政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118573

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/118573

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政大典藏 > College of Informatics > Taiwan International Graduate Program > Theses > Item 140.119/118573

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/118573

Title:	行動社群網路之內容分享技術 Content sharing in mobile social networks
Authors:	奧菲利 Awuor, Fredrick MZee
Contributors:	王志宇蔡子傑 Wang, Chih-Yu Tsai, Tzu-Chieh 奧菲利 Fredrick MZee Awuor
Keywords:	內容分享 Content prcicing Content trustworthiness Congestion control Concurrent access
Date:	2018
Issue Date:	2018-07-10 16:00:09 (UTC+8)
Abstract:	作为智能型移动设备和电话变得更加普遍存在和普遍存在各种各样的传感器和通信技术，我们可以开发的移动社交网络(MSN)应用程序，启用这些设备将自动创造的虚拟社区中的内容可以是共有的含蓄的。这种方式，MSN使用户有类似兴趣的发现并与每一其他使用智能手机和分享内容和联系，在一个即兴的方式，因为他们移动。例如，你的智能手机可以帮助你有效地收到与其他MSN用户通知你关于他们的利益和有价值的内容，他们可以与你分享的。 MSN能够随时随地homophilic社会互动，特点是投机性的、自发的，而短暂的。然而，需要有一种激励用户之间的合作和激励内容共享，并且一个机制，以评估的可信赖性的内容，正在分享在MSN网络。此外，一个内容提供者的高度排序的内容，例如在展览会或会议，很可能会收到多个同时请求其他用户和因此可能需要一个机制来控制的碰撞和支持多的访问。为此目的，该论文提出了一个框架，内容共享MSN对解决这些挑战。第一，这篇论文来解决问题的激励内容共享MSN。多媒体使用户能够发现和分享内容，尤其是在短暂的活动，如展览和会议。尽管如此，鼓励用户的积极分享它们的内容在多媒体可能缺乏，如果相应的成本是高的。因此，在增加内容共享率，这篇论文提出的内容的定价和分享框架，该框架是建立在用户的集体投标和内容的成本共享，促使用户分享他们的内容与他们的共同定位的遭遇使用一个单一的拍卖。内容共享问题，制定作为一个分布式系统实现合作的结果，同时保持不合作的决定，使用户之间的通过拟议的集体投标和广播的性质无线通信。就是说，共同事单独提出支付给他们的遭遇，其内容感兴趣他们是根据他们的感知价值的内容。相关的内容拥有者分享他们的内容，如果拟议的付款可以集体补偿成本的共享它们的内容与这些同龄人。我们表明，这保证了个人的合理性，并促进内容之间分享的机会遇到在网络。绩效评估显示，拟议的机制减少了时间和成本以收集的内容感兴趣的网络中的80％和40％，并提高了网络的利用率为50%。接下来，这个论文的地址问题的内容可信性在MSN。用户在MSN大都是陌生人谁愿意去发现的同行具有类似兴趣在他们的遭遇并分享的内容和接触，在一个即兴的方式，因为他们移动。然而，这是在风险MSN用户，因为他们可能不会有知识的用户，他们是社会上的连接。因此，在使临时社交网络在MSN，有必要建立一个机制，以评估信任的未知的用户及其内容，并减少审查攻击此类西比尔和拒绝攻击。因此，这篇论文提出了一种分布式内容相信评价框架的基础上加密散列链内容的审查，检测和弹性以西比尔和拒绝攻击的内容的评论。作为审查是哈希链，它们不可删除和阻要的修改，因为修改一次审查中作用的变化的审查记录的散列值和散列值的所有随后的审查记录的审查。这使得这样的审查记录无效，因为他们签署的散列值就不符合他们各自的公用钥匙。而且，在提议的机制，用户分享他们的审查历史上与他们同在的机会遇到这些同龄人使用建立信誉的审查链共享他们的未来遭遇。结果显示，拟议的机制有效地评估内容的可信赖性的检测和鉴别审查链的声誉受到损害，由于西比尔或拒绝攻击。最后，该问题的同时访问是到处理。当多个同时进行的内容的请求内容的主机，我们可能遇到的请求碰撞将导致访问的延误和浪费的带宽因重发。为了解决这一问题，我们提出了一个分布式聚类和排队机制位于同的用户(例如，在一个展览)人感兴趣的内容由他们的遭遇(例如展览会的立场)自组织自己进入一个当地集群和建立一个合乎逻辑的队列。然后，用户提出请求的内容提供商在轮流的基础上他们的位置在队列中。作为一个群集的成员被选为领袖和分配的责任的建筑队列中，我们获得的用户的投票权的策略和分析的系统理论性能。由于这个问题也是经验丰富的中随机访问通道(迪)，我们使用RACH作为一个例子，评价建议的机制在这样一个系统。模拟结果显示，拟议的机制，减少碰撞的概率和访问的延迟的65%和19%。 As smart mobile devices and phones become more ubiquitous and pervasive with wide array of sensors and communication techniques, we can develop mobile social network (MSN) apps that enable these devices to automatically create virtual communities where contents can be shared implicitly. This way, MSN enable users with similar interests to discover and connect with each other using smart phones and to share contents and contacts in an impromptu way as they move. For instance, your smartphone could assist you have a productive encounter with other MSN users by informing you about their interests and valuable contents that they may share with you. MSN enables anytime-anywhere homophilic social interactions that are characteristically opportunistic, spontaneous, and ephemeral. However, there is need to motivate cooperation among users and incentivize content sharing, and a mechanism to evaluate trustworthiness of contents being share in the MSN network. In addition, a content provider of highly sort content, for instance at exhibition or conference, is likely to receive multiple simultaneous requests from other users and thus may need a mechanism to control collision and to support multiple access. To this end, this dissertation proposes a framework for content sharing in MSN towards addressing these challenges. First, this dissertation addresses the problem of motivating content sharing in MSN. MSNs enable users to discover and share contents with each other, especially at ephemeral events such as exhibitions and conferences. Nevertheless, the incentive of users to actively share their contents in MSNs may be lacking if the corresponding cost