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UNIVERSITY OF CALIFORNIA

Santa Barbara

Delay, Reliability and Trust in Mobile Ad Hoc

Networks: A Space-Centric Approach to Routing

A dissertation submitted in partial satisfaction

of the requirements for the degree of

Doctor of Philosophy

in

Electrical and Computer Engineering

by

Amir Aminzadeh Gohari

Committee in Charge:

Professor Volkan Rodoplu, Chair

Professor Michael Melliar-Smith

Professor Louise Moser

Professor Ben Zhao

July 2011


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The dissertation of

Amir Aminzadeh Gohari is approved:

Professor Michael Melliar-Smith

Professor Louise Moser

Professor Ben Zhao

Professor Volkan Rodoplu, Committee Chairperson

July 2011


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Delay, Reliability and Trust in Mobile Ad Hoc Networks:

A Space-Centric Approach to Routing

Copyright c 2011

by


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To my parents,for their unconditional love and everlasting sacrifice

To Diana,

for her support and encouragement

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Pasternak, Diana Ramazanova, Adam Lugowski, and Jacob Serup. I will never for-

get our coffee hours, parties and of course our trips. I would also like to thank the

Persian community at UCSB, particularly Mehdi Nasr Azadani, Meysam Rezaei, Nilo-

ufar Pourian, Tad Dougherty, Farshad Pour Safaei, Saeed Shamshiri, Hadi Rasouli, Ali

Nabi, and anyone I might be forgetting, for our fun times together. It has been a privi-

lege knowing each and every one of you.

Of course, my deepest appreciation goes to my parents, my brothers Amin and

Omid and my sister Anahita for their unconditional love, and everlasting sacrifice. I

will not forget the endless discussions and debates about everything and nothing with

Amin. I have always found Amins brilliant knowledge of mathematics fascinating and

learned a lot from his points of view on my research problems. Finally, there is no

doubt in my mind that my life here has been a truly fruitful one, because of Diana, her

support, encouragements and affection.

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Curriculum Vit


Education

09/2007 08/2011 Doctor of Philosophy in Electrical and Computer Engineering,

University of California, Santa Barbara, CA 93106-9560, USA.

09/2003 12/2005 Master of Science in Electrical Engineering,

Sharif University of Technology, Tehran, 11365-9363, Iran.

09/1999 09/2003 Bachelor of Science in Electrical Engineering,

Sharif University of Technology, Tehran, 11365-9363, Iran.

Honors and Recognitions

ECE Department Dissertation Fellowship, University of California, Santa Bar-bara, Mar. 2011.

Session Chair, IEEE Global Communications Conference (GLOBECOM), Dec.2010.

Session Chair, IEEE GLOBECOM Workshop on Mobile Computing and Emerg-ing Communication Networks, Dec. 2010.

University of California Presidents Work Study Award, 2008 and 2009.

Ranked 11th in Nationwide Electrical Engineering Students Olympiad, Iran,2003.

Ranked 10th in Nationwide Graduate School Entrance Examinations for Mas-ters in Electrical Engineering, Iran, 2003.

Ranked 30th in Nationwide College Entrance Examinations in Math and Physics,Iran, 1999.

Academic Experience

09/2007 08/2011 Graduate Research Assistant,

University of California, Santa Barbara.

06/2011 08/2011 Teaching Assistant, ECE 152A: Digital Design Principles,


05/2011 Guest Lecturer, ECE 250: Wireless Communications and Net-

working, University of California, Santa Barbara.

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01/2011 03/2011 Teaching Assistant, ECE 152A: Digital Design Principles,

University of California, Santa Barbara.05/2010 Guest Lecturer, ECE 250: Wireless Communications and Net-

working, University of California, Santa Barbara.

01/2006 08/2007 Research Engineer, Optical Networks Research Lab.,

Sharif University of Technology.

01/2004 07/2004 Head Teaching Assistant, EE 25-543: Microprocessors I,


09/2003 12/2003 Teaching Assistant, EE 225-504 Microprocessors Lab I,


Leadership Experience

09/2009 09/2010 Vice President, International Students Association,


09/2008 09/2009 Treasurer, International Students Association,


09/2002 09/2003 Member of the Board, Student Association,


09/2001 09/2002 President, Sharif Film Club,


Publications

Book Chapter

Volkan Rodoplu and Amir A. Gohari, MAC Protocol Design for UnderwaterNetworks: Challenges and New Directions, in Underwater Acoustic Sensor Net-

works, Ed. Yang Xiao, Auerbach Publications, Taylor and Francis, CRC Press,

May, 2010.

Journal Papers

Amir A. Gohari, Ryan Pakbaz, P. Michael Melliar-Smith, Louise E. Moser, andVolkan Rodoplu, RMR: Reliability Map Routing for Tactical Mobile Ad HocNetworks, to appear in IEEE Journal of Selected Areas in Communications, Nov.

2011.

Amir A. Gohari and Volkan Rodoplu, Congestion Aware Spatial Routing in Hy-brid High-Mobility Wireless Multihop Networks, submitted to IEEE Transaction

on Mobile Computing.

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Abstract

Delay, Reliability and Trust in Mobile Ad Hoc Networks:

A Space-Centric Approach to Routing


We establish a methodology for handling high mobility in wireless ad hoc networks.

We present a novel design framework for the development of scalable ad hoc routing

protocols that are capable of providing QoS guarantees (delay, reliability and trust) in

the high-mobility regime. In the first part of this dissertation, we consider the problem

of providing delay guarantees for ad hoc routing protocols under high mobility. The

novel aspect of our work is the attribution of network and MAC layer congestion to

space, which enables congestion-aware routing and provides delay guarantees over a

much longer duration than that achieved by traditional ad hoc routing protocols. We

prove that, over the duration during which the node density and the offered traffic pat-

tern remain roughly constant, the spatial congestion of the network remains roughly

invariant. We present an accurate method of spatial delay estimation, named path

integration, between distinct locations, and derive an upper bound for the estimation

error. Furthermore, we develop a congestion-aware routing protocol to enable delay-

optimized routing for real-time applications. Through extensive QualNet simulations,

we perform a detailed evaluation of the presented framework in a realistic simulation

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set-up. The simulation studies demonstrate that the proposed scheme provides substan-

tial improvements in the delivery of real-time applications such as Voice over IP (VoIP)

for a wide range of node densities, velocities and offered traffic.

In the second part of this dissertation, we turn our attention to the problem of reli-

able and trustworthy routing in mobile ad hoc networks. We consider the implications

of applying our spatial approach to improve routing reliability through difficult terrains

with possibly untrustworthy regions in tactical mobile ad hoc networks. The proposed

approach provides maps of spatial reliability and trust, that reflect the probabilities for

finding trustworthy routes between distinct locations. We develop a routing protocol,

named Reliability Map Routing (RMR), which discovers routes over spatial cells

whose local reliability and trust metrics are distributed throughout the network via a

fast dissemination algorithm. Furthermore, the RMR protocol is capable of reliable

geocasting with low overhead. Via QualNet simulation studies, we compare the per-

formance of the RMR protocol in terms of packet delivery ratio, delay, and overhead,

and quantify the effects of node density, velocity, and traffic load on these performance

metrics.

