with the Dynamic Source Routing (DSR) algorithm in terms of packet delivery ratio, number of packet drops,

31 17 normalized routing load, and hop distance, which is the number of hops from source to destination. The two versions of SMR differ according to the route recovery process. In one version, route recovery is performed when one of the paths fails, and in the other version, it is performed when both of the paths fail. According to the results, SMR performs better than DSR in highly mobile environments, and its relative performance increases as mobility of the network increases. However, in more stable systems, DSR is better in terms of the number of control packets generated and hop distance of the paths used. Wu and Harms (2002) proposed an on-demand multipath routing algorithm in which the paths are node disjoint and the length differences between the paths (primary and alternative paths) are small to minimize average delay, bandwidth cost, and provide load and energy balancing. The algorithm uses non-redundant dispersity routing where all the paths are used to transmit data. The probability of selection of a path is inversely proportional to the length of that path. If a path fails, the traffic on that path will be transmitted by other paths that are alive. A new multipath discovery is initiated if all paths are broken. A heuristic method was also presented to decrease the overhead of the proposed method. The proposed methods were compared with unipath routing and the diversity injection method, which uses path information from the received route queries to send the route replies to the source using different paths (Pearlman and Haas (1999)). The results show that multipath routing improves end-to-end delay if the alternative paths used are also short and multipath routing provides better load balancing. However, traffic load in the route discovery or path selection process were not considered in the study.

32 18 Das et al. (2003) proposed a methodology in which the data packets are distributed through different paths to minimize the end-to-end delay. Temporal and spatial paths are used to send a large volume of data from source to destination. The data is distributed into small packets that are sent through a set of temporal paths at different time instants. These small volumes of the original data can be split further and sent through a number of paths (spatial paths). Lagrangian relaxation and a subgradient algorithm were used to find the paths and the size of the packets allocated to each path. The stability of the links and paths were also investigated. The results show that by using temporal and spatial paths, end-to-end delay can be decreased and large volumes of data can be sent at the same time. The path delays were estimated by route discovery times in order to take congestion into account. However, this means that the route discovery process takes as much time as sending a packet from source to destination, and therefore, causes larger end-to-end delays for a specific traffic flow. Pham and Perreau (2004) proposed a multipath routing algorithm with load balancing (MRP-LB). The routing algorithm takes the number of packets at each node on a path into account to evaluate the paths for allocating the packets. The algorithm was analyzed for different arrival rates and compared with single-path routing, which uses the shortest path. MRP-LB was compared with Dynamic Source Routing (DSR) in terms of average delay, throughput, and overhead. The results show that at the expense of increased overhead, higher throughput is obtained by using MRP-LB. In terms of average delay, the results show that MRP-LB performs better than DSR when there are frequent arrivals. Overhead and traffic distribution were also analyzed analytically. The results show that when the number of paths used in MRP-LP exceeds 3, overhead increases

33 19 significantly. An upper bound is obtained for the average path length to be used in MRP-LB, which will decrease congestion compared to single-path routing. The main drawback with the model is that mobility is not considered. Dulman et al. (2003) also studied multiple paths in wireless sensor networks. The data packet was split into k sub-packets. Forward error-correcting codes were used that add redundancy to the data but allow reconstruction of the original packet at the destination without using all sub-packets. Methodologies were presented to predict the number of paths that are needed to successfully deliver the original packet and analyze the effect on the traffic of the network and percentage of failed transmissions. It is assumed that there is a routing algorithm that finds multiple paths from source to destination. Although node-disjoint paths in which the paths do not have nodes in common were considered, the methodology can also be applied to braided multipaths where paths are not completely node disjoint. The simulation results indicate that when more paths than the optimal number of paths calculated are used, the failure probability increases. Pearlman et al. (2000) studied the impact of alternative paths routing (APR) in mobile ad hoc networks and the impact of route coupling on alternative paths. Route coupling is high if two routes have common links or nodes. The results show that as route coupling decreases, load balancing becomes more effective. The system was analyzed under different traffic patterns for multiple-channel and single-channel ad hoc networks. Based on the simulations, APR has less effect on end-to-end delay for reactive routing protocols than proactive routing protocols. Also, with the use of alternative paths, the

