thesis hyungjune lee final-augmented

8/7/2019 Thesis HyungJune Lee Final-Augmented

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ROUTING TO MOBILE USERS BY EXPLOITING MOBILITYPREDICTION IN WIRELESS MESH NETWORKS

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF ELECTRICAL

ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITYIN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

HyungJune LeeAugust 2010


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http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/kg407mt1632

2010 by HyungJune Lee. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

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http://creativecommons.org/licenses/by-nc/3.0/us/http://purl.stanford.edu/kg407mt1632http://purl.stanford.edu/kg407mt1632http://creativecommons.org/licenses/by-nc/3.0/us/http://creativecommons.org/licenses/by-nc/3.0/us/


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I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Leonidas Guibas, Primary Adviser


Fouad Tobagi, Co-Adviser


Nicholas Bambos

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation inelectronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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Abstract

With the advent of ubiquitous wireless networks, supporting user mobility has becomecrucial. Routing algorithms face challenges in supporting mobility while remainingenergy efficient. Mobility of human or vehicle makes wireless links in these mobilenetworks much more volatile than the links in wireless networks of stationary nodes,and the wireless connectivity varies frequently due to moving speeds. Hence reliabledata delivery to mobile users is often hard to achieve.

In this thesis, we propose a new routing algorithm for wireless mesh networks withmobile users based on the following components: (i) predicting likely next associationnode of mobile user (short-term mobility prediction), and (ii) predicting a sequenceof the future association nodes of mobile user (long-term mobility prediction). Ourapproach is to understand and characterize the mobility of the mobile users by lookingat connectivity patterns over stationary mesh nodes using past history of connectivityinformation. Our main contribution is the design of techniques that can be used byrouting algorithms to leverage the predictive knowledge of user mobility to efficientlydeliver data to those users. This work enables a long-term routing plan through anetwork optimization process, called data stashing . The data stashing scheme enablesreliable data delivery from stationary mesh nodes to mobile users. In this scheme,each mesh node selects a set of possible association nodes on which to stash its data,

to be picked up whenever the mobile user passes the stashing node.We show that data stashing signicantly decreases routing cost for delivering data

from stationary mesh nodes to multiple mobile users compared to immediate routingprotocols where mesh nodes immediately deliver data to the last known associationnodes of mobile users. We also show that the scheme provides better load balancing,

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avoiding collisions and consuming energy resources evenly throughout the network,

leading to longer overall network lifetime over the immediate routing protocols. Moreimportantly, we demonstrate that given even limited information about the futurenode associations of mobile users, optimization of routing paths leads to signicantimprovements in routing performance.

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Acknowledgments

I am so blessed to enjoy my graduate student life at Stanford. The great sunnyweather in the Bay Area kept me staying with positive and active mind, and pro-vided a great environment to engage in free thinking without constraint in indoorand outdoor atmospheres. I enjoyed the high quality courses offered by the distin-guished professors at Stanford both in Electrical Engineering and Computer Science.I was able to learn many state-of-the-art technical contents from in-depth theoreticalknowledge to practical system implementation, which have been essential researchtools for my Ph.D. research work.

First of all, I would like to thank my Ph.D. advisor, Leonidas Guibas for hisvaluable and insightful advising throughout my Ph.D. study. He provided me withenough freedom to choose interesting research topics, and taught me a variety of re-search essentials brainstorming from group discussion, positioning research work,and interdisciplinary methodology by introducing many different approaches fromother research areas. I learned how to communicate research ideas to people who donot necessarily have the same research background. I gave many research presenta-tions during the group meetings and improved my presentation skills based on hisfeedback. I learned a lot from these valuable opportunities.

I also thank Fouad Tobagi and Nicholas Bambos for serving as my reading com-

mittee. Thanks to their helpful suggestions and feedback, I was able to improve andnalize this dissertation.

Next, I would like to thank our Guibas group members for their valuable feed-back and suggestions throughout my research. Especially, Martin Wicke, Omprakash

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Gnawali, Arik Motskin, Branislav Kusy, and Nikola Milosavljevic have been great col-

laborators. Numerous fruitful discussions enabled me to develop initially crude ideasinto publishable research work. I also thank Kyle Heath, Primoz Skraba, Mirela Ben-Chen, Dmitriy Morozov, Eunjoon Cho, Kevin Wong, Xiaoye Jiang, Qixing Huang,Daniel Chen, and others for being my colleagues and having great moments with mein the office.

I would also like to express my gratitude to my funding sources: Samsung Schol-arship and Army High Performance Computing Research Center (AHPCRC) for sup-porting my graduate studies and research work.

I would like to thank my mother, father, grand mother, and my older brotherfor their love, faith, and sacrice throughout my life. Their prayers enabled me toovercome hardships and difficulties upon me through Gods way. Without them, Iwould never have nished my Ph.D. study. I thank my beloved wife, Haejin Song.She has been always on my side, and supportive with her love and exceptionallybeautiful singing. Without her, everything that I achieved with all my effort wouldbe meaningless. This dissertation is a result of her constant love and encouragement.I also thank my parents-in-law for all their love and support for our family of Haejinand me.

Finally, I devote this dissertation to my Savior and God, Jesus Christ for Hisgrace, mercy, and love in my entire life.

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Contents

Abstract iv

Acknowledgments vi

1 Introduction 11.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.1 Mesh Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.2 Mobile Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.3 Association Nodes . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.3.1 Short-Term Mobility Prediction . . . . . . . . . . . . . . . . . 81.3.2 Long-Term Mobility Prediction . . . . . . . . . . . . . . . . . 91.3.3 Predictive Data Delivery . . . . . . . . . . . . . . . . . . . . . 11

2 Short-Term Mobility Prediction 132.1 Short-Term Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4 Constructing the Mobility Model . . . . . . . . . . . . . . . . . . . . 16

2.4.1 Mobility Graph . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4.2 Mobility Graph Extraction . . . . . . . . . . . . . . . . . . . . 21

2.5 Connectivity Prediction . . . . . . . . . . . . . . . . . . . . . . . . . 222.5.1 Matching Segments Using Dynamic Time Warping . . . . . . 24

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2.6 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.6.1 Evaluation Setup . . . . . . . . . . . . . . . . . . . . . . . . . 262.6.2 Prediction Performance . . . . . . . . . . . . . . . . . . . . . . 282.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 31

3 Long-Term Mobility Prediction 333.1 Long-Term Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . 343.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.4 Constructing the Mobility Model . . . . . . . . . . . . . . . . . . . . 37

3.4.1 Trajectory Representation . . . . . . . . . . . . . . . . . . . . 383.4.2 Similarity Measure . . . . . . . . . . . . . . . . . . . . . . . . 383.4.3 Cluster Representation . . . . . . . . . . . . . . . . . . . . . . 39

3.5 Connectivity Prediction . . . . . . . . . . . . . . . . . . . . . . . . . 423.5.1 Cluster Matching . . . . . . . . . . . . . . . . . . . . . . . . . 423.5.2 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.6 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 47

4 Predictive Data Delivery 484.1 Routing Benets from Mobility Prediction . . . . . . . . . . . . . . . 494.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.3.1 Reactive Approach . . . . . . . . . . . . . . . . . . . . . . . . 534.3.2 Proactive Approach . . . . . . . . . . . . . . . . . . . . . . . . 544.3.3 Predictive Approach . . . . . . . . . . . . . . . . . . . . . . . 54

4.4 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554.5 Network Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 564.6 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.6.1 Evaluation Setup . . . . . . . . . . . . . . . . . . . . . . . . . 584.6.2 Network Performance . . . . . . . . . . . . . . . . . . . . . . . 61

4.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 72

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5 Conclusion 75

5.1 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Bibliography 78

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List of Figures

1.1 Overview of our routing algorithm using predictive knowledge of theanticipated association nodes . . . . . . . . . . . . . . . . . . . . . . 6