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Contents

Acknowledgements v

Curriculum Vit vii

Abstract x

List of Figures xiv

1 Introduction 1

2 Congestion-Aware Spatial Routing in Hybrid Mobile Ad Hoc Networks 12

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3 Network Model and Assumptions . . . . . . . . . . . . . . . . . . . 192.4 Mathematical Framework . . . . . . . . . . . . . . . . . . . . . . . 212.5 DMQR Routing Protocol . . . . . . . . . . . . . . . . . . . . . . . 36

2.5.1 Call Admission Control . . . . . . . . . . . . . . . . . . . . 382.5.2 Spatial Path Selection . . . . . . . . . . . . . . . . . . . . . 402.5.3 Neighbor Discovery . . . . . . . . . . . . . . . . . . . . . . 422.5.4 Congestion Map Construction/Dissemination . . . . . . . . . 43

2.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 452.6.1 Mathematical Framework Validation . . . . . . . . . . . . . 462.6.2 DMQR Protocol Simulation . . . . . . . . . . . . . . . . . . 51

2.6.2.1 Performance Evaluation. . . . . . . . . . . . . . . 532.6.2.2 VoIP Performance of DMQR . . . . . . . . . . . . 63

2.7 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

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3 RMR: Reliability Map Routing for Tactical Mobile Ad Hoc Networks 71

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 763.3 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.3.1 Spatial Reliability . . . . . . . . . . . . . . . . . . . . . . . 803.3.2 Spatial Trust . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.4 RMR: Reliability Map Routing Protocol . . . . . . . . . . . . . . . . 913.4.1 Reliability Map Measurement, Estimation, Dissemination . . 92

3.4.1.1 Measurement Procedure . . . . . . . . . . . . . . 953.4.1.2 Estimation Procedure . . . . . . . . . . . . . . . . 953.4.1.3 Dissemination Procedure . . . . . . . . . . . . . . 96

3.4.2 Reliable/Trustworthy Path Discovery . . . . . . . . . . . . . 973.4.2.1 Reliable Geocast Routing . . . . . . . . . . . . . . 98

3.4.3 Neighbor Node Lookup Table . . . . . . . . . . . . . . . . . 1003.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 101

3.5.1 Maps of Reliability, Packet Delivery Ratio, and Delay. . . . . 1033.5.2 Performance Comparison . . . . . . . . . . . . . . . . . . . 106

3.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

4 Conclusions and Future Directions 116

Bibliography 129

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List of Figures

1.1 Node mobility affects wireless modules and networking protocols in

physical, MAC and network layers . . . . . . . . . . . . . . . . . . . . . 3

2.1 Congestion attributed to spatial cells remains invariant longer than the

congestion levels of highly mobile individual nodes. . . . . . . . . . . . . 162.2 Effective region (interference range) of a wireless random access MAC

protocol. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.3 Construction of the local delay (congestion) map: The ith element of

the congestion map is defined as the average of the local delay of the nodes

located within cellCi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.4 Construction of the end-to-end delay map: The ith element of the end-

to-end delay map i is the average of the end-to-end delay of the nodeslocated within cellCi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.5 The -coherence time of the network variable (t), T

(,)c (t)is the max-

imum interval during which does not deviate by more than from itsexpected value at timet. . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.6 The network snapshot duringT()c (t). (a) R20is the set of cells for the

route fromC20to C1. (b) The shaded area H7is the set of upstream cells forC7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.7 The high-level state diagram of the DMQR protocol. . . . . . . . . . 372.8 Network graph construction. (a) Network dynamics and local delay

values, (b) the weighted network graph of the center cell; note the values ofthe cell without connectivity.. . . . . . . . . . . . . . . . . . . . . . . . . 41

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2.9 Effect of small displacements and network parameters on the end-to-

end and local delays. (a) Effect of node velocity, N = 400, G(D, t) 3Mb/s. (b) Effect of node density,v = 10m/s,G(D, t) 1.6Mb/s. (c) Effectof the offered trafficN= 400,v = 10, (d) The standard deviation of localdelay with respect to changes in traffic and node velocity, N= 400. . . . . 492.10 (a) Delay map, (b) Congestion map of a network with N= 400, v= 10m/s, andG(D, t) = 3Mb/s. . . . . . . . . . . . . . . . . . . . . . . . . . 502.11 Effect of network parameters on the relative error. (a) Effect of node

velocity,N= 400, (b) Effect of node density, v = 10m/s. . . . . . . . . . 512.12 Simulated terrain with holes and sample node trajectories.. . . . . . . 522.13 Performance maps of a realistic network running DMQR protocol. (a)

Congestion (local delay) map, (b) PDR contour map, (c) end-to-end delay

map, (d) average delay estimate map. . . . . . . . . . . . . . . . . . . . . 552.14 Performance maps of AODV routing protocol. (a) Packet Delivery Ra-

tio (PDR) contour map, and (b) end-to-end delay map. . . . . . . . . . . . 562.15 Performance maps of OLSR routing protocol. (a) PDR contour map,

and (b) end-to-end delay map. . . . . . . . . . . . . . . . . . . . . . . . . 572.16 DMQR performance comparison as a function of node velocity. N =200andG(D, t) =1.6, 2.8 Mb/s. (a) Packet delivery ratio, (b) average delay(logarithmic scale), and (c) routing overhead. . . . . . . . . . . . . . . . . 582.17 DMQR performance comparison as a function of node density.v = 10m/s andG(D, t) = 2.44Mb/s. (a) Packet delivery ratio, (b) average routing

delay, and (c) routing overhead. . . . . . . . . . . . . . . . . . . . . . . . 612.18 DMQR performance comparison as a function of total offered traffic.

N = 200 and v = 10 m/s. (a) Packet delivery ratio, (b) average routingdelay, and (c) routing overhead. . . . . . . . . . . . . . . . . . . . . . . . 622.19 Performance maps of the DMQR protocol carrying VoIP traffic. (a)

End-to-end delay map, and (b) PDR contour map. . . . . . . . . . . . . . . 642.20 The MOS quality maps of the network in Fig. 2.12 with (a) DMQR,

and (b) AODV routing protocol. . . . . . . . . . . . . . . . . . . . . . . . 652.21 (a) Average delay, and (b) packet delivery ratio (PDR) of the VoIP packets 66

3.1 (a) In the space-centric approach, a route is thought of as a sequence

of node presences. (b) Selected route is still reliable even though the topol-ogy has changed.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.2 It is essential that the routing protocol avoids untrustworthy regions. . 753.3 Different views of route reliability in MANETs. . . . . . . . . . . . . 823.4 Reliable geocasting with RMR. The geocast region is covered by two

sets of cells, solid black arrows indicate the unicast data transmission and the

curved dashed arrows are the data broadcasts to the target cells. . . . . . . . 100

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3.5 Deployment Region with holes. (a) Partitioned into cells. (b) Trajecto-

ries of nodes moving within the deployment region. . . . . . . . . . . . . . 1023.6 Contour plot of the reliability map of the network generated by the

RMR protocol; the darker the regions the less reliable they are. (a) N= 200,v = 10 m/s, (b) N = 200, v = 40 m/s, (c) N = 400, v = 10 m/s, (d)N= 400,v = 40m/s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1053.7 Performance of the RMR protocol in the same set-up as Fig. 3.6(a). (a)

Spatial map of packet delivery ratio, (b) Spatial map of end-to-end delay. . . 1073.8 Performance evaluation of RMR as a function of node velocity. 200

mobile nodes are deployed in D with a total load of3.2Mbps.(a) Packet de-livery ratio comparison. (b) Average routing delay comparison. (c) Routing

overhead comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083.9 Performance evaluation of RMR as a function of node density. Speed

of the nodes is set tov = 10m/s and a total load of3.2Mbps is transmitted.(a) Packet delivery ratio comparison. (b) Average routing delay comparison.

(c) Routing overhead comparison. . . . . . . . . . . . . . . . . . . . . . . 1103.10 Performance evaluation of RMR as a function of total offered load.

There are 200 transmitting mobile nodes in D, moving with the speed ofv= 10m/s. (a) Packet delivery ratio comparison. (b) Average routing delaycomparison. (c) Routing overhead comparison. . . . . . . . . . . . . . . . 1123.11 The RMR protocol switches to a longer (less reliable) route in order to

avoid the untrustworthy region that is flagged by a distress signal.. . . . . . 113

3.12 The RMR protocol switches to a longer, less reliable route to satisfy thetrust requirement, as shown by (a) route hop count, and (b) packet delivery

ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

4.1 Cell size adaptation idea: the regions can vary and change dynamically

in size, depending on the node density in the network.. . . . . . . . . . . . 1264.2 Node-centric and space-centric hybrid operation idea: the sparse re-

gions in the right side of the figure adopt a node centric routing protocol

until the packets reach the space-centric enabled areas. . . . . . . . . . . . 128

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Chapter 1

Introduction

The wireless revolution is continuing, and wireless communication networks are

proliferating. The number of mobile subscribers has increased from 49% of the worlds

population in 2007 [1], to more than 70% of its population (5 billion mobile connec-

tions) in July 2010 [2]. The wireless penetration rate in the United States was reported

to be 93% in June 2010 [3] and has exceeded 100% in many of the western Euro-

pean countries, where there is more than one mobile connection per person [2]. As

a result, the design challenges of such networks have become the high rate data sup-

port in large-scale, high-mobility scenarios. As the fourth generation (4G) of wireless

networks is being deployed, the significant demands of mobile users continues to be

ubiquitous connectivity, seamless mobility support, and the ability to roam between

different wireless networks, e.g., WiFi for local access, Bluetooth for short range data

exchange, ZigBee for personal area networks, and WiMax and LTE for urban broad-

band access [4, 5]. Mobility support is essential for providing uninterrupted access to

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Chapter 1. Introduction

mobile content and for maintaining real-time connectivity with automatic switching be-

tween networks, protocols, and communication channels. Such requirements present a

number of research challenges including the design of scalable solutions that support

high node mobility, that range from physical layer communication schemes to network

layer protocols.

