모바일 소셜네트워크에서 사용자간 기회적인 공유기반트래픽 오프로딩

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2013. 8. 최양희.최근 모바일 트래픽의 빠른 증가는 이동통신 사업자에게 심각한 문제가 되고 있다. 이러한 문제를 해결하기 위해 단거리 통신 기술 및 모바일 소셜 네트워크 등을 이용하여 사용자간 직접 데이터를 주고 받는 오프로딩 기법을 사용하는 것에 대한 연구가 이루어지고 있다. 본 논문에서는 사용자간 직접 통신을 통한 효율적인 콘텐츠 공유 및 오프로딩 기법을 제안하고자 한다. 첫번째 연구로, 모바일 소셜 네트워크에서 사용자간 직접 전송기회를 활용해 데이터를 공유하는 모바일 트래픽 오프로딩의 핵심 기술인 TOSS를 제안 하였다. TOSS에서는 셀룰러 네트워크에서 급속히 증가하고 있는 트래픽으로 인한 네트워크 과부하를 경감시키기 위해 온라인 소셜 네트워크에서 사용자의 연결성 및 오프라인 네트워크에서 사용자의 이동성을 고려하여 콘텐츠를 전달할 사용자를 결정하고 블루투스나 와이파이 다이렉트 등의 기술을 이용해 콘텐츠를 직접 전달 하였다. 또한 소셜네트워크 서비스 사용자의 서로 다른 콘텐츠 접근 패턴, 즉 각 사용자가 콘텐츠 생성으로부터 오프로딩을 위해 콘텐츠에 접근하기까지의 시간을 고려 하였다. 본 연구에서는 이러한 요건을 고려하여 트래픽 오프로딩과 콘텐츠 확산을 모델링하고 분석 하였다. 모바일 소셜 네트워크의 데이타 셋을 기반으로 분석 결과에서 TOSS는 모든 사용자의 딜레이 요구조건을 만족시키면서 최대 86.5의 셀룰러 트래픽을 경감시키는 것을 보였다. 두번째의 연구에서는 모바일 네트워크에서 멀티셀을 고려하여 콘텐츠를 배포하는 프레임워크에 대한 연구를 진행 하였다. 해당 프레임워크에서 콘텐츠는 셀룰러 링크와 모바일 사용자간 로컬 링크를 통해 푸시-공유 기반의 통신으로 전달 되였다. 이러한 기법을 바탕으로 multi-compartment 모델을 이용하여 셀 간 핸드오버 및 콘텐츠 전달을 모델링 및 분석하고, 콘텐츠 전달 딜레이와 에너지 소모 사이의 trade-off를 수학적인 최적화 기법을 사용하여 해결 하였다. 본 논문에서는 이와 같이 기존의 측정 연구에 기반한 trace-driven 분석, 모델링 및 시스템 최적화에 대한 연구를 통해 모바일 소셜 네트워크에서 사용자간 직접 전송을 통한 오프로딩 기법이 고효율적임을 보였다. 또한 본 논문은 제안된 기법의 상용화 전망 및 이를 위한 이슈들에 대한 논의도 포함 하였다.The fast increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been efforts to offload the traffic from cellular links to local short-range communications among mobile users that are moving around and forming mobile social networks. In my thesis, I mainly focus on the user-to-user opportunistic sharing and try to elaborate its effectiveness and efficiency for to offload mobile traffic. In the first work, I propose the Traffic Offloading assisted by Social network services via opportunistic Sharing in mobile social networks, TOSS. In TOSS, initially a subset of mobile users are selected as initial seeds depending on their content spreading impact in online social network services (SNSs) and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct) with each other. Due to the distinct access patterns of individual SNS users, TOSS further exploits the user-dependent access delay between the content generation time and each user's access time for the purpose of traffic offloading. I model and analyze process of the traffic offloading and content spreading by taking into account various options in linking SNS and MSN data sets. The trace-driven evaluation shows that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users. In the second work, I focus on the analytical research on Push-Share framework for content disseminating in mobile