博碩士論文 106583602 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:13 、訪客IP:3.14.132.164
姓名 米爾雅(Mohd Yaseen Mir)  查詢紙本館藏   畢業系所 通訊工程學系在職專班
論文名稱 行動機會網路下訊息傳遞及中繼節點選擇機制之研究
(Message Forwarding and Relay Selection Schemes in Mobile Opportunistic Networks)
相關論文
★ 非結構同儕網路上以特徵相似度為基準之搜尋方法★ 以階層式叢集聲譽為基礎之行動同儕網路拓撲架構
★ 線上RSS新聞資料流中主題性事件監測機制之設計與實作★ 耐延遲網路下具密度感知的路由方法
★ 整合P2P與UPnP內容分享服務之家用多媒體閘道器:設計與實作★ 家庭網路下簡易無縫式串流影音播放服務之設計與實作
★ 耐延遲網路下訊息傳遞時間分析與高效能路由演算法設計★ BitTorrent P2P 檔案系統下載端網路資源之可調式配置方法與效能實測
★ 耐延遲網路中利用訊息編碼重組條件之資料傳播機制★ 耐延遲網路中基於人類移動模式之路由機制
★ 車載網路中以資料匯集技術改善傳輸效能之封包傳送機制★ 適用於交叉路口環境之車輛叢集方法
★ 車載網路下結合路側單元輔助之訊息廣播機制★ 耐延遲網路下以靜態中繼節點(暫存盒)最佳化訊息傳遞效能之研究
★ 耐延遲網路下以動態叢集感知建構之訊息傳遞機制★ 跨裝置影音匯流平台之設計與實作
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 移動機會網絡在具有無線連接和移動能力的網路設備中建立了一種新的數據傳播方法,其中,移動節點在短時通訊的機會接觸期間發現位置接近的鄰居並交換消息。此外,頻繁的鏈路故障以及缺乏最新的網絡拓撲信息,儲存-攜帶-轉送的數據傳遞模型被用來以延遲容忍的方式中繼消息,由於節點間通訊僅在未經排程的期間發生,在移動機會網絡中,數據傳遞效能將很大程度的受到節點移動的特性以及接觸的概率分佈影響。

在移動機會網絡中,基於複製的路由技術通常用於發送重複的訊息,以增加傳遞到目的地的機會,然而,這將造成巨大的傳輸、存儲與功耗成本,為此,相關的研究包括有效的中繼選擇、訊息轉發、訊息傳輸調度和緩衝區管理被用來降低重複複製訊息的相關成本。本論文的研究也將鎖定在移動機會網絡中的數據傳播方式,提出一種結合緩衝區管理和傳輸調度的方案以及兩種新穎的路由方法:(1) 在移動機會網絡中採用啟發式策略的基於訊息限額的路由和緩衝區管理;(2) 在移動機會網絡中利用移動聯繫模式進行訊息轉發; (3) 利用群體移動性在移動機會網絡中進行訊息傳播。

本論文的研究首先討論了關於節點群內選擇高效率中繼節點和訊息轉發的聯繫模式與其驅動知識的理解。在第二章中, 我們展示了中繼節點之間接觸頻率不同的發現,如一些節點可能永遠不會與另一個節點建立聯繫,而一些節點能夠聯繫多個節點,此外,節點具有聯繫週期性或重複模式,以及群體內的節點聯繫本質上是短暫的。

儘管之前的研究提出了各種緩衝區管理和訊息調度方案,但這些研究主要基於幾個前提,例如全球網絡知識的可用性、無限頻寬容量和同質聯繫模式,此外,先前的研究對結合緩衝區管理和中繼選擇的主題關注較少。在第三章中,我們提出了一種新方案,名為具有有限緩衝區管理與基於訊息限額的路由方法(QRBP),我們透過選擇一部分移動性較高的節點,委託這些節點攜帶一定配額的訊息副本,並將其傳播給移動性較低的節點。這些節點可以根據訊息限額值、剩餘生存時間與目的地聯繫率的多參數啟發式演算法來調整訊息調度和丟棄以提高資料成功傳遞率,實驗結果顯示,QRBP的方法比傳統的Epidemic, SprayAndWait, Temporal Closeness and Centrality-Based (TCCB) 路由方法效能更加,此外在緩衝區管理方面,我們的方法也比 DropOldest (DO), DropNewest (DN), DropRandom (DR), and Space-Time-Drop (ST-Drop)表現更好。