A mobile ad hoc network (MANET) is a self-configuring network of mobile de-

vices that are connected by wireless links. Each node in a MANET can freely move

in any direction and is expected to act as a router in the network if necessary. The

main goal of this dissertation is the design of scalable ad hoc routing protocols that are

capable of providing QoS guarantees (delay, reliability and trust) in the high-mobility

regime of network operation. Before we address the challenges of mobility support

in ad hoc routing, it is useful to review the state of the art of mobility management

approaches in the physical and Medium Access Control (MAC) layer of wireless net-

works. Fig. 1.1 illustrates various mobility management techniques in wireless net-

works and their cross-layer interactions. In the physical layer, node mobility causes

significant frequency-selective, time-varying fading and decreases the coherence time

of the channel [6]. Digital multi-carrier modulation methods in general, and Orthogonal

Frequency Division Multiplexing (OFDM) and its multiple access evolution, OFDMA,

are particularly robust against co-channel interference, intersymbol interference (ISI)

and fading due to node mobility [7, 8]. Spatial diversity techniques (e.g., MIMO sys-

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Network Layer

MAC Layer

Physical LayerMulticarrier Modulation

(OFDM)

Diversity Techniques

(MIMO)

Channel Monitoring

Power ManagementDynamic Bandwidth

Allocation

Horizontal Handoff

Decision

Route Discovery Route Maintenance

QoS Provisioning

Network

Discovery

QoS Metric

Measurements

VerticalHandoff

Decision

Figure 1.1: Node mobility affects wireless modules and networking protocols in phys-

ical, MAC and network layers

tems) improve the range and capacity of wireless connections by taking advantage of

multipath rather than fighting it [9]. Mobility support in the MAC layer covers a wide

range of problems, from power management [1012] and dynamic bandwidth alloca-

tion [1315], to horizontal and vertical (a.k.a. media independent) handoff [1618].

Cross layer interactions between the physical layer, the MAC layer and the network

layer are necessary to achieve the above tasks [4], as shown in Fig. 1.1.

The effect of node mobility on network layer protocols could be even more crucial.

The reason is that unlike that of the physical and MAC layer protocols, the scope of net-

work layer protocols is expanded to the end-to-end connection between the source and

the destination. The performance of routing protocols in wireless networks is affected

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not only by the mobility of the source and the destination but also by the mobility of

the relay nodes. There are two necessary requirements for a routing protocol that sup-

ports seamless mobility. First and foremost is the robustness of route discovery and

maintenance. Frequent network topology changes in high mobility scenarios make it

challenging and sometimes impossible to discover routes without interruption or to re-

establish the ones that have been broken due to node mobility. The second requirement

is the ability to provide accurate measures of Quality of Service (QoS). The end-to-end

QoS guarantees, such as reliability, timeliness, and throughput, are of paramount im-

portance in achieving acceptable quality of experience for the mobile users as well as

providing the comparative metrics for vertical handoffs, as shown in Fig. 1.1.

In this dissertation, our focus is on the design of scalable QoS-enabled routing pro-

tocols for high-mobility ad hoc networks. The primary challenges in achieving end-to-

end QoS over wireless mobile ad hoc networks are: (1) mobility of the nodes places

constraints on the success of reservation-based schemes; the predictions of the avail-

able resources at each node, made in mobile environments, can quickly become obso-

lete, and (2) mobility of the nodes poses significant challenges for finding, tracking and

maintaining the routes over which data traffic will be transmitted. In such scenarios,

finding a QoS-guaranteed route between a source-destination pair might be impossible,

if there is not enough time to propagate the updates of the latest topology changes to all

of the pertinent nodes.

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The behavior of a mobile ad hoc network is called combinatorially stable if the

topology changes that affect routing occur sufficiently more slowly than the rate at

which information propagates across the network [19]. Combinatorial stability of an

ad hoc network is determined not only by the mobility patterns of the nodes but also by

the complexity and the capabilities of the routing protocol. As a result, the traditional

QoS ad hoc routing protocols [20, 21] that build routes from sequences of nodes and

attribute QoS metrics to the nodes do not remain combinatorially stable under high

mobility. The category of ad hoc routing protocols that build routes as sequences of

nodes are called node-centric in this dissertation.

Another category of ad hoc routing protocols are geographic protocols in which

relay nodes send the packets toward the geographic location of the destination node

instead of using the network address of the destination node. With accurate and inex-

pensive positioning devices, such as Global Positioning System (GPS) modules, that

are available to mobile users, geographic ad hoc routing (georouting) protocols have

gained much attention recently (cf. [22]). Geographic routing protocols are not node-

centric in the sense that in their routing algorithm, there is no notion of an end-to-end

route as a chain of individual nodes. Therefore, they are able to operate efficiently in

high-density, high-mobility scenarios. However, georouting protocols come with the

disadvantage of low reliability and inability to provide QoS guarantees. The reason

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is that in the geographic approach, the routes are only dynamically and locally deter-

minable and end-to-end routes do not exist1.

In this dissertation, we present a spatial solution to the combinatorial stability prob-

lem of routing protocols in mobile ad hoc networks. The novelty of this work is that we

consider the implications of a space-centric view of communication in which node

mobility is examined in the aggregate in order to provide QoS guarantees. Our space-

centric approach takes advantage of the fact that in achieving an end-to-end route, the

nodes on the route are interchangeable. If a particular node moves away from a loca-

tion and another node moves in its place or proximity, no change will have occurred

from an end-to-end route quality perspective [23]. Similar to georouting protocols, we

think of aroutenot as asequence of nodesfrom the source to the destination, but rather

as a chain of regions each of which contains one or more nodes. We maintain that

significant improvements in the design of a QoS routing protocol can be achieved by

attributing local QoS metrics to patches of space rather than to individual nodes (this

idea first appeared in [23] in the context of energy minimization). In particular, we

present two scalable QoS routing protocols that take advantage of this novel idea to

deliver QoS guarantees (delay, reliability and trust) in high mobility ad hoc networks.

1In general, georouting strategies rely on the greedy forwarding algorithm. This algorithm makes

locally optimal choices at each step (which is a heuristic), with the aim of finding the global optimum.

That is, it takes the packet towards the destination by using only local information. As a result, the

end-to-end route to the destination node is not known prior to data transmission.

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In Chapter 2, we present a congestion-aware routing protocol, named Delay Map

QoS Routing (DMQR), which provides delay guarantees for multihop routing between

distinct locations. ITUs recommendation for next-generation mobile systems (IMT-

Advanced) requires a VoIP capacity of 30 active users/sector/MHz and a latency of at

most 100 ms in high-speed mobility environments (up to 350 km/h) [24]. These re-

quirements introduce unique opportunities that are not present in traditional voice and

data applications over wireless networks, when we focus on mobile VoIP as the tar-

get application. While mobile VoIP consumes only a fraction of the data bandwidth of

traditional data applications, it must meet stringent delay constraints of voice communi-

cation with a high Packet Delivery Ratio (PDR). Furthermore, to support mobile VoIP,

a gateway station is required to ensure connectivity to the outside world. In Chapter 2,

we consider such a hybrid mobile ad hoc network with a single fixed gateway. The

DMQR protocol that is developed in that chapter estimates the end-to-end delay be-

tween physical locations in space by using a spatial map of network congestion called

a congestion map, which is the set of MAC and network layer congestion values at-

tributed to spatial regions. This map of network congestion can be stable over a much

longer duration than the congestion levels of the individual nodes.

