立 政 治 大 學

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國立政治大學資訊科學系

Department of Computer Science National Chengchi University

碩士論文 Master’s Thesis

考慮時間價值的兩階段群組訊息 網路編碼的散播機制

A Two-Phase Network Coding Design for Mobile Time-Valued Group-Message Dissemination

研 究 生：劉亭侁 指導教授：蔡子傑

中華民國一零六年一月

January 2017

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l C h engchi U ni ve rs it y 考慮時間價值的兩階段群組訊息

網路編碼的散播機制

A Two-Phase Network Coding Design for Mobile Time-Valued Group-Message Dissemination

研 究 生：劉亭侁 Student：Ting-Shen Liu 指導教授：蔡子傑 Advisor：Tzu-Chieh Tsai

國立政治大學 資訊科學系

碩士論文

A Thesis

submitted to Department of Computer Science National Chengchi University

in partial fulfillment of the Requirements for the degree of

Master in

Computer Science

中華民國一零六年一月

January 2017

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考慮時間價值的兩階段群組訊息網路編碼的散播機制 摘要

現今因無線通訊技術的進步，使得人們能方便地利用智慧型裝置透過 3G，

4G 和 Wi-Fi 等技術彼此溝通聊天。其中，聊天應用是最受智慧型裝置使用者歡 迎的應用程式。大部分的聊天應用程式需依賴網路以達到訊息交換的目的。然而 網路的頻寬是非常有限的，當使用者處在擁擠的環境中時，他們可能會面臨資源 耗盡問題。此外，例如在漫遊的情況下有些使用者並沒有行動網路的存取，導致 使用者無法使用聊天應用。

因此我們希望利用無線廣播傳輸的特性，開發一個應用於間歇性網路連接的 聊天應用程式。然而，廣播傳輸的散播策略若沒有設計得宜，可能導致廣播風暴 的問題，使得整體網路效能低落。我們研究的目標是要如何在間歇性網路增加訊 息的傳輸效率。為了達成此目標，在我們的研究中考量了許多技術要求，如：訊 息具有截止時間與優先權特性、多聊天室應用、傳輸效率。

我們提出了一種兩階段基於網絡編碼設計的訊息散播方法，實現在機會性社 群網路中的訊息散播。網絡編碼階段，提高網路頻寬的傳輸效率，也能增加網路 傳輸的可靠性；預熱階段能提升網路編碼訊息被解開的機率。最後，利用政大的 真實軌跡紀錄評估我們所設計的訊息傳播方法。結果顯示，我們的方法是有效率 且優於氾濫式的路由協議和一般的網絡編碼散播技術。

關鍵字：聊天應用程式、間歇性連接網路、廣播傳輸、網絡編碼、政大軌跡紀錄、

機會性社群網路。

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A Two-Phase Network Coding Design for Mobile Time-Valued Group-Message Dissemination

Abstract

Nowadays, the advancement of wireless communication technology has allowed people to use smart phones to communicate with each other more easily via 3G/4G, Wi-Fi, etc. One kind of popular mobile Apps is

“chat” App. Most chat Apps rely on the Internet to exchange the messages. However, the bandwidth of network is limited in some circumstances. When users stay in the crowded environment, they will face the resource depletion problem. Besides, some people may not subscribe to any cellular network access, e.g. in roaming scenarios.

Therefore, we want to develop a novel mobile Chat APP in

intermittently connected networks. We utilize the characteristic of the wireless broadcast transmission. However, it may cause the broadcast storm problem without careful design. How to increase the efficiency of message delivery in such intermittently connected networks is our

research goal. To achieve this, technical issues in our research involve message priority, multi-chatroom, deadline and transmission efficiency.

We proposed a two-phase network coding design for message

dissemination to enable the multi-hop instant messaging in Opportunistic Mobile Social Networks. The network coding phase can increase the bandwidth utility and transmission efficiency. Moreover, it can improve transmission robustness and adaptability. The warm up phase can increase the decoding probability of coded packets. Finally, we evaluated our approach with real trace data from NCCU. The results showed that our approach is effective and superior to the flooding based routing protocol and the pure network coding technique.

Keywords: Chat App, Intermittently Connected Networks, Broadcast,

Network Coding, NCCU Trace Data, Opportunistic Mobile Social Network

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誌謝辭

回顧這兩年半來在政大的碩士生生涯，依稀記得從一開始考上政大，懷抱著 興奮與充滿幹勁的心情，不知不覺也來到了尾聲，擁有了許多的收穫，心裡更是 滿懷著感激。從一開始什麼都不懂的碩零研究生，跟著蔡子傑教授及學長們的一 步一步指導，從中學習、成長，除了學習到了非常多與網路相關的專業知識，也 因為參與了大型的整合型研究計畫：跨領域行動商務，接觸到了跟原實驗室研究 完全不同的領域，並且學習到了巨量資料的處理平台以及先前所未觸及的財經知 識，這些都是非常難能可貴的學習機會。

在研究的歷程中，特別感謝指導教授 蔡子傑老師用心的教導。碩論的研究 過程並不如預期的順利，每每碰到研究的瓶頸，老師總能提出關鍵的問題點及改 善方法，使我能更有效率的修正自己的研究方向。更是感謝老師，即使身兼研發 處主管等職務，使得時間的壓縮緊繃，仍願犧牲不管是假日或是自己的休息時間，

撥空與學生討論。老師除了提供予我正確的研究方法之外，並從老師身上學習到 了許多的生活道理，在在令我受益良多。

在政大的生活裡，也遇見了許多一同相互成長的夥伴們，讓我的碩士生涯增 添了不少趣味及色彩。感謝建淳學長對於程式撰寫提供了很多非常寶貴的建議。

謝謝賀翔學長細心指導並提供了許多針對研究方向及方法加以改善。謝謝泰銘與 仲祐學長帶領學弟們從大學生變成研究生。謝謝冠宇學長教導了很多跨領域的知 識以及生活態度。謝謝學弟們與育銓，在課業上一起討論並相互鼓勵。在政大所 遇見的人事物，都編織成了一段令人回味無窮的回憶。