2.1 The differences between mobility and connectivity graph . . . . . . . 182.2 Extracting the mobility graph from an observation sequence . . . . . 202.3 Dynamic time warping determines the best-matching outgoing edge

and predicts the next relay node. . . . . . . . . . . . . . . . . . . . . 232.4 Dynamic time warping example . . . . . . . . . . . . . . . . . . . . . 242.5 Routes used for training and testing short-term mobility prediction . 272.6 Experimental evaluation of prediction algorithm for varying time to

transition to the node . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.7 Experimental evaluation of prediction algorithm depending on speed

variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.8 Impact of size and variability of training data . . . . . . . . . . . . . 30

3.1 Clustering and alignment procedures. . . . . . . . . . . . . . . . . . . 413.2 Sequences belonging to a cluster, the aligned sequences, and their

graphical prole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.3 Sequence alignment of a partial trajectory with a cluster prole. . . . 44

3.4 Typical trajectories of moving buses in UMass from the DieselNet dataset 453.5 Average entropy of wireless associations within a cluster . . . . . . . 46

4.1 Optimal selection of stashing nodes for each mesh node, given a set of trajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

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4.2 Wireless mesh sensor network testbed in Clark building at Stanford . 58

4.3 Wireless mesh sensor network in downtown San Francisco for simulation 594.4 Routing cost depending on the number of mobile sinks in Clark testbed. 604.5 Fraction of packets stashed on nodes that are actually visited by the

mobile node depending on number of nodes used for prediction in theDieselNet dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.6 Moving paths of mobile vehicles in simulation dataset . . . . . . . . . 644.7 Routing cost and delivery reliability depending on the number of mo-

bile sinks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.8 Routing cost and delivery reliability depending on the number of pre-

dicted trajectory nodes for 10 mobile sinks . . . . . . . . . . . . . . . 674.9 Packet delivery reliability depending on number of nodes used for pre-

diction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684.10 Distributions of the number of hops and node transition time of mobile

sinks in evaluation data, and packet delay performance . . . . . . . . 694.11 Running time for a sensor node to solve an optimization problem for

stashing in each platform/tool depending on the number of mobile sinks. 704.12 Load balancing throughout the networks (for 10 mobile sinks case). . 714.13 Packet delivery reliability depending on speed of mobile users . . . . 724.14 Storage overhead over the mesh nodes for 10 mobile sinks. . . . . . . 73

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Chapter 1

Introduction

A variety of wireless devices based on 802.11x, ZigBee, WiMAX, and Bluetooth havebecome affordable for large scale use in mobile and wireless applications and areubiquitous in our working and living environments. These wireless devices typicallyrely on infrastructure (such as cellphone towers or wireless access points) for com-munication. When such infrastructure is not available, or when the infrastructure isnot in direct range of radio transmission of these devices, they can be congured toform a multi-hop wireless network called mesh networks. The mesh nodes, which arenodes in these mesh networks, act not only as packet source or destination, but alsoas routers. The mesh nodes forward data from the source to the destination, whichmight be multiple hops away. Recently, mesh networks have been publicly deployed tosupport Wi-Fi sharing over San Francisco area [59], Mountain View in California [25],and Champaign-Urbana in Illinois [9]. A special type of mesh nodes equipped withsensors are deployed and congured to form sensor mesh networks in Cambridge,MA [61], and UC Berkeley campus [30], for monitoring weather conditions and airpollutants or auditing electrical usage in buildings.

Many mesh network applications require communication between mobile wirelessdevices moving across the network and the nodes in the mesh network. For example,humans or vehicles carry wireless devices and communicate with the mesh networksin order to either send data to the networks or receive data from them. This datadelivery for mobile users can be categorized into two scenarios: i) from a mobile user

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CHAPTER 1. INTRODUCTION 2

to a mesh node, and ii) from a mesh node to a mobile user. In the rst scenario,

when a mobile user wants to deliver data to a mesh node in the network, the mobileuser rst sends the data to the currently associated mesh node, and thereafter thedata can be delivered to the destination node through a series of intermediate meshnodes. The second scenario raises a more challenging problem. When a mesh nodeneeds to deliver data to a mobile user, the mesh node sends data toward the lastknown associated node of the mobile user. If the mobile user moves away from thecommunication range of the associated node while the packets are in transit, thosepackets will be lost unless additional mechanisms ensure path discovery to the newlocation of the mobile user.

In this dissertation, we study the problem of data delivery from mesh nodes (asdata sources ) to mobile users in wireless mesh networks the goal is to design areliable and scalable routing algorithm for mesh nodes to deliver data to multiplemobile users.

There is a large body of prior work in the eld of routing from mesh nodes tomobile users. We can categorize them into two schemes: 1) proactive scheme suchas OLSR (Optimized Link State Routing) [11] and DSDV (Destination-SequencedDistance Vector routing) [67], and 2) reactive scheme such as DSR (Dynamic SourceRouting) [31] and AODV (Ad hoc On-demand Distance Vector routing) [68]. Thestate-of-the-art ad-hoc routing protocols can discover routes without initially knowingthe topology of the networks, and this aspect is considered as a big advantage of theseprotocols over traditional routing protocols like OSPF(Open Shortest Path First) [12]and RIP(Routing Information Protocol) [57]. However, the problem is that theirrouting performance degrades rapidly with increasing mobility, i. e., resulting in higherroute update cost for proactive scheme or higher bandwidth usage of on-demandooding for reactive scheme as investigated in [71].

Since mobile users movement is restricted by environmental structures such asbuildings, bridges, roads, and walkways, we can assume that not all possible move-ments within space are actually realized. Rather, a recent study [74] investigated thatthe users move along a limited set of typical spatial trajectories, and the movementshows a certain degree of regularity. This suggests that we can learn the structure


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in humans movement, called the mobility pattern from repeated observation, and

exploit the mobility pattern for designing a more reliable and efficient routing schemethat works even under high mobility.The ad-hoc routing protocols in prior work do not explicitly consider connectivity

pattern of user mobility for the routing problem. Our work opens a new direction byinterconnecting routing algorithm with mobility pattern modeling of mobile users forimproving routing performance in wireless mesh networks.

In this chapter, we introduce the system model, describe our main contributionscompared to prior work in the literature, and then give an overview of the mainchapters.

1.1 Preliminaries

We describe the system model that we consider throughout this thesis. We assumetwo-tier communication structure, consisting of 1) mesh nodes and 2) mobile nodes.Stationary mesh nodes are congured to form a mesh network, considered as anunderlying communication structure, and mobile nodes communicate with the meshnetworks. We describe several types of nodes in more detail below.

1.1.1 Mesh Nodes

Mesh nodes are wireless nodes that connect with other wireless nodes to form a multi-hop wireless network. In this thesis, each mesh node operates as a data source or actsas a router (called a relay node ) in the network. Mesh nodes can be either 802.11 Wi-Fi ad-hoc devices, 802.15.4 sensor nodes [14, 70], or any other wireless ad-hoc devices.A mesh node communicates with several neighboring mesh nodes, and mesh nodes

are congured to form a mesh network. Packet transmission from a stationary sourceto a stationary destination in the mesh network needs multi-hop delivery throughintermediate mesh nodes.

We assume that each mesh node is stationary and has enough memory to recordand keep any collected data from the environment using sensor equipment and buffered


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packets for packet relay.

1.1.2 Mobile Nodes

Mobile nodes are wireless nodes that move in the area covered by the mesh network.We assume that a human or vehicle carries a wireless device, and it is considered as amobile node. In this dissertation, we consider a data delivery scenario in which meshnodes have data to deliver to mobile users serving as sinks (data consumers). Mobileusers can receive data through the underlying stationary mesh networks. We will usethe terms mobile user, mobile sink, and mobile node interchangeably.

1.1.3 Association Nodes

If a mobile node is in the mesh network, one of stationary mesh nodes which usuallyhas the highest signal strength from the mobile node, called association node , isselected, and a communication link between two nodes is established. When themobile node roams through the network, the association node changes, and a sequenceof association nodes is given for a physical path of the user.