In the first part of Chapter 2, we prove that over the duration during which the net-

work node density and its traffic pattern remain roughly constant, the expected values

of local congestion and end-to-end delay remain roughly invariant. Then, we present

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an accurate method of spatial delay estimation, named path integration, and derive an

upper bound for its estimation error.

Several questions arise in development of the DMQR protocol based on our math-

ematical model:

How will the mobile nodes utilize the spatial congestion information?

Where is the congestion map maintained and how long does it remain valid?

The number of measurements that need to be made and distributed might have a

large overhead. How does DMQR approximate this map with a minimum amount

of dissemination overhead?

The answers to these questions are presented in the second part of Chapter 2 and play

an important role in the design of the DMQR protocol.

In the second part of this dissertation, we focus on the implications of adopting this

space-centric view of node mobility in the context of reliable and trustworthy routing in

MANETs. Mobile ad hoc networks can be used wherever there is a need for establish-

ing a network environment where a communication infrastructure does not exist or is

difficult to establish. Military tactical command and control is one of the major applica-

tions of MANETs that demands attention [25]. In a military operation, different mobile

units (sensors, soldiers, and vehicles) are involved and need to maintain communication

with each other. Tactical MANETs differ from commercial wireless networks in their

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security requirements, lack of centralized arbitration, and, most importantly, dynamic

topology changes due to node mobility. Furthermore, supporting seamless mobility

in tactical networks is not a matter of selecting the best wireless network to which to

switch, as usually there are not many alternatives for data transmission. Therefore, the

level of mobility support in tactical MANETs is determined by the robustness, relia-

bility, and security of the routing protocol in delivering mission-critical information.

Hence, the end-to-end guarantees of reliability and trustworthiness become essential in

achieving mobility support in tactical MANETs.

In Chapter 3, we attribute reliability measures directly to patches of space and keep

track of these measures in the network via a fast dissemination algorithm. Following

this idea, we construct reliability maps, which are maps of reliable communication

regions in an unknown territory where the nodes have been deployed. Furthermore, we

utilize the term spatial trust in the context of tactical MANETs to quantify whether

a region in the network has shown security vulnerabilities (e.g. malicious activities

such as cyber-attacks) or has been physically compromised (e.g. enemy contact or

misbehaving nodes) in the past. The trust management of our work is based on the

reputation of an area in the network. Hence, we employ monitoring signals to build a

trust map of the deployment region and update this map as information from different

nodes arrives. We then present a routing protocol, named Reliability Map Routing

(RMR), which uses the spatial reliability and trust maps of the deployment region and

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reactively discovers spatial routes while it avoids unreliable or untrustworthy regions.

The RMR protocol acquires timely information about the network and the environment

for situation-awareness purposes (cf. [26]). It also reacts promptly to a distress signal

sent by a node or potential threats in some of the areas of the deployment region.

The distinguishing features of our work in Chapter 3 are:

The development of a reliability function by which the degree of reliability of

spatial locations over a new, unknown terrain can be characterized.

The introduction of a spatial trust management methodology based on monitoring

and making observations on possible environmental risks (such as enemy contact)

and networking attacks (such as denial of service and intrusion).

Efficient and reliable geocast [27] routing for geographic command and control

functionality.

The rest of this dissertation is organized as follows: In Chapter 2, we present a

method of delay estimation and the DMQR protocol based on our novel space-centric

approach to ad hoc routing. We perform a detailed evaluation of the presented frame-

work and of the DMQR protocol in a realistic simulation set-up and show that signifi-

cant improvements in delay-sensitive routing are achieved.

In Chapter 3, we describe the building blocks of the RMR protocol for reliable and

trustworthy routing in tactical MANETs. Via QualNet simulations, we show that the

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RMR protocol successfully finds routes through reliable regions, and avoids potentially

untrustworthy or compromised regions based on the trust management signals trans-

mitted by the nodes. The results of this section indicate that a high packet delivery

ratio and a reasonable average delay can be achieved end-to-end with minimum routing

overhead.

Finally, in Chapter 4, we summarize and discuss some of the broader impacts of

this dissertation: First, we argue that our space-centric idea can be extended to other

QoS metrics, such as throughput, to perform multi-objective routing. Second, we ex-

plain how our framework can be extended to the construction of QoS maps in cooper-

ative communication and networking, which allow cellular networks to move towards

seamless mobility support. Finally, we shed some light on future improvements to the

problem of routing in tactical mobile ad hoc networks.

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Chapter 2

Congestion-Aware Spatial Routing in

Hybrid Mobile Ad Hoc Networks

In this chapter, we develop a space-centric framework to provide delay guarantees

in mobile multihop networks. As explained in Chapter 1, the novel aspect of this ap-

proach is the attribution of network and MAC layer congestion to space, which enables

congestion-aware routing and provides delay guarantees over a much longer duration

than that achieved by node-centric routing protocols. First, we discuss the challenge

of delay-optimized routing that can enable multihop extensions to cellular networks.

We discuss the related work in this area to emphasize the distinguishing features of our

approach. Second, we prove that, if the node density and the network traffic pattern

remain roughly constant, the expected values of local congestion and end-to-end delay

approximately remain invariant. In this scenario, the end-to-end routes encounter node

densities that change much more slowly than the positions of the nodes themselves. A

network of vehicles traveling in urban areas and city highways, pedestrians walking in

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Chapter 2. Congestion-Aware Spatial Routing in Hybrid Mobile Ad Hoc Networks

shopping malls, museums, and campus areas are examples of networks with the above

characteristics.

We present a method of end-to-end delay estimation and derive an upper bound for

its estimation error. Third, we develop a congestion-aware routing protocol to enable

delay-optimized routing for real-time applications. Finally, we perform a detailed eval-

uation of the presented framework and the routing protocol through extensive QualNet

simulations.

2.1 Introduction

Multihop relaying and routing technologies have been actively studied for cover-

age extension and throughput enhancement in wireless mobile networks in the past

decade [28]. However, only recently have they emerged as an important research topic

for the next generation (4G) mobile wireless communication systems [2932]. For mul-

tihop communication, one paradigm is collaborative relaying in which a relay station

helps forward user information to a base station [30, 31]. Another paradigm, which is

the focus of this chapter, is using QoS-enabled ad hoc routing protocols over the set of

mobile users [20]. Heterogeneous characteristics of next generation networks make it

more attractive to utilize the latter paradigm to build an integrated heterogeneous access

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network [33, 34]. Node mobility and high node density cause significant performance

challenges in such networks.

In this chapter, we consider the implications of examining node mobility in the

aggregate to provide QoS. In particular, we examine how this could enable the opti-

mization of network congestion. Our proposition is that the congestion levels in spatial

cells remain invariant longer than the congestion levels (measured at the nodes MAC

and network layer buffers) of individual, erratic nodes. We suggest that significant

improvements in the design of a delay-sensitive, high-mobility, multihop network can

be achieved if we attribute the local QoS metrics to space and maintain and refine the

end-to-end QoS metrics in the joint memory of nodes.

The main challenges in achieving end-to-end Quality of Service (QoS) over mobile,

wireless multihop networks are: (1) the mobility of nodes places constraints on the

success of reservation-based schemes; the predictions of the available resources at each

node, made in mobile environments, can quickly become obsolete, (2) the mobility of

nodes poses significant challenges for finding, tracking and maintaining the routes over

which data traffic is transmitted, and (3) the queuing delays incurred at each hop add

up to quickly fill up the required delay deadline. We focus on the end-to-end delay

as our QoS metric, which has been known to be essential for multimedia traffic and

particularly difficult to achieve in mobile multihop networks [35, 36].

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The novel idea of this chapter is the attribution of local delay to the patches of space,

e.g., cells, as shown in Fig. 2.1. This means that whenever a node transmits a packet in

a spatial cell, it makes measurements of the packet delay and associates that delay with

the cell location at which it was measured. We allow the nodes to collect measurements

of these local delays at each cell and efficiently disseminate these among themselves.