在第三章中,我們考慮了節點在較大區域快速移動的實際情況,這將導致節點在地圖上不同的移動模式,當中,我們的研究注意到只要有足夠數量的高移動性節點,縱使只複製任何特定限額的訊息仍然可以維持傳遞的效率。因此在第四章中,我們進一步釋放了這種受限的考量,透過多次複製訊息以增加到達目的地的機會,特別是需要檢查聯繫模式的內在屬性並確定兩種聯繫類型,即是在移動機會網絡中的定期聯繫和零星聯繫,其中定期聯繫為具有週期性的接觸,零星聯繫則為不具有週期性的接觸。第四章提出了一種有效的訊息轉發方法,稱為基於移動節點之間的聯繫模式的定期和零星聯繫路由 (RSCR),首先,我們根據移動機會網絡中任何節點對的接觸持續時間平均值推導出定期聯繫和零星聯繫模式,接著我們結合衍生的接觸模式與剩餘的訊息生存時間來準確確定兩個節點在移動過程中遇到時是否傳遞訊息。最後實驗結果顯示,我們提出的方法與Epidemic 和 PRoPHETv2 方案相比,以更低的成本獲得相同的訊息抵達率。

繼第三章與第四章中的有效緩衝區管理、傳輸調度和節點中繼選擇方案後,我們在第五章中研究節點組群內的聯繫模式。傳統方案通常分析節點之間的直接接觸與長時間觀察接觸資訊,並評估移動機會網絡中節點組群形成的靜態拓樸,然而,這樣的形成方法可能導致節點組群聯繫關係薄弱,並且隨機節點被錯誤地指定到群組中。相反地,我們的研究檢查了一些重要因素,例如特定時期的接觸一致性、接觸頻率和接觸持續時間,用以形成K個群組,並且著重在節點的接觸模式,我們利用接觸持續時間、接觸頻率和在特定時間區段內關於組內和組間接觸模式的聚合接觸屬性,定義了一個新的指標,時間聯繫強度。基於此,我們提出了一種新的路由方案,稱為基於時間聯繫強度的路由 (TS-GBR),用以提高移動機會網絡中訊息轉發的成本效益性能,並且我們透過幾個真實數據集的模擬證明了此方法與Epidemic、TCCB 、Transient Community-based (TC)、與 Dynamic Transient Social Community (DTSC)相比,獲得更佳的效能。

最後,本文的貢獻可以提高移動機會網絡中訊息傳遞和中繼選擇方案的效率,我們堅信這些努力可集成到當前和新興的無線通信系統中,應用在例如人聯網 (IoP)、裝置到裝置通信 (D2D)、社群物聯網 (SIoT)、無人機網路 (Flying Ad-hoc Networks)、邊緣計算 (Edge Computing)與車聯網 (Vehicular Ad-hoc Networks)。


摘要(英) The prevalence of networked devices capable of wireless connectivity and mobility propels a new data dissemination paradigm -- mobile opportunistic networking (MON). Wherein, mobile nodes discover neighbors in location proximity and exchange messages during their opportunistic contacts in short-time reciprocal communications. Because of frequent link failures and the lack of up-to-date network topology information in MONs, the extit{store-carry-and-forward} data delivery model is employed to relay messages in a delay-tolerant manner. Since inter-node communications occur only during unscheduled meetings between nodes, the performance of data dissemination in MONs is heavily influenced by the dynamic nature of node mobility and contact opportunity in mobile environments.

In MONs, replication-based routing techniques are often used to distribute duplicate messages to increase the chances of delivering messages to a destination, which unfortunately leads to enormous costs of transmission, storage, and energy resources. To reduce the costs associated with replicating messages repeatedly, several essential techniques are required certainly: efficient relay selection, message forwarding, transmission scheduling, and buffer management. The study in this dissertation proposes one joint buffer management and message scheduling scheme and two novel routing schemes for data dissemination in MONs: (1) Quota-Based Routing and Buffer Management with Heuristic Strategies in Opportunistic Ad Hoc Networks; (2) Exploiting Mobile Contact Patterns for Message Forwarding in Mobile Opportunistic Networks; (3) Exploiting Group Mobility for Message Dissemination in Mobile Opportunistic Networks.

The study in this dissertation first discusses the understanding of contact patterns and contact-driven knowledge regarding the novel schemes of efficient relay node selection and message forwarding inside node communities. In Chapter
ef{chap2}, we show the findings that contact frequency is heterogeneous between relay nodes, i.e., some nodes may never establish contacts with the others, whereas some nodes may contact more than one node. Thus, nodes have contact periodicity or repeating patterns, and nodal contacts in communities are transient in nature.

Although previous studies proposed various buffer management and scheduling schemes, their efforts were mainly based on several premises, e.g., the availability of global network knowledge, unlimited bandwidth capacity, and homogeneous contact patterns. Prior research has paid less attention to the theme of joint buffer management and relay selection. In Chapter
ef{chap3}, we propose a novel scheme, named Quota-Based Routing Scheme with Finite Buffer Management (QRBP). This scheme selects only a portion of nodes that have higher mobility, and then delegates those nodes to carry a certain quota of message replicas and disseminate those messages to nodes with lower mobility. Nodes can manipulate message scheduling and dropping to improve the successful delivery rate according to heuristic strategies based on several measures of the quota value, remaining time-to-live (TTL), and contact rate with the destination. Simulation results manifest that the QRBP scheme obtains better message delivery performance than Epidemic, SprayAndWait, and Temporal Closeness and Centrality-Based (TCCB) routing and DropOldest (DO), DropNewest (DN), DropRandom (DR), and Space-Time-Drop (ST-Drop) buffer management schemes.

