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

—In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

Video Content Placement At the Network Edge: Centralized and Distributed Algorithms

Opportunistic Mobile Social Networks (MSNs) as a kind of social network in which nodes are opportunistically connected. Many data routing strategies have been proposed for opportunistic MSNs. Most of them apply a utility for relay selection that a node with a higher utility value is selected as relay. However, such utility-based routing strategies run into dead end problem in which the data is stuck into a node with local maximal utility value. In this paper, we propose a virtual routing method named VIRO, which exerts conformal mapping to convert the local topology of dead end into a virtual geometric map to guarantee data dissemination to the next relay. We first convert the utility into geometric planner, and then conduct the discrete Ricci Flow bypass the gap between the dead end and next relay. Extensive experiment results suggest VIRO can reduce the ratio of dead end effectively so that the data delivery ratio is enhanced up to 42% by increasing only a slight amount of delay and cost.

VIRO: A virtual routing method for eliminating dead end in Opportunistic Mobile Social Network

Metabolisms of field plankton community, including gross primary production (GPP), community respiration (CR), and net community production (NCP), usually indicate the status of the health, resource production, and carbon budget of marine ecosystems. In this study, we explored the regional variance and driving forces of plankton metabolism in coastal waters of the Guangdong-Hong Kong-Macao Greater Bay Area (GGBA), a fast-developed area with complex hydrological and environmental states. The results showed that the maximum GPP and CR occurred in the estuarine plume of the GGBA in summer, while in winter the more active metabolisms of plankton community occurred in the Daya Bay, with the GPP and CR being respectively mediated by the nutrient level and temperature. Moreover, four regional zones were divided on the base of the environments and biological factors in surface water of the GGBA i.e., the river-runoff zone, river-plume zone, nearshore and far-offshore zones. The metabolic states in these zones varied significantly due to the regional and seasonal variations of, for example, the nutrient level, temperature, and turbidity driven by multiple factors including land-derived runoffs, anthropogenic activities, the Yuedong Coastal Current, and offshore seawater-intrusions. On the whole, the GGBA areas exhibited the weak heterotrophic processes in both summer (NCP = -24.9 ± 26.7 mg C m-3 d-1) and winter (NCP = -51.2 ± 8.51 mg C m -3 d-1). In addition, we found that the higher CR occurred to the bottom layers of the river plume and nearshore zones wherein hypoxia happened, indicating a possible attribution of plankton community respiration to the hypoxia in the GGBA.

/pdf/plankton-metabolism-in-coastal-waters-of-the-guangdong-hong-2fooe1lg.pdf

Plankton Metabolism in Coastal Waters of the Guangdong-Hong Kong-Macao Greater Bay: Regional Variance and Driving Factors

In today’s telecommunications networking and services are changing in a rapid way to support next generation Internet (NGI) user environment. Wireless networks will play an important role in NGI. Wireless broadband networks are being increasingly deployed and used in the last mile for extending or enhancing Internet connectivity for fixed and/or mobile clients located on the edge of the wired network [1].

Routing and Scheduling for WiMAX Mesh Networks

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/dac.3092

Xiaoming Fu

Papers

Video Content Placement At the Network Edge: Centralized and Distributed Algorithms

VIRO: A virtual routing method for eliminating dead end in Opportunistic Mobile Social Network

Plankton Metabolism in Coastal Waters of the Guangdong-Hong Kong-Macao Greater Bay: Regional Variance and Driving Factors

Routing and Scheduling for WiMAX Mesh Networks

Mobile internet: content, security and terminal