On structure less and everlasting data collection in wireless sensor networks

Computing and maintaining network structures for efficient data aggregation curs high overhead for dynamic events where the set of nodes sensing an event changeswith time.. In addition t

ON STRUCTURE-LESS AND EVERLASTING DATA COLLECTION IN WIRELESS SENSOR NETWORKS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

By

Kai-Wei Fan, B.S., M.S.

* * * * * The Ohio State University

Kai-Wei Fan2008

Computing and maintaining network structures for efficient data aggregation curs high overhead for dynamic events where the set of nodes sensing an event changeswith time Prior works on data aggregation protocols have focused on tree-based orcluster-based structured approaches Although structured approaches are suited fordata gathering applications, they incur high maintenance overhead in dynamic scenar-ios for event-based applications The goal of this dissertation is to design techniquesand protocols that lead to efficient data aggregation without explicit maintenance of

in-a structure

We propose the first structure-free data aggregation technique that achieves highefficiency Based on this technique, we propose two semi-structured approaches tosupport scalability We conduct large scale simulations and real experiments on atestbed to validate our design The results show that our protocols can performsimilar to an optimum structured approach which has global knowledge of the eventand the network

In addition to conserving energy through efficient data aggregation, renewableenergy sources are required for sensor networks to support everlasting monitoringservices Due to low recharging rates and the dynamics of renewable energy such assolar and wind power, providing data services without interruptions caused by batteryrunouts is non-trivial Moreover, most environment monitoring applications require

data collection from all nodes at a steady rate The objective is to design a solutionfor fair and high throughput data extraction from all nodes in the network in presence

of renewable energy sources Specifically, we seek to compute the lexicographicallymaximum data collection rate for each node in the network, such that no node willever run out of energy We propose a centralized algorithm and an asynchronousdistributed algorithm that can compute the optimal lexicographic rate assignmentfor all nodes The centralized algorithm jointly computes the optimal data collectionrate for all nodes along with the flows on each link, while the distributed algorithmcomputes the optimal rate when the routes are pre-determined We prove the op-timality for both the centralized and the distributed algorithms, and use a testbedwith 158 sensor nodes to validate the distributed algorithm

To my family.

First and foremost, I would like to express my sincerest gratitude to my Adviser,

Dr Prasun Sinha, for the guidance and support in the last four years This workwould have never reached completion without all the discussions and brainstormingwith him His advice and patience make this work possible I am fortunate to havehim as my adviser I am also thankful to my research committee members, Dr AnishArora and Dr David Lee for their invaluable inputs and comments to make this workcomplete

I would also like to express my gratitude to my colleagues in our research group,Sha Liu, Ren-Shiou Liu, and Zizhan Zheng, and my friends Ming-Feng Hsieh, Yen-Chen Lu, and Yi-Wen Kuo, for numerous collaborations and discussions I wouldalso like to thank Chi-Hsien Yao, Yu-Neng Li, Xu Wang, for being such wonderfulfriends

Finally, I would like to thank all my family members for their unconditional loveand support To my parents for giving my such a wonderful family, to my sister forlooking after me, and to my brother for being so supportive

Formosoft Inc., Taiwan

2007 M.S

Computer Science & Engineering,The Ohio State University

2004-present Graduate Teaching & Research Associate,

The Ohio State University

PUBLICATIONS

Research Publications

Kai-Wei Fan, Sha Liu, and Prasun Sinha “Dynamic Forwarding over Tree-on-DAGfor Scalable Data Aggregation in Sensor Networks” IEEE Transactions on MobileComputing (TMC), preprint, 3 Apr 2008, doi:10.1109/TMC.2008.55

Ren-Shiou Liu, Kai-Wei Fan, and Prasun Sinha “ClearBurst: Burst Scheduling forContention-Free Transmissions in Sensor Networks” IEEE Wireless Communicationsand Networking Conference (WCNC), pages 1899-1904, March 2008

Sha Liu, Kai-Wei Fan, and Prasun Sinha “CMAC: An Energy Efficient MAC LayerProtocol Using Convergent Packet Forwarding for Wireless Sensor Networks” Fourth

Annual IEEE Communications Society Conference on Sensor, Mesh, and Ad HocCommunications and Networks (SECON), pages 11-20, June 2007.

