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

This chapter begins with a workplace phenomenon in which a supervisor categorizes his staff into in-group and out-group members, using different rules of social exchange with the different groups. It presents a novel comprehensive methodology to quantify guanxi in workplaces, which incorporates both quantitative data and qualitative results in a complementary manner. The chapter identifies informal leaders’ guanxi circles and check for overlapping areas among various circles, that is, bridges. Guanxi circles are actually pseudofamilies in a person’s working life and at work usually develop from ego-centered social networks around one focal person. The main guanxi circle in a workplace is generally centered on the supervisor at the highest level. The concept of guanxi circles is similar to action sets rather than a closed group or an association. Flexible guanxi operation usually makes a guanxi circle’s boundary open.

Measurement of Guanxi Circles: Using Qualitative Study to Modify Quantitative Measurement *

The usage of testbeds is considered a key tool for exploring the development of new protocols and network architectures in the area of network research. Testbeds, together with simulations, are the basic tool set of network researchers to drive research, but often it is impossible to get feedback from real deployments and their respective data traffic. Today's major testbed facilities, e.g., VINI and PlanetLab, aim at emulating the behavior of large-scale networks, but they are still several orders of magnitude smaller than the deployed operational network infrastructure. We argue that it is time to extend network research beyond theoretical and testbed approaches towards a dynamic, peer-to-peer based testbed environment, similar to the approach taken by seti@home and BOINC. We aim at expanding the total number of participating nodes in an experiment and at experimenting on existing operational infrastructure with its entirely uncontrollable environment. Our vision presented in this paper, the Testbed on Real Infrastructure (TORI), includes regular end hosts (peers) in an experiment by deploying and executing the experimental software on these peers and to form an overlay network upon them. The main difference of our TORI approach compared to others is installing new technologies and testing them with the operational infrastructure.

TORI: User Provided Future Networking Testbeds

We propose a simple yet effective method to perform surface remeshing with hard constraints, such as bounding approximation errors and ensuring Delaunay conditions. The remeshing is formulated as a constrained optimization problem, where the variables contain the mesh connectivity and the mesh geometry. To solve it effectively, we adopt traditional local operations, including edge split, edge collapse, edge flip, and vertex relocation, to update the variables. Central to our method is an evolutionary vertex optimization algorithm, which is derivative‐free and robust. The feasibility and practicability of our method are demonstrated in two applications, including error‐bounded Delaunay mesh simplification and error‐bounded angle improvement with a given number of vertices, over many models. Compared to state‐of‐the‐art methods, our method achieves higher remeshing quality.

Constrained Remeshing Using Evolutionary Vertex Optimization

—Artiﬁcial intelligence (AI) has been introduced in communication networks and services to improve efﬁciency via self-optimization. Cooperative intelligence (CI), also known as collective intelligence and collaborative intelligence, is expected to become an integral element in next-generation networks because it can aggregate the capabilities and intelligence of multiple devices. However, privacy issues may intimidate, obstruct, and hinder the deployment of CI in practice because collaboration heavily relies on data and information sharing. Additional practical constraints in communication (e.g., limited bandwidth) further limit the performance of CI. To overcome these challenges, we propose PP-MARL, an efﬁcient privacy-preserving learning scheme based on multi-agent reinforcement learning (MARL). We apply and evaluate our scheme in two communication-related use cases: mobility management in drone-assisted communication and network control with edge intelligence. Simulation results reveal that the proposed scheme can achieve efﬁcient and reliable collaboration with 1.1-6 times better privacy protection and lower overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art approaches.

PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication

Although Mobile IPv6 allows maintaining transport layer connections alive when an IPv6 node roams to different access networks, certain enabling mechanisms are needed for it to work in large scale network scenarios, including, most notably, issues with Mobile IPv6 bootstrapping and firewall traversal. This paper tries to address these problems by extending the IETF PANA and NSIS protocols to form an extensible framework for wide deployment of a secure, light-weight mobility service in operational IPv6 environments.

Xiaoming Fu

Papers

Measurement of Guanxi Circles: Using Qualitative Study to Modify Quantitative Measurement *

TORI: User Provided Future Networking Testbeds

Constrained Remeshing Using Evolutionary Vertex Optimization

PP-MARL: Efficient Privacy-Preserving MARL for Cooperative Intelligence in Communication

Enabling mobile ipv6 in operational environments