networks. One content is firstly pushed the to a subset of subscribers via cellular links, and mobile users spread the content via opportunistic local connectivity. I theoretically model and analyze how the content can be disseminated, where handovers are modeled based on the multi-compartment model. I also formulate the mathematical optimization framework, by which the trade-off between the dissemination delay and the energy cost is explored. Based on the measurement study, trace-driven analysis, theoretical modeling and system optimization in above papers, the traffic offloading by user-to-user opportunistic sharing in mobile social networks is proved to be effective and efficient. Additionally, further discussions on the practical deployment, future vision, and open issues are discussed as well.Abstract i I. Introduction 1 II. RelatedWork 7 2.1 Opportunistic Sharing in DTNs/MSNs 7 2.2 Mobile Traffic Offloading 9 2.3 Information/Content Spreading in SNSs 10 III. TOSS 13 3.1 Framework Details 13 3.1.1 Preliminaries 13 3.1.2 Spreading Impact in the Online SNS 16 3.1.3 Access Delays of Users in the SNS 18 3.1.4 Mobility Impact in the Offline MSN 21 3.2 System Optimization 25 3.3 Trace-Driven Measurement 26 3.3.1 Measurement of the Online SNS 26 3.3.2 Measurement of Offline MSNs, λi j and IM 33 3.3.3 Content Obtaining Delays 36 3.3.4 How C Impacts the Obtaining Delay 38 3.4 Performance Evaluation 39 3.4.1 How C Impacts the Total Access Utility 39 3.4.2 Satisfying 100%, 90%, and 80% of Users 44 3.4.3 On-Demand Delivery 47 3.5 Conclusion 48 IV. Push-Share 50 4.1 Framework Details 50 4.2 System Model 53 4.3 Content Dissemination in Single Cell 56 4.3.1 Content Dissemination by Sharing Only 57 4.3.2 Content Dissemination with Initial Push and Final Push 59 4.3.3 Content Dissemination Energy Cost 62 4.4 Content Dissemination in Multiple Cells 63 4.4.1 Non-steady-state Modeling of MSs in Multiple Cells 66 4.4.2 Steady-State Modeling of MSs in Multiple Cells 66 4.4.3 How Handovers Affect the Content Dissemination 67 4.5 Optimization Framework 69 4.5.1 Minimum Dissemination Completion Delay 69 4.5.2 Minimum Dissemination Completion Cost 70 4.5.3 Conjunctive Minimization of Delay and Cost 71 4.6 Evaluation Results 73 4.6.1 Content Dissemination within One Single Cell 74 4.6.2 Content Dissemination within Multiple Cells 77 4.6.3 Optimization Framework 80 4.7 Conclusion 82 V. Summary and Future Work 84 5.1 A Comparison with Traffic Offloading based on Wi-Fi APs 85 5.2 Practical Deployment and Application 86 5.3 Future Work and Vision 88 Bibliography 90Docto

