We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Part I: AN INTRODUCTION TO INQUIRY. 1. Human Inquiry and Science. 2. Paradigms, Theory, and Research. 3. The Ethics and Politics of Social Research. Part II: THE STRUCTURING OF INQUIRY: QUANTITATIVE AND QUALITATIVE. 4. Research Design. 5. Conceptualization, Operationalization, and Measurement. 6. Indexes, Scales, and Typologies. 7. The Logic of Sampling. Part III: MODES OF OBSERVATION: QUANTITATIVE AND QUALITATIVE. 8. Experiments. 9. Survey Research. 10. Qualitative Field Research. 11. Unobtrusive Research. 12. Evaluation Research. Part IV: ANALYSIS OF DATA:QUANTITATIVE AND QUALITATIVE . 13. Qualitative Data Analysis. 14. Quantitative Data Analysis. 15. Reading and Writing Social Research. Appendix A. Using the Library. Appendix B. Random Numbers. Appendix C. Distribution of Chi Square. Appendix D. Normal Curve Areas. Appendix E. Estimated Sampling Error.

社会研究方法基础 = The Basics of Social Research

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Various approaches exist to detect gestures and movements via smartphones. Most of them, however, require that the smartphone is carried on-body. The abscence of reliable ad-hoc on-line gesture detection from environmental sources inspired this project for on-line hand gesture detection on a smartphone using only WiFi RSSI. We highlight our line of work and explain problems at hand to provide information for possible future work. We will furthermore introduce wifiJedi, a smartphone application, that is able to detect movement in front of the smartphone by reading the WiFi RSSI and use this information to control a Slideshow.

Demo abstract: Use the force, Luke: Implementation of RF-based gesture interaction on an android phone

Information sharing plays a significant role in human daily life. With the development of the Internet and applications over it, we share almost all kinds of information in a much wider space and time scope that we never had. However, when it comes to sharing information about resources which are available in an instant manner and expire in a very short time, such as the cases in carpooling, train ticket resale and short-term discount information, the Internet is not that all-powerful. On the other hand, we have witnessed the rapid development of smart mobile phones over recent years, and which is gradually reshaping our way to learn and communicate. To the best of our knowledge, instant resource information sharing systems have not been available in this new environment. In this work, we develop and demonstrate Thumb, a system aiming to leverage the new functionalities of smart mobile phones and the Internet to allow users to share instant availability of resource information, especially for resources with extreme short life time.

Thumb: A real-time resource information sharing application over mobile phones

To ensure state synchronization of signalling operations, many signaling protocol designs choose to establish “soft” state that expires if it is not refreshed. The approaches of refreshing state in multi-hop signaling system can be classified as either end-to-end (E2E) or hop-by-hop (HbH). Although both state refresh approaches have been widely used in practical signaling protocols, the design tradeoffs between state synchronization and signaling cost have not yet been fully investigated. In this paper, we investigate this issue from the perspectives of state refresh and state removal. We propose simple but effective Markov chain models for both approaches and obtain closed-form solutions which depict the state refresh performance in terms of state consistency and refresh message rate, as well as the state removal performance in terms of state removal delay. Simulations verify the analytical models. It is observed that the HbH approach yields much better state synchronization at the cost of higher signaling cost than the E2E approach. While the state refresh performance can be improved by increasing the values of state refresh and timeout timers, the state removal delay increases largely for both E2E and HbH approaches. The analysis here shed lights on the design of signaling protocols and the configuration of the timers to adapt to changing network conditions.

/pdf/end-to-end-versus-hop-by-hop-soft-state-refresh-for-multi-456p6k2bkb.pdf

End-to-end versus hop-by-hop soft state refresh for multi-hop signaling systems

Social community question answering (SCQA) sites not only provide regular question answering (QA) service but also form a social network where users can follow each other. Identifying topical opinion leaders who are both expert and influential in SCQA becomes a hot research topic. However, existing works focus on either using knowledge expertise to find experts for improving the quality of answers, or measuring user influence to identify influential ones. In this paper, we propose QALeaderRank, a topical opinion leader identification framework, incorporating both the topic-sensitive influence and the topical knowledge expertise. To measure a user’s topic-sensitive influence, we design a novel ranking algorithm that exploits both the social and QA features of SCQA, taking account of the network structure, topical similarity and knowledge authority. Besides, we incorporate three topic-relevant metrics to infer the topical expertise. Extensive experiments along with a user study demonstrate that QALeaderRank outperforms the compared state-of-the-art methods. QALeaderRank can also be used to identify multi-topic opinion leaders.

Identifying Topical Opinion Leaders in Social Community Question Answering

One of the key requirements of today's and future network infrastructures is to provide Quality of Service (QoS) support for end-to-end applications, by distinguishing the application flows and properly handling them in network nodes As an important component to achieve Internet QoS, explicit signaling schemes for resource reservation have been proposed, which deal with admission, installation and refreshment of QoS reservation state information To be useful, any QoS signaling protocol should provide a capability for authentication and authorization of the QoS requests, especially in environments where the end points are not trusted by the network nodes However, existing protocols for QoS signaling encounter a number of authentication and authorization issues, which limit their application scenarios The advent of NSIS QoS Signaling Layer Protocol (QoS-NSLP) offers the prospect to overcome some of these issues After describing the overall design of QoS-NSLP, we present an approach to support advanced authentication and authorization capabilities by using the Extensible Authentication Protocol (EAP) In comparison with existing approaches, this approach, combined with the support for effective interaction with the Authentication, Authorization and Accounting (AAA) infrastructure, provides flexible and extensible authentication and authorization methods for the QoS signaling

Xiaoming Fu

Papers

Demo abstract: Use the force, Luke: Implementation of RF-based gesture interaction on an android phone

Thumb: A real-time resource information sharing application over mobile phones

End-to-end versus hop-by-hop soft state refresh for multi-hop signaling systems

Identifying Topical Opinion Leaders in Social Community Question Answering

Advanced authentication and authorization for quality of service signaling