1.2 Main Contributions

This dissertation focuses on designing optimal routing algorithm based on under-standing user mobility using wireless association in wireless mesh networks. Thisthesis comprises three main parts: 1) short-term mobility pattern modeling for pre-dicting the future association nodes of mobile users, 2) long-term mobility patternmodeling for predicting a sequence of the future association nodes, and 3) reliablerouting algorithm to multiple mobile users with energy efficiency by taking a long-term routing plan through network optimization. We describe each of these in moredetail below.

1) We propose a prediction scheme to infer highly probable next association node of mobile users in wireless mesh networks. We introduce the mobility graph , a directed


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graph structure that encodes a direct link of association transition from a mesh node

to another, corresponding to users physical movement. The mobility graph capturesmovement patterns of mobile users in the space of wireless connectivity using pasthistory of RSSI (Received Signal Strength Indicator) for packet transmission frommesh nodes to mobile user. The mobility graph is extracted from wireless trace of mobile users who explore a number of physical paths over the networks in a learningphase. Then the mobility graph is used to predict the future mesh nodes to whichthe mobile node will connect after the link to the current association node becomesunreliable. The experimental evaluation from real testbed deployment demonstratesthat we can predict the next association node of a mobile user 1-2 seconds in advancewith 90% accuracy.

Our work is different from prior work for predicting the next association for mobileusers in Wi-Fi networks recently presented in [64, 77], in that their approaches arebased on Markov model with only few states of the current and past associations,whereas our approach exploits more detailed signal strength information as well asassociations for embedding association patterns in a graph.

2) Beyond the short-term prediction algorithm described above, we present moregeneral prediction algorithm which allows us to predict a sequence of node associ-ation nodes of mobile users, called long-term mobility prediction. To do this, wepresent a method for representing trajectories with wireless association, learning typ-ical trajectories from observations as well as predicting likely association patternsgiven observed partial association history, where we borrowed ideas of sequence simi-larity, clustering, and alignment, from computational biology. Wireless device carriedby mobile user runs the prediction algorithm to compute and supply informationabout its future association sequences to the network. We characterize a trajectory

as a sequence of node associations, and compute similarity between two sequencesout of all association data acquired in a learning phase. Using this similarity, wecompute clusters representing typical moving paths through the network. We designa compact probabilistic representation for the clusters which we use to efficiently nd


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Figure 1.1: Overview of our routing algorithm using predictive knowledge of the antic-ipated association nodes. Red node is the immediate future association node (throughshort-term prediction), and blue nodes are sequences of the future association nodes(through long-term prediction).

Data Source

likely trajectories during prediction. This work provides an efficient probabilistic tra-jectory, which is used for prediction of the anticipated trajectory nodes for mobileusers.

There are prior works [19, 39] to model long-term movement patterns of movingusers using GPS traces, and predict the goal destination of the users. Our work isdifferent since we use wireless association data which is more coarse and noisy, andour long-term prediction algorithm provides more detailed view of all possible futurepaths, not just the destination.

3) Based on the mobility prediction algorithms, we design a routing scheme that

exploits knowledge about the long-term association pattern of mobile users withina network of data sources to minimize energy consumption and network congestionenabling the routing scheme to scale to multiple mobile users and a large number of data sources. For delay-tolerant network applications, which do not require immediatereal-time data retrieval, we propose to route data not to the mobile sink directly,


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but to relay nodes along a predicted path of the mobile node that is close to the

data source in terms of communication hops (see Fig. 1.1). The selected relay nodewill stash the information to be picked up when the mobile node passes within thetransmission range of the relay node. We use an integer programming technique tond optimal relay nodes that minimize the number of necessary transmissions whileguaranteeing robustness against link and node failures. We demonstrate that thistechnique can drastically reduce the number of transmissions necessary to deliverdata to mobile sinks. We derive mobility and association models from real-worlddata traces and evaluate our techniques in real-world testbeds and simulations. Weexamine the inuence of uncertainty in the trajectory prediction on the performanceand robustness of the routing scheme.

There is a large body of previous work on data delivery algorithms in mesh net-works, which minimizes the routing cost. One class of the previous algorithms [20, 56]controls the movement of mobile users through optimization to minimize routing cost.The other class of the algorithms [49, 52, 55] does not control the sink movement,but has not explicitly considered user mobility in the routing problem. To the best of our knowledge, our proposed scheme is the rst to incorporate the analysis of long-term user mobility into the problem of mobile data delivery, without controlling anymovements of mobile users.

Together these three contributions offer a characterization and analysis of usermobility captured in the space of wireless connectivity and provide a practical wayto design a routing algorithm to compute routes to deliver data to mobile users.The proposed work builds sophisticated mobility pattern structures to help networkrouting protocols to proactively plan to route data to mobile users. Using intermedi-ate relay nodes spreads the traffic evenly across the network, leading to better load

balancing and more even utilization of network resources, compared to immediaterouting protocols in which all the mesh nodes immediately deliver data to the lastknown association nodes of mobile users. This work demonstrates a key claim thateven probabilistic knowledge of the future trajectory of mobile users can signicantlyimprove routing performance.


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1.3 Thesis Organization

Mobile nodes move in physical space which is restricted by environmental structures,constraining possible trajectories, while continuously connecting to stationary meshnodes. Further, mobile users often follow short(-est) paths from a starting point toa destination, greatly limiting the set of possible paths. In this thesis, we study howmuch association patterns of mobile users can be extracted from repeated observationof mobile users physical movement. And then, we design techniques that can beused by routing algorithms to leverage the predictive knowledge of user mobility toefficiently deliver data to those users.

In each chapter, we introduce the problem that we propose to solve, and raisethe importance of the problem statement. We position our work in the ow of pre-vious literature, and then describe our algorithm: short-term connectivity predictionalgorithm (Chapter 2), long-term connectivity prediction algorithm (Chapter 3), andpredictive routing algorithm for mobile users using the prediction algorithms (Chap-ter 4).

1.3.1 Short-Term Mobility Prediction

Since stationary mesh nodes are congured as an underlying communication network,wireless association and signal strength data of mobile nodes to the mesh networkscan be gathered and analyzed. Because of inherent regularity of humans movementbehavior, the movement pattern can be collected and analyzed in a learning phase,and this knowledge can be exploited to predict the future relay mesh node in the nextfew seconds based on the current and past RSSI history. Chapter 2 focuses on theproblem of predicting the immediate next associated nodes of a mobile user, calledshort-term mobility prediction .

Since wireless connectivity of a mobile user to the mesh networks is more dy-namic due to user mobility, extracting connectivity patterns of mobile users is nota straightforward problem. It requires an understanding of characteristics of bothdynamic wireless channel and humans mobility. The goal is to construct an efficientstructure to encode association patterns of mobile users using RSSI measurements


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over time, and to exploit it to predict the highly probable association transitions.

The algorithm in Chapter 2 introduces the mobility graph , which is a directedgraph structure to encode connectivity transitions of mobile users and embed knowl-edge about likely local mobility patterns within the network. The mobility graphis used to predict the future relay nodes for the mobile node, taking into accountwireless dynamics as well as mobility.

The output of this algorithm is next relay transitions as well as expected timeto transition. The algorithm is validated with a real testbed of 10 wireless devices.The experimental evaluation in real testbeds shows that we can predict the nextassociation node 1-2 seconds in advance with 90% accuracy. The algorithm canbe used to precompute and efficiently store additional routing plans for non-localmovement of mobile users, which would take signicant time to update the correctrouting path.

Chapter 2 is based on [42], where I proposed the mobility graph, designed theshort-term mobility prediction algorithm, and evaluated the algorithm in a real-worldtestbed.

1.3.2 Long-Term Mobility Prediction

Chapter 2 presents the techniques to predict the next association node after the cur-rent association, i. e., short-term transition. However, the inferred future associationnodes can be wrong when there is a signicant moving speed difference in between thelearning phase and the usage phase. We extend the mobility prediction to capture asequence of the future association nodes likely to be encountered a few minutes intothe future, called long-term transitions, by learning dynamic transitional patterns of mobile users while they are walking and connecting to stationary mesh nodes. The

topic of Chapter 3 is the problem of predicting the long-term future association nodesof mobile users, called long-term mobility prediction .