Kai-Wei Fan, Sha Liu, and Prasun Sinha “Structure-free Data Aggregation in SensorNetworks” IEEE Transactions on Mobile Computing (TMC), August 2007

Kai-Wei Fan, Sha Liu, and Prasun Sinha “Scalable Data Aggregation for DynamicEvents in Sensor Networks” 4th ACM Conference on Embedded Networked SensorSystems (SenSys), pages 181-194, November 2006

Kai-Wei Fan, Sha Liu, and Prasun Sinha “On the Potential of Structure-free DataAggregation in Sensor Networks” IEEE INFOCOM, pages 1-12, April 2006

Sha Liu, Kai-Wei Fan, and Prasun Sinha “Dynamic Sleep Scheduling using OnlineExperimentation for Wireless Sensor Networks” in Proceedings of SenMetrics, July2005

Wen-Her Yang, Kai-Wei Fan, and Shiuh-Pyng Shieh “A Secure Multicast Protocolfor The Internet’s Multicast Backbone” ACM/PH International Journal of NetworkManagement, March/April 2001

Wen-Her Yang, Kai-Wei Fan, and Shiuh-Pyng Shieh “A Scalable and Secure cast Protocol on MBone Environments” Information Security Conference, Taiwan,May 2000

Multi-Instructional Publications

Sha Liu, Kai-Wei Fan, and Prasun Sinha “Protocols for Data Aggregation in sor Networks, chapter in book titled Wireless Sensor Networks and Applications”.Springer Verlag’s book series Network Theory and Applications, 2005

Sen-Kai-Wei Fan, Sha Liu and Prasun Sinha “Ad-hoc Routing Protocols, chapter inbook titled Algorithms and Protocols for Wireless and Mobile Networks” CRC/HallPublisher, 2004

FIELDS OF STUDY

Major Field: Computer Science and Engineering

Studies in:

Computer Networking Prof Prasun Sinha

Prof Anish AroraProf David Lee

TABLE OF CONTENTS

Page

Abstract ii

Dedication iv

Acknowledgments v

Vita vi

List of Tables xii

List of Figures xiii

Chapters: 1 Introduction 1

1.1 Background 1

1.2 Data Aggregation in Wireless Sensor Networks 3

1.2.1 Cluster-Based Approaches 4

1.2.2 Tree-Based Approaches 6

1.3 Rate Allocation in Rechargeable Sensor Networks 8

1.4 Contributions 10

1.5 Organization of the Dissertation 11

2 Structure-Free Data Aggregation 12

2.1 Objective 12

2.2 Spatial Convergence for Data Aggregation 14

2.3 Temporal Convergence for Data Aggregation 22

2.4 Analysis 24

2.4.2 Alternate Analysis 27

2.4.3 Comparison with Simulation Results 29

2.5 Evaluation Results 30

2.5.1 Simulation Scenario 32

2.5.2 Maximum Delay 34

2.5.3 Node Density 37

2.5.4 Event Speed 40

2.5.5 Event Size 41

2.5.6 Number of Events 41

2.5.7 Distance to the Sink 44

2.5.8 Aggregation Ratio 44

2.6 Summary 47

3 Semi-structured Data Aggregation 50

3.1 Objective 50

3.2 ToD in One Dimensional Networks 51

3.2.1 Construction of One Dimensional ToD 52

3.2.2 Dynamic Forwarding 54

3.3 ToD in Two Dimensional Networks 56

3.3.1 Construction of Two Dimensional ToD 56

3.3.2 Dynamic Forwarding 57

3.3.3 Clustering and Aggregator Selection 62

3.3.4 ToD in Irregular Topology Networks 66

3.4 Analysis 71

3.5 Evaluation Results 73

3.5.1 Testbed Evaluation 73

3.5.2 Large Scale Simulation 78

3.5.3 Event Size 79

3.5.4 Scalability 81

3.5.5 Aggregation Ratio 82

3.5.6 Cell Size 84

3.5.7 Random Deployment for Irregular Topology 86

3.6 Summary 88

4 Scale-Free Data Aggregation 90

4.1 Objective 90

4.2 Alternative Forwarding Tree 91

4.2.1 AFT Construction 91

4.2.2 Alternative Forwarding on AFT 93

4.2.3 Construction and Maintenance 98

4.2.4 Irregular Network Topology 99

4.2.5 Implementation of AFT 99

4.3 Analysis 101

4.4 Evaluation Results 106

4.4.1 Baseline Simulations 106

4.4.2 Cluster Size 110

4.4.3 Amorphous Event 111

4.4.4 Packet Loss 112

4.5 Summary 113

5 Rate Allocation in Perpetual Sensor Networks 115

5.1 Objective 115

5.1.1 Problem Formulation 118

5.2 Optimal Lexicographic Rate Assignment 121

5.3 Distributed Lexicographic Rate Assignment 128

5.4 Evaluation Results 138

5.4.1 Optimality 139

5.4.2 Recharging Profile 139

5.4.3 Control Overhead 141

5.4.4 Initial Battery Level 143

5.4.5 Topology 148

5.5 Summary 149

6 Conclusions 150

6.1 The Thesis 150

6.2 Future Work 152

Bibliography 153

LIST OF TABLES

LIST OF FIGURES

2.1 Enhancing opportunistic aggregation with spatial convergence 15

2.2 Unicast vs Anycast 18

2.3 Spatial convergence by DAA 18

2.4 CTS priorities 21

2.5 Packet forwarding in lock-step 23

2.6 The order of transmissions with randomly delay 28

2.7 A network topology with k=3 downstream nodes 29

2.8 Analysis and simulation results 30

2.9 Simulation results for maximum randomized waiting time 33

2.10 End-to-end transmission delay 35

2.11 Simulation results for node densities 38

2.12 Simulation results for event moving speeds 39

2.13 Simulation results for event size 42

2.14 Simulation results for numbers of events 43

2.15 Simulation results for distances to the sink 45

2.16 Simulation results for aggregation ratios 48

3.1 Long-stretch for fixed tree structure 51

3.2 Illustration for one row of the network 52

3.3 The construction of F-Tree, S-Tree, and ToD 52

3.4 Grid-clustering for a two-dimension network 57

3.5 Cells triggered by an event 58

3.6 Scenarios in an F-aggregator’s view 58

3.7 Forwarding to S-aggregators 59

3.8 Aggregating cluster 64

3.9 Scenarios of a void aggregating cluster 68

3.10 Aggregating clusters within void 69

3.11 The worst case scenario for ToD 71

3.12 Normalized number of transmissions for event sizes 76

3.13 Normalized number of transmissions for maximum delays 77

3.14 Simulation results for event sizes 80

3.15 Simulation scenario for scalability 81

3.16 Simulation results for distances to the sink 83

3.17 Simulation results for aggregation ratios 85

3.18 Simulation results for cell sizes 87

3.19 Simulation results for random deployments 89

4.1 Illustration for Q-clusters and A-clusters of AFT 91

4.2 Possible parents of an A-cluster 92

4.3 Overview of a four level AFT 93

4.4 Forwarding decisions for an A-cluster with four parents 96

4.5 Dilution of neighboring information 101

4.6 Percentage of cases that do not aggregate all packets 101

4.7 Possible Qi−1 and Ai−1 having packets for Qi 103

4.8 Worst case scenario 104

4.9 CDF of number of transmissions of QT/AFT (σ = 64m) 107

4.10 CDF of ToD/AFT (σ = 64m) 108

4.11 CDF of number of transmissions of ToD/AFT (σ = 256m) 109

4.12 CDF of ∆/δ (σ = 64m) 110

4.13 Average of normalized number of transmissions 111

4.14 CDF of ∆/δ in random topology with amorphous event (σ = 64m) 112

4.15 CDF of ∆/δ in grid network with amorphous event (σ = 64m) 112

4.16 Normalized number of transmissions for packet loss rates (σ = 64m, δ = 100m) 113

5.1 A network of four nodes with solar cells 116

5.2 Recharging profile for nodes in Fig 5.1 117

5.3 Battery levels of nodes in Fig 5.1 117

5.4 Current measured from a solar cell in 48 hours 118

5.5 Distributed lexicographic rate computation 137

5.6 Rate assignments of LP solver and DLEX 140

5.7 The difference between LP and DLEX 140

5.8 Rate assignments for sunny and cloudy days 141

5.9 Number of control messages and children of each node in DLEX 142

5.10 The size of a subtree v.s the rate 143

5.11 Rates of nodes in different rate assignment approaches 145

5.12 Number of packets received for each source 145

5.13 Percentage of time a node runs out of energy 146

5.14 Total number of packets received (top) and ratio of nodes out of energy.147 5.15 Rate assignment in different network topology 148

One key to the success for such sensor networks is sustainability Due to theremoteness of the deployments of sensor networks where wall-power is not accessible,sensor nodes are usually powered by batteries When sensor nodes run out of batteries,they cease to operate and may compromise the network functionalities It is usuallynot economical, sometimes impossible, to replace the depleted batteries Therefore,energy conservation and its efficient utilization are both critical for increasing thelifetime of sensor networks.

There have been many approaches proposed for efficient energy consumption in

aware routing [18, 83, 76] This dissertation focuses on data aggregation, a techniquethat can reduce the amount of data transmitted in the network, and thus, reduce theenergy consumption.

Data aggregation is an effective technique for conserving energy in sensor networks

In sensor networks, the communication cost is often several orders of magnitude higherthan the computation cost Due to the inherent redundancy in raw data collectedfrom sensors, in-network data aggregation can often reduce the communication cost

by eliminating redundancy and forwarding only the extracted information from theraw data As reducing communication energy consumption extends the network life-time, it is critical for sensor networks to support in-network data aggregation Fordata collection applications where sensor nodes send collected readings to the sinkperiodically, the packet forwarding routes for facilitating data aggregation can beplanned in advance for optimality However, for applications where only a subset ofnodes is triggered by an event, designing solutions for efficient aggregation of dataoriginating from these nodes is not trivial In Chapters 2, 3, and 4, data aggregationstructures and techniques for packet forwarding that are efficient and scalable forevent triggered sensor networks are presented

However, no matter how hard we try to conserve energy, the batteries will bedepleted one day To support perpetual sensor networks that provide everlastingmonitoring services, an alternate source of energy is required More recently, renew-able energy harvested from natural sources, such as solar [37, 68], wind [63], thermal[78], and vibration [72, 60], have been used as alternate sources of energy In addition

to being an energy source which can boost network lifetime, renewable energy can

be used to optimize network performance if planned carefully Chapter 5 presents

solutions for achieving fair and high throughput data extraction from all nodes indata collection networks in presence of renewable energy sources.