另外，我要感謝我的家人，一直是我最強大的後盾，讓我能無後顧之憂的學 習，也因為有家人無私的付出與支持鼓勵，終能讓我完成研究所的碩士學位。

完成了這段研究生生涯，更豐富了我的人生經歷，再次立下了一個新的里程 碑，然而這不是學習的終點，期許我們帶著收穫與持續成長，前往下一段更精采 的旅程。

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TABLE OF CONTENT

CHAPTER 1 Introduction ... 1

1.1 Background & Motivation ... 1

1.2 Goal ... 2

1.3 Opportunistic Mobile Social Network ... 3

1.4 Network Coding ... 4

CHAPTER 2 Related work ... 6

2.1 Social Trace Data ... 6

2.1.1 Reality Mining: MIT [10] ... 6

2.1.2 Cambridge [11] ... 7

2.1.3 INFOCOM [12] ... 8

2.1.4 UPB Trace [29]... 8

2.1.5 NCCU [9] ... 9

2.2 Flooding-based routing protocol ... 9

2.2.1 Direct Delivery Routing Protocol ... 9

2.2.2 Epidemic Routing protocol ... 10

2.2.3 Spray and wait routing protocol [16] ... 10

2.3 Network coding technique in the network ... 11

2.3.1 Delay-tolerant networks and network coding [13] ... 11

2.3.2 Broadcasting with hard deadlines in wireless multi-hop networks [14] ... 11

2.3.3 Deadline-aware Broadcasting in Wireless Networks [15]... 12

CHAPTER 3 NCCU Trace Data ... 13

3.1 The composition of trace data ... 14

3.1.1 Participant ... 14

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3.1.2 Interest Relation... 14

3.1.3 Trace Data ... 15

CHAPTER 4 A Two-Phase Network Coding Design for Mobile Time-valued Group- message Dissemination ... 16

4.1 Environment Definition ... 16

4.2 Time Value ... 18

4.3 Warm Up phase ... 22

4.4 Network Coding Phase ... 22

4.5 2-Phase: Dynamic Threshold ... 23

4.6 Activity Ratio (AR) ... 25

4.7 Meta-data ... 26

4.8 Buffer management ... 28

4.9 Routing Strategy ... 29

4.9.1 Exchange Meta-data ... 29

4.9.2 Generate Message Candidate Set (CS) ... 30

4.9.3 Message Forwarding Sequence ... 33

4.9.4 Update Meta-data ... 36

Chapter 5 Simulation setting ... 37

5.1 Simulation environment ... 37

5.2 Simulation Setting ... 38

5.3 Simulation results ... 40

5.3.1 Delivery ratio ... 41

5.3.2 Delay time ... 42

5.3.3 Time value ... 43

5.3.4 Overhead ratio ... 44

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5.4 Insight Analysis ... 45

5.4.1 Delivery Ratio ... 45

5.4.2 Dynamic 2-phase Optimization ... 47

5.4.3 Other method ... 49

Chapter 6 Conclusion and Future Work ... 50

Reference ... 51

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LIST OF FIGURE

Figure 1. Store-Carry-Forward ... 3

Figure 2. Broadcast Transition with Network Coding ... 5

Figure 3. MIT Reality Mining Dataset ... 7

Figure 4. iMote Device for the Experiment ... 8

Figure 5. Social Relation of NCCU Trace Data ... 14

Figure 6. Experiment Environment ... 16

Figure 7. Architecture of Message Buffer ... 17

Figure 8. Transmission Scenario ... 18

Figure 9. Encounter List ... 26

Figure 10. History Table ... 27

Figure 11. Un-coded Message List ... 27

Figure 12. Message State Table ... 28

Figure 13. Flowchart of Routing Strategy ... 29

Figure 14. History Table ... 30

Figure 15. Un-coded Message List ... 30

Figure 16. Message Forwarding Sequence ... 34

Figure 17. The ONE Simulator ... 37

Figure 18. NCCU Map ... 39

Figure 19. Result: Delivery Ratio ... 41

Figure 20. Result: Delivery Delay ... 42

Figure 21. Result: Time Value ... 43

Figure 22. Result: Overhead Ratio ... 44

Figure 23. Result: Delivery Ratio-Phase ... 45

Figure 24. Result: Delivery Ratio-Priority ... 46

Figure 25. Optimization Result: Delivery Ratio ... 47

Figure 26. Optimization Result: Delivery Delay Time ... 48

Figure 27. Optimization Result: Time Value ... 48

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LIST OF TABLE

Table 1. Notation for Time Value Calculation ... 21

Table 2. Notation for Dynamic Threshold ... 24

Table 3. Notation for Activity Ratio ... 25

Table 4. Notation for History Table ... 27

Table 5. Simulation Setting ... 39

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CHAPTER 1 Introduction

1.1 Background & Motivation

Nowadays, the advancement of wireless communication technology has allowed people using the smart phone to communicate with each other more easily via 3G, 4G and Wi-Fi, etc. Most popular service in the mobile platform is communication service.

The general chat application relies on the internet to exchange the message, however, the bandwidth of network is limited. When users in crowded environments, they will face the resource depletion problem. For example, a large-scale live concert,

demonstration and New Year's Eve event, it is difficult to communicate with each other via internet in those environments.

In 2014, the team named Open Garden built a messaging application “FireChat”

[1]. FireChat is a free messaging app for public and private communications that works even without Internet access or cellular data. In the range of communication, each user will compose the ad-hoc networks by Bluetooth or Wi-Fi direct, so the users of FireChat can chat with each other.