As a mobile user moves along a physical path from a starting location to thedestination, the corresponding wireless association sequence (also called trajectory ) tothe stationary mesh networks can be obtained. Due to imperfect links and radio signal


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strength uctuations, and also moving speed variance, the association trajectories

from the same spatial path are not necessarily identical. A trajectory may includesome additional association nodes, miss some nodes, or have different associationsamong nearby connections compared to the trajectory collected at a different time.The goal is to learn typical movement clusters given only association trajectories of mobile users, and to predict not only the immediate future transitions, but also asequence of the anticipated trajectory nodes.

In Chapter 3, we present a mobile trajectory clustering algorithm given wireless as-sociation trajectories using sequence matching, alignment, and clustering techniques,borrowing from computational biology, in order to extract typical movement patternsof mobile users. We characterize a trajectory as a sequence of node associations anduse multiple sequence alignment techniques to compute pair-wise similarity out of all sequences. After clustering, a set of wireless trajectories can belong to a charac-teristic mobile trajectory cluster. We propose a probabilistic representation for setsof similar trajectories to compactly describe a cluster of trajectories, and efficientlynd the best matching cluster given a partial trajectory. The constructed mobiletrajectory clusters are used to predict a set of the anticipated trajectory nodes bynding out the current node association of the mobile node within the probabilisticrepresentation of the best-matching cluster.

The output of this prediction algorithm is a set of long-term future associationsequences. The algorithm is validated with real-world mobility dataset from UMassDieselNet [4] and simulations. The algorithm can be used to design a scalable andenergy-efficient routing scheme for mobile users, by using a subset of the predictedtrajectory nodes as intermediate storage nodes from which the mobile users can laterreceive the data, instead of immediate data delivery to mobile users. Also, if thenetwork proactively knows anticipated associations of mobile users, it can search user-specic information of possible interests using a collaborative ltering technique, andproactively push the data to the nodes which the mobile user will be associated within the future.

Chapter 3 is based on [46], where I proposed the long-term mobility predictionalgorithm, and evaluated the algorithm with real-world wireless traces.


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1.3.3 Predictive Data Delivery

Chapters 2 and 3 present connectivity-based mobility models for learning the short-term and the long-term transitional patterns of mobile users, and algorithms to pre-dict the anticipated associations of the mobile users (i. e., the immediate future relaynodes as well as the anticipated trajectory relay nodes). A key connection of thepredictive knowledge of connectivity to the mobile data delivery is to deal with theuncertainty of the future connections of mobile users, and to provide a specic pro-tocol and algorithm to effectively use the probabilistic knowledge to ensure packetrouting to multiple mobile users, achieving high packet delivery reliability, and low

network overhead. The topic of Chapter 4 is the problem of designing a data routingalgorithm, which delivers data from stationary mesh nodes to multiple mobile nodes,using the predictive knowledge of connectivity.

Since the unstable wireless links and moving speed variance make the predictionproblem difficult, we should design a robust prediction algorithm to deal with theuncertainty of the future associations of mobile users. When we use the predictiveknowledge of user associations to design a routing algorithm, we must take into ac-count the uncertainty of the predicted associations for ensuring reliable data delivery

to mobile users. The goal is to design a robust data delivery scheme which ensureshigh packet delivery and low routing cost, by exploiting the predictive knowledge of mobile users movement.

In Chapter 4, we demonstrate a key claim that using only the probabilistic futureassociations of mobile users can greatly improve routing performance in terms of net-work overhead, packet delivery reliability, and load balancing, compared to immediatedata delivery schemes such as proactive and reactive ad-hoc routing protocols. Toestablish this claim, we present a data delivery scheme, called data stashing , which

routes data not to the sink directly, but to relay nodes along a predicted path of the mobile node that is close to the data source in terms of communication hops.To ensure packet delivery, a data source can select multiple storage nodes (calledstashing nodes ) and send data to them in order to cover all likely future paths of each mobile node. We formulate this problem into a binary integer program wherethe output of the algorithm is the optimal relay nodes that minimize the number of


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necessary transmissions while guaranteeing robustness against link and node failures.

We show that our routing scheme provides better load balancing, avoiding collisionsand consuming energy resources economically and evenly throughout the network.The algorithm provides not only a routing protocol, but also a way to improve

existing protocols by learning and exploiting mobility patterns. Existing mobile ad-hoc routing protocols can benet from the short-term and the long-term connectivityanalysis and prediction of mobile users, which provides lower routing cost, morereliable packet delivery, and better load balancing. Also, our optimization problemformulation itself can be a separate contribution by adding an interesting class of wireless mobile routing into Network Utility Maximization (NUM) problems [33, 34].

Chapter 4 is based on [46], where I proposed the data stashing scheme, designedalgorithms, and evaluated with real-world testbed experiments and simulations.

Finally, in Chapter 5, we summarize our results and contributions, and proposeinteresting directions for future research.


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Chapter 2

Short-Term Mobility Prediction

In this chapter, we present a mobility modeling that encodes the movement patternsof mobile nodes using radio signal strength information, and allows the prediction of highly probable relay node transitions. This structure can support proactive routeupdate for data delivery to mobile users. Network connectivity of a mobile user overdeployed stationary mesh nodes changes rapidly with high moving speed. In orderfor stationary mesh nodes to deliver data to a mobile user, setting up the route to themobile user must be fast to avoid high latency and packet loss. Further, dependingon the network topology, the route can change signicantly even if the mobile usermoves only a short distance. Packets already en route will be lost unless additionalmechanisms ensure path discovery to the new location of the mobile user.

We address this problem by introducing the mobility graph , which is a directedgraph structure to encode connectivity transitions of mobile users and embed knowl-edge about likely local mobility patterns within the network. The mobility graph canbe extracted from training data and is used to predict the next future transition whichthe mobile user will be associated with immediately after the current association node.

The rest of this chapter is structured as follows: In Sec. 2.1, we introduce theproblem of short-term mobility prediction, and discuss the importance of short-termprediction for data delivery to mobile users, and we present our contributions inSec. 2.2. We discuss related work in Sec. 2.3, and in Sec. 2.4, we present the mobilitymodel and introduce the mobility graph . We describe our approach for prediction of

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CHAPTER 2. SHORT-TERM MOBILITY PREDICTION 14

future relay nodes in Sec. 2.5, and in Sec. 2.6, we present an experimental evaluation

of the approach and discuss the results. In Sec. 2.7, we conclude this chapter.

2.1 Short-Term Connectivity

In our data delivery scenario, mesh nodes are stationary (i. e., not mobile), and con-gured as mesh network. In our approach, one relay node is designated as a proxy forthe mobile node, through which the data is forwarded to the user. Thus, the proxynode becomes the stationary sink for all traffic destined to the mobile node.

We dene the mobility graph , a directed graph structure that allows for associationtransition to a new relay node, resulting in signicant improvements in the accuracyof predicting the correct future relay node. We describe how the mobility graph canbe extracted from received signal strength indicator (RSSI) traces from mesh nodesto mobile user. By comparing the measured RSSI traces with RSSI traces in edgesof the mobility graph, which are collected in a learning phase, we can predict thefuture association nodes and time to the transition. We use dynamic time warping (DTW) to match the current RSSI trace of the mobile node to the traces recorded inthe past, and calculate similarity between two traces which may vary in time due tomoving speed variance.

To minimize the impact of mobility on packet delivery performance, a routingalgorithm needs to update its destination nodes toward both the current and thepredicted association node, guaranteeing that the new routing path to the predictedfuture association node is ready once it is needed. The prediction algorithm canbe used to precompute and efficiently store additional routing plans for non-localmovement of mobile users, which would take signicant time to update the correctrouting paths.