Modeling and Measuring Performance of Data Dissemination in Opportunistic Networks

In this thesis we focus on understanding, measuring and describing the performance of Opportunistic Networks (ONs) and their applications. An “opportunistic network” is a term introduced to describe a sparse, wireless, ad hoc network with highly mobile nodes. The opportunistic networking paradigm deviates from the traditional end-to-end connectivity concept: Forwarding is based on intermittent connectivity between mobile nodes (typically, users with wireless devices); complete routes between sources and destinations rarely exist. Due to this unique property of spontaneous link establishment, the challenges that exist in ONs are specific. The unstructured nature of these networks makes it difficult to give any performance guarantees on data dissemination. For this reason, in Part I of this thesis we explore the dynamics that affect the performance of opportunistic networks. We choose a number of meaningful scenarios where our models and algorithms can be validated using large and credible data sets. We show that a drift and jump model that takes a spatial approach succeeds in capturing the impact of infrastructure and mobile-to-mobile exchanges on an opportunistic content update system. We describe the effects of these dynamics by using the age distribution of a dynamic piece of data (i.e., information updates) as the performance measure. The model also succeeds in capturing a strong bias in user mobility and reveals the existence of regions, whose statistics play a critical role in the performance perceived in the network. We exploit these findings to design an application for greedy infrastructure placement, which relies on the model approximation for a large number of nodes. Another great challenge of opportunistic networking lies in the fact that the bandwidth available on wireless links, coupled with ad hoc networking, failed to rival the capacity of backbones and to establish opportunistic networks as an alternative to infrastructure-based networks. For this reason, we never study ONs in an isolated context. Instead, we consider the applications that leverage interconnection between opportunistic networks and legacy networks and we study the benefits this synergy brings to both. Following this approach, we use a large operator-provided data set to show that opportunistic networks (based on Wi-Fi) are capable of offloading a significant amount of traffic from 3G networks. At the same time, the offloading algorithms we propose reduce the amount of energy consumed by mobiles, while requiring Wi-Fi coverage that is several times smaller than in the case of real-time offloading. Again we confirm and reuse the fact that user mobility is biased towards certain regions of the network. In Part II of this thesis, we treat another issue that is essential for the acceptance and evolution of opportunistic networks and their applications. Namely, we address the absence of experimental results that would support the findings of simulation based studies. Although the techniques such as contact-based simulations should intuitively be able to capture the performance of opportunistic applications, this intuition has little evidence in practice. For this reason, we design and deploy an experiment with real users who use an opportunistic Twitter application, in a way that allows them to maintain communication with legacy networks (i.e., cellular networks, the Internet). The experiment gives us a unique insight into certain performance aspects that are typically hidden or misinterpreted when the usual evaluation techniques (such as simulation) are used. We show that, due to the commonly ignored factors (such as the limited transmission bandwidth), contact-based simulations significantly overestimate delivery ratio and obtain delays that are several times lower than those experimentally acquired. In addition to this, our results unanimously show that the common practice of assuming infinite cache sizes in simulation studies, leads to a misinterpretation of the effects of a backbone on an opportunistic network. Such simulations typically overestimate the performance of the opportunistic component, while underestimating the utility of the backbone. Given the discovered deficiencies of the contact-based simulations, we consider an alternative statistical treatment of contact traces that uses the weighted contact graph. We show that this approach offers a better interpretation of the impact of a backbone on an opportunistic network and results in a closer match when it comes to modeling certain aspects of performance (namely, delivery ratio). Finally, the security requirements for the opportunistic applications that involve an interconnection with legacy networks are also highly specific. They cannot be fully addressed by the solutions proposed in the context of autonomous opportunistic (or ad hoc) networks, nor by the security frameworks used for securing the applications with continuous connectivity. Thus, in Part III of this thesis, we put together a security framework that fits the networks and applications that we target (i.e., the opportunistic networks and applications with occasional Internet connectivity). We then focus on the impact of security print on network performance and design a scheme for the protection of optimal relaying capacity in an opportunistic multihop network. We fine-tune the parameters of our scheme by using a game-theoretic approach and we demonstrate the substantial performance gains provided by the scheme

Automated Hierarchical, Forward-Chaining Temporal Planner for Planetary Robots Exploring Unknown Environments

The transition of mobile robots from a controlled environment towards the real-world represents a major leap in terms of complexity coming primarily from three different factors: partial observability, nondeterminism and dynamic events. To cope with them, robots must achieve some intelligence behaviours to be cost and operationally effective. Two particularly interesting examples of highly complex robotic scenarios are Mars rover missions and the Darpa Robotic Challenge (DRC). In spite of the important differences they present in terms of constraints and requirements, they both have adopted certain level of autonomy to overcome some specific problems. For instance, Mars rovers have been endowed with multiple systems to enable autonomous payload operations and consequently increase science return. In the case of DRC, most teams have autonomous footstep planning or arm trajectory calculation. Even though some specific problems can be addressed with dedicated tools, the general problem remains unsolved: to deploy on-board a reliable reasoning system able to operate robots without human intervention even in complex environments. This is precisely the goal of an automated mission planner. The scientific community has provided plenty of planners able to provide very fast solutions for classical problems, typically characterized by the lack of time and resources representation. Moreover, there are also a handful of applied planners with higher levels of expressiveness at the price of lowest performance. However, a fast, expressive and robust planner has never been used in complex robotic missions. These three properties represent the main drivers for the outcomes of the thesis. To bridge the gap between classical and applied planning, a novel formalism named Hierarchical TimeLine Networks (HTLN) combining Timeline and HTN planning has been proposed. HTLN has been implemented on a mission planner named QuijoteExpress, the first forward-chaining timeline planner to the best of our knowledge. The main idea is to benefit from the great performance of forward-chaining search to resolve temporal problems on the state-space. In addition, QuijoteExpress includes search enhancements such as parallel planning by division of the problem in sub-problems or advanced heuristics management. Regarding expressiveness, the planner incorporates HTN techniques that allow to define hierarchical models and solutions. Finally, plan robustness in uncertain scenarios has been addressed by means of sufficient plans that allow to leave parts of valid plans undefined. To test the planner, a novel lightweight, timeline and ROS-based executive named SanchoExpress has been designed to translate the plans into actions understandable by the different robot subsystems. The entire approach has been tested in two realistic and complementary domains. A cooperative multirover Mars mission and an urban search and rescue mission. The results were extremely positive and opens new promising ways in the field of automated planning applied to robotics