In Section 2.4, we prove that the integration of these local delays over the spatial routes

accurately estimates the end-to-end average delay. We argue that when the node den-

sity and the traffic patterns are more stable than the individual erratic nodes, as shown

in Fig. 2.1, the space-centric method gives a better estimate of the end-to-end delay.

We then present a delay-aware routing protocol that adopts this mathematical frame-

work and operates over spatial cells rather than individual nodes. Extensive network

simulations show that the Delay Map estimation QoS Routing (DMQR) protocol of

this chapter provides significant performance improvements over the existing routing

protocols for mobile multihop networks.

The rest of this chapter is organized as follows: In Section 2.2, we discuss the

related work. Section 2.3 states the assumptions for the network model that will be used

throughout this chapter. In Section 2.4, we present our mathematical framework. In

Section 2.5, we propose the DMQR routing protocol based on the ideas of Section 2.4.

Section 2.6 provides extensive simulation studies to validate the results of Sections 2.4

and 2.5. Finally, we summarize this chapter in Section 2.7.

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00

00

Figure 2.1: Congestion attributed to spatial cells remains invariant longer than the

congestion levels of highly mobile individual nodes.

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2.2 Related Work

The challenge of providing QoS delivery for delay-sensitive applications, such as

VoIP, over mobile wireless networks has received much attention in the past. It has been

shown that high latency and low packet delivery ratio are major shortcomings due to

the contention, congestion and user interference in wireless cellular networks [37, 38]

and 802.11 LANs [13, 39]. These issues tend to be more deteriorating for multihop

communications since the routing or scheduling issues have to be addressed [40]. Oth-

erwise, only a couple of VoIP sessions can be transmitted over three or more hops [41].

Hence, the choice of routing protocol plays an important role on network capacity and

performance.

In many of the past reactive and proactive QoS routing protocols, e.g., [4245],

the routes are conceived as sequences of individual, erratic nodes. Consequently, this

node-centric approach is sensitive to node mobility [20] because (1) the routes break

frequently and (2) the topology changes occur faster than the spreading period of QoS

information [19]. For instance, the Ad hoc QoS On-demand Routing (AQOR) [42]

protocol is vulnerable to both of the above issues, because it makes bandwidth and de-

lay measurements during route discovery, and route maintenance is performed by the

senders route discovery re-initiation. ADQR [43] aims to predict the route breaks via

power measurements of the received signals. However, the reservations are made in the

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route discovery phase and what the data packets encounter in reality can be substan-

tially different because of node mobility. The location-based routing protocol of [45]

addresses these issues by attaining a global view of the network, which comes at the

high cost of overhead for mobility updates. Trigger-based distributed QoS routing [46]

limits the routing overhead by keeping its scope to a local neighborhood database.

However, all of the above node-centric approaches fail to achieve combinatorial sta-

bility [19] in medium-to-high node mobilities. Our work is also related to [47, 48] that

optimize the performance of routing for real-time applications via congestion control.

Again, these approaches work well only in stationary or low mobility scenarios as the

congestion is attributed to the nodes, and thus moves with the nodes.

In contrast, geographic routing protocols, e.g., [22, 49], operate efficiently at high-

density, high-mobility scenarios. Nevertheless, they are unable to provide QoS guar-

antees, since the routes are only dynamically and locally determinable and end-to-end

paths do not exist. The first space-centric approach to the delivery of QoS, to our

knowledge, was [23]. In [23], the authors demonstrated that the end-to-end energy

consumption over space is stable within a time period during which the node density

remains constant. Statistical QoS routing, presented in [50], enhances the GPSR pro-

tocol [49] by providing stochastic end-to-end delay guarantees. However, the delay

guarantees still rely on the individual nodes that have previously been discovered by

GPSR; that is, such guarantees soon become stale because of node mobility. Finally, a

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geographic routing protocol with congestion avoidance is proposed in [51] to maximize

VoIP capacity. The routing protocol of [51] forwards data packets towards the locations

in which the number of busy nodes is less than a threshold. This protocol, however, is

unable to give any end-to-end delay guarantees.

In contrast, we inherit the advantages of geographic routing in our QoS provisioning

scheme in order to achieve soft delay guarantees for real-time applications. Our method

constructs, shares, and refines the more stable, spatial local delays, in the joint memory

of the nodes. The DMQR routing protocol of this chapter not only avoids the congested

regions with less overhead, but also provides average and stochastic delay guarantees,

in high-mobility and high density networks, without any end-to-end route discovery or

reservation requests.

2.3 Network Model and Assumptions

We constrain ourselves in this chapter to a hybrid mobile network, with a fixed

gateway node, as shown in Fig. 2.1. We assume that the stationary gateway serves

as the destination for the traffic generated in the network. This assumption is made to

simplify the mathematical framework and to help avoid the need for location lookup

algorithms. Our formulation can be extended to multiple gateway nodes or to ad hoc

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communication provided that the network is geographically addressed, similar to the

underlying assumption of geographical routing protocols.

Each user in the network is a location-aware mobile node that moves inside a con-

nected deployment region D R2, and potentially has data traffic destined for the

gateway. The deployment region D can contain obstacles and holes into which the

nodes cannot enter, however, over which the wireless signal can propagate. The mobile

nodes are usually energy constrained; hence, a multihop transmission scheme is used

to conserve energy, and further to limit the multiple access contention, thus forming a

multihop mobile network.

We partition D into M cells C1, . . . , C M D and assume that the partitioning

method is known by the nodes in advance. For instance, if M is a set of known distinct

positions x(i)

D, (i = 1, . . . , M ), the Voronoi cells of the set M is a well-defined

set of cells. Our investigations in the following sections suggest that a cell diameter of

half the transmission range will provide an acceptable delay estimation accuracy while

keeping the complexity of the protocol limited.

The end-to-end delay of the packets in a wireless multihop network is comprised

of the following: (1) the sum of the network and MAC layer delays of all the relay

nodes, (2) the sum of wireless transmission delays at each hop, which is usually neg-

ligible when compared with the other sources of delay. It is further assumed that the

application layer delay, e.g., encoder/decoder/buffer delay for VoIP, is subtracted from

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the delay budget in calculating the maximum tolerable networking delay. To enable

multihop operation, we assume that the nodes are deployed with IEEE 802.11 modules

used in their DCF mode of operation, possibly forming an integrated heterogeneous

mobile network [34]. However, the choice of the MAC layer protocol is not limited to

802.11, and the results of this chapter can be extended to any self-organizing wireless

MAC layer protocol that is capable of supporting multihop IP routing.

2.4 Mathematical Framework

In this section, we develop a framework to investigate the trackability of congestion

changes and the possibility of delay predictions in multihop networks. First, we define

the spatial counterparts of the network variables that are usually attached to the nodes,

such as generated traffic and congestion. The spatial maps of network congestion and

end-to-end delay are introduced as the prime metrics of emphasis in this work. Then,

we show that under conditions that we will specify, these maps have bounded averages

and the expected value of end-to-end delay can be estimated with limited error. Third,

a stochastic delay guarantee is provided in addition to average delay estimation. This

framework will eventually lead to the design of a congestion-aware routing protocol in

Section 2.5.

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We choose the Cartesian coordinate system such that the gateway is located at 0def=

(0, 0). LetNdenote the set ofNnodes moving over D. The mobile network is defined

by the trajectories of the nodes X(k)(t), k N, i.e., the locations of the nodes at time

t, and the generated traffic of the nodesG(k)(t), k N, i.e., the data generation rates

of the nodes in bits per second (bps) at timet.

To study the implications of our space-centric approach, we define the spatial coun-

terparts of the network variables. Below, the node density and the spatial generated

traffic are presented as the spatial counterparts of node trajectories and generated traf-

fic.

Definition 1(Node Density)

Let the random processS(A, t) denote the density of the nodes on a bounded region

A D with area |A|, at time t; that is,

S(A, t)def=

Nk=11[X(k)(t)A]

|A| (2.1)

where1[X(k)(t)A], the indicator function, is equal to one ifX(k)(t) Aand zero other-

wise.