is high. Thus, in increasing content sharing rate, this dissertation proposes a content pricing and sharing framework that is built on users` collective bidding and content cost sharing that motivates users to share their contents with their co-located encounters using a single auction. The content sharing problem is formulated as a distributed system that achieves cooperative outcome while preserving non-cooperative decision making among the users through the proposed collective bidding and broadcast nature of wireless communication. That is, co-located peers individually propose payments to their encounters whose contents they are interested in based on their perceived values of the contents. The respective content owners share their contents if the proposed payments can collectively compensate the cost of sharing their contents with these peers. We show that this guarantees individual rationality and promotes content sharing among the opportunistic encounters in the network. Performance evaluation shows that the proposed mechanism reduces the time and cost to collect contents of interest in the network by 80% and 40% respectively and improves network utilization by 50%. Next, this dissertation addresses the problem of content trustworthiness in MSN. Users in MSN are mostly strangers who wish to discover peers with similar interests among their encounters and to share contents and contacts in an impromptu way as they move. However, this is at a risk for the MSN users since they may not have knowledge about the users they are socially connecting with. Therefore, in enabling impromptu social networking in MSN, there is need for a mechanism to evaluate trust of unknown users and their contents, and to mitigate review attacks such sybil and rejection attacks. Thus, this dissertation proposes a distributed content trust evaluation framework based on cryptographic hash-chained content review that detects and is resilient to sybil and rejection attacks on content reviews. As the reviews are hash-chained, they are undeletable and resistive to modifications since modifying a review in effect changes the review record`s hash value and the hash values of all the subsequent review records in the review-chain. This renders such review records invalid as their signed hash values would not match their respective public keys. Moreover, in the proposed mechanism, users share their review history with their peers during the opportunistic encounters which these peers use to establish the reputation of review-chains shared by their future encounters. The results show that the proposed mechanism efficiently evaluates content`s trustworthiness by detecting and discriminating review-chains whose reputation are compromised due to sybil or rejection attacks. Lastly, the problem of concurrent access is addressed. When multiple simultaneous content requests are made to the content host, we are likely to experience request collision that would lead to access delays and wastage of bandwidth due to retransmissions. To address this issue, we propose a distributed clustering and queuing mechanism where co-located users (for instance, at an exhibition) who are interested in contents hosted by their encounter (e.g., exhibition stand) self-organize themselves into a local cluster and build a logical queue. Users then submit their requests to the content provider in turns based on their positions in the queue. As one of the cluster members is voted as the leader and assigned the responsibility of building the queue, we derive the users voting strategy and analyze the systems theoretical performance. Since this problem is also experienced in random access channel (RACH), we use RACH as an example and evaluate the performance of the proposed mechanism in such a system. Simulation results show that the proposed mechanism reduces collision probability and access delays by 65% and 19% respectively.