2.2 Contribution

In this section, we present our contributions in the eld of wireless mesh networks asfollows:


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We introduce the mobility graph , a directed graph structure that encodes knowl-

edge about the possible local movements of mobile users roaming the area cov-ered by a mesh network. We show how to compute a mobility graph from RSSImeasurements, and demonstrate its utility for prediction.

We propose a method for short-term mobility prediction that infers the futureassociation nodes based on knowledge of the mobility graph, as well as RSSItraces that represent the edges of the graph. Experiments show that the correctfuture association node for a mobile user can be predicted with up to 90%accuracy seconds before the transition to that node happens.

The proposed prediction scheme supports proactive routing decision for data de-livery to mobile users, and can also be applied to other applications such as improvingbandwidth reservation for dynamic usage of spectrum.

The main advantage of our algorithm is that likely movement patterns of mobileusers are modeled in the space of wireless connectivity. The input of the algorithmis raw RSSI measurements, which reect wireless dynamics well, and can be easilyobtained by every single wireless radio chip. The mobility graph constructed by theextensive real-world wireless traces can provide accurate future connectivity for theusage of data delivery application.

2.3 Related Work

Short-term prediction problems have been widely studied using Markov models incellular, wireless, and GPS networks. Liu and Maguire [53] predict users futurelocation using the users movement history in cellular networks. Their proposedmodel captures both long-term regular user movements through Movement Circlemodel, and less-constrained random movements through Movement Track model.However, the prediction method is validated only analytically with synthetic dataset,leaving its practicality with real mobility trace still unanswered. Similar models havealso been used for predicting next association and supporting proactive hand-off inwireless LAN networks in [18]. Using Wi-Fi traces of mobile users in long-term real


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testbed experiment presented in [77], the authors have shown that Markov models

can predict the next wireless access point with around 70% accuracy for a medianuser. Together with simulations, the method improves packet reception ratios as wellas latency by fast hand-off. Further, Lempel-Ziv text compression algorithms werealso successfully applied to mobility prediction problem by building and maintainingdictionary of mobile users association list based on analytical study in [6]. Lee et al . [47] construct a semi-Markov model for capturing the steady-state and transientbehaviors of user mobility using Dartmouth WLAN trace [37] by ltering out noisyassociations.

Kim et al . [36], Liu et al . [54], and Yoon et al . [83] aim to capture mobilitypatterns and build mobility models for generating simulated movement of mobileusers. The approaches use Wi-Fi access point (AP) association in Dartmouth dataset,and apply various stochastic techniques such as Kalman ltering and Markov modelafter discarding noisy associations (called ping-pong events). Markov-style predictorsare particularly well suited for applications involving large networks in which usersmove in a repeatable, non-random way in [40, 64]. However, most of the previousapproaches fall into one of the following problems: 1) the prediction algorithm isvalidated with analytical or simulation studies, 2) dynamic associations are lteredout for making steady-state Markov model, and 3) Markov-based prediction algorithmis based on current and past states only, not using more informative radio signalstrength information.

Our work is different in that the mobility graph is extracted from real mobilitytrace with dynamic association transitions, and our short-term prediction algorithmis based not just on few states, but rather on sound analysis in signal strength uc-tuations over time, also considering changing speed.

2.4 Constructing the Mobility Model

The mobility graph captures the movement patterns using RSSI measurements frommesh nodes to mobile users. We assume that the mobility graph is constructed by anumber of physical movements which mobile users explored in a learning phase.


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Assuming that we can observe users as they go about their everyday activities

over a certain period of time, it is possible to extract mobility patterns of the usersin the observed environment.More specically, we assume that users carry a mobile computing device that ac-

tively communicates with surrounding mesh nodes. This allows us to observe physicalmovements of the users indirectly via RSSI traces 1 of communication links betweenmesh nodes and the user. Formally, we assume that N mesh nodes measure signalstrength r i (t ), i = 1 . . . N of a packet from a user U received at time t . A location of the user at time t corresponds to an observation vector

R (t) = ( r 1(t),...,r N (t )) .

At each point in time, we dene the association node

B (t) = argmaxi=1 ...N

r i (t)

as the node measuring the highest signal strength at a given point in time.The trajectory of a user corresponds to an observation sequence

R (t1 : tk ) = R (t 1)R (t2) . . . R (tk )

where t1, t 2, . . . t k is the packet reception time from the user in a regular basis. Givensuch an observation sequence, we can dene the sequence of association nodes

B (t1 : tk ) = B (t1)B (t2) . . . B (tk ).

Note that we do not assume the ability to measure locations of the users directly,

nor do we assume any relation between the location of the user and the measuredsignal strengths. However, we do assume that if the user follows the same movingpath at different times, the corresponding observation sequences will be similar. Inessence, we assume that the environment does not change drastically over time, and

1 Or in general, traces of any other link quality estimator.


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(a) Mobility Graph

(b) Network Connectivity Graph

Figure 2.1: The differences between mobility and connectivity graph. (a) Note theadditional (blue) edges in the mobility graph in regions where the network providesno coverage. (b) The connectivity graph is signicantly more dense (red edges) inareas where movement is constrained by walls.


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we aim to optimize routing protocols for more reliable packet delivery to mobile users

for the case of frequently repeated mobility patterns. Even though we require trainingphase for constructing the mobility graph in this thesis, data aging techniques can beused to relax this assumption.

2.4.1 Mobility Graph

The mobility graph is a high level directed graph structure that encodes local mobil-ity patterns extracted from observation sequences. Formally, the mobility graph isdened on a set of N vertices, corresponding to the infrastructure mesh nodes. Two

vertices vm and vn are connected by a (directed) edge if there exists an observationsequence such that at some point, the association node switched from m to n :

i : {B (t i ) = m } {B (t i+1 ) = n}.

Intuitively, an edge in the mobility graph is inserted whenever the user moves fromnode vm to vn . This edge assignment essentially cuts the observation sequences intoshort segments, each segment corresponding to the transition between two nodes. Fora trajectory R i , each edge en m connecting vertices vn and vm is associated with thesegment R n mi = R i (t j n , t j m ) for which the association node is n :

B i (t j n 1) = n

t j n t t j m : B i (t) = n

B i (t j m +1 ) = m.

This denes a set of segments S e for each graph edge e.Although other ways of associating the data with the graph exist, we found this

to be most useful for routing and prediction as described below.

Mobility Graph vs. Network Connectivity Graph Even though the mobilityand network connectivity graphs are dened on the same set of vertices, they canhave substantially different edge sets (see Fig. 2.1). On one hand, the mobility graph


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(a) Mobility graph from a path

(b) RSSI measurements over time

Figure 2.2: Extracting the mobility graph from an observation sequence. (a) Thelayout of our office with 10 infrastructure nodes. The trajectory is shown by a dottedline, arrows show the extracted mobility graph. (b) RSSI data recorded during theexperiment. An edge between nodes 2 and 1 is highlighted. This corresponds to thedata segment in which node 2 is the association node.


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can contain edges that are not present in the connectivity graph, if a user moves

through areas without network coverage. On the other hand, the connectivity graphcan contain edges that are not present in the mobility graph, since radio signals canbypass obstacles, or travel through walls.

Using the Mobility Graph We use the mobility graph in two ways. The mobilitygraph constrains future location of a user in the network. We show in Sec. 2.5 that if we store typical signal strength observation sequences at edges of the mobility graph,both future relay nodes for a mobile user as well as the time to transition to thatnode can be predicted with high accuracy.

Edges in the mobility graph that do not exist in the connectivity graph are the ba-sis for a routing algorithm. We use the differences between the two graphs to identifyregions in the network for which routing information needs to be precomputed.

We believe that the mobility graph is a valuable data structure, independent of theusage laid out in this thesis. For example, the differences between the mobility andconnectivity graph can guide network administrators in deploying additional nodesor redeploying existing nodes to improve the quality of the network coverage, or tounderstand data traffic patterns.

2.4.2 Mobility Graph Extraction

The mobility graph is extracted from a set of observation sequences R = {R i (t i1 : t i2)}that correspond to users moving in the environment. We assume that the sequencesare preprocessed and the set R contains only continuous, densely sampled observationsequences. An example of one such trajectory is shown in Fig. 2.2.