We are particularly interested in the average node density of the cells, i.e., S(Ci, t), i

{1..M}.

Definition 2(Spatial Generated Traffic)

The random processG(A, t)represents the traffic generation rate of the regionA D

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at timet; that is,G(A, t)is the sum of the ratesG(k)(t)of the nodes that reside inA at

timet:

G(A, t) =N

k=1

1[X(k)(t)A]G(k)(t). (2.2)

Let the random process (A, t) denote the cumulative traffic rate of region A

D. The cumulative traffic rate of regionA is the generated data traffic in that region

G(A, t), plus the relay traffic that was forwarded to A and has not been sent out yet.

Note that, unlike the generated traffic, the total amount of data present in the region A

depends on the choice of the routing protocol, and so does (A, t). The expected value

of this process, (A, t) = E[(A, t)], is the average rate that has to be transmitted out

of the regionAtoward the gateway.

Generally speaking, the congestion level of a node located at x depends on the node

density and the traffic generated in a region around that node. To be precise, let the

effective region of a location x be the areaA(x)E such that the network and MAC layer

delays of a node located at x depend only on the network dynamics inside A(x)E . The

effective region of a wireless MAC layer, also known as the interference range, is the

range within which transmission from an interferer makes the signal to interference and

noise ratio (SINR) of the legitimate receiver smaller than is required by the receiver to

correctly receive the message from the transmitter, as shown in Fig. 2.2. For instance,

the effective region of 802.11 MAC layer is the union of the transmission range, and

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Transmission

Range

Interferencerange

These nodes potentially affectthe data transmission to the

intended receiver.

Figure 2.2: Effective region (interference range) of a wireless random access MAC

protocol.

the hidden and exposed areas of a node located at x; i.e., the changes inS(A(x)E , t)and

(A(x)E , t)affect MAC layer delay.

The effect of node density, total offered traffic and network characteristics on the

packet delay has been investigated in the literature, e.g., [5254]. Even though it is

not straightforward to formulate the packet delay in a closed form, we can compute

it numerically as a function of the above parameters, based on simulation studies or

empirical models. Therefore, the local delay of a node k located at X(k)(t), denoted

by(k)L (t), can be written as a function ofX

(k)(t), node density and cumulative traffic

within the effective area ofX(k)(t).

(k)L (t) =fL

X

(k)(t); S

A(X(k)(t))E , t

;

A(X(k)(t))E , t

(2.3)

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where fL is a non-linear and possibly random function (due to MAC layer random

access schemes, such as collision avoidance and random back-off) that returns the sum

of the network and MAC layer delays of successful packet transmission.

We now introduce the spatially attributed counterpart of the local and end-to-end

delays of the nodes as the important metrics in the space-centric view of the network.

Definition 3(Local Delay Map (Congestion Map))

The local delay map (congestion map) of the network is defined as the average of the

local delay of the nodes located within the cells C1, C2, . . . , C M. We denote the conges-

tion map by (t) = [1(t), 2(t),..., M(t)]. Theith element of(t), namelyi(t)is

defined as the average delay, faced by a data packet in the protocol stack of a node in

cellCi, in order to be transmitted to its next hop at timet. That is,

i(t)def=

k:X(k)(t)Ci

[(k)L (t)]

|{k: X(k)(t) Ci}| (2.4)

Unlike the end-to-end delay measurements that require the collaboration of the des-

tination node, the local delays can bemeasured locallyto represent the congestion level

of cellCi, as shown in Fig. 2.3. In the example of Fig. 2.3,i(t)is calculated as the

average of the local delay of the three nodes that are located within cell Ci at a timet.

Furthermore, i(t) = E[i(t)] is the expected value of the packet delay at a node in

cellCi, until the packet has been successfully transmitted out of that cell.

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ected v lue

i=Av(

1,

2,

3)

APPTL

NL

MAC

PHY

i

APP

TL

APP

TL

NL

MACPHYMAC

PHYCi

Figure 2.3: Construction of the local delay (congestion) map: The ith element of the

congestion map is defined as the average of the local delay of the nodes located within

cellCi.

Definition 4(End-to-end Delay Map)

The end-to-end delay from cellCito the gateway at timet, denoted by i(t), is defined

as the end-to-end delay of sending a data packet, at timet, from cellCito the gateway,

averaged over the nodes inside that cell, as shown in Fig. 2.4.

i(t)def=

k:X(k)(t)Ci

(k)E (t)

|{k: X(k)(t) Ci}| (2.5)

where

(k)

E (t), k Nis the end-to-end delay of sending a packet, generated at time

t, from the mobile nodek to the gateway. The end-to-end delay map of the network

is, therefore, denoted by (t) = [1(t), 2(t),..., M(t)], and i(t) = E[i(t)] is

the expected value of the end-to-end delay of a packet transmission initiated inside

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=Av(1, 2,3)

Figure 2.4: Construction of the end-to-end delay map: The i th element of the end-to-

end delay mapi is the average of the end-to-end delay of the nodes located withincellCi.

cellCi. Note that the end-to-end delay map is not locally measurable and requires the

collaboration of the destination node, as shown in Fig. 2.4.

Next, we shall define the coherence time of the spatial network variables to quantify

and keep track of the topology changes. Park and Rodoplu [23] defined the coherence

time of a QoS map. To quantify the temporal changes in a network variable, we improve

upon that definition by generalizing it to-neighborhood variations.

Definition 5(-coherence time)

Let(t)be a network variable1 defined overD. The-coherence timeof,T(,)c (t), is

defined as the maximum time intervalduring which does not deviate by more than

1From this point onwards in the chapter, whenever we say network variable, we are referring to a

spatial network variable; which can be any of the following variables:S, G, , , or.

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)(),( tTc

2

tt

2

t

Figure 2.5: The-coherence time of the network variable (t), T(,)c (t) is the maxi-mum intervalduring whichdoes not deviate by more thanfrom its expected valueat timet.

from its expected value at timet. That is t (t 2 , t+ 2)

T(,)c (t)def= arg max

(t) (t)< (2.6)

Note that T(,)c (t) is a functional, i.e., it is a function of the function (t). The

-coherence time of the network, denoted by T()c (t)is defined as the minimum of the -

coherence time of all the independent network variables, i.e., T()c (t)

def= min{T

(,)c (t)}.

To satisfy the above equation for the network as a whole, the network characteris-

tics, such as node density and cumulative traffic rate, should remain roughly constant

for at leastT()c (t). For example, if we take a snapshot of the network duringT

()c (t), a

route that is initiated by any node withinCi goes through the same chain of cells. We

defineRi as the set cells that a route passes through from the cell Ci to the gateway.

For example, Fig. 2.6(a) presents a sample snapshot of the network during T()c (t); here,

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1 2 3 4 5

6 7 8 9 10

11 12 13 14 15

16 17 18 19 20

21 22 23 24 25

},,,,{

17

13192020

CC

CCCR

(a)

1 2 3 4 5

6 7 8 9 10

11 12 13 14 15

16 17 18 19 20

21 22 23 24 25

},,,,,,,,,{2524232019

1815141377

CCCCC

CCCCCH

(b)

Figure 2.6: The network snapshot during T()c (t). (a) R20 is the set of cells for the

route fromC20 to C1. (b) The shaded area H7is the set of upstream cells forC7.

R20 = {C20, C19, C13, C7, C1}is the routing path from C20 to the gateway located at

C1. Similarly, we define Hi as the set of cells that use Cias an intermediate cell along

the route to the gateway; that is, Hi = {Cj|Ci Rj}. For instance, the shaded area H7

in Fig. 2.6(b) is the set of cells that use C7along their route in the snapshot of Fig 2.6(a).

Our goal is to show that during the -coherence time of the network, (1) the end-to-

end delay from each cell Ci to the gateway has a bounded average, (2) the congestion

(local delay) map of the network also has a bounded average, (3) the average end-to-end

delay can be estimated by summation of the local delay map elements along the route

of the packet, and (4) a stochastic delay guarantee can be provided in addition to the

average delay estimation. Below, we provide two fundamental assumptions that relate

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the experienced delay of a node to the spatial characteristics of the network. We discuss

the validity of these assumptions and also provide experimental results in Section 2.6.1

to support their claims.