Because of the energy constraint of wireless sensor networks and relatively highcommunication cost, the computation cost of sensor nodes becomes less significant.Pottie and Kaiser [67] reported that the energy consumption for executing 3000 in-structions is equivalent to sending a bit 100 meters by radio For this reason, dataaggregation and in-network processing are very important to extend the lifetime ofwireless sensor networks

An example of data aggregation is obtaining AVERAGE, MAX, MIN, or SUM

of readings from all sensors For example, if the sink wants to collect the averagetemperature of the area monitored by a sensor network, the naive approach would

be to let each sensor node send its temperature reading back to the sink, and thesink can then compute the average temperature from collected readings However,instead of sending individual readings back to the sink, intermediate sensor nodescan combine their temperature readings with the received readings, and send onlythe average temperature of all readings it has, together with the number of readingscontributing to the average temperature, to the sink The average temperature can

be updated while being forwarded toward the sink, and eventually the sink can stillcompute the average temperature of all sensor readings In this way, each node onlysends the number of readings and the average temperature, which is significantly lessdata than forwarding all readings for others

Data aggregation has been an active research area in sensor networks for its ability

to reduce energy consumption Many works have focused on different aspects of dataaggregation Some focus on how to aggregate data from different nodes [40, 41, 57, 58],some focus on how to construct and maintain a structure to facilitate data aggregation[34, 35, 53, 51, 52, 88, 87, 38, 82, 23, 24, 56, 32, 19, 73], and some focus on how

to efficiently compress and aggregate data by taking the correlation of data intoconsideration [75, 74, 20, 19, 64] As this dissertation focuses on how to forwardpackets to facilitate data aggregation, we briefly review protocols for routing packetsfor data aggregation in current research These protocols can be categorized into twofamilies: cluster-based and tree-based protocols In Sections 1.2.1 the cluster-basedprotocols are presented and in Section 1.2.2 the tree-based protocols are presented

LEACH [34, 35] and PEGASIS [53, 51, 52] are representatives of this family In[34], the authors propose the LEACH protocol to cluster sensor nodes and let thecluster-heads aggregate data and communicate directly with the base station To dis-tribute energy consumption evenly among all nodes, the cluster-heads are randomlyelected in each round In [35], authors propose a modified version named LEACH-C.LEACH-C uses the base-station to broadcast the cluster-head assignment, thus fur-ther spreading out the cluster-heads evenly throughout the network and extendingthe network lifetime Based on LEACH, authors refine the cluster-head election al-gorithm in [89] by letting every node broadcast and count neighbors at each setupstage, where qualified potential nodes bid for the cluster-head position This modi-fication scatters cluster-heads more evenly across the network without requiring the

participation of the base-station As it also requires every node to broadcast at itshighest transmission power at the setup stage of each round, it achieves only slightimprovement (around 6%) over LEACH.

Reducing the number of cluster-heads is critical to conserve energy as these nodesstay awake and transmit to the base-station using high power Lindsey et al proposePEGASIS [51], which organizes all nodes in a chain and lets them play the role ofheads in turn Since there is only one head node in PEGASIS, and there are no simul-taneous transmissions, latency is an issue in PEGASIS To address this, the authorspropose two chain-based PEGASIS enhancements in [52, 53] In [53] the authors pro-pose a binary hierarchical approach for CDMA-capable sensor nodes, and in [52] theauthors propose a chain-based three-level approach that allows simultaneous trans-missions for non-CDMA-capable sensor nodes These two approaches usually save lessenergy than PEGASIS, but outperform PEGASIS in Energy × Delay metric Based

on both LEACH and PEGASIS, Culpepper et al propose Hybrid Indirect sion (HIT) [23], a hybrid scheme of these two HIT uses LEACH-like clusters, butallows multi-hop routes between cluster-heads and non-head nodes

Transmis-LEACH and PEGASIS based protocols assume that the base-station can bereached by any node in only one hop, which limits the size of the network for whichsuch protocols are applicable The combination of CSMA, TDMA and CDMA makesthe design complex and cost-inefficient In addition, in scenarios where the data cannot be perfectly aggregated, LEACH-based protocols do not necessarily have signif-icant advantage since the cluster-head has to send many packets to the base stationusing high transmission power The chain-based nature of PEGASIS-based protocols

also makes them suitable only for scenarios where multiple packets can be perfectlyaggregated into one packet.