Since the vertex set of the mobility graph is dened by the set of infrastructure

nodes, we only have to decide which edges should be present in the mobility graph.We determine the edges solely from the observation sequences. Given an observationsequence R i (t1 : tk ), we add an edge in the mobility graph whenever the correspondingassociation node B i (t) changes.

In practice, this algorithm might construct a large number of edges in the mobility


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graph due to the noise in the link quality measurements. Mobility of the users exacer-

bates the effects of reections and signal fading in structured buildings or other urbanenvironments. We have implemented a number of lters that prevent constructingunnecessary edges.

The observation sequence is low-pass ltered and the nodes in the best neighborsequence are retained only if they provide a high quality link for at least two seconds.The obtained mobility graph is further pruned by removing its infrequently observededges. This nal ltering step can be adapted to provide a simple yet efficient dataaging mechanism: as new measurements are taken, rarely visited edges in the mobilitygraph are deleted, enabling the mobility graph to adapt to gradual changes in theenvironment.

The ltering criteria are motivated by a cost analysis of the routing algorithm.The cost of briey losing a connection to a node (a link failed because we did notchoose the best node with the highest signal strength) is much lower than the costof setting up a new connection (if we switch nodes for less than two seconds). Otherapplications may dictate other criteria, resulting in a slightly different mobility graph.

2.5 Connectivity PredictionIn this section, we present an algorithm to predict future relay nodes using the con-structed mobility graph in Sec. 2.4. We use pattern matching to determine the currentposition in the graph.

With each edge e of the mobility graph, we associate a set S e of observationsequence segments that are representative for this edge. This set is determined inthe training phase during which the mobility graph is extracted. Set S e contains allsegments that witnessed the mobility edge e, normalized to the same transmissionpower. 2 While the network is deployed, new data can be added to the graph, anddata aging techniques can be applied to adapt the graph to gradual changes in the

2 We simply calculate the mean segment-wise RSSI value for each segment and scale the segmentsto have the same mean. This way, the mobility prediction algorithm works even if the transmittedsignal had variable signal strength.


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Figure 2.3: Dynamic time warping determines the best-matching outgoing edge andpredicts the next relay node.

t

RSSI

environment.The prediction problem can then be stated as follows: At each time t , we know

the current position of the mobile user in the mobility graph, given by the currentrelay node vt . Given the current RSSI measurements R (t0 : t) since the last changeof relay node, what is the next relay node and when will the transition occur?

Using the mobility graph extracted in the training phase, we can restrict thesearch by only considering the set of outgoing edges E (vt ) from the current graphvertex vt . In order to determine the correct edge, we match the current RSSI trace tothe stored RSSI segments of all edges in E (vt ). The edge emin associated with thebest-matching segment is chosen, and its end-vertex is the predicted next relay node.

emin = argmineE (vt )

minR G S e

D DTW (R, R G )

The distance function D DTW is discussed below. Fig. 2.3 illustrates the matchingalgorithm.


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comparing each RSSI sample from R to each sample from R G :

D ij = d(R (t i ), R G (t j )) , (2.1)

where i {1, . . . , k }, j {1, . . . , l }, and d(, ) is a distance function operating onvectors of RSSI samples; we simply use the L2 distance.

The warp path distance matching the rst i samples of R to the rst j samples of R G can then be dened recursively by

D DTW (i, j ) = D ij + min D DTW (i 1, j ) + ,

D DTW (i 1, j 1),D DTW (i, j 1) + .

The penalties and are applied when a sample is skipped in the stored or cur-rent data, respectively. The traditional warp distance assuming a complete matchis now D DTW (k, l ). The path taken by the recursion denes matches between in-dividual samples in the sequences. We will write it as a sequence of index pairsW = [(1 , 1), . . . , (k, l )]. Fig. 2.4 illustrates the matching.

By default, DTW matches the complete sequence and computes errors accordingly.This behavior favors sequences of equal length. Since we are interested in partialmatches, we consider only the error incurred until R is fully matched: we nd therst sample amin of R G that matches the end of R , amin = min {a | (k, a ) W } (seeFig. 2.4 for an illustration). The nal DTW distance for the partial match is thenD DTW (k, a min ).

In our experiments, we have obtained the best results penalizing stretching of longer sequences, and compression of shorter sequence. Thus, if tk t1 t l t1,we use = 50 and = 0, while otherwise we use = 0 and = 50. The results,however, are not very sensitive to the choice of these parameters.

Expected Time to Transition In order to synchronously change the routingbehavior throughout the network, it is useful to have advance warning when therelay node changes. Using the partial DTW matching outlined above, we can easily


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Figure 2.5: Routes used. (1) (5) for training, (6) (9) for testing.


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90%

100%

)70%

80%

cy (%

50%

60%

ccura

40%

tion

20%redic

0%

10%

1 2 3 4 5 6 7 8 9 10

Time to transition (sec)

Figure 2.6: Experimental evaluation of prediction algorithm. Mean accuracy of pre-diction of the next node, for varying time to transition to the node.

testing trajectories overlapped in some segments. The trajectories were collected overa one week period to account for variance of RSSI signals over time.

2.6.2 Prediction Performance

The prediction algorithm needs to be able to reliably predict the next relay nodesufficiently in advance, to leave enough time for the routing protocol to adapt to thenew relay node. In general, prediction of a few seconds ahead is sufficient.

We rst evaluate accuracy of the prediction algorithm: for a given observationsequence R (t1 : tk ), we construct the best neighbor sequence B (t1 : tk ) to obtain theground truth of which node is the relay node throughout the experiment. In the online

phase, we predict the next relay node and estimate the time to transition to the newrelay node, t . We initially use only the rst observation R (t1) for prediction andincrementally consider more observations R (t1 : t), until t = tk . For each prediction,we compare the predicted relay node and the predicted time to transition to theground truth and calculate the ratio of correct predictions of relay nodes. We show


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50%

60%

70%

80%

90%

100%

ccuracy (%)

Medium speed

0%

10%

20%

30%

40%

1 2 3 4

Prediction A


Varying speed

(a)

6

8

10

12

14

ror (sec)

Fast speed

Slow speed

Medium speed

-2

0

2

4

1 2 3 4

Timing e


(b)

Figure 2.7: Experimental evaluation of prediction algorithm depending on speed vari-ance. (a) Prediction accuracy for different speeds. : Training and testing speeds aresimilar, : Training and testing speeds differ by 30%. (b) Error in estimation of time to transition, showing the mean error and the standard deviation. : Trainingand testing speeds are similar. : Slower testing speed. : Faster testing speed.


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60%

70%

80%

90%100%

ccuracy (%)

0%

10%

20%

30%

40%

1 2 3 4

P

rediction A


100% trajectories

80% trajectories

60% trajectories

(a)

90%

)

70%

80%

cy (

50%

60%

ccur

30%

40%

ction 100% use

60% use40% use

20%

Predi

use

0%


(b)

Figure 2.8: Impact of size and variability of training data. (a) Prediction accuracydepending on the size of the training set. (b) Prediction accuracy depending on thevariability of the training set.


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the histogram of prediction accuracy depending on the actual time to transition in

Fig. 2.6. As we can see in the gure, the accuracy of the prediction is initially low,since only few data points are available for prediction. As the number of data pointsgrows (with decreasing time to transition), the accuracy of the prediction improvessignicantly, around 90% for prediction 1-2 seconds before the transition.

We further test how well the DTW algorithm can compensate for speed differences.Fig. 2.7(a) shows the prediction accuracy of the same trajectory when the walkingspeed in training and testing samples were identical, and when we changed the walkingspeed in the testing phase by 30%.

Additionally, we measure the error in the estimation of time to transition. Fig. 2.7(b)shows the mean error in the estimate, along with the standard deviation of the er-ror. The estimated time to transition can be used to let the network know when therouting path should be updated for ensuring packet delivery.