The users of FireChat can only exchange the messages in their own ad-hoc networks group, so the range of communication is limited; moreover, the connectivity of Bluetooth or Wi-Fi is intermittent and un-robust. Thus, this research combines the DTN (Delay Tolerant Networks) [4] approach. With the help of store-carry-forward method, users can relay and forward messages to other group. The DTN not only

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extend the rage of transmission but also increase the delivery ratio.

In order to increase the delivery ratio, traditional DTN usually adopt flooding- based routing protocol or forwarding-based routing protocol to exchange packets, but it will cause the Broadcast Storm [2]. Broadcast Storm is the accumulation of

broadcast and multicast traffic on a network. If all users adopt high redundancy routing approach, it will give rise to network congestion and unnecessary waste of storage capability and battery energy. Therefore, we take the advantage of network coding with broadcasting mechanism which can decrease the network's overhead ratio. Network coding is a technique which can be used to improve a network's throughput, efficiency and scalability.

In an actual case, each message can be divided into different priority in the chat room. High priority message should be sent delivered to the destination as soon as possible. Priority message of salient features are: timeliness, TTL (Time to Live), delivery delay and delivery ratio. We develop a novel mobile Chat APP in

intermittently connected multi-hop ad hoc networks. Technical Requirements includes message priority, multi-chatroom, deadline-aware and transmission efficiency.

1.2 Goal

We propose a network coding design dissemination approach to enable multi- hop instant messaging in mobile social network.

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1.3 Opportunistic Mobile Social Network

Opportunistic mobile social network is a kind of delay tolerant network with social relations between the nodes [17]. Delay-tolerant networking (DTN) is a routing protocol to computer network architecture that may lack continuous network

connectivity. There are many DTN routing protocols based on “Store-Carry- Forward”, which can be divided into two categories: flooding-based protocol and forwarding-based protocol [5]. In figure 1, the concept of Store-Carry-Forward is that message is sent to an intermediate node where it is kept and sent at a later time to the final destination node or to another intermediate node.

Figure 1. Store-Carry-Forward

Flooding-based protocols is a simple computer network routing algorithm in which every message in the buffer will be sent to receivers. In flooding-based protocols, each node acts as both a sender and a receiver. The disadvantages of

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flooding-based protocols are that: it will duplicate too many copies of message into network further causing the waste of bandwidth and storage resources.

Forwarding-based protocol is the relaying of messages from sender to receiver in a computer network. Nonetheless, the most different between flooding-based and forwarding-based is that forwarding-based protocols will not transmit all message to the receiver. Before sender transmit messages to receiver, sender will select the appropriate forwarding sets to be transmitted according to the network situation.

However, the Delay-Tolerant Network is a distributed network, and the disadvantages of forwarding-based protocol is that it is difficult for sender to collect network

information completely.

1.4 Network Coding

Network information flow [3] was proposed by (Ahlswede, et al.). With network coding, relay nodes can code several packets into one packet, instead of transmitting packet directly. The spirit of network coding is let intermediate node sender broadcast coded packets, then receiver can decode it with their message in buffer. According to the XOR property, network coding approach can increase the bandwidth utility and transmission efficiency. When the network condition is high link-failure, network coding technique also can improve transmission robustness and adaptability [6].

Because Delay-Tolerant Network lacks continuous network connectivity, network coding is suitable for Delay-Tolerant Network in our scenario.

Broadcast transition approach shows in figure 2, the left part of figure is traditional broadcast and the right part of figure is network coding. It is the same on

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the step 1and step2. Node A broadcast the message 1 to node C. Node B broadcast message 2 to node C. At Step 3 is the key point, with the help of network coding, node C just only broadcast XOR packet one time. Then, the node A and B can decode the missing message from there buffer. In contrast, traditional broadcast must

broadcast 4 time, so network coding approach can improve the delivery overhead easily. To decrease the number of transmissions, for each transmitted packet, Sender has to maximize the number of neighbors which can decode a missing packet. In [24], it is proven that it is NP-Complete problem.

Figure 2. Broadcast Transition with Network Coding

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CHAPTER 2 Related work

2.1 Social Trace Data

The characteristics of trace data have been studied in several studies [19,20,21].

There are many mobility model based on specific participants or particular group;

such as, MIT reality [10], INFOCOM [12] and Cambridge [11]. Mobility patterns affect the performance heavily in opportunistic network. We hope that the trace data is close to real situation. Thus, choosing the appropriate mobility model is important for evaluating results in opportunistic network.

2.1.1 Reality Mining: MIT [10]

Reality mining experiment was conducted by MIT in 2006 and the total

participants is 100 students. The composition of the groups is MIT Media Laboratory and MIT Sloan business school respectively. Participants were rationed to a smart phone in this experiment. They were asked to use smart phones to communicate with each other; besides, the movement trajectory, contact time, encounter time and communicating data were recorded by their smart phone too. Participant can

exchange their data over short distances by Bluetooth wireless technology standard.

In figure 3, researchers can analyze and make prediction for the relation of

participant's movement and social relations. The reason why for move model lacks of objectivity is that all participants belong to specific groups.

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Figure 3. MIT Reality Mining Dataset

2.1.2 Cambridge [11]

Cambridge social-based forwarding experiment was conducted by computer lab of UC in 2008 and the total participants are 54 students. Figure 4 shows that those researchers invented a device, named iMote, to collect the experiment data. The composition of participants are students. Each student was equipped with iMote device, which recorded the main active area, the other students they encounter and the length of communicating time of each student. The experiment time is eleven days.

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Figure 4. iMote Device for the Experiment

2.1.3 INFOCOM [12]

This experiment was conducted in IEEE INFOCOM Conference and the total participants are 98 people. The most participants who attended INFOCOM conference are students and researchers respectively. Participants were rationed to an iMote device in the beginning. There were lots of different topics in the conference, the participants will go to attend the conference that they interest in. Thus, researchers can analyze the relation between participants and their social relation from recorded data.