pro-accordingly Through real field experiments, TAG shows the benefit of data gation, not only on saved energy, but also on data quality improvement due to lesscontention Ding et al use shortest path tree with parent energy-awareness in [24],where the neighbor node of the shortest distance to the sink with higher residualenergy is chosen as the parent All the above tree-based data aggregation routingprotocols need a lot of message exchanges to construct and maintain the tree.Most of these tree-based aggregation routing protocols are not designed for eventtracking applications GIT can be used in such a scenario, but it suffers from the cost

aggre-of pruning branches, which might lead to high cost in moving event scenarios Zhangand Cao propose Dynamic Convoy Tree-Based Collaboration (DCTC) in [88] Theypropose a conservative scheme and a prediction-based scheme to wake up and prunenodes from the convoy tree, and favor the latter one given reasonable prediction ac-curacy They also propose message-intensive sequential reconfiguration scheme which

is suitable for sparse networks, and heuristic-based localized reconfiguration which

is suitable for dense networks In [87], the authors further optimize the tree figuration schemes They compare optimized complete reconfiguration (OCR) andoptimized interception-based reconfiguration (OIR), and show that OIR is suitablefor small data size and small monitoring regions, while OCR is suitable for othercases Essentially, DCTC tries to balance the tree in the monitoring region to reducethe energy consumption But it assumes the knowledge of distances to the center

recon-of the event at sensor nodes, which may not be feasible to compute with the sensedinformation in all tracking applications In addition, DCTC involves heavy messageexchanges, which is not desired when the data rate is high, and the performance ofDCTC highly depends on the accuracy of mobility prediction algorithms

There is another class of tree-based data-centric routing protocols, which takes

Servetto propose a broadcast routing protocol to disseminate the information fromeach node to all other nodes in the network under the capacity constraint, but thebroadcast scenario is different from ours In [19, 20], Cristescu et al first study thedistributed source coding and its approximation for sensor networks But source cod-ing or even its approximation is hard to deploy since the real information distribution

is hard to know The authors then study the routing metric that considers joint tropies through explicit communications The authors prove that to find the optimalrouting tree using the new routing metric is NP-complete, and propose approxima-tion algorithms such as Leaves Deletion approximation and Balanced SPT/TSP tree.But these algorithms are centralized They assume the global knowledge of the infor-mation entropy of each sensor node and the joint entropy of each pair, which makessuch approaches impractical Pattern et al study the impact of spatial correlation

en-on routing for some special cases in [64] and derive the optimal cluster size for thesecases Although a cluster structure is used, the basic tree-based routing is maintainedinstead of transmitting packet to the base-station in one hop However, whether thededuced result can be generalized to other cases is unclear

There have been many works on developing sensors with capability of harvestingenergy from solar or wind resources, such as Prometheus [44], Trio [25], and Ambi-Max [63] There are also many studies on exploiting renewable energy to increase

system performance or network lifetime [86, 81, 69, 47, 46, 45, 48, 80] In [81], the thors consider solar-aware routing based on directed diffusion in rechargeable sensornetworks They use a simple heuristic that preferably routes packets through solar-powered nodes, and the extra energy harvested from the environment is only a means

au-to boost network lifetime In [47], the authors propose au-to measure the environmentalenergy properties and renewable opportunities at each node, and use the informa-tion to schedule tasks to increase network lifetime In [45] and [46], the authorsfurther consider maximizing system performance while maintaining Energy-Neutraloperation, i.e., the energy used is always less than the energy harvested so that thesystem can operate perennially In [48], the authors study how sensor nodes should

be activated dynamically so as to maximize a utility function defining the coveragearea of sensors In [80], in addition to adjusting the duty cycle of sensors to achieveEnergy-Neutral operation, the authors consider the variability of environmental en-ergy resource and attempt to reduce the variation of duty cycle using adaptive controltheory However these works either only consider the workload of individual sensorsand do not consider the influence on overall network performance by the individualdecisions, or only try to maximize system performance but do not consider the impact

on individual sensors

To maximize system performance while balancing workload among sensors, ness has to be considered Maxmin fairness, or lexicographic fairness, has been widelyused to define the fairness of a system In [15], a distributed maxmin rate computa-tion algorithm for fixed flows that are routed through capacity constrained switches

fair-in wired networks is proposed The proposed algorithm computes a maxmfair-in rate

assignment for each flow and guarantees quick convergence In [36], the authors eralize the problem by adding maximum and minimum rate requirement for each flow,and propose a centralized algorithm similar to the one proposed in [12] that identifiesbottleneck links first, and assigns rates equally to all flows passing through these bot-tleneck links A distributed algorithm is also proposed that is based on the algorithmproposed in [15] In [16], a centralized algorithm is proposed that iteratively useslinear programming to find lexicographic rate assignment for all sensor nodes thatperiodically report readings to the sink The proposed algorithm does not requirethese flows to be forwarded through fixed routes In [70] a distributed congestioncontrol scheme is proposed to achieve maxmin rate allocation through overhearingand propagating congestion announcement, but it requires sophisticated parametertuning to achieve stable operation and the rates oscillate up and down after con-verging, even if the topology remains unchanged All these works solve the fairnessproblem with static constraints, such as based on switches or battery capacity, and

gen-do not consider dynamic resources such as changing harvested energy

We make the following contributions in this dissertation:

• We propose the first structure-free data aggregation protocol that achieves tial and temporal convergence without incurring control overhead for event-based sensor networks

spa-• We propose an efficient and scalable data aggregation mechanism that canachieve early aggregation without incurring overhead of constructing a structurefor event-based sensor networks

• We prove that the distance the packets traveled before they are aggregated isbounded by a constant factor of event diameter for event-based sensor networks.