Assumption 1

If the location of nodek at timetis X(k)(t), then >0, x = x such that

E (k)L

(t)X(k)(t) = x E (k)L

(t)X(k)(t) = x < (2.7)Given (2.3), equivalently,

E fL x; SA(x)E , t; A(x)E , tE

fL

x; S

A

(x)E , t

;

A(x)E , t

< (2.8)Assumption 1 above claims that a small change in the position of a node will result

in a relatively small change in the delay of a packet in the nodes queue. The reason

is that a reasonably small change in the nodes location makes a small change in the

effective region of that node. Therefore, the change in the local delay of that node is

bounded by, as the delay functionfLis a bounded function.

Corollary 1

rc > 0, pe(rc)> 0 where, > pe(rc), >0 such that < rc,

|x x| < E fL x; SA(x)E , t; A(x)E , t

E

fL

x; S

A

(x)E , t

;

A(x)E , t

< . (2.9)30


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The corollary of Assumption 1 is important because it limits the variation of the

local node delay bype(rc), when the node moves no more thanrc.

Assumption 2

LetQ be the event that the position of nodekat timetis equal to its position at timet;

i.e., {X(k)(t) = X(k)(t)}. Then, >0, 1, 2> 0 such that

S(Ci, t

) S(Ci, t)< 1,

(Ci, t

) (Ci, t)< 2

E (k)L (t)Q E (k)L (t)Q < (2.10)

The assumption above claims that small changes in the node density and the cumu-

lative traffic will result in a relatively small change in the local delay of a fixed node.

Even though the methods similar to the ones in [5254] do not give a closed form

equation for the average delay, we can still see that, for either single-hop or multihop

communication, the MAC layer delay does not change abruptly provided that the node

density and offered traffic do not change abruptly. Furthermore, the route discovery

delay is also a bounded function of the node density. Therefore, small changes in the

node density or offered traffic do not cause arbitrary large changes in the local delay of

the nodes.

Lemma 1

LetBki be the event{X(k)(t) Ci}. Letricbe the maximum possible displacement of a

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node in cellCi. Then Ci D andk N, pe(ric)such that:

E i(t)Bki E (k)L (t)Bki < pe(ric) (2.11)Lemma 1 places an upper bound on the error between the actual local delays of

the nodes at cell Ci and the local delay attributed to that cell. This error has a direct

relationship with the relative size of the cells in comparison with the transmission range

of the nodes. The trade-off here is that, while choosing smaller cells results in higher

accuracy, it also increases the estimation complexity and the convergence time of the

delay map construction. The proofs of all of the Lemmas and Theorems in this section

are given in the appendix of this chapter.

Let K be the event that there is at least one node in each cell; that is, {Ci D,{k:

X(k)(t) Ci} = 0}. Furthermore, let1, 2> 0andT(1,G)c (t) = min{T(1,G(Ci,t))c (t) :

Ci D}, T(2,S)c (t) = min{T

(2,S(Ci,t))c (t) : Ci D}. Finally, let T

(1,2)c (t) =

min{T(2,S)c (t), T

(2,G)c (t)}.

Lemma 2

t

t T(1,2)c (t)/2, t+T

(1,2)c (t)/2

, te, pe> 0 such thatCi D

E i(t)K E i(t)K < te+ 3pe (2.12)Lemma 2 suggests that if the node density and generated traffic remain roughly the

same over time, the local delay map of the network remains roughly the same. Here,

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te is a function of(1, 2, maxCj Hj) representing the error due to temporal variation

andpeis a function ofricwhich stands for the error that arises due to location changes.

Theorem 1

t

t T(1,2)c (t)/2, t+T

(1,2)c (t)/2

, te, pe> 0 such thatCi D

E [i(t

)|K] E [i(t)|K]< te+pe (2.13)

Theorem 1 states that if the node density and the traffic generation rate of the cells

remain roughly close to their average over time, the expected value of the end-to-end

delay map of the network is approximately invariant during that time.

Theorem 2(Path Integration)

Leti(t) =CjRi j(t). Then, t t T

(1,2)c (t)/2, t+T

(1,2)c (t)/2, pe >

0,te> 0, andpe> 0 such thatCi D

Ei(t)K Ei(t)K < pe+ (pe+te)maxCi

|Ri| (2.14)

Theorem 2 states that the end-to-end delay of the cells can be approximated by

adding up the congestion map values along the spatial route Ri. We call this method of

end-to-end delay estimation path integration (following [23]). To achieve an accurate

estimation of end-to-end delay in the high mobility regime, we need to make sure that

the routes do not change abruptly during the course of statistical measurements and

estimation. Therefore, a safe choice of routing protocol is geographic routing, where

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the routes are selected as a chain of cells. More importantly, the routing protocol can

exploit the local delay map of the network to avoid congested areas, reaching a better

delay performance and capacity. This insight is the key motivation and fundamental

building block of the routing protocol presented in Section 2.5.

Most of the delay-sensitive applications of wireless networks can tolerate only a

limited amount of jitter. Therefore, besides the end-to-end average delay guarantees,

it is of great importance to provide an upper bound on the variations of the end-to-end

delay. The rest of this section is dedicated to the presentation of a statistical scheme to

determine the probability of delay violations. Similar to [50], we adopt and simplify

the effective capacity approach of [55]. The stochastic end-to-end delay guarantee for

the nodes located in cellCiis defined as:

P r{i(t)> Dmax} (2.15)

whereDmaxis the delay threshold requirement, and is the violation probability that

can be handled by the application.

The effective capacity approach, derived by [55] and extended to multihop and

parallel channels by [56], states that the delay at a node is exponentially bounded if the

data rate is limited to the effective capacity defined below. Let S(t)denote the service

process of the channel (either parallel or sequential) in bits over [0, t], i.e., the amount

of data that the channel can carry. The effective capacity function of the channel for

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ergodic and stationary service process is defined as [56]:

() = limt

1

tlog(eS(t)) (2.16)

where() minh h()or () =

h h()for h sequential and parallel channels,

respectively [56].

It is shown in [56] that the probability that the total experienced delay of nodek

exceeds a threshold ofDmaxis:

P r{(k)E (t)> Dmax} e

Dmax (2.17)

where, the QoS exponent of the connection, is given by

= 1() (2.18)

As stated in [55] and confirmed for VoIP traffic by [57], the distribution of end-to-

end delay is approximately exponential. Moreover, it is shown that the QoS exponent

of the connection is equal to the inverse of the delay average [55]. Therefore, for every

nodekin cellCi, the QoS exponentcan be estimated by using Theorem 2 as follows:

() = 1

E[

(k)

E (t)]

1

CjRi j(t)

(2.19)

The delay violation probability of (2.15) for each cell Ci follows from (2.17) and

(2.19).

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2.5 DMQR Routing Protocol

In the previous section, we showed that, during the coherence time of the network,

the congestion map has a bounded average and that the end-to-end delay map can be es-

timated by the path integration method. In this section, we develop a congestion-aware

routing protocol that utilizes such spatial maps of network congestion to enable delay-

optimized routing over high-density mobile multihop wireless networks. The main

procedures of the Delay Map QoS Routing (DMQR) protocol are those that help the

protocol make use of the space-centric framework. DMQR is a hybrid routing protocol

that constructs the congestion map of the network, as well as a neighbor lookup table in

a proactive fashion. However, it does not maintain an end-to-end route database for any

of the source/destination pairs. DMQR discovers a delay-optimized geographical path

(namely, a chain of cells) to the destination on demand, while it selects the individual

relay nodes proactively. To accomplish its mission, DMQR builds a distributed view

of the congestion map via dissemination of locally measured MAC and network layer

delays. This strategy can be exploited to (1) find a delay-optimized route upon request

via path integration, (2) give a soft end-to-end delay guarantee while avoiding the con-

gested areas of the network, and (3) perform resource planning in a delay-sensitive,

high-density, and high-mobility network.