Reference:	[1] Nikolaos Vastardis and Kun Yang. Mobile social networks: Architectures, social properties, and key research challenges. IEEE Communications Surveys & Tutorials, 15(3):1355–1371, 2013. [2] Yufeng Wang, Li Wei, Athanasios V Vasilakos, and Qun Jin. Device-to-device based mobile social networking in proximity (msnp) on smartphones: Framework, challenges and prototype. Future Generation Computer Systems, 74:241–253, 2017.   [3] Sancheng Peng, Aimin Yang, Lihong Cao, Shui Yu, and Dongqing Xie. Social influence modeling using information theory in mobile social networks. Information Sciences, 379:146–159, 2017.   [4] Yufeng Wang, Athanasios V Vasilakos, Qun Jin, and Jianhua Ma. Survey on mobile social networking in proximity (msnp): approaches, challenges and architecture. Wireless networks, 20(6):1295–1311, 2014. [5] NipendraKayastha,DusitNiyato,PingWang,andEkramHossain.Applications,archi- tectures, and protocol design issues for mobile social networks: A survey. Proceedings of the IEEE, 99(12):2130–2158, 2011.   [6] Zhifei Mao, Yuming Jiang, Geyong Min, Supeng Leng, Xiaolong Jin, and Kun Yang. Mobile social networks: Design requirements, architecture, and state-of-the-art technology. Computer Communications, 100:1–19, 2017.   [7] Xiping Hu, Terry HS Chu, Victor CM Leung, Edith C-H Ngai, Philippe Kruchten, and Henry CB Chan. A survey on mobile social networks: Applications, platforms, system architectures, and future research directions. IEEE Communications Surveys & Tutorials, 17(3):1557–1581, 2015.   [8] Aaron Beach, Mike Gartrell, Sirisha Akkala, Jack Elston, John Kelley, Keisuke Nishimoto, Baishakhi Ray, Sergei Razgulin, Karthik Sundaresan, Bonnie Surendar, et al. Whozthat? evolving an ecosystem for context-aware mobile social networks. IEEE network, 22(4):50–55, 2008.   [9] Martin Atzmueller, Mark Kibanov, Christoph Scholz, Juergen Mueller, and Gerd Stumme. Conferator–a ubiquitous system for enhancing social networking at confer- ences. In Proceedings of the International UIS Workshop, 2015.   [10] Martin Atzmueller, Dominik Benz, Stephan Doerfel, Andreas Hotho, Robert Jäschke, Bjoern Elmar Macek, Folke Mitzlaff, Christoph Scholz, and Gerd Stumme. Enhancing social interactions at conferences. it-Information Technology Methoden und innovative Anwendungen der Informatik und Informationstechnik, 53(3):101–107, 2011.   [11] Alvin Chin and Daqing Zhang, editors. Mobile Social Networking: An Innovative Approach. Springer-Verlag, 2014. [12] Raghu K Ganti, Nam Pham, Hossein Ahmadi, Saurabh Nangia, and Tarek F Abdelza- her. Greengps: a participatory sensing fuel-efficient maps application. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 151–164. ACM, 2010.   [13] Linda Deng and Landon P Cox. Livecompare: grocery bargain hunting through participatory sensing. In Proceedings of the 10th workshop on Mobile Computing Systems and Applications, page 4. ACM, 2009.   [14] Zhou Su, Qichao Xu, Haojin Zhu, and Ying Wang. A novel design for content delivery over software defined mobile social networks. IEEE Network, 29(4):62–67, 2015.   [15] Jialu Fan, Jiming Chen, Yuan Du, Wei Gao, Jie Wu, and Youxian Sun. Geocommunity- based broadcasting for data dissemination in mobile social networks. IEEE Transac- tions on Parallel and Distributed Systems, 24(4):734–743, 2013.   [16] Jie Li, Zhaolong Ning, Behrouz Jedari, Feng Xia, Ivan Lee, and Amr Tolba. Geo- social distance-based data dissemination for socially aware networking. IEEE Access, 4:1444–1453, 2016.   [17] James Scott, Jon Crowcroft, Pan Hui, and Christophe Diot. Haggle: A networking architecture designed around mobile users. In WONS 2006: Third Annual Conference on Wireless On-demand Network Systems and Services, pages 78–86, 2006.   [18]MarcoConti,SilviaGiordano,MartinMay,andAndreaPassarella.Fromopportunistic networks to opportunistic computing. IEEE Communications Magazine, 48(9):126– 139, Sept 2010.   [19] Laura Ferrari and Marco Mamei. Discovering daily routines from google latitude with topic models. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pages 432–437. IEEE, 2011.   [20] Zhou Su, Qichao Xu, Kuan Zhang, and Xuemin (Sherman) Shen. Modeling and Optimization for Mobile Social Networks. Springer, 2016.   [21] Kate Ching-Ju Lin, Chun-Wei Chen, and Cheng-Fu Chou. Preference-aware content dissemination in opportunistic mobile social networks. In IEEE INFOCOM, 2012.   [22] Jie Hu, Lie-Liang Yang, H Vincent Poor, and Lajos Hanzo. Bridging the social and wireless networking divide: Information dissemination in integrated cellular and opportunistic networks. IEEE Access, 3:1809–1848, 2015.   [23] Jie Hu, Lie-Liang Yang, and Lajos Hanzo. Delay analysis of social group multicast- aided content dissemination in cellular system. IEEE Transactions on Communications, 64(4):1660–1673, 2016.   [24] Jie Hu, Lie-Liang Yang, Kun Yang, and Lajos Hanzo. Socially aware integrated cen- tralized infrastructure and opportunistic networking: A powerful content dissemination catalyst. IEEE Communications Magazine, 54(8):84–91, 2016.   [25] Jaeok Park and Mihaela van der Schaar. A game theoretic analysis of incentives in content production and sharing over peer-to-peer networks. IEEE Journal of Selected Topics in Signal Processing, 4(4):704–717, 2010.   [26] Chiranjeeb Buragohain, Divyakant Agrawal, and Subhash Suri. A game theoretic framework for incentives in p2p systems. arXiv preprint cs/0310039, 2003.   [27] Qichao Xu, Zhou Su, and Song Guo. A game theoretical incentive scheme for relay selection services in mobile social networks. IEEE Transactions on Vehicular Technology, 65(8):6692–6702, 2016.   [28] Ting Ning, Yang Liu, Zhipeng Yang, and Hongyi Wu. Incentive mechanisms for data dissemination in autonomous mobile social networks. IEEE Transactions on Mobile Computing, 16(11):3084–3099, 2017.   [29] Ting Ning, Zhipeng Yang, Hongyi Wu, and Zhu Han. Self-interest-driven incentives for ad dissemination in autonomous mobile social networks. In INFOCOM, 2013 Proceedings IEEE, pages 2310–2318. IEEE, 2013.   [30] Xin Kang and Yongdong Wu. Incentive mechanism design for heterogeneous peer- to-peer networks: A stackelberg game approach. IEEE Transactions on Mobile Computing, 14(5):1018–1030, 2015.   [31] Richard TB Ma, Sam Lee, John Lui, and David KY Yau. A game theoretic approach to provide incentive and service differentiation in P2P networks, volume 32. ACM, 2004.   [32] KevinLeyton-BrownandIlyaMironov.Incentivesforsharinginpeer-to-peernetworks. In Proc. of the ACM Conference on Electronic Commerce, 2001.   [33] MichalFeldman,KevinLai,IonStoica,andJohnChuang.Robustincentivetechniques for peer-to-peer networks. In Proceedings of the 5th ACM conference on Electronic commerce, pages 102–111. ACM, 2004.   [34] Iordanis Koutsopoulos. Optimal incentive-driven design of participatory sensing systems. In Infocom, 2013 proceedings ieee, pages 1402–1410. IEEE, 2013.   [35] Dejun Yang, Guoliang Xue, Xi Fang, and Jian Tang. Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the 18th annual international conference on Mobile computing and networking, pages 173–184. ACM, 2012.   [36] Xinglin Zhang, Zheng Yang, Zimu Zhou, Haibin Cai, Lei Chen, and Xiangyang Li. Free market of crowdsourcing: Incentive mechanism design for mobile sensing. IEEE transactions on parallel and distributed systems, 25(12):3190–3200, 2014.   [37]TieLuo,Hwee-PinkTan,andLirongXia.Profit-maximizingincentiveforparticipatory sensing. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pages 127–135. IEEE, 2014.   [38] Jin-Hee Cho, Ananthram Swami, and Ing-Ray Chen. A survey on trust management for mobile ad hoc networks. IEEE Communications Surveys Tutorials, 13(4):562 – 583, 2011.   [39] Wenjun Jiang, Guojun Wang, Md Zakirul Alam Bhuiyan, and Jie Wu. Understanding graph-based trust evaluation in online social networks: Methodologies and challenges. ACM Computing Surveys (CSUR), 49(1):10, 2016. [40] Wanita Sherchan, Surya Nepal, and Cecile Paris. A survey of trust in social networks. ACM Computing Surveys (CSUR), 45(4):47, 2013. [41] Chii Chang, Sea Ling, and Satish Srirama. Trustworthy service discovery for mobile social network in proximity. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on, pages 478–483. IEEE, 2014. [42] Tie Luo, Salil S. Kanhere, Jianwei Huang, Sajal K. Das, and Fan Wu. Sustainable incentives for mobile crowdsensing: Auctions, lotteries, and trust and reputation systems. IEEE Communications Magazine, 55(3):68 – 74, 2017.   [43] Yashar Najaflou, Behrouz Jedari, Feng Xia, Laurence T Yang, and Mohammad S Obaidat. Safety challenges and solutions in mobile social networks. IEEE Systems Journal, 9(3):834–854, 2015.   [44] Sacha Trifunovic, Franck Legendre, and Carlos Anastasiades. Social trust in oppor- tunistic networks. In INFOCOM IEEE Conference on Computer Communications Workshops, 2010, pages 1–6. IEEE, 2010.   [45] Xiaohui Liang, Xiaodong Lin, and Xuemin Sherman Shen. Enabling trustworthy service evaluation in service-oriented mobile social networks. IEEE Transactions on Parallel and Distributed Systems, 25(2):310–320, 2014.   [46] Fei Hao, Geyong Min, Man Lin, Changqing Luo, and Laurence T Yang. Mobi- fuzzytrust: an efficient fuzzy trust inference mechanism in mobile social networks. IEEE Transactions on Parallel and Distributed Systems, 25(11):2944–2955, 2014.   [47] Kemal Bicakci and Nazife Baykal. Infinite length hash chains and their applications. In Enabling Technologies: Infrastructure for Collaborative Enterprises, 2002. WET ICE 2002. Proceedings. Eleventh IEEE International Workshops on, pages 57–61. IEEE, 2002.   [48] Maithili Narasimha and Gene Tsudik. Authentication of outsourced databases using signature aggregation and chaining. In International conference on database systems for advanced applications, pages 420–436. Springer, 2006.   [49] Gang Sheng, Chunming Tang, Hongyan Han, Wei Gao, and Xing Hu. Authentication of outsourced linear function query with efficient updates. Cluster Computing, pages 1–9, 2017. [50] HweeHwa Pang and Kyriakos Mouratidis. Authenticating the query results of text search engines. Proc. VLDB Endow., 1(1):126–137, August 2008.   [51] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system, 2008.   [52] Michael Crosby, Pradan Pattanayak, Sanjeev Verma, and Vignesh Kalyanaraman.  Blockchain technology: Beyond bitcoin. Applied Innovation, 2:6–10, 2016.   [53] Roman Beck, Jacob Stenum Czepluch, Nikolaj Lollike, and Simon Malone. Blockchain-the gateway to trust-free cryptographic transactions. In ECIS, page Re- searchPaper153, 2016.   [54] Andreas M Antonopoulos. Mastering Bitcoin: Programming the Open Blockchain. " O’Reilly Media, Inc.", 2017.   [55] Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven Goldfeder. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press, 2016.   [56] Chunchun Wu, Tie Luo, Fan Wu, and Guihai Chen. Endortrust: An endorsement- based reputation system for trustworthy and heterogeneous crowdsourcing. In Global Communications Conference (GLOBECOM), 2015 IEEE, pages 1–6. IEEE, 2015.   [57] Audun Josang and Roslan Ismail. The beta reputation system. In Proceedings of the 15th bled electronic commerce conference, volume 5, pages 2502–2511, 2002.   [58] Ming Li, Ning Cao, Shucheng Yu, and Wenjing Lou. Findu: Privacy-preserving personal profile matching in mobile social networks. In INFOCOM, 2011 Proceedings IEEE, pages 2435–2443. IEEE, 2011.   [59] Qichao Xu, Zhou Su, and Song Guo. A game theoretical incentive scheme for relay selection services in mobile social networks. 2015.   [60] Kan Zheng, Suling Ou, Jesus Alonso-Zarate, Mischa Dohler, Fei Liu, and Hua Zhu. Challenges of massive access in highly dense lte-advanced networks with machine-to- machine communications. IEEE Wireless Communications, 21(3):12–18, 2014.   [61] 3GPP. 3rd generation partnership project; technical specification group radio access network; evolved universal terrestrial radio access (e-utra); medium access control (mac) protocol specification. Technical Report TS 36.321 V11.5.0, 2014 March.   [62] 3GPP TR 23.888 V11.0.0. System improvements for machine-type communications. Technical report, Sept 2012.   [63] J.-P. Cheng, C. h. Lee, and T.-M. Lin. Prioritized random access with dynamic access barring for ran overload in 3gpp lte-a networks. IEEE GLOBECOM Workshops (GC Wkshps), pages 368–372, 2011.   [64] Shao-Yu Lien, Tzu-Huan Liau, Ching-Yueh Kao, and Kwang-Cheng Chen. Coopera- tive access class barring for machine-to-machine communications. IEEE Transactions on Wireless Communications, 11(1):27–32, 2012.   [65] Chih-Hua Chang and Ronald Y Chang. Design and analysis of multichannel slot- ted aloha for machine-to-machine communication. IEEE Global Communications Conference (GLOBECOM), 2015.   [66] Ki-Dong Lee, Sang Kim, and Byung Yi. Throughput comparison of random access methods for m2m service over lte networks. In GLOBECOM Workshops (GC Wkshps), 2011 IEEE, pages 373–377. IEEE, 2011.   [67] Anthony Lo, Yee Wei Law, Martin Jacobsson, and Michal Kucharzak. Enhanced lte-advanced random-access mechanism for massive machine-to-machine (m2m) communications. In 27th World Wireless Research Forum (WWRF) Meeting, pages 1–5. WWRF27-WG4-08„ 2011.   [68] J. Kim, D. Munir, S. Hasan, and M. Chung. Enhancement of lte rach through extended random access process. Electronics Letters, 50:1399–1400, 2014. [69] Nuno KPratas, Henning Thomsen, Cedomir Stefanovic ́,and Petar Popovski. Code- expanded random access for machine-type communications. In Globecom Workshops (GC Wkshps), 2012 IEEE, pages 1681–1686. IEEE, 2012. [70] Andrea Zanella, Michele Zorzi, Andre F dos Santos, Petar Popovski, Nuno Pratas, Cedomir Stefanovic, Armin Dekorsy, Carsten Bockelmann, Bryan Busropan, Toon Norp, et al. M2m massive wireless access: challenges, research issues, and ways forward. In IEEE Globecom Workshops (GC Wkshps), pages 151–156, 2013. [71] Andres Laya, Luis Alonso, Periklis Chatzimisios, and Jesus Alonso-Zarate. Massive access in the random access channel of lte for m2m communications: An energy per- spective. In Communication Workshop (ICCW), 2015 IEEE International Conference on, pages 1452–1457. IEEE, 2015.   [72] Andres Laya, Charalampos Kalalas, Francisco Vazquez-Gallego, Luis Alonso, and Jesus Alonso-Zarate. Goodbye, aloha! IEEE Access, 4:2029–2044, 2016.   [73] Andres Laya, Luis Alonso, and Jesus Alonso-Zarate. Contention resolution queues for massive machine type communications in lte. In Personal, Indoor, and Mobile Radio Communications (PIMRC), 2015 IEEE 26th Annual International Symposium on, pages 2314–2318. IEEE, 2015.   [74] Mehdi Naderi Soorki, Walid Saad, Mohammad Hossein Manshaei, and Hossein Saidi. Stochastic coalitional games for cooperative random access in m2m communications. IEEE Transactions on Wireless Communications, 16(9):6179–6192, Sept 2017.   [75] Taehoon Kim, Han Seung Jang, and Dan Keun Sung. An enhanced random ac- cess scheme with spatial group based reusable preamble allocation in cellular m2m networks. IEEE Communications Letters, 19(10):1714 – 1717, 2005.   [76] Han Seung Jang, Su Min Kim, Kab Seok Ko, Jiyoung Cha, and Dan Keun Sung. Spatial group based random access for m2m communications. IEEE Communications Letters, 18(6):961–964, 2014.   [77] Huai-Lei Fu, Phone Lin, Hao Yue, Guan-Ming Huang, and Chia-Peng Lee. Group mobility management for large-scale machine-to-machine mobile networking. IEEE Transactions on Vehicular Technology, 63(3):1296–1305, 2014.   [78] Shao-Yu Lien, Kwang-Cheng Chen, and Yonghua Lin. Toward ubiquitous massive accesses in 3gpp machine-to-machine communications. IEEE Communications Maga- zine, 49(4):66–74, 2011.   [79] Hyun-Kwan Lee, Dong Min Kim, Youngju Hwang, Seung Min Yu, and Seong-Lyun Kim. Feasibility of cognitive machine-to-machine communication using cellular bands. IEEE Wireless Communications, 20(2), April 2013.   [80] Chih-Yuan Tu, Chieh-Yuan Ho, and Ching-Yao Huang. Energy-efficient algorithms and evaluations for massive access management in cellular based machine to machine communications. In Vehicular Technology Conference (VTC Fall), 2011 IEEE, pages 1–5. IEEE, 2011. [81] Chieh Yuan Ho and Ching-Yao Huang. Energy-saving massive access control and resource allocation schemes for m2m communications in ofdma cellular networks. : IEEE Wireless Communications Letters, 1(3), 2012.   [82] Shih-En Wei, Hung-Yun Hsieh, and Hsuan-Jung Su. Joint optimization of cluster formation and power control for interference-limited machine-to-machine communi- cations. In Global Communications Conference (GLOBECOM), 2012 IEEE, pages 5512–5518. IEEE, 2012.   [83] Ameer Ahmed Abbasi and Mohamed Younis. A survey on clustering algorithms for wireless sensor networks. Computer communications, 30(14):2826–2841, 2007.   [84] Ping Ding, JoAnne Holliday, and Aslihan Celik. Distributed energy-efficient hierar- chical clustering for wireless sensor networks. In Distributed computing in sensor systems, pages 322–339. Springer, 2005.   [85] Jenq-Shiou Leu, Tung-Hung Chiang, Min-Chieh Yu, and Kuan-Wu Su. Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. Communications Letters, IEEE, 19(2):259–262, 2015.   [86] Ossama Younis and Sonia Fahmy. Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. Mobile Computing, IEEE Transactions on, 3(4):366–379, 2004.   [87] Kaixin Xu and Mario Gerla. A heterogeneous routing protocol based on a new stable clustering scheme. In MILCOM 2002. Proceedings, volume 2, pages 838–843. IEEE, 2002.   [88] Wendi B Heinzelman, Anantha P Chandrakasan, and Hari Balakrishnan. An application-specific protocol architecture for wireless microsensor networks. Wireless Communications, IEEE Transactions on, 1(4):660–670, 2002.   [89] Jesús Alonso and Luis Alonso. A novel mac protocol for dynamic ad hoc wireless net- works with dynamic self-configurable master-slave architecture. In Personal, Indoor and Mobile Radio Communications, 2004. PIMRC 2004. 15th IEEE International Symposium on, pages 2392–2396. IEEE, 2004.   [90] Seema Bandyopadhyay and Edward J Coyle. An energy efficient hierarchical clus- tering algorithm for wireless sensor networks. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, volume 3, pages 1713–1723. IEEE, 2003. [91] Mainak Chatterjee, Sajal K Das, and Damla Turgut. Wca: A weighted clustering algorithm for mobile ad hoc networks. Cluster computing, 5(2):193–204, 2002.   [92] Jesus Alonso-Zarate, Christos Verikoukis, Elli Kartsakli, Alex Cateura, and Luis Alonso. Saturation throughput analysis of a passive cluster-based medium access control protocol for ad hoc wireless networks. In Communications, 2008. ICC’08. IEEE International Conference on, pages 2348–2352. IEEE, 2008.   [93] Fredrick Mzee Awuor, Chih-Yu Wang, and Tzu-Chieh Tsai. Motivating content shairing in mobile social network through collective bidding. In IEEE Wireless Communications and Networking Conference (WCNC) 2018. IEEE, 15-18 April, 2018.   [94] Fredrick Mzee Awour, Chih-YuWang, and Tzu-Chieh Tsai. Motivating content sharing and trustworthiness in mobile social network. IEEE Access, 6(1):28339–28355, May 2018.   [95] David Crandall, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, and Siddharth Suri. Feedback effects between similarity and social influence in online communities. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 160–168. ACM, 2008.   [96] Ruining Lan, Wei Wang, Aiping Huang, and Hangguan Shan. Device-to-device offloading with proactive caching in mobile cellular networks. In 2015 IEEE Global Communications Conference (GLOBECOM), pages 1–6. IEEE, 2015.   [97] Charu C Aggarwal. An introduction to social network data analytics. Social network data analytics, pages 1–15, 2011.   [98] Wei Dong, Vacha Dave, Lili Qiu, and Yin Zhang. Secure friend discovery in mobile social networks. In INFOCOM, 2011 Proceedings IEEE, pages 1647–1655. IEEE, 2011.   [99] Daniele Quercia and Stephen Hailes. Sybil attacks against mobile users: friends and foes to the rescue. In INFOCOM, 2010 Proceedings IEEE, pages 1–5. IEEE, 2010.   [100] Fredrick Mzee Awuor and Chih-Yu Wang. Massive machine type communication in cellular system: A distributed queue approach. In Communications (ICC), 2016 IEEE International Conference on, pages 1–7. IEEE, 2016.   [101] Fredrick Mzee Awour, Chih-Yu Wang, and Tzu-Chieh Tsai. Distributed queue based congestion control in massive machine type communication. IEEE Transactions on Vehicular Technology, (Under review).   [102] A. Laya, L. Alonso, and J. Alonso-Zarate. Is the random access channel of lte and lte-a suitable for m2m communications? a survey of alternatives. IEEE Communications Surveys & Tutorials, 16:4–16, 2014.   [103] Andres Laya, Luis Alonso, and Jesus Alonso-Zarate. Efficient contention resolution in highly dense lte networks for machine type communications. In Global Communications Conference (GLOBECOM), 2015 IEEE, pages 1–7. IEEE, 2015.   [104] Thit Minn and Kai-Yeung Siu. Dynamic assignment of orthogonal variable-spreading- factor codes in w-cdma. IEEE Journal on Selected Areas in communications, 18(8):1429–1440, 2000.   [105] Yunnan Wu, Xiang-Gen Xia, Qian Zhang, Wenwu Zhu, and Ya-Qin Zhang. Mac- throughput analysis of cdma wireless networks based on a novel collision model. In Combinatorial Optimization in Communication Networks, pages 233–257. Springer, 2006.   [106] D. P. Bertsekas, R. G. Gallager, and P. Humblet. Data networks, volume 2. Prentice- Hall International New Jersey, 1992.   [107] Mikhail Gerasimenko, Valentin Petrov, Olga Galinina, Sergey Andreev, and Yevgeni Koucheryavy. Energy and delay analysis of lte-advanced rach performance under mtc overload. In Globecom Workshops (GC Wkshps), 2012 IEEE, pages 1632–1637. IEEE, 2012.   [108] X. Jian, Y. Jia, X. Zeng, and J. Yang. A novel class-dependent back-off scheme for ma- chine type communication in lte systems. IEEE Wireless and Optical Communication Conference (WOCC), pages 135–140, 2013.
Description:	博士國立政治大學社群網路與人智計算國際研究生博士學位學程(TIGP) 103761505
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0103761505
Data Type:	thesis
DOI:	10.6814/DIS.NCCU.TIGP.001.2018.B02
Appears in Collections:	[Taiwan International Graduate Program] Theses

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