• We propose a centralized algorithm to compute the optimal data collection ratefor each node, along with the amount of flow on each link for data collectionnetworks with energy recharging capability

• We propose an optimum distributed and asynchronous algorithm for data lection networks with energy recharging capability assuming that the routingtree is pre-determined

col-• We conduct experiments in sensor network testbed and extensive large-scalesimulations to validate our proposed solutions

The rest of this dissertation is organized as follows Chapter 2 presents a free data aggregation protocol, DAA (Data Aware Anycast), that increases the chance

structure-of data aggregation without incurring control overhead Chapter 3 presents the ToD(Tree-on-DAG), a semi-structured data aggregation approach that guarantees aggre-gation within constant distance from the sources when the maximum event size isknown Chapter 4 presents the AFT (Alternative Forwarding Tree) that further im-proves ToD by guaranteeing aggregation irrespective of network size, event size, andevent location Chapter 5 presents a centralized and a distributed algorithm for datacollection rate assignment that achieves fair and high throughput data extraction fromall sensor nodes with energy recharging capability We conclude this dissertation inChapter 6

a tree Structured approaches though suited for data gathering applications, havehigh overhead for event-based applications In event-based applications, nodes thatare triggered by an event are not known in advance Therefore a structure has to

be constructed dynamically for these nodes, and this requires message exchanges andincurs control overhead Moreover, when the event moves, the structure has to beadjusted for a new set of nodes that are triggered by the event This also incurs heavymessage exchanges

The other extreme is to use opportunistic aggregation where packets are gated only if they happen to meet at a node at the same time There is no overhead

aggre-of structure construction; however it may result in inefficient data aggregation out explicit coordination, the performance of the opportunistic aggregation technique

With-is non-determinWith-istic, and the chance of aggregation may be limited To avoid theoverhead of structured approaches and the limitations of opportunistic aggregation,

we study and design structure-free techniques for data aggregation

Spatial convergence and temporal convergence during transmission are two sary conditions for aggregation Packets have to be transmitted to a node at the sametime to be aggregated Structured approaches achieve these two conditions by let-ting nodes transmit packets to their parents in the aggregation tree and parents waitfor packets from all of their children before transmission Without explicit messageexchange in structure-free aggregation, nodes do not know where they should sendpackets to and how long they should wait for aggregation Therefore improving spa-tial convergence or temporal convergence can improve the chance of aggregation Wepropose the Data-Aware Anycast (DAA) protocol for improving spatial convergenceand the Randomized Waiting (RW) technique for improving temporal convergence.These two approaches are described in the rest of this section

neces-For the design of the structure-free convergence protocol we have the followinggoals

1 Early aggregation: Packets must get aggregated as early as possible on theirjourney to the sink

2 Tolerance to event dynamics: If the event’s region of influence changes,the overhead must not increase and the aggregation performance must remainunchanged

3 Robust to interference: Intermittent link failures should not affect the gregation performance.

ag-4 Fault tolerance: The aggregation performance must not be affected by nodefailures

In this section we present the Data-Aware Anycast (DAA) protocol which achievesthe goals described above The idea behind DAA is, instead of constructing a struc-ture in advance for optimal aggregation which is impossible without global knowledge

of the network topology and traffic pattern, an independent set among sources is ated Nodes in the independent set act as aggregation points The independent set iscreated distributedly and automatically while packets are forwarded to the sink, thusreducing the maintenance overhead incurred by structured approaches To betterdescribe the DAA protocol, we make the following assumptions:

cre-• Nodes know the geographic location of their one-hop neighbors and the sink.Geographic information is essential in sensor networks and it can be acquired

by GPS devices or localization protocols [14, 61]

• The interference range is at least twice as the transmission range This ensuresthat the neighbors of the sender will interfere with each other and no CTS frommultiple nodes will collide However if this does not hold, other mechanisms,such as [42], can be used to prevent collision from multiple CTS packets

• Nodes are time-synchronized We aggregate packets that are generated at thesame time therefore nodes have to be time-synchronized However, if packets

are aggregated according to other properties, such as geographic location, synchronization is not necessary.