Finally, we explore the impact of the size and variability of the training set onprediction accuracy. First, we discard datasets of some training trajectories (out of the ve trajectories we used in total). Fig. 2.8(a) shows that the prediction accuracydegrades signicantly as we remove trajectories from the training set. The main rea-son is that the remaining training trajectories capture mobile patterns only partiallyand it is difficult to predict trajectories that have never been observed. Next, wekept all training trajectories, but removed some of the repetitive training rounds foreach trajectory. Fig. 2.8(b) shows that the prediction accuracy is less dependent onthe variability of the training set. Even when removing 60% of the training data,the prediction accuracy is quite high. Overall, this evaluation implies that exploringthe complete routes over the network with fairly many training runs is an importantfactor to achieve higher prediction accuracy.

2.7 Summary and Discussion

We have presented an algorithm to predict future association nodes of mobile usersfor proactive route plan using a novel mobility prediction algorithm. We build onthe concept of the mobility graph, a data structure that encapsulates and formalizes


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knowledge about possible mobility patterns of users roaming the mesh network. Es-

pecially in structured, man-made environments, the mobility graph is a valuable toolfor understanding and optimizing wireless networks.However, in case of varying moving speed, predicting only the next future con-

nectivity is not enough because the predicted node using the mobility graph could bethe second or third next connected nodes depending on the moving speeds we usedin the learning and testing phases. To take into account the moving speed variancemore reliably, we would need more sophisticated mobility model, which can predictlong-term future connectivity of mobile nodes beyond the short-term connectivity.Also, our concept of mobility graph requires storing all RSSI signatures in each corre-sponding edge, increasing the complexity of storage and computation. In Chapter 3,we present a long-term mobility model, which captures long-term mobility patternbehavior, and allows the prediction of a set of future trajectory nodes using onlyhigh-level association node list, not using the raw RSSI measurements.

Although we use the short-term prediction algorithm for routing, it would beinteresting to apply the short-term prediction to dynamic bandwidth allocation toproactively avoid possible bandwidth shortage. Also, in this thesis, our mobilitygraph needs a separate learning phase (i. e., off-line learning phase), and we do notprovide a specic way to dynamically update it in the usage phase. On-line learningand updating of mobility graph is another interesting future research topic.


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Chapter 3

Long-Term Mobility Prediction

In this chapter, we present an algorithm that classies mobile users movements intotypical mobility pattern clusters, not using raw RSSI measurements. Instead, ourlong-term mobility prediction algorithm uses high-level node associations to formulatea compact and efficient representation of those clusters. The proposed algorithmprovides anticipated trajectory node lists, which will be exploited to design a routingscheme for delivering data to mobile users in Chapter 4.

Network connectivity is highly volatile due to environmental effects such as multi-path fading, scattering, and more importantly the changing speed of mobile users.Predicting only the next association node is not enough and can be wrong when thereis a huge difference in moving speeds between the learning and usage phases.

We address this problem by introducing mobile trajectory clusters, which arecaptured using wireless association, to encode knowledge about likely long-term tran-sitional patterns within the network. This algorithm needs only the association tracesover time while mobile users are roaming within the network, not geographical coor-dinates. The constructed mobile trajectory clusters are used as a crucial part of thedata delivery scheme described in Chapter 4. They are used to provide all possiblepredicted association nodes in the near future trajectory of the mobile user. Thisenables a routing algorithm to deliver data to mobile users while minimizing packetrouting cost throughout the networks by letting data sources push data to the closerelay nodes which the mobile users pass through in the future, instead of sending

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CHAPTER 3. LONG-TERM MOBILITY PREDICTION 34

immediately toward mobile users.

The rest of this chapter is organized as follows: In Sec. 3.1, we introduce theproblem of long-term mobility prediction, and discuss the benets and signicance of long-term prediction for data delivery to mobile users. We present our contributionsin Sec. 3.2, and discuss related work in Sec. 3.3. In Sec. 3.4, we present the mobil-ity model and introduce mobile trajectories representation in the space of wirelessconnectivity association, and clustering algorithms. We describe our approach forprediction of a set of future trajectory nodes using the constructed mobile trajectoryclusters through learning in Sec. 3.5, and in Sec. 3.6, we present an experimentalevaluation of the approach and discuss the results. In Sec. 3.7, we conclude thischapter.

3.1 Long-Term Connectivity

We predict likely long-term association nodes of mobile users by using the currentassociation and a past history of association trajectories. We present a method forlearning typical movement patterns from observations, representing trajectories, aswell as predicting likely trajectories, given observed partial trajectories. The predic-tion algorithm is used by the mobile node to compute and supply information aboutits future trajectory to the network. We characterize the trajectories as sequences of node associations, and compute similarity between two trajectories out of all collectedtrajectories in the learning phase. Using this similarity metric, we compute clustersrepresenting typical trajectories through the network. Using multiple sequence align-ment techniques to identify similar regions and dissimilar regions in a cluster, we nda compact probabilistic representation for the clusters which we use to efficiently ndlikely future trajectories during prediction.

The predicted long-term trajectory of mobile users can benet network-wise appli-cations: especially providing an efficient data delivery to mobile users. For example,when each information source in the network needs to deliver data to multiple mobileusers, it can select intermediate storage nodes which are close to itself in terms of communication hops, and are also along the anticipated trajectory of the sinks, and


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stash the data to the selected nodes, instead of routing the data directly to the sinks

at their current positions. As a mobile user passes through the network, it can pickup the data at the intermediate storage node. The anticipated long-term connectivityprediction can contribute to designing a scalable data delivery scheme for multiplemobile users, which can reduce redundant packet transmissions by sharing data deliv-eries via intermediate storage relays, which are popular overlapped trajectory nodesamong mobile users.

3.2 Contribution

In this section, we present contributions we make in the eld of wireless mesh networksas follows:

We introduce a network-centric representation for trajectories. In this represen-tation, a trajectory is represented as a sequence of associated nodes, giving us allthe information we need for data delivery, while abstracting from unnecessaryand possibly misleading spatial information. We also develop useful similaritymeasures for this motion representation which allows us to perform clustering.

We propose a probabilistic representation for sets of similar (but potentiallypartial) trajectories. This representation can be used to compactly describea cluster of trajectories, and efficiently nd the best-matching cluster given apartial trajectory.

We present a method for predicting a set of anticipated trajectory nodes by nd-ing out the current node association of the mobile node within the probabilisticrepresentation of the best-matching cluster, and using the rest of association

nodes.

To the best of our knowledge, this work is the rst to classify dynamic real-worldwireless traces into several unique mobile clusters from the perspective of long-term connectivity, using wireless association traces.


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The proposed prediction scheme is closely coupled with a routing scheme for mo-

bile users, enabling the whole network to nd efficient intermediate relays in orderto distribute data to mobile users, minimizing packet routing cost throughout thenetworks. It should be pointed out that our mobility model and prediction algorithmcharacterize likely movement patterns of mobile users, and provide necessary futureconnectivity information to enable long-term route pre-planning for later mobile usersretrieval.

3.3 Related Work

Long-term mobility pattern modeling has been studied using GPS data, or associationdata from cellular networks or wireless LANs. In the case of GPS, since the raw GPSdata contain many outliers, most of the previous research approaches [3, 19, 39] lterout the noisy and unreasonable measurements rst, and then identify the possible goallocations from the ltered GPS positions, and construct prediction models. Ashbrookand Starner [3] nd signicant places where a user spent over a threshold amount of time, and cluster them into locations with the k-means clustering algorithm. Finally,a Markov model is applied for each location, and used for predicting the next goallocation. Froehlich and Krumm [19, 39] obtain the end-to-end routes from the rawGPS data, and use a Bayesian model and a trip similarity clustering algorithm topredict the next goal location. Further, Liao et al. [50, 51], and Yin et al. [82] notonly extract signicant places from ltered GPS data, but also try to associate theplaces with activities that a person can undertake in each different place. Their workis the rst to suggest exploiting high-level context (i.e., users activities) to detect thegoal place for a mobile user with higher delity. Although the previous approachesinfer long-term destination of mobile users, our work provides more detailed view of all possible future paths, not just the destination.