In Chapter
ef{chap3}, we consider the practical situation that some nodes move quickly over a larger area, which leads to different movement patterns in a network map. Then, our study notices that replicating only a quota of any particular message can still sustain the efficiency of message delivery provided with only a sufficient number of high-mobility nodes. Our study in Chapter
ef{chap4} further releases this restricted consideration and appeals to a general case, which is to replicate messages multiple times to increase the chances of reaching a destination. In particular, this general study should examine the intrinsic properties of contact patterns and identify two contact types, i.e., regular and sporadic contacts in MONs. Contacts occurring periodically are defined as $regular$, and occasional contacts are termed as $sporadic$. Chapter
ef{chap4} presents an efficient message forwarding scheme, named Regular and Sporadic Contact-Based Routing (RSCR), which is based on contact patterns among mobile nodes. First, we derive the regular and sporadic contact patterns based on the mean of inter-contact durations with respect to any node pair in MONs. Then, we jointly use the derived contact patterns together with a remaining TTL value to accurately determine whether or not to hand over messages when two nodes encounter during movement. Simulation results show that the proposed scheme attains the same delivery rate at a lower cost as compared with Epidemic and PRoPHETv2 schemes.

Following the efforts of efficient buffer management, scheduling, and relay selection schemes in Chapters
ef{chap3} and
ef{chap4}, we aim to investigate contact patterns inside groups/communities in Chapter
ef{chap5}. Conventional schemes often analyzed direct contacts between nodes, observed contact formation for an extended period, and evaluated a static graph for community formation in MONs. However, such formation methods can result in communities with weak contact relationships and incorrectly appointing random nodes to a community. Instead, our study examines essential factors such as contact consistency in a certain period, contact frequency, and contact duration to form $k$ groups of which nodes render strong contact patterns. Further, we specify a new metric, temporal-tie strength, by utilizing contact duration time, contact frequency, and aggregate contact properties with respect to intra- and inter-group contact patterns during a specific time window. We propose a novel routing scheme, named Temporal-Tie-Strength Group-Based Routing (TS-GBR), which is able to improve the cost-effective performance of message forwarding in MONs. Our simulation with several real-life data sets demonstrates the efficiency of the proposed scheme as compared with Epidemic, TCCB, Transient Community-based (TC), and Dynamic Transient Social Community (DTSC) routing schemes.

Therefore, the contribution of this dissertation can promote the efficiency of message delivery and relay selection schemes in MONs. We believe that these efforts to MONs can be integrated to the current and new emerging wireless communication systems such as Internet of People (IoP), Device-to-Device communication (D2D), Social Internet of Things (SIoT), unmanned aerial vehicles (UAVs), Edge computing, and Vehicle-to-Everything (V2X) in the coming future.


關鍵字(中) ★ 延遲/中斷容忍網絡
★ 移動機會網絡
★ 移動社交網絡
★ 移動 Ad hoc 網絡
關鍵字(英) ★ Delay/Disruptive Tolerant Network
★ Mobile Opportunistic Network
★ Mobile Social Networks
★ Mobile Ad hoc Networks
論文目次 Abstract (Chinese)
Abstract (English)
Acknowledgement
List of Figures
List of Tables
List of Symbols and Abbreviations
1 Introduction
1.1 Motivation
1.2 Overview of the Dissertation
1.2.1 Quota-Based Routing and Buffer Management with Heuristic Strategies in Opportunistic Ad Hoc Networks
1.2.2 Exploiting Mobile Contact Patterns for Message Forwarding in Mobile Opportunistic Networks
1.2.3 Exploiting Group Mobility for Message Dissemination in Mobile Opportunistic Networks
1.3 Dissertation Structure
2 Related Works
2.1 Message Scheduling and Dropping Schemes
2.1.1 Heuristic-based Scheme
2.1.2 Optimal-based Scheme

2.2 Contact Pattern based Routing
2.3 Group-based Routing in MONs
3 Quota-Based Routing and Buffer Management with Heuristic Strategies in Opportunistic Ad Hoc Networks
3.1 Introduction
3.2 Problem Formulation and Utility Function
3.2.1 System Environment and Assumptions
3.2.2 Message Utility Calculation
3.2.3 Problem Formulation
3.3 Quota-Based Routing and Buffer Management scheme
3.3.1 Properties
3.3.2 QRBP’s Node Type Definitions
3.3.3 QRBP’s Quota Notion and Relay Node
3.3.4 QRBP’s Message States
3.3.5 QRBP’s Message Routing Procedure
3.3.6 QRBP’s Priority-Based Message Scheduling and Dropping
3.4 Performance Results