2.1.4 UPB Trace [29]

Trace data collected at the University Politehnica of Bucharest in the spring of 2012. This experiment lasted for 63 days, and total participants are 72 people. They collected contextual data from Android smartphones and gathered information about a device's encounters with other nodes. The experiment data includes participants'

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Facebook profiles, Facebook profiles and trace of wireless contacts.

2.1.5 NCCU [9]

The NCCU Trace experiment was conducted by NCCU in 2014. The NCCU Trace is collected by mobility patterns from several departments’ students in National Chengchi University. This experiment lasted for two weeks, from December 17 to December 31. The researchers built an android application to collect experiment data.

Participants can participate this experiment by installing the application on their own mobile device easily. All the details will be described in the next chapter.

2.2 Flooding-based routing protocol

Flooding-based routing protocol is based on redundancy approach. It is the easiest way to transmit messages, because each node will transmit multiple copies of messages to receiver, but they don’t need to obtain the network condition. The core of flooding-based routing protocol is that decreasing the average delay time by

duplicating the messages. The advantage of this protocol is simple and easy; however, too much duplicated messages in the network will result in broadcast storming, bandwidth consuming and low network efficiency.

2.2.1 Direct Delivery Routing Protocol

Direct delivery routing protocol is just like the literal meaning, senders only can transmit the messages when they encounter with the destination node. This protocol will reduce the overhead significantly, but the delay time of message will be much

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long. Thus, in order to improve the network efficiency and decrease the resource usage, it have identified a tradeoff between maximizing throughput and minimizing message delay [7].

2.2.2 Epidemic Routing protocol

Epidemic routing is resource hungry routing protocol because the senders will continuously replicate and transmit messages to receivers that messages not in the buffer. The concept of replicated database maintenance was proposed by A. Demers in epidemic routing protocol [8]. The bottleneck of this routing protocol is buffer size. If the buffer size is infinite, it will bring on the minimal delivery latency.

2.2.3 Spray and wait routing protocol [16]

In the “Spray and Wait” routing protocol, protocol is composed of two phases:

the spray phase and the wait phase. When a new message is created, which will be limited in maximum allowable relay copies in the network. The size L indicating the relay of copies. In other word, the message can be forwarded with L times. Each of these nodes then transfers half of its copies (Remain of L/2) to the node‘s encounters.

When a relay node receives the copy which belongs to maximum allowable copies, it enters the wait phase, where the relay node simply holds that particular message until the destination is encountered directly.

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2.3 Network coding technique in the network

Network coding technique is not the new concept in wireless network. For this purpose, network coding can be used to mix packets together to reduce the number of transmissions. Opportunistic mobile social network is a kind of delay tolerant network with social relations between the nodes. [18] shows that network coding technique can improve network performance compared to transmitting messages directly.

2.3.1 Delay-tolerant networks and network coding [13]

Researchers compared network coding technique in the simulation and running applications on real mobile devices. It shows that they found conditions where their results match. Thus, it is possible to use network coding technique for message dissemination in larger delay-tolerant networks at a much manageable cost.

2.3.2 Broadcasting with hard deadlines in wireless multi-hop networks [14]

Each messages with characteristic of deadlines in this research. The broadcast mechanism on the basis of broadcasting trees. According to the results, broadcasting message with network coding technique by constructing broadcasting trees, it shows that good performance on delivery ratio and transmission times.

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2.3.3 Deadline-aware Broadcasting in Wireless Networks [15]

Delay and deadline constraints are important metrics in many scenarios. In order to increases the coding opportunity, relay nodes need to wait to receive more packets.

This research provides local waiting time of the packets at relay nodes, which improves the efficiency of the network coding.

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CHAPTER 3 NCCU Trace Data

Generally, the mobility pattern can be divided into synthetic and trajectory models. Synthetic models are based on mathematical method to generate the mobility pattern. Trajectory models can be collected by people’s moving record, daily behavior in real life, etc.

The feature of DTN (Delay-tolerant network) is that nodes lake continuous connectivity between source and destination. All nodes only can deliver messages to other nodes when they encounter each other. Therefore, mobility pattern is important for researchers. The mobility pattern will cause great influence on routing protocol design.

There are many previous work focus on trace data, for example, MIT trace data [10], Cambridge trace data [11] and INFOCOM [12] trace data. However, most of those trace data only emphasis on specific social groups or features. In the MIT trace data, the participants belong to media laboratory and business school. In the

Cambridge trace data, only computer laboratory students participate this project.

Finally, the participants only belong to specific conference in the INFOCOM experiments.

We take [22] for reference, and we want to develop our own mobility model.

Thus, we built an android application to collect the experiment data. NCCU Trace Data is close to the real environment. There are 115 available participants in trace data. This experiment lasted for two weeks, from December 17 to December 31. All trace data can be downloaded at [23].

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3.1 The composition of trace data

NCCU trace data includes not only movement pattern but also the social relation, shown in figure 5. Next, we will describe the composition of trace data, and why those attributes will be chosen in the experiment.

Figure 5. Social Relation of NCCU Trace Data

3.1.1 Participant

In order to improve shortcomings of previous studies, all the participants should not come from one specific group. Therefore, the participants are from different departments in this experiment. Then, we define the relation in the same group is that two participants must get to know each other. The candidate participants will be eliminated, if their relation contravenes the previous rule mentioned above.

3.1.2 Interest Relation

We asked all the participants do the interesting questionnaire in the beginning.

The interest can be divided into five categories, which are sports, reading, social, arts and service respectively, shown in figure 5. We built a questionnaire based on Likert scale, so we can find the interest relation for all participant by induction the

questionnaire.

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3.1.3 Trace Data

Movement was recorded by GPS, and the scan period is 5 minutes. Application recorded not only the trajectory but also the encounters. Besides, we also track the application usage in Android devices. With those experiment data, we can analyze the relation between participants and trajectory.