34 20 median lifetime of a path decreases. The number of alternative paths was limited to 1 or 2 and source-destination nodes that are separated by at least six hops were considered. Wang et al. (2001) proposed a multipath routing algorithm, Multipath Source Routing (MSR), which is based on DSR. This algorithm uses weights that are calculated based on the delays of the paths. The theoretical analysis of this algorithm was given by Zhang et al. (2002). The algorithm proposed in Wang et al. (2001) was analyzed by using a queueing model of the network. The results show that if traffic is large enough, splitting the traffic into two paths is optimal. MSR is a source-based algorithm; therefore, the paths are found by the source node, and the intermediate nodes are used to transmit the packets according to the paths assigned to them. This algorithm uses disjoint paths to send the packets to their destinations, and it minimizes end-to-end delay and balances the load in the network. Another issue that needs to be considered in wireless networks is the interference between the paths, which is called route coupling. This can have a negative effect on the performance. Saha et al. (2003) proposed an algorithm to find zone disjoint paths to eliminate the route coupling problem in multipath routing. Directional antennas are proposed for obtaining a smaller transmission zone and the effects were analyzed. In order to minimize the delay, zone disjoint shortest paths were considered. The proposed algorithm assigns packets to two zone disjoint paths alternately at the source node. One of the main differences of this algorithm is that intermediate nodes decide the next hops for the packets instead of forwarding them based on a path found by their source nodes. The results show that the use of directional antennas decreases average end-to-end delay.

35 21 The proposed algorithm was also compared with AODV and the results show that it outperforms AODV in terms of throughput. Marbukh (2003) approached the multipath routing problem using a game theoretic approach and provided analytical results for finding the optimal allocation to multiple paths to minimize the average cost of the paths. In a later study, Kumar and Marbukh (2004) used Marbukh s work to find the approximate solutions to this problem. The proposed solution produces an optimal solution if there are only two possible routes. The equation for the proposed splitting mechanism is based on the weights of the links, which can be represented in terms of delay, packet loss probability, or availability of bandwidth. Stability of the multiple paths was analyzed by Shi et al. (2003) to determine routes that can be used for a longer time. The effect of using two paths was analyzed for two different cases. In the first case, the probability of having one path failure was considered, whereas in the second case, the probability of failure in both paths was considered. Based on the results of the stability analysis of multiple paths, a methodology that uses totally independent routes with no nodes in common was presented to send the data to the destination node. Valera et al. (2003) proposed the Caching and Multipath (CHAMP) routing protocol which uses a packet caching methodology together with a multipath routing methodology. The nodes keep a copy of the packets they send at the cache, and if transmission is not successful, the alternative path is used for retransmission. Also, the paths are selected according to the number of times they are used. The least used node is selected as the next hop. Since the decision is made per packet, this algorithm can be considered as multipath routing with splitting if a flow is considered to consist of more

36 22 than one packet. Due to the retransmission policy, it also uses the multipath information as backup paths. The algorithm was analyzed for the following performance measures: packet delivery ratio, end-to-end delay, routing overhead, and fraction of packets that arrive out of order. At the end of the analysis, the results show that keeping two disjoint path information is optimal with this setting for the performance measures mentioned above. The proposed algorithm performs better than DSR and AODV in terms of packet delivery ratio and end-to-end delay. In terms of routing overhead, it performs better when mobility is high. A summary of the literature on routing can be seen in Table 2-1 and Table 2-2. Table 2-1: Literature review on routing in ad hoc networks-unipath and backup paths Author Algorithm # of paths / Application area Johnson and DSR Single path routing Maltz Ad Hoc Networks (1996) Perkins and Royer (1999) Zhang and Mouftaf (2004) Lee and Gerla (2000) Nasipuri et al. (2001) Marina and Das (2001) Sambasivam et al. (2004) Li et al. (2004) De et al. (2003) AODV Extension of DSR AOMDV MP- AOMDV MDAR M-MPR Single path routing Ad Hoc Networks Multiple paths, Ad Hoc Networks Multiple paths, Ad Hoc Networks Multiple paths, Ad Hoc Networks Multiple paths, Ad Hoc Networks Multiple paths, Ad Hoc Networks Multiple paths, Ad Hoc Networks Multiple paths, Ad Hoc Networks Shortest path is used Problem structure Alternative paths in case of failures Alternative paths in case of failures Alternative paths in case of failures Alternative paths in case of failures Primary path is dynamically selected among multiple paths Alternative paths in case of failures Alternative paths in case of failures Source-based Routing Table Source-based Meshed structure Routing Table Routing Table Routing Table

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