3.4.1 Simulation Settings
3.4.2 Sensitivity to QRBP’s Factors of Q and µ0
3.4.3 Relative Performance among QRBP, SprayAndWaitB, SprayAndWait, Epidemic, and TCCB
3.4.4 Relative Performance by QRBP with Different Buffer Management schemes
3.5 Summary

4 Exploiting Mobile Contact Patterns for Message Forwarding in Mobile Opportunistic Networks
4.1 Introduction
4.2 Regular and Sporadic Contact-Based Routing
4.2.1 System Model and Notation
4.2.2 Inter-Contact Time Period for Regular Links
4.2.3 Regular and Sporadic Links in Markov Model
4.2.4 RSCR: Scheme Design
4.3 Performance Results

4.3.1 Simulation Settings
4.3.2 Sensitivity to Values of α and T
4.3.3 Sensitivity to the Value of TTL Duration
4.4 Summary
5 Exploiting Group Mobility for Message Dissemination in Mobile Opportunistic Networks
5.1 Introduction
5.2 Temporal-Tie-Strength Group-Based Routing
5.2.1 Network Model
5.2.2 Group Formation Process
5.2.3 Path Formation and Probability Calculation
5.2.4 TS-GBR: Scheme Design
5.3 Performance Results
5.3.1 Simulation Settings
5.3.2 Sensitivity to the parameters in TC, TCCB, and DTSC
5.3.3 Sensitivity to TS-GBR’s Factors of γ, δ, K, β, α, and Ti
5.3.4 Impact of Time-to-Live (TTL) value
5.3.5 Impact of Buffer size and TTL value
5.3.6 Impact of Message Traffic
5.3.7 Impact of Node Density
5.3.8 Discussion and Future Works
5.4 Summary

6 Conclusion and Future Prospects
6.1 Summary of the dissertation
6.2 Future Research Work
6.3 Other Research Directions

A The ONE Simulator
B Time-Variant Community Mobility Model (TVCM)
C Self-Similar Least Action Walk (SLAW)
C.1 Parameters for generating SLAW trace
D Real Traces
D.1 Infocom’06 and Infocom’05
D.2 NCCU trace