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CHAPTER 4

A Two-Phase Network Coding Design for Mobile Time- valued Group-message Dissemination

4.1 Environment Definition

According to the interest questionnaire from NCCU trace data, there are five group chatrooms in the scenario. Those groups were named sports, reading, social, arts, and services respectively. The destination of message is the group rather than person. Each user can participate in more than one chatroom.

Each chatroom member can forward the different group’s message as figure 6.

Figure 6. Experiment Environment

Furthermore, user can assign the different priority value to new messages. We illustrate this concept in figure 7. The first priority messages represent the most important messages, and so on.

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Figure 7. Architecture of Message Buffer

We adopt the broadcast transition to deliver message, and each node will

broadcast the beacon signal to identify the same group member. Before sender want to forward message to receiver, they will exchange the meta-data initially. With the help of meta-data, senders can forward message more efficiently. The meta-data includes the history of encounters, buffer information and environment situation, etc.

Based on broadcast transmission, not only receiver will receive the messages from sender, but also the neighbors of sender will overhear the messages too. The most different form traditional routing scenarios is that neighbor can receive the messages even if they do not belong to the same group chatroom. The concept of overhear is describe in the figure 8.

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Figure 8. Transmission Scenario

In order to improve the transmission efficiency and performance, we propose a network coding design method for message transmission. In figure 7, transmission scheme can be divided into two phase, the first phase is warm up phase and the second phase is network coding phase. We will describe each phases in detail, and how to improve the problem of current protocols.

4.2 Time Value

Time value is term of finance [25], which also be called Time value of money or Time value of an option. The time value describes the greater benefit of receiving money now rather than later. It is founded on time preference. In mathematical finance, the Greek: THETA representing the decay sensitivity of the time value in Black-Scholes model [26, 27].

As we mentioned in previous section, we have assigned priority and TTL value

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to every message. We define time-value of message depends on the receiving time and priority, and then it is subject to exponential decay by formula 1. Derivation of the solution to this equation can be seen below. For example, two messages with the same receiving time (short time) but different priority, the high priority message will get more time value than low priority message. On the contrary, two messages with the same receiving time (long time) but different priority, the high priority message will get less time value than low priority message. The high priority message will get more time value; however, the decay of time value is much fast. In other words, larger decay constants make the time value vanish much more rapidly for high priority message. We can calculate the time value of message by equation 2.

𝑑𝑣(𝑡)

𝑑𝑡 = −𝛼𝑣(𝑡) 𝑣(𝑡)=𝑣₀𝑒^−𝛼𝑡

(1)

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𝑑𝑣(𝑡)

𝑑𝑡 = −𝛼𝑣(𝑡) 𝑑𝑣(𝑡)

𝑣(𝑡) = −𝛼 𝑑𝑡 1

𝑣(𝑡)𝑑𝑣(𝑡) = −𝛼 𝑑𝑡

∫ 1

𝑣(𝑡)𝑑𝑣(𝑡) = ∫ −𝛼 𝑑𝑡 𝑙𝑛 𝑣(𝑡)=−𝛼𝑡 + 𝑐

𝑒^{𝑙𝑛 𝑣(𝑡)}=𝑒^{−𝛼𝑡+𝑐} 𝑣(𝑡)=𝑒^𝑐𝑒^−𝛼𝑡=𝑣₀𝑒^−𝛼𝑡

(𝑣(0) = 𝑒^𝑐 = 𝑣₀)

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Let 𝑡 = ⌊ ^{𝑇−𝐶𝑇𝑚𝑖}

𝑃𝑒𝑟 ⌋ ; 𝑇𝑉(𝑚_𝑖) = { 𝑣⁰𝑒

−𝑡

α∗𝑃𝑚𝑖, 𝑖𝑓 𝑡 < 𝑇𝑇𝐿 0 , 𝑖𝑓 𝑡 ≥ 𝑇𝑇𝐿

∀𝑖, 𝑚_𝑖 ∈ 𝑀

(2)

Table 1. Notation for Time Value Calculation

Notation Description

𝑣(𝑡) Time value function

𝑡 Time

α Exponential decay constant

𝑣₀ Initial quantity of time value

α Exponential decay constant

𝑇𝑉(𝑚_𝑖) Time value of message i

𝑃_𝑚𝑖 Priority of message

𝑃𝑒𝑟 Time period constant

𝑇𝑇𝐿 Time to live of message

T Current system time

𝐶𝑇_𝑚𝑖 Message create time

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4.3 Warm Up phase

In the pure network coding transmission scheme, we analyzed the buffer

utilization and we found that there are seldom coded packets which can be decoded in the beginning. Members need to wait a period of time to receive more packets, so they will have more chance to decode the coded packet. Thus, we take the advantages of flooding routing protocol. We take “Spray and Wait” routing protocol for reference, and we limit the copies of forwarding messages. If messages belong to warm up phase, it only can be transmitted directly. Furthermore, un-coded packet will improve the delay time and probability of decoding message.

In our network coding transmission design, when the forwarded messages reach to the maximum allowable copies. It will stop to be transmitted directly, but it will be transmitted in coded-packet way (i.e. it belongs to network coding phase and it will be encoded with another packet).

4.4 Network Coding Phase

Network coding transmission scheme is a technique which can be used to improve a network's throughput, efficiency and scalability. Message belong to network coding phase, which represents that the messages have been transmitted or overhear a lot of times. If messages still be transmitted directly, it will cause too much overhead and poor utilization in the network. Therefore, messages which belong to network coding phase will be encoded in to coded packet before be transmitted.

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4.5 2-Phase: Dynamic Threshold

The simplest way to delivered message is flooding, however it will cause the broadcast storming and energy-consumption, and most importantly it will decrease network's throughput. In the “Spray and Wait” routing protocol, protocol is composed of two phases: the spray phase and the wait phase. When a new message is created, a number L is attached to that message indicating the maximum allowable copies of the message in the network. In other word, the message can be forwarded with L times.