Similarly, in cellular networks, some previous work [5, 43, 44, 66] uses cell identi-ers to identify signicant locations, and constructs prediction models by clusteringalgorithms. In wireless LAN networks, a long-term large-scale measurement study


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of user-access point (AP) association at Dartmouth [37] has inspired work in mo-

bility prediction. It has been noted that wireless users locations can be predictedwith up to 72% accuracy using an order-2 Markov predictor [76] for users with longtrace lengths. Further analysis of the same dataset has suggested the feasibility of predicting the future associations of a mobile user in space and time [75]. Using adifferent dataset, Ghosh et al. [22] describe techniques to predict a users locationwith respect to social hubs such as buildings and classrooms, rather than individualwireless APs. Although the approaches work with real world mobility data, and useonly association data for predicting the future association, they do not explicitly dealwith noisy association for classifying mobility pattern clusters. Our work provides aspecic way to address possible insertion, deletion, and replacement of associations,and to do ne clustering considering the dynamics.

3.4 Constructing the Mobility Model

In this section, we introduce a trajectory in terms of wireless association and presentour mobile trajectory clustering method using given trajectories for an off-line learningphase.

In most scenarios, mobile users travel along a fairly limited set of trajectories.Oftentimes, this is due to obstacles present in the environment: buildings, bridges,roads, and walkways constrain the possible trajectories. Even without any environ-mental restrictions, there are usually few interesting start- and endpoints for anygiven journey, and sinks often follow short(-est) paths from a starting point to adestination, greatly limiting the set of possible trajectories.

It therefore makes sense to nd and exploit the structure that is present in thelikely trajectories through a network. We will do so by clustering similar trajectories,thus creating a database of historical trajectories, arranged in clusters of similartrajectories in the off-line learning phase. In order to perform practical clusteringon trajectories, we require a trajectory representation, a similarity measure, and acompact representation of a cluster of sequences. The following sections describethese concepts in turn.


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3.4.1 Trajectory Representation

In the following, we will represent a single trajectory through the network not interms of spatial position, but in terms of the associated mesh node at any given time.

Let us consider a mobile user moving through the network on a given spatial path.Sending periodic beacons and listening for replies, the mobile node can record thenodes in radio range at each beacon time. In each of these sets, we can determine theassociation node, for example, by measuring signal strength on the acknowledgmentor the beacon packet. This is the node that the mobile node would associate with tosend or receive data. We represent trajectories through the network as a sequence of

association nodes:T = N 1N 2N 3 . . . N k .

We only record changes in the associated node list, i. e. N i = N i+1 . For example,given s s a a a r r r a n n g h h h h a a e e e e y y o o , thecorresponding trajectory is represented as T = s a r a n g h a e y o .

Note that due to imperfect links and radio signal strength uctuations in dynamicenvironments, two node sequences recorded from the same spatial path are not nec-essarily identical, or even of the same length. To compensate for noisy uctuations incapturing similar trajectory patterns, we borrow a similarity measure from computa-tional biology where functional, structural, or evolutionary relationships between se-quences encoding biological macromolecules have been thoroughly investigated. Also,note that once trajectory data is collected, the corresponding mobile user ID can bediscarded, allaying possible privacy concerns.

3.4.2 Similarity Measure

We use a variant of the longest common subsequence metric known from string the-ory and a variant of the Smith-Waterman algorithm [73] to calculate this similaritymeasure between two sequences.

Informally, to compute the similarity between two sequences T A = A1 . . . A n A andT B = B 1 . . . B n B , we count how many nodes we have to insert , delete, or substitute inT A to obtain T B .


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We dene the partial match function F AB (i, j ), which computes the similarity

between the prexes of length i and j of T A and T B , A1 . . . A i and B 1 . . . B j . F AB canbe dened recursively:

F AB (i, 0) = 0 for 0 i n A ,

F AB (0, j ) = 0 for 0 j n B , (3.1)

F AB (i, j ) = max F AB (i 1, j 1) + s (A i , B j ),

F AB (i 1, j ) + d,

F AB (i, j 1) + d,0 ,

where the similarity for insertion or deletion operations, d, as well as the similarityfunction on individual nodes are free parameters. In our experiments, we use d = 0,meaning we see no similarity in deletion or insertion operations, and we set s (A, A ) =1 and s(A, B ) = 0 A = B . With these parameters, F AB (n A , n B ) is the length of the longest common subsequence in the two sequences.

We often need to compare several partial trajectories A to a signicantly longercomplete trajectory B . As it is dened above, F AB (n A , n B ) will be lower the shorterA is, even if (in the matching part of B ) there is a perfect match. To compensate fordifferences in length of A or B , we normalize the similarity measure by dividing bythe length of the shorter sequence:

S (A, B ) =F AB (n A , n B )min(n A , n B )

.

Note that the similarity measure we dene is not a distance metric.

3.4.3 Cluster Representation

Based on the pairwise similarities between all pairs of sequences, we apply a hierarchi-cal clustering method for classifying each mobility trajectory into a certain number of


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characteristic mobility pattern clusters. We use the average linkage metric which uses

the average similarity between objects in two clusters to determine whether clustersare merged. For a more detailed description of the hierarchical clustering method, werefer to [32].

Each cluster consists of a number of similar sequences. During the predictionstage of our algorithm, we will be presented with a partial trajectory T and asked tond the most likely cluster for this trajectory. While it would be possible to computeaverage linkage for T and each cluster, this would entail computing the similaritybetween T and each trajectory in the database. To avoid limiting the size of ourdatabase, we instead propose a probabilistic representation for each cluster, so thatwe can efficiently query for the best matching cluster.

We create a representation for our clusters in two steps: for each cluster, werst align all its sequences and then create a probabilistic summary of the alignedsequences.

Multiple Sequence Alignment

Given a set of sequences, multiple sequence alignment algorithms compute how the

sequences should be lined up in order to maximize overlap. Our algorithm for comput-ing the similarity between two sequences essentially computes a sequence alignmentfor these two sequences. In the general case, however, multiple sequence alignmentis an NP-hard problem [79]. Heuristic alignment methods are widely used for DNAor protein alignments in bioinformatics [65]. We use a modied version of ClustalW,one of the most popular alignment tools [78].

The ClustalW algorithm starts by aligning the most similar sequences, and pro-gressively adds more distant sequences one by one. This iterative procedure yields agood alignment of all sequences. We have changed the alphabet of twenty amino acidsor four DNA base pairs used in computational biology to the set of node IDs moresuitable for our situation. We also use an unweighted substitution matrix, makingeach substitution equally likely. The computation complexity of ClustalW algorithmis O(N 2L 2) where N is the number of sequences and L is the sequence length [2].To construct a cluster prole database, the aligned trajectory sequences need to be


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z a y u r s m a

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t z q b q m

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a l o r t z t b o r t

l o z t r z

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Clustering

- a - - r s l t -

z a y u r s m - a

z - - u r s - t a

m t u z y q p b v m

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m - - - - q - b v n

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Alignment

Figure 3.1: Clustering and alignment procedures.

stored with storage cost O (NL ).The output of the algorithm is aligned sequences that have the same length. Gaps

in the aligned sequences are marked with a special gap symbol (see Fig. 3.1). Wecompute a probabilistic representation from these aligned sequences within a cluster.

Probabilistic Cluster Representation

Given the set of aligned sequences of length n , we construct a probabilistic repre-sentation for the cluster, which we call the cluster prole. A prole is a sequenceof probability distributions P = P 1 . . . P n . At each position i, the probability dis-tribution P i (A) denotes the probability that node A appears in position i . Thisrepresentation can also be considered a 0 th order Markov model of the set of aligned


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sequences.

The cluster proles allow us to efficiently nd the most likely cluster, given apartial test sequence. See Fig. 3.1 for an illustration of clustering a

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