Bibliography
參考文獻 [1] P. Sommer, B. Kusy, P. Valencia, R. Dungavell, and R. Jurdak, “Delay-tolerant networking for long-term animal tracking,” IEEE Internet Computing, vol. 22, no. 1, pp. 62–72, 2018.
[2] B. E. Pataki and L. Kovacs, “Sensor data collection experiments with chaoster in the fed4fire federated testbeds,” ser. Proceedings of 2014 10th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Larnaca, Cyprus, 2014, pp. 306–313.
[3] A. Boukerche, B. Turgut, N. Aydin, M. Z. Ahmad, L. Boloni, and D. Turgut, “Routing protocols in ad hoc networks: A survey,” Computer Networks, vol. 55, no. 13, pp. 3032–3080, 2011.
[4] H. Gao, C. Liu, Y. Li, and X. Yang, “V2vr: Reliable hybrid-network-oriented v2v data transmission and routing considering rsus and connectivity probability,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3533–3546, 2021.
[5] X. Wang, Y. Weng, and H. Gao, “A low-latency and energy-efficient multimetric routing protocol based on network connectivity in vanet communication,” IEEE Transactions on Green Communications and Networking, vol. 5, no. 4, pp. 1761–1776, 2021.
[6] G. Yang, R. Wang, K. Zhao, X. Zhang, W. Li, and X. He, “Queueing analysis of dtn protocols in deep-space communications,” IEEE Aerospace and Electronic Systems Magazine, vol. 33, no. 12, pp. 40–48, 2018.
[7] S. Trifunovic, S. T. Kouyoumdjieva, B. Distl, L. Pajevic, G. Karlsson, and B. Plattner, “A decade of research in opportunistic networks: challenges, relevance, and future directions,” IEEE Communication Magazine, vol. 55, no. 1, pp. 168–173, 2017.
[8] D. Xu, Y. Li, X. Chen, J. Li, P. Hui, S. Chen, and J. Crowcroft, “A survey of opportunistic offloading,” IEEE Communications Surveys and Tutorials, vol. 20, no. 3, pp. 2198–2236, 2018.
[9] S. T. Kouyoumdjieva and G. Karlsson, “From opportunistic networks to 3gpp network-independent deviceto-device communication,” vol. 20, no. 2, 2016.
[10] D. Xu, Y. Li, X. Chen, J. Li, P. Hui, S. Chen, and J. Crowcroft, “A survey of opportunistic offloading,” IEEE Communications Surveys and Tutorials, vol. 20, no. 3, pp. 2198–2236, 2018.
[11] S. CC, V. Raychoudhury, G. Marfia, and A. Singla, “A survey of routing and data dissemination in delay tolerant networks,” Journal of Network and Computer Applications, vol. 67, pp. 128–146, 2016.
[12] Y. Cao, K. Wei, G. Min, J. Weng, X. Yang, and Z. Sun, “A geographic multicopy routing scheme for dtns with heterogeneous mobility,” IEEE Systems Journal, vol. 12, no. 1, pp. 790–801, 2018.
[13] Y. Yang, C. Zhao, S. Yao, W. Zhang, X. Ge, and G. Mao, “Delay performance of network-coding-based epidemic routing,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 3676–3684, 2016.
[14] T. Abdelkader, K. Naik, A. Nayak, N. Goel, and V. Srivastava, “A performance comparison of delay-tolerant network routing protocols,” IEEE Network, vol. 30, no. 2, pp. 46–53, 2016.
[15] A. Krifa, C. Barakat, and T. Spyropoulos, “Optimal buffer management policies for delay-tolerant networks,” Proceedings of 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), San Francisco, CA, USA, 2008, pp. 260–268.
[16] Q. Li, S. Zhu, and G. Cao, “Routing in socially selfish delay-tolerant networks,” ser. Proceedings of 2010 IEEE International Conference on Computer Communications (INFOCOM), San Diego, CA, USA, 2010, pp. 1–9.
[17] A. Krifa, C. Barakat, and T. Spyropoulos, “Message drop and scheduling in dtns: Theory and practice,” IEEE Transactions on Mobile Computing, vol. 11, no. 9, pp. 1470–1483, 2012.
[18] T. Le, H. Kalantarian, and M. Gerla, “A joint relay selection and buffer management scheme for delivery rate optimization in dtns,” ser. Proceedings of 2016 IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Coimbra, Portugal, 2016, pp. 1–9.
[19] Y.-F. Hsu and C.-L. Hu, “Enhanced buffer management for data delivery to multiple destinations in dtns,” IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8735–8739, 2016.
[20] A. Elwhishi, P.-H. Ho, K. Naik, and B. Shihada, “A novel message scheduling framework for delay-tolerant networks routing,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 5, pp. 871–880, 2013.
[21] C.-H. Lee and D. Y. Eun, “On the forwarding performance under heterogeneous contact dynamics in mobile opportunistic networks,” IEEE Transactions on Mobile Computing, vol. 12, no. 6, pp. 1107–1119, 2013.
[22] E. Wang, Y. Yang, and J. Wu, “A knapsack-based buffer management strategy for delay-tolerant networks,” Journal of Parallel and Distributed Computing, vol. 86, pp. 1–15, 2015.
[23] P. Matzakos, T. Spyropoulos, and C. Bonnet, “Buffer management policies for dtn applications with different qos requirements,” ser. Proceedings of 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA, 2015, pp. 1–7.
[24] T. Le, H. Kalantarian, and M. Gerla, “A dtn routing and buffer management strategy for message delivery delay optimization,” ser. Proceedings of 2015 8th IFIP Wireless and Mobile Networking Conference (WMNC), Munich, Germany, 2015, pp. 32–39.
[25] A. Vahdat and D. Becker, “Epidemic routing for partially-connected ad hoc networks,” Duke University, Durham, NC, USA, Tech. Rep., 2000.