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Initial

State

Spatial Path

Selection

Neighbor

Discovery

Call Admission

Control

Cong. Map

Construction &

Dissemination

Packet in Buffer

Neighb

orN

ot

Accessible

No Connectivity

PacketT

x

Call Request

Neighbor Lookup

Timeo

ut/

HEL

LOPacke

tRec

v.

Figure 2.7: The high-level state diagram of the DMQR protocol.

The high-level state diagram of the DMQR protocol is shown in Fig. 2.7 and con-

sists of the following building blocks: (1) call admission, (2) spatial path selection,

(3) neighbor discovery, and (4) congestion map construction and dissemination. In

DMQR, whenever a node initiates a data session or a VoIP call, the call admission con-

trol (CAC) block of the routing protocol decides whether or not to admit the session or

not. However, the routing protocol does not keep any information about the admitted

sessions and treats all of the incoming and relay packets independently. That is, when a

node has a data packet to send, it uses its local copy of the congestion map to discover

the best chain of cells over which the sum of the local delays is a minimum. The path

integration theorem (Theorem 2, Section 2.4) implies that, if the average node density

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On the other hand, CAC based on the delay average performs only slightly worse than

the more precise CAC methods [61]. Therefore, we adopt a CAC algorithm that makes

its admission decisions based on the networks congestion map as follows: (1) Upon a

call admission request, the source node finds the delay optimized path to the destination

by calling the spatial path selection procedure. (2) The local delay values of the cells

along the discovered path are tweaked to reflect the effect of incoming traffic; e.g.,

adding a single VoIP call will approximately increase the local delay by 5-10% in most

cases2. (3) The expected value of end-to-end delay of the selected path is estimated via

path integration. (4) The violation probability of the tolerable delay is derived by (2.17)

and (2.19). (5) The call is accepted if the estimated delay and its violation probability

are within the acceptable range.

Furthermore, in a more realistic scenario, there are possibly multiple gateways that

can be selected as the packets destination. This is the case when there are multiple base

stations in the cellular network that the packet can be routed towards or when there are

multiple base stations and wireless access points in a heterogeneous environment [34].

In such cases, an optimized choice of the gateway can exceptionally improve the per-

formance of the underlying network. In DMQR, the above CAC algorithm can simply

be amended to run the path selection algorithm for all the possible destinations and

choose the one with the best delay performance, with no additional routing overhead.

2This assumption is valid as long as the network remains in the VoIP regime as defined in [57].

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2.5.2 Spatial Path Selection

The spatial path selection algorithm constructs a graph in which the vertices repre-

sent the cells and the edges connect each vertex (cell) to its neighbor cells. The edges

of the network graph are weighted by the local delay (congestion) map values or a large

number (e.g., 10 seconds) if the local delay for a cell is undeterminable, as shown in

Fig. 2.8. Doing so helps the protocol avoid routing over the obstacles in D. The neigh-

bor cells here are considered as the adjacent cells, plus possibly further cells in the

transmission range, that contain at least one node. However, this extension of neigh-

boring cells is possible only locally and has to be repeated by the relay nodes along the

spatial route. There is also another piece of information fed back from the neighbor

discovery algorithm that overwrites the weights of those edges that do not contain a

node inside their connecting cells (to infinity); i.e., it removes the connecting edge of

those two vertices. This feedback, which is available only for one hop, substantially

enhances the path selection performance as it adds real-time connectivity information

to the long-run average of delay measurements.

DMQR runs Dijkstras algorithm on demand, at each hop, to obtain the best set of

cells, over which the sum of the local delays, i.e., the estimated end-to-end delay, is

minimum. We clarify the operation of the path discovery procedure by the following

examples. Assume that the node of interest joins the network or the network is in

cold start mode, i.e., there is no congestion information available. Initially, each node

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1ms 1.1ms 1.05ms

1.2ms 2ms 1.05ms

2ms 1.2ms 1.1ms

(a)

1.2ms

1.2ms

1.2m

s

1.2ms

1.2ms

1.2ms

1ms

1.05ms

2ms

2ms

1.1ms

1.2ms

(b)

Figure 2.8:Network graph construction. (a) Network dynamics and local delay values,

(b) the weighted network graph of the center cell; note the values of the cell without

connectivity.

builds a network graph that is weighted by long delays as explained above. In this case,

the output of Dijkstras algorithm points to the cell along the geographic route to the

destination. As the congestion map becomes available, the node updates the path to

the delay optimized one. Furthermore, similar to geographic routing, the obstacles and

holes are avoided by the nodes close to them at the beginning, as the feedback from

the neighbor discovery module removes those edges. However, once the congestion

map becomes available, the path selection is done long before the packet gets close to

the holes. Therefore, unlike geographic perimeter routing protocols, DMQR does not

overload the nodes around the edges of the holes.

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2.5.3 Neighbor Discovery

The output of the path selection algorithm points to the next cell along the optimal

route. So, the protocol has to find a relay node as the recipient of the packet in that

cell. The challenge of fast neighbor discovery comes from the high-mobility, high-

density nature of the network. Note that here, a node is used as the next hop as long

as it remains in the desired region (cell). This approach is in contrast to the node-

centric protocols that use the same node until the route breaks. We employ a proactive

neighbor discovery algorithm that is similar to the one in the TDR protocol [46]. This

choice is made due to the fact that, in a high-mobility network, neighbor nodes change

frequently, so the packets cannot meet the end-to-end delay constraint if a neighbor

is found reactively. To achieve proactive neighbor discover, every node maintains a

neighbor database (lookup table). Each element of this lookup table holds the following

information: (1) neighbor node ID, (2) mobility information of the neighbor, i.e., the

location and velocity, (3) lifetime of that element, and (4) a validity flag. The lifetime

of an element is defined as the duration for which the neighbor node remains within

its current associated cell. The validity flag is used for route maintenance as explained

before. To update the neighbor lookup tables, nodes are required to periodically broad-

cast Hello packets that carry their location and mobility information (refer to Fig. 2.7).

Upon the reception of a Hello packet, the receiver updates the neighbor database by

calculating the lifetime of that piece of information. The transmission frequency of the

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Hello packets plays an important role in the performance of the protocol. The shorter

the time period between the updates, the more recent the neighbor lookup database of

a node is, and the more likely that a node can find a neighbor located in the desired

zone, albeit at the cost of increasing the control overhead. Once the nodes build their

neighbor lookup database, the routing protocol chooses the neighbor with the longer

lifetime to maintain continuous traffic flow, and to decrease the delay jitter.

2.5.4 Congestion Map Construction/Dissemination

Another novel aspect of DMQR is the construction and dissemination of the con-

gestion map by using local delay measurements by different nodes, at different times

and locations. It is required that we construct and maintain the congestion map in the

joint memory of the nodes in a time period (thespreading period) shorter than the co-

herence time of the congestion map. To do so, every node measures the network and

MAC layer delays of the actual data packets and attributes them to the cell at which it

is located. The local delay measurements of the packets build an enormous distributed

data set, that usually contains bursts of measurements by multiple nodes, each of which

becomes obsolete at some point. The challenge is to find a minimum amount of dis-

semination overhead such that the nodes reach a consensus on the approximation of the

congestion map, with acceptable error.

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To construct and retain the congestion map, a simple moving average (SMA) within

the coherence time window is used. In this algorithm, for each cell, a node keeps (1)

the moving average of the local delays, (2) the number of measurements (samples) over

which the average is taken, (3) the window start time and end time. Whenever a new

measurement is made, the node updates its local version of the congestion map includ-

ing the above variables. Furthermore, as the time passes, some of the measurements

become obsolete and the algorithm should remove them from the averaging process.

Even though we cannot remove the expired samples from the moving average one by

one, we can normalize the weight of the moving average with respect to the portion

of the window that has not expired. This method works without error if the samples

are distributed uniformly, but it causes some error because the measurements are made

in bursts. Finally, to make local measurements available to the network, the nodes

broadcast their local copies of the congestion map along with the periodic Hello packet

broadcasts. Every node consolidates the received map information with its own using

the timestamps and the weights mimicking the error and avoiding the measurement

duplicates.

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Chapter 2. Congestion-Aware Spatial Routing in Hybrid Mob

delay reliability and trust in manets

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