Each of these nodes then transfers half of its copies (Remain of L/2) to the node‘s encounters. When a relay receives the copy which belongs to maximum allowable copies, it enters the wait phase, where the relay simply holds that particular message until the destination is encountered directly.

We take the advantages of network coding and flood routing protocols. Using flooding method will increase the deliver ratio and decoding probability, but it also will cause the overhead issue. Network coding mechanism can improve the efficiency in the network, however, too much coded packets in the buffer which cannot be decode effectively will lead to too much delay time. Thus, we propose 2-phase network coding design for message dissemination. Messages can be classified into two phases by threshold with equation 3. Furthermore, the threshold is not a constant, it will be updated with remaining relay copies and activity ratio of receiver

accordingly by formula 4.

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𝑚_𝑖^𝑔 = { 𝑚_𝑖^𝑔 ∈ 𝑃ℎ_{_𝑤𝑎𝑟𝑚} , 𝑖𝑓 𝑟_𝑚𝑖+ 𝑜_𝑚𝑖 < 𝑇ℎ_𝑚𝑖 𝑚_𝑖^𝑔 ∈ 𝑃ℎ_{_𝑛𝑐} , 𝑖𝑓 𝑟_𝑚𝑖+ 𝑜_𝑚𝑖 ≥ 𝑇ℎ_𝑚𝑖

∀𝑖, 𝑔 ; 𝑚_𝑖^𝑔 ∈ 𝑀 ; 𝑔 ∈ 𝐺

(3)

𝑇ℎ_𝑚𝑖 = 𝑅𝑁(𝑚_𝑖^𝑔) ∗ 𝛾 ∗ (1 − 𝐴𝑅_𝑔)_{𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑟}

∀𝑖 ; 𝑚_𝑖^𝑔 ∈ 𝑀

(4) Table 2. Notation for Dynamic Threshold

Notation Description

𝑚_𝑖^𝑔 Message i of group g

𝑟_𝑚𝑖 Total relay time of message

𝑜_𝑚𝑖 Total overhear time of message

𝑇ℎ_𝑚𝑖 Threshold for message i

𝑃ℎ_{_𝑤𝑎𝑟𝑚} Warm up phase

𝑃ℎ_{_𝑛𝑐} Networking coding phase

𝑅𝑁(𝑚_𝑖) Remaining relay copies of message i

𝛾 Network environment constant

𝐴𝑅_𝑔 Activity ratio for group g

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4.6 Activity Ratio (AR)

Activity ratio is the activity level in specific group, which can be calculated by the formula 5. High activity ratio means nodes have high probability to encounter the group, and vice versa.

𝐴𝑅_𝑔=^{∑ 𝐸𝐶𝑔}

∑ 𝐸𝐶𝐺

𝑔 ∈ 𝐺

∀𝑖, 𝑔 ; 𝑚_𝑖^𝑔 ∈ 𝑀 ; 𝑔 ∈ 𝐺

(5)

Table 3. Notation for Activity Ratio

Notation Description

𝐴𝑅_𝑔 Activity ratio for group g

𝐸𝐶_𝑔 Encounter Count for group g

𝐸𝐶_𝐺 Encounter Count for all group

𝛽 Aging constant

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Activity ratio will be updated periodically and calculated with size w. Moreover, the activity ratio for group are "aged" with flowing equation 6.

(𝐴𝑅_𝑔)_𝑛𝑒𝑤= (𝐴𝑅_𝑔)_𝑜𝑙𝑑∗ 𝛽 𝑔 ∈ 𝐺

(6)

Figure 9. Encounter List

4.7 Meta-data

In order to make the precise decision for message forwarding, we need to exchange some meta-data before deliver message. Figure 10 is the meta-data of history table. Data format can be divided into four attribute. Group_ID represents the Identification number of group chatrooms; we create four different interests group in our scenario. AR is the abbreviation of activity ratio. Sender can understand the environment of network through the AR, when the value is high which means node have strong relation with specific chatroom. TS (timestamp) is used to identify the elapse time since last updated. Timestamp will be update periodically, moreover, if

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elapse time is too long to take for reference. Finally, member of attribute represents that whether nodes belong to chatroom’s member or not.

Un-coded message list (UML) is that messages have been received from warm up phase or decoded by network coding phase recently, shown in figure 11. The size of UML is limited by window size w.

Figure 10. History Table

Table 4. Notation for History Table

Notation Description

Group_ID Identification number for group chatroom

AR Activity Ratio

TS Time Stamp since last update time

Member Belong to group member

Figure 11. Un-coded Message List

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4.8 Buffer management

We use two buffers to store coded and un-coded packets, besides we will maintain a packet delivery status table, showed in figure 12. In Figure 12, the first row and the first column are the message id, the other attributes record the delivery status. The value-pair is relay times and overhear times in the message state table.

Figure 12. Message State Table

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4.9 Routing Strategy

The message dissemination flowchart is shown in figure 13.

Figure 13. Flowchart of Routing Strategy

4.9.1 Exchange Meta-data

If two nodes encounter each other, firstly they will exchange meta-data, such as history table (HT) and un-coded message list (UML). Figure 14 is the history table.

The attribute of TS (Time stamp) is calculated by comparing the current system time and activity ration updated time, which is used to remove outdated information. The attribute of Member is used to identify the group member in the chatroom. In figure 15, UML is the un-coded message list; which node have received or decoded recently.

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Figure 14. History Table

Figure 15. Un-coded Message List

4.9.2 Generate Message Candidate Set (CS)

Algorithm of Generate Message Candidate Set is shown in Algorithm 1. Sender will choose specific group messages to temperate buffer randomly by analyzing the HT. Take figure 14 as example, we assume that sender is the group member of 1 and 3. Sender will pick the message of group 1 and 3, because Both of them are the member of group 1 and 3. Then, according the history table of receiver, sender will also pick the message of group 2, the reason is that activity ratio of group 2 with the highest value. Next, each message will be classified into different phase by its threshold, and we will get three sets by the following expression 7. Finally, sender will pick messages to forwarding sequence buffer from candidate set buffer randomly.