Figure 2.1: Enhancing opportunistic aggregation with spatial convergence

When nodes send packets to the sink, they may follow different routes dictated bythe routing protocol Fig 2.1 shows an example comparing opportunistic aggregationwith optimal forwarding strategy In Fig 2.1, S is the sink, solid lines are routesconstructed by the routing protocol, dotted lines are other wireless links, and thearrows on the links represent packet transmissions A node with data are representedwith a dark circle Fig 2.1(a) shows the packet transmissions assuming opportunistic

constructed by the routing protocol Fig 2.1(b) shows how information about istence of data in neighboring nodes can be exploited to make dynamic forwardingdecisions to achieve more aggregation Fig 2.1(b) improves spatial convergence byallowing nodes to send packets to nodes that still have packets for aggregation Theblack circles are nodes that have packets to send In Fig 2.1(a), as there is no mes-sage exchange to construct a structure for aggregation, packets from C and E followtwo different routes constructed by the routing protocol The distributed MAC pro-tocol determines the order of transmissions in opportunistic aggregation which doesnot achieve any aggregation in this case However, in Fig 2.1(b), if node C knowsthat node B does not have packets for aggregation but node E does, it can send thepacket to E for immediate aggregation As a result there are only two packets left

ex-in the network (as opposed to three for opportunistic aggregation) This process can

be repeated until a node does not have neighbors with packets for aggregation, such

as E in Fig 2.1(b), and we call E an aggregation point This shows that if therouting protocol provides the freedom to the MAC layer to decide among a set ofnodes (rather than a single next-hop), and if it can determine which node has packetsfor aggregation, efficient spatial convergence can be achieved In typical deployments

of sensor networks, nodes have multiple choices for the next-hop For example, inthe ExScal [2] demonstration of the world’s largest sensor network, each sensor hadanywhere between 3 to 32 nodes in its communication range

We present the mechanisms of the DAA approach by discussing the base approachand enhancements to the base approach

• DAA - The base approach: DAA is based on anycasting [42, 90, 91] atthe MAC layer to determine the next-hop for each transmission Anycasting

requires the use of RTS packets to elicit CTS responses from the neighbors beforetransmission of the packet We define the Aggregation ID (AID) to associatepackets that can be aggregated The RTS contains the AID of the transmittingpacket and any neighbor that has a packet with the same AID can respond with

a CTS Depending on the application, AID can be any type of data, such asgeographic location or time instance In this chapter we use the measurementtimestamp as the AID Therefore two packets that are generated at the sametime can potentially be aggregated As there could be multiple receivers capable

of aggregating the packet, the receivers randomly delay the CTS transmissions

to avoid CTS collision Fig 2.2 shows the difference between unicasting in802.11 and randomized CTS response in anycasting In 802.11, the receiversends a CTS immediately after receiving the RTS, while in randomized CTS,the receiver sends a CTS with a random delay to avoid collision between nodessending the CTS In Fig 2.2, the CTS of receiver 2 has longer delay and hence

is canceled after hearing CTS from receiver 1

Because we assume that the interference range is more than twice of the mission range, the neighbors of the sender can interfere with each other Nodeswill cancel their CTS transmission if they overhear any packet transmissionduring the random delay to prevent CTS collision

trans-• DAA on all hops: To further increase aggregation, we also use the DAAapproach rather than unicast while forwarding packets from the aggregationpoints to the sink However, in order to forward packets to the sink using DAA,

we enhance the mechanism as follows Instead of dropping RTS if nodes do

(a) 802.11 based RTS/CTS

sender receiver

RTS

CTS

SIFS

sender receiver 1

Figure 2.2: Unicast vs Anycast

the sink, but with lower priority than nodes that have packets for aggregation.Therefore, packets are still aggregated when they have the chance to meet;otherwise the packets are forwarded greedily toward the sink

Fig 2.3(a) shows an example network of 50 nodes in a 200m × 200m square withthe sink at (0, 0) The communication range of each node is 50m Fig 2.3(b) showsroutes taken by packets using DAA before they reach the aggregation points (blacknodes) where they first fail to get aggregated any further The result shows thatthe 50 packets are aggregated to seven aggregation points (not including the sink).Compared to the 50 packets in the beginning, DAA reduces the packets to only sevenwithout incurring any overhead of constructing or maintaining a structure.

The average number of aggregation points selected in DAA is roughly n/(k + 1)where n is the number of nodes generating packets and k is the average degree ofnodes This can be explained as follows Consider a node that has k neighbors

It will become an aggregation point only if all its neighbors have sent their packetsbefore itself The probability that the node sends its packet later than all its neighbors

is 1/(k + 1); therefore the average number of aggregation points is n/(k + 1) Thismeans that the number of packets remaining in the network is reduced by a factor

of k + 1 automatically, which saves a lot of energy if the network is large and manysource nodes are far away from the sink

We now discuss details of the CTS priorities and the distance metrics

CTS Priorities: Nodes are assigned with different priorities in responding to anRTS The three classes in decreasing order of their priorities are as follows:

closer to the sink than the sender

Class C: The receiver does not have a packet with the same AID but is closer tothe sink than the sender.