[26] T. Spyropoulos, K. Psounis, and C. S. Raghavendra, “Spray and wait: An efficient routing scheme for intermittently connected mobile networks,” ser. Proceedings of ACM SIGCOMM workshop on Delay-tolerant networking (WDTN), Philadelphia, Pennsylvania, USA, 2005, pp. 252–259.
[27] Y. Cao, Z. Sun, N. Wang, F. Yao, and H. Cruickshank, “Converge-and-diverge: A geographic routing for delay/disruption-tolerant networks using a delegation replication approach,” IEEE Transactions on Vehicular Technology, vol. 62, no. 5, pp. 2339–2343, 2013.
[28] K. Wei, D. Zeng, S. Guo, and K. Xu, “On social delay-tolerant networking: Aggregation, tie detection, and routing,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 6, pp. 1563–1573, 2014.
[29] H. Zhou, V. C. M. Leung, C. Zhu, S. Xu, and J. Fan, “Predicting temporal social contact patterns for data forwarding in opportunistic mobile networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 11, pp. 10 372–10 383, 2017.
[30] Y. Liu, H. Wu, Y. Xia, Y. Wang, F. Li, and P. Yang, “Optimal online data dissemination for resource constrained mobile opportunistic networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 6, pp. 5301–5315, 2017.
[31] W. Wang, Y. Bai, P. Feng, Y. Gu, S. Liu, W. Jiang, and J. Huang, “Dtn-knca: A high throughput routing based on contact pattern detection in dtns,” in Proceedings of IEEE COMPSAC’18, vol. 01, Tokyo, Japan, July 2018, pp. 926–931.
[32] Y.-F. Hsu, C. Hu, and H. Hsiao, “On exploiting temporal periodicity for message delivery in mobile opportunistic networks,” in Proceedings of IEEE COMPSAC’18, vol. 01, Tokyo, Japan, July 2018, pp. 809–810.
[33] F. Li, H. Jiang, H. Li, Y. Cheng, and Y. Wang, “Sebar: Social-energy-based routing for mobile social delaytolerant networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 8, pp. 7195–7206, 2017.
[34] X. Bi, T. Qiu, W. Qu, L. Zhao, X. Zhou, and D. O. Wu, “Dynamically transient social community detection for mobile social networks,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1282–1293, 2021.
[35] W. Gao, G. Cao, T. L. Porta, and J. Han, “On exploiting transient social contact patterns for data forwarding in delay-tolerant networks,” IEEE Transactions on Mobile Computing, vol. 12, no. 1, pp. 151–165, 2013.
[36] J. Tao, H. Wu, S. Shi, J. Hu, and Y. Gao, “Contacts-aware opportunistic forwarding in mobile social networks: A community perspective,” in IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018, pp. 1–6.
[37] Z. Li, C. Wang, L. Shao, C. Jiang, and C. Wang, “Exploiting traveling information for data forwarding in community-characterized vehicular networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 7, pp. 6324–6335, 2017.
[38] M. N. Soorki, W. Saad, M. H. Manshaei, and H. Saidi, “Social community-aware content placement in wireless device-to-device communication networks,” IEEE Transactions on Mobile Computing, vol. 18, no. 8, pp. 1938–1950, 2019.
[39] I. O. Nunes, P. O. S. V. de Melo, and A. A. F. Loureiro, “Group mobility: Detection, tracking and characterization,” in IEEE International Conference on Communications (ICC’16), Kuala Lumpur, Malaysia, 2016, pp. 1–6.
[40] X. Zhang and G. Cao, “Transient community detection and its application to data forwarding in delay tolerant networks,” IEEE/ACM Transactions on Networking, vol. 25, no. 5, pp. 2829–2843, 2017.
[41] M. J. Williams, R. M. Whitaker, and S. M. Allen, “There and back again: Detecting regularity in human encounter communities,” IEEE Transactions on Mobile Computing, vol. 16, no. 6, pp. 1744–1757, 2017.
[42] H. P. Bahman Ravaei, Masoud Sabaei and S. Valaee, “Community-aware single-copy content forwarding in mobile social network,” Wireless Networks, vol. 24, no. 7, pp. 2705–2721, 2018. 131
[43] S. H. Kyunghan Lee, S. J. Kim, I. Rhee, and S. Chong, “Slaw: A new mobility model for human walks,” in Proceedings of 2009 IEEE International Conference on Computer Communications (INFOCOM), Rio de Janeiro, Brazil, 2009, pp. 855–863.
[44] J. Scott, R. Gass, J. Crowcroft, P. Hui, C. Diot, and A. Chaintreau, “CRAWDAD dataset cambridge/haggle (v. 2009-05-29),” May 2009, https://crawdad.org/cambridge/haggle/20090529.
[45] S. Samo Grasic, E. Davies, A. Lindgren, and A. Doria, “The evolution of a dtn routing protocol - prophetv2,” in Proceedings of ACM CHANTS’11, 2011, pp. 27–30.
[46] G. Goudar and S. Batabyal, “Optimizing bulk transfer size and scheduling for efficient buffer management in mobile opportunistic networks,” IEEE Transactions on Mobile Computing, pp. 1–1, 2021.
[47] J. Burgess, B. Gallagher, D. Jensen, and B. N. Levine, “Maxprop: Routing for vehicle-based disruption tolerant networks,” ser. Proceedings of 2006 IEEE International Conference on Computer Communications (INFOCOM), Barcelona, Spain, 2006, pp. 1–11.
[48] Y. Liu, J. Wang, S. Zhang, and H. Zhou, “A buffer management scheme based on message transmission status in delay-tolerant networks,” ser. Proceedings of 2011 IEEE Global Telecommunications Conference (GLOBECOM), Houston, TX, USA, 2011, pp. 1–5.