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𝑆₁ = {𝑚_𝑖^𝑔 | 𝑚_𝑖^𝑔 ∈ 𝑃ℎ_{_𝑤𝑎𝑟𝑚}}

𝑆₂ = {𝑚_𝑖^𝑔⊕ 𝑚_𝑗^𝑔 | 𝑚_𝑖^𝑔 ∈ 𝑃ℎ_{_𝑤𝑎𝑟𝑚}× 𝑚_𝑗^𝑔 ∈ 𝑃ℎ_{_𝑛𝑐}} 𝑆₃ = {𝑚_𝑖^𝑔⊕ 𝑚_𝑗^𝑔 | 𝑚_𝑖^𝑔 ∈ 𝑃ℎ_{_𝑛𝑐}× 𝑚_𝑗^𝑔 ∈ 𝑃ℎ_{_𝑛𝑐}}

∀𝑖, 𝑗, 𝑔 ; 𝑚_𝑖^𝑔, 𝑚_𝑗^𝑔 ∈ 𝑀 ; 𝑔 ∈ 𝐺

CS= 𝑆₁∪ 𝑆₂∪ 𝑆₃

(7)

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4.9.3 Message Forwarding Sequence

In social opportunistic network, the contact time of two nodes may not be a long time. However, the contact is short and unstable due to the mobility pattern of node.

Therefore, sender node probably does not have enough time to transmit all selected candidate set to receiver node. The features of carry-and-forward is important in social opportunistic network. Thus, the message forwarding sequence will influence the performance of delivery rate and delivery delay significantly. Algorithm of

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Message Forwarding Sequence is shown in Algorithm 4.

If messages belong to warm phase, which means there are few copies in the network, vice versa. Consequently, the messages of warm phase should be transmitted firstly. We illustrate our idea in figure 16. If two messages belong to same phase, the transmission sequence will be scheduled by decode probability and expected time value. The decode probability can be known from history table and the expected time value can be calculated by following equation 8.

Figure 16. Message Forwarding Sequence

TV (𝑚^′_𝑘) = { TV(𝑚_𝑖^𝑔) , 𝑖𝑓 𝑚′_𝑖 𝑖𝑠 𝑢𝑛𝑐𝑜𝑑𝑒𝑑 , 𝑚′_𝑘 = 𝑚_𝑖^𝑔 𝑀𝐴𝑋 (TV(𝑚_𝑖^𝑔), TV(𝑚_𝑗^𝑔)) , 𝑖𝑓 𝑚′_𝑖 𝑖𝑠 𝑐𝑜𝑑𝑒𝑑 , 𝑚′_𝑘 = 𝑚_𝑖^𝑔⊕ 𝑚_𝑗^𝑔

∀𝑖, 𝑗, 𝑘, 𝑔 ; 𝑚^′_𝑘 ∈ 𝐹𝑆 ; 𝐹𝑆 ⊂ 𝐶𝑆 ; 𝑚_𝑖^𝑔, 𝑚_𝑗^𝑔 ∈ 𝑀 ; 𝑔 ∈ 𝐺

(8)

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4.9.4 Update Meta-data

After nodes finish the transmission process, they will update the meta-data, the meta-data including HST (History table) and MST (Message state table). History table will be updated on the basis of encounters. Message state table will be updated with the message receiving approach (relay directly or by overhearing) for receivers and message forwarding list for senders.

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Chapter 5 Simulation setting

5.1 Simulation environment

We use The ONE (Opportunistic Network Environment simulator) [28] to conduct our simulation and evaluation our research, which is an open source platform of opportunistic network simulator and shown in figure 17. The one simulator doesn’t support network coding by default, so we extend the one simulator and integrate the network coding module manually.

Figure 17. The ONE Simulator

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5.2 Simulation Setting

Our goal is that networking design for group message dissemination. There are many mobility model such as, MIT reality [10], INFOCOM [12] and Cambridge [11];

nonetheless, they all collected from some specific groups. Finally, we choose the trace data from our previous work [9], NCCU trace data, as our mobility model.

In our scenario, we choose NCCU trace data to be our mobility model.

According to the interest questionnaire form NCCU trace data, all of the pedestrian can be classified into 5 groups (sports, reading, social, arts and services). Each pedestrian can participate more than one chatroom group.

Finally, we define the simulation coverage of NCCU campus, figure 18 shows the main active area of students. The size of map is about 12 square kilometers (3764*3420m). The simulation time is from 12:00 a.m. to the next day of 12:00 p.m., which is equivalent to two days (172800 seconds). The data rate is 250KB/s, and the TTL of message is 1080 minutes (18 hours).

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Table 5. Simulation Setting

Map NCCU Trace

Area 3764*3420m

Simulation Time 172800 sec

Data Rate 250KBps

Radio Range 30m

Buffer Size 100MB

Number of chatroom 5

Time to Live 1080mins

Figure 18. NCCU Map

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5.3 Simulation results

It is not suitable for the evaluation with General DTN routing protocols in our scenario, such as Spray and Wait, PRoPHET and MaxProp, etc. First, those routing protocols do not support the network coding method. Second, the distribution of messages does not base on the broadcast transmission in those routing protocols.

Therefore, we evaluated our dissemination on the principle of fair comparison, we only focus on the methods of flooding and network coding.

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5.3.1 Delivery ratio

Delivery ratio is calculated by percentage of messages delivered to destination.

Figure 19 shows that 2-phase networking coding design is better than the other routing method. In the networking coding approach, too many codes packets can be decoded in the buffer. Flooding method will have better performance than network coding method; however, it will lead to low buffer utilization and overhead issue.

In our approach, we can improve the delivery ratio by decoded more network coding packet in the buffer. Besides, we increase the decoding probability through the un-coded packet from warm up phase.