If the receiver does not have the packet with the same AID and is also fartherfrom the sink than the sender, it does not send a CTS Corresponding to these threeclasses of neighbors that can respond to the RTS, three slots are reserved for the CTSpackets providing exclusively higher priorities for Class A over Class B, and Class Bover Class C (Fig 2.4) Nodes in the same class select a mini-slot to send their CTS

to avoid collision with other nodes in the same class In order to further reduce thenumber of transmissions, we divide Class C into 3 different priorities Nodes thatare on the shortest path to the sink have the highest priority in Class C Nodes canknow this information either by relative physical locations to their neighbors, or if therouting protocol indicates that they are the next-hop of the sender Second, nodesthat are at least closer to the sink by half of the transmission range than the senderare assigned with priority two, and the remaining nodes in Class C are assigned withpriority three This can reduce the number of transmissions since it takes fewer hops

to reach the sink by forwarding packets to farther nodes when there is no aggregation.Note that the actual transmission time of the CTS could be larger than the mini-slot or slot time The slots and mini-slots are used to stagger the starting time ofCTS transmissions Based on the assumption of interference between neighbors, weexpect only the first CTS transmission to succeed since the others will suppress theirtransmissions due to the resulting interference

Distance Metrics: In the DAA protocol, nodes need to know whether they arecloser to the sink than the sender to set the priority for sending the CTS This pri-ority is used for selecting the CTS-slot We use geographic distance to compare the

Class B Class A

Canceled CTS Canceled CTS Canceled CTS

RTS

CTS

Sender Class A Nbr

Class B Nbr Class C Nbr Class A Nbr

CTS slot mini-slot

Class C

Figure 2.4: CTS priorities

distance to the sink between two nodes Nodes have to know their location and alsothe sink’s location Furthermore, nodes have to know the sender’s location Thesender’s location can be either contained in the RTS packet, or can be exchangedbetween neighbors during network deployment Geographic voids and protocols to

go around voids have been well studied [49, 29] The DAA approach can be easilyadapted to account for voids For example the perimeter-mode forwarding approachfor dealing with voids [49] can make use of the anycast approach where Class C can

be restricted only to the designated next-hop on the perimeter The DAA approachcan also be used with other metrics such as the number of hops to the sink The maindifference from the geographic approach is that the number of hops will be used tomeasure closeness to the sink rather than the geographic distance

The DAA approach meets the design goals outlined in the beginning of this tion The DAA approach is used at each hop resulting in aggregation as early aspossible on the routes to the sink (Goal 1) As there is no computed structure, eventmobility has no impact on the performance of DAA (Goal 2) As transmission links

Sec-and next-hop nodes are chosen dynamically, DAA is tolerant to interference Sec-and nodefailures, and therefore is very robust even in unreliable networks (Goals 3 and 4).However, in the DAA approach packets may not get aggregated if they are spatiallyseparated (more than one hop) and if they are forwarded in lock-step by the MAClayer For such cases, we study the temporal convergence technique for improvedperformance.

The second condition for aggregation requires packets to be present in the samenode at the same time Structure-free aggregation does not guarantee that aggrega-tion will happen even when packets follow the same route If the order of transmissionsdoes not result in packets meeting temporally at intermediate nodes, the benefit ofaggregation may be limited The order of transmissions may be governed by severalfactors including interference from other flows and interference from the same flow.Assume that the backoff intervals are much smaller in comparison to the packettransmission time For such a configuration, packets that are only a few hops apartmay get forwarded in lock-step till they reach the sink even though they are on thesame route To illustrate this point, consider a simple topology where all nodes arelined up in a chain as shown in Fig 2.5 Suppose the radio signal can interfere withnodes that are two hops away If node D transmits first, node B and C will remainsilent during the transmission Therefore no nodes will contend for the channel with

A Although C will send a CTS packet and the channel will not be idle for node A,node A will only backoff for a short period, which is shorter than a packet transmissiontime, and will sense the channel as idle after that Since there is no contention, node

A will send its packet and it will not be aggregated with other packets from upstreamnodes Note that when packets are more than one hop apart or when packets followthe same route, the DAA approach is ineffective in improving aggregation.

Figure 2.5: Packet forwarding in lock-step

Deterministically assigning the waiting time to nodes such that nodes closer tothe sink wait longer can avoid the problem However nodes have no knowledge ofthe event size (the area in which nodes are triggered by the event) and location Inaddition, they do not know their relative position compared to other nodes sensing theevent The only information that a node knows is its distance to the sink Therefore itcan only set the delay inversely proportional to its distance to the sink This results in

a fixed delay for all packets wherever the event is, and the delay will be proportional

to the size of the network, which would be intolerable in large network deployments.Therefore we propose Randomized Waiting (RW) at sources for each packet tointroduce artificial delays and increase temporal convergence Each source delays itstransmission by an interval chosen from 0 to τ , where τ is the maximum delay InFig 2.5, if node A chooses a higher delay than node D and nodes B and C have