[49] K. Shin and S. Kim, “Enhanced buffer management policy that utilises message properties for delay-tolerant networks,” IET Communications, vol. 5, no. 6, pp. 753–759, 2011.
[50] D. Wu, J. Zhou, P. Zhang, and R. Wang, “Intelligent dynamical buffer scheduling mechanism for intermittently connected mobile network,” Wireless Personal Communications, vol. 73, no. 3, pp. 1269–1288, 2013.
[51] K. Wei, S. Guo, D. Zeng, and K. Xu, “A multi-attribute decision making approach to congestion control in delay-tolerant networks,” ser. Proceedings of 2014 IEEE International Conference on Communications (ICC), Sydney, NSW, Australia, 2014, pp. 2742–2747.
[52] A. Balasubramanian, B. Levine, and A. Venkataramani, “Dtn routing as a resource allocation problem,”. Proceedings of 2007 ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOM), Kyoto, Japan, 2007, pp. 373–384.
[53] P. Matzakos, T. Spyropoulos, and C. Bonnet, “Joint scheduling and buffer management policies for dtn applications of different traffic classes,” IEEE Transactions on Mobile Computing, vol. 17, no. 12, pp. 2818–2834, 2018.
[54] S. Rashid, Q. Ayub, M. S. Zahid, and A. H. Abdullah, “Message drop control buffer management policy for dtn routing protocols,” Wireless Personal Communication, vol. 72, no. 1, p. 653–669, 2013.
[55] P. Sermpezis and T. Spyropoulos, “Delay analysis of epidemic schemes in sparse and dense heterogeneous contact networks,” IEEE Transactions on Mobile Computing, vol. 16, no. 9, pp. 2464–2477, 2017.
[56] K. Sakai, M. Sun, W. Ku, J. Wu, and F. S. Alanazi, “Performance and security analyses of onion-based anonymous routing for delay-tolerant networks,” IEEE Transactions on Mobile Computing, vol. 16, no. 12, pp. 3473–3487, 2017.
[57] S. Moon and A. Helmy, “Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis,” in Proceedings of IEEE GLOBECOM’10, Miami, Fl, USA, Dec. 2010, pp. 1–5.
[58] W.-J. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy, “Modeling time-variant user mobility in wireless mobile networks,” in Proceedings of IEEE INFOCOM’07, Barcelona, Spain, May 2007, pp. 758–766.
[59] W. Gao, Q. Li, B. Zhao, and G. Cao, “Multicasting in delay tolerant networks: A social network perspective,” in Proceedings of ACM MobiHoc’09, 2009, pp. 299–308.
[60] E. Bulut, S. C. Geyik, and B. K. Szymanski, “Efficient routing in delay tolerant networks with correlated node mobility,” in Proceedings of IEEE MASS’10, San Francisco, CA, USA, Nov. 2010, pp. 79–88.
[61] P. Hui, E. Yoneki, S. Y. Chan, and J. Crowcroft, “Distributed community detection in delay tolerant networks,” in Proceedings of 2nd ACM/IEEE International Workshop on Mobility in the Evolving Internet Architecture (MobiArch’07), Kyoto, Japan, August 2007, pp. 1–8.
[62] D. Pisinger and P. Toth, Knapsack problems. Boston, MA: Springer, 1998, iSBN 978-1-4419-7996-4.
[63] E. Wang, Y.-J. Yang, J. Wu, and W.-B. Liu, “A buffer scheduling method based on message priority in delay-tolerant networks,” Journal of Computer Science and Technology, vol. 31, no. 6, pp. 1228–1245, 2016.
[64] A. Picu and T. Spyropoulos, “Dtn-meteo: Forecasting the performance of dtn protocols under heterogeneous mobility,” IEEE/ACM Transactions on Networking, vol. 23, no. 2, pp. 587–602, 2015.
[65] S. Batabyal and P. Bhaumik, “Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey,” IEEE Communications Surveys Tutorials, vol. 17, no. 3, pp. 1679–1707, 2015.
[66] H. Zhou, J. Chen, H. Zhao, W. Gao, and P. Cheng, “On exploiting contact patterns for data forwarding in duty-cycle opportunistic mobile networks,” IEEE Transactions on Vehicular Technology, vol. 62, no. 9, pp. 4629–4642, 2013.
[67] A. Ker¨anen, J. Ott, and T. K¨arkk¨ainen, “The one simulator for dtn protocol evaluation,” in Proceedings of the 2nd International Conference on Simulation Tools and Techniques (SIMUTools), Rome, Italy, 2009, pp. 55:1–55:10.
[68] M. D. Silva, I. O. Nunes, R. A. Mini, and A. A. F. Loureiro, “St-drop: A novel buffer management strategy for d2d opportunistic networks,” in Proceedings of 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece, 2017, pp. 1300–1305.
[69] J. Scott, R. Gass, J. Crowcroft, P. Hui, C. Diot, and A. Chaintreau, “CRAWDAD dataset cambridge/haggle (v. 2006-09-15),” Sep 2006, https://crawdad.org/cambridge/haggle/20060915.
[70] T. Spyropoulos, T. Turletti, and K. Obraczka, “Routing in delay-tolerant networks comprising heterogeneous
node populations,” IEEE Transactions on Mobile Computing, vol. 8, no. 8, pp. 1132–1147, 2009.
[71] W.-J. Hsu, T. Spyropoulos, K. Psounis, and A. Helmy, “Modeling spatial and temporal dependencies of user
mobility in wireless mobile networks, rome, italy,” IEEE/ACM Transactions on Networking, vol. 17, no. 5, pp. 1564–1577, 2009.
[72] T.-C. Tsai and H.-H. Chan, “Nccu trace: Social-network-aware mobility trace,” IEEE Communications Magazine, vol. 53, no. 10, pp. 144–149, 2015.
[73] J. Sun, W. Zheng, Q. Zhang, and Z. Xu, “Graph neural network encoding for community detection in attribute networks,” 2020.