Figure 19. Result: Delivery Ratio

0 0.2 0.4 0.6 0.8

Delivery Ratio

Enhanced-Flooding N.C 2-Phase

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5.3.2 Delay time

Delay time is critical factor in our scenario. Message delay is calculated by the message created time and message delivered time to all nodes. Because too many coded packets can be decoded in the buffer, the network coding method have the longest wait waiting time. However, if we can use this shortcoming wisely, we can increase the decoding probability by sending un-coded packets directly. As we mentioned above this is the spirit of two phase dissemination approach. In figure 22, it is observed that two phase mechanism greatly out performs flooding in terms of message delivery delay.

Figure 20. Result: Delivery Delay

10000 15000 20000 25000 30000 35000 40000 45000

Delivery Delay

Enhanced-Flooding N.C 2-Phase

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5.3.3Time value

Time value is corresponding to the message priority and delay time. There are two priority messages in the network, and we define messages’ time value decay exponentially. High priority messages with larger decay constants make the time value vanish much more rapidly. Next, we define our time value in the semi-open range [0.0, 1.0). Figure 23 shows that our two phase design is superior to other routing protocols.

Figure 21. Result: Time Value

0 0.2 0.4 0.6 0.8

Time Value

Enhanced-Flooding N.C 2-Phase

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5.3.4 Overhead ratio

Overhead ratio is calculated by the following formula 9.

∑ 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑 𝑚𝑒𝑠𝑠𝑎𝑔𝑒 − ∑ 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑚𝑒𝑠𝑠𝑎𝑔𝑒

∑ 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑑 𝑚𝑒𝑠𝑠𝑎𝑔𝑒

(9) In figure 24, we can observe that network coding and two phase network coding design performs closely in terms of transmission overhead. Network coding approach will provide better buffer utilization than flooding approach in limited buffer size.

Figure 22. Result: Overhead Ratio

0 10 20 30 40 50 60 70

Overhead Ratio

Enhanced-Flooding N.C 2-Phase

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5.4 Insight Analysis

5.4.1 Delivery Ratio

We analysis the delivery ratio by phase. Figure 20 shows that most of nodes received messages through the network coding phase in general cases. However, if the average activity ratio is low in the social network, such as day 4-5 and day 11-12, two phase dissemination approach will not provide significant improvement in the

performance of delivery ratio.

Figure 23. Result: Delivery Ratio-Phase

0 0.2 0.4 0.6 0.8

Delivery Ratio-Phase

2-Phase Phase1 Phase2

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There are two kind of priority messages in the network. The high priority messages will have more probability to be transmitted directly in our dissemination mechanism. As expected, figure 21 shows that high priority message will have more chance to be delivered to the destination.

Figure 24. Result: Delivery Ratio-Priority

0 0.2 0.4 0.6 0.8

Delivery Ratio-Priority

2-Phase Priority1 Priority2

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5.4.2 Dynamic 2-phase Optimization

According to our formula 4, γis the network environment constant in the equation. We want to know performance issue with different value of γ.

In figure 25, we can find that when value equal to tree with the highest delivery ratio, but value is inverse proportional to delivery ratio when value is getting larger. It is obviously that the performance of delivery ratio is close to flooding routing

protocol when value is greater than ten.

Figure 25. Optimization Result: Delivery Ratio

0 0.2 0.4 0.6 0.8

γ=1 γ=3 γ=5 γ=10

Delivery ratio

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Figure 26 shows that increasing the value of γ can improve the performance in the terms of delay time and time value, but if the value increases too much it will result in poor performance.

Figure 26. Optimization Result: Delivery Delay Time

Figure 27. Optimization Result: Time Value

25000 26000 27000 28000 29000 30000 31000 32000

γ=1 γ=3 γ=5 γ=10

Delay Time

0 0.2 0.4 0.6 0.8

γ=1 γ=3 γ=5 γ=10

Time Value

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Consequently, after we integrated the simulation results mentioned above, we think that value γ equals three, which is the best case in our simulation environment scenario.

5.4.3 Other method

We have compared many methods in our simulation scenario. However, they do not perform outstanding results. We have tried the implement different number of relay copies for receiver. Each of these nodes transfers half (n/2) of the total number of copies they have encounter, it will decrease the decoding probability significantly.

Thus, we adopt “n-1” in our routing approach.

Also, we have tried random phase for message classification. We can find that the buffer utilization is the worst case in our scenario. The reason is that the buffer size is limited. Classifying messages randomly will decrease the decode probability and increase the redundancy of messages.

Finally, we compared the sender-oriented and receiver-oriented method in our scenario. Sender-oriented method will ignore the situation of node with low activity ratio. Nodes with low activity ratio mean that they seldom meet the group members and they do not have enough packets to decode the packet in the buffer. Thus, if senders transmit the too much coded-packets to receivers, it will cause the overhead issue. In contrast, receiver-oriented method will prevent this situation happening by let forwarded messages with high probability for belonging to warm up phase.

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Chapter 6 Conclusion and Future Work

In this thesis, we proposed “A 2-phase Network Coding Design for Mobile Time- valued Group-message Dissemination” in opportunistic mobile social network. Warm Up phase can increase the decode probability in buffer; moreover, network coding technique phase can improve network performance. Simulation result shows that our proposed protocol is effective and superior to flooding based message broadcasting in performance of message delivery ratio, message delivery delay and message time value. Furthermore, we evaluated our dissemination approach with real trace data from campus instead of random trajectory. Therefore, the simulation results are closer to the real environment.

For the further research, the phase decision is worth to study. It is expected that more accurate phase decision leads to better buffer utilization. Maybe phase decision is not only related to receiver-oriented but social relation. Besides, there is a strong relationship between social relation and participants, and the relay node selection is important in real environment. Finally, maximizing the number of neighbors which can decode a missing packet is proven that this problem is NP complete. Therefore, if another novel, wise and non- greedy algorithm is used to address this problem, it can be expected that performance of network and efficiency of network coding will be improved obviously.

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