[74] N. Eagle and A. S. Pentland, “CRAWDAD dataset mit/reality (v. 2005-07-01),” Downloaded from https://crawdad.org/mit/reality/20050701, jul 2005.
[75] M. Piorkowski, N. Sarafijanovic-Djukic, and M. Grossglauser, “CRAWDAD dataset epfl/mobility (v. 2009-
02-24),” Downloaded from https://crawdad.org/epfl/mobility/20090224/cab, feb 2009, traceset: cab.
[76] Q. Hao, M. Sheng, D. Zhou, and Y. Shi, “A multi-aspect expanded hypergraph enabled cross-domain resource
management in satellite networks,” IEEE Transactions on Communications, vol. 70, no. 7, pp. 4687–4701, 2022.
[77] J. Zhang, T. Chen, S. Zhong, J. Wang, W. Zhang, X. Zuo, R. G. Maunder, and L. Hanzo, “Aeronautical ad hoc networking for the internet-above-the-clouds,” Proceedings of the IEEE, vol. 107, no. 5, pp. 868–911, 2019.
[78] L. Pelusi, A. Passarella, and M. Conti, “Opportunistic networking: Data forwarding in disconnected mobile ad hoc networks,” Comm. Mag., vol. 44, no. 11, p. 134–141, nov 2006.
[79] R. Wang, A. Sabbagh, S. C. Burleigh, K. Zhao, and Y. Qian, “Proactive retransmission in delay-/disruption tolerant
networking for reliable deep-space vehicle communications,” IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9983–9994, 2018.
[80] P. Hui, A. Chaintreau, J. Scott, R. Gass, J. Crowcroft, and C. Diot, “Pocket switched networks and human mobility in conference environments,” in Proceedings of the 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking, 2005, p. 244–251.
[81] L. Jiang, L. Shi, L. Liu, J. Yao, B. Yuan, and Y. Zheng, “An efficient evolutionary user interest community
discovery model in dynamic social networks for internet of people,” IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9226–9236, 2019.
[82] A. Khelloufi, H. Ning, S. Dhelim, T. Qiu, J. Ma, R. Huang, and L. Atzori, “A social-relationships-based service recommendation system for siot devices,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1859–1870, 2021.
[83] C.-L. Hu, S.-Z. Huang, Z. Zhang, and L. Hui, “Energy-balanced optimization on flying ferry placement for data gathering in wireless sensor networks,” IEEE Access, vol. 9, pp. 70 906–70 923, 2021.
[84] 3GPP, “Feasibility study for proximity services (prose),” 3rd Generation Partnership Project (3GPP), Tech. Rep., TR 22.803, 2013.
[85] M. Usman, A. A. Gebremariam, U. Raza, and F. Granelli, “A software-defined device-to-device communication
architecture for public safety applications in 5g networks,” IEEE Access, vol. 3, pp. 1649–1654, 2015.
[86] L. Gallo and J. Haerri, “Unsupervised long- term evolution device-to-device: A case study for safety-critical v2x communications,” IEEE Vehicular Technology Magazine, vol. 12, no. 2, pp. 69–77, 2017.
[87] M. H¨oyhty¨a, J. Huusko, M. Kiviranta, K. Solberg, and J. Rokka, “Connectivity for autonomous ships: Architecture,
use cases, and research challenges,” in Proceedings of 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea (South), 2017, pp. 345–350.
[88] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.
[89] M. Amadeo, C. Campolo, A. Iera, and A. Molinaro, “Information centric networking in iot scenarios: The
case of a smart home,” in Proceedings of 2015 IEEE International Conference on Communications (ICC), London, UK, 2015, pp. 648–653.
[90] E. Borgia, R. Bruno, and A. Passarella, “Making opportunistic networks in iot environments ccn-ready: A performance evaluation of the mobccn protocol,” Computer Communications, vol. 123, pp. 81–96, 2018.
[91] A. Khelloufi, H. Ning, S. Dhelim, T. Qiu, J. Ma, R. Huang, and L. Atzori, “A social-relationships-based service recommendation system for siot devices,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1859–1870, 2021.
[92] C. Han, H. Yao, T. Mai, N. Zhang, and M. Guizani, “Qmix aided routing in social-based delay-tolerant networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1952–1963, 2022.
[93] M. Liu, J. Li, and H. Lu, “Routing in small satellite networks: A gnn-based learning approach,” 2021.
[94] J. Zhang, D. Liu, S. Chen, S. X. Ng, R. G. Maunder, and L. Hanzo, “Multiple-objective packet routing optimization for aeronautical ad-hoc networks,” 2022.
指導教授 胡誌麟(Chih-Lin Hu) 審核日期 2023-1-3
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明