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

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Convex Analysisの二,三の進展について

Digital processing of speech signals

Vehicular networking has significant potential to enable diverse applications associated with traffic safety, traffic efficiency and infotainment. In this survey and tutorial paper we introduce the basic characteristics of vehicular networks, provide an overview of applications and associated requirements, along with challenges and their proposed solutions. In addition, we provide an overview of the current and past major ITS programs and projects in the USA, Japan and Europe. Moreover, vehicular networking architectures and protocol suites employed in such programs and projects in USA, Japan and Europe are discussed.

Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions

A comprehensive survey on vehicular Ad Hoc network

Traffic routing in stochastic network function virtualization networks

Speeding is one of the biggest threatens to road safety. However, facilities like radar detector and speed camera are not deployed everywhere, as roads in some areas like campus and residential areas often lack these facilities. Several solutions either depend on pre-deployed infrastructures, or require additional devices, which motivate us to explore the practicability of using smartphones' acoustic sensors to detect vehicle speed. In this paper, we propose a Hybrid Detection system for vehicle Speed (HDSpeed). We first investigate the relationship between acoustic pattern and vehicle speed. According to our findings on typical patterns of both Electric Vehicles (EVs) and Gasoline Vehicles (GVs), we separately extract different features from the acoustic signals of EVs and GVs. A CNN and an LSTMN are designed for training EV and GV models, respectively. Considering that applying neural networks obtains coarse-grained information like a speed section, we propose a detection method based on active acoustic sensing, in which method HDSpeed calculates the fine-grained speed by detecting the distance change between the smartphone and the passing vehicle. In addition, the previously detected speed section can eliminate interferences of surrounding moving objects. Through extensive experiments in real driving environments, HDSpeed achieves an average error of 2.17km/h.

HDSpeed: Hybrid Detection of Vehicle Speed via Acoustic Sensing on Smartphones

In this work, we investigate the selfish load balancing problem in mobile distributed crowdsourcing networks. Conventional methods heavily relied on cooperations among users to achieve balanced resource utilization in platform-centric view. In achieving fairly low communication and computational overhead, and maintaining good load balancing property among selfish users, we resort to the 'd-choice' method based on 'Ball and Bin' theory [1] for balancing with limited information, and proliferate the 'Proportional Allocation' [2] scheme for selfish load balancing. We combine the good properties in aforementioned schemes and propose 'Chance-Choice', a lightweight distributed load balancing scheme for selfish users with fast convergence property. We find that, even with limited information, the balancing performance could be improved significantly, under the rule of opportunistic offloading and selfish behavior. Extensive evaluations have been made to show that, 'Chance-Choice' outperforms several existing algorithms. Typically, comparing with 'Proportional Allocation' scheme [2], ours could decrease the load gap by 50% to 80%, and reduce the overhead complexity from O(n) to O(1) comparing with the 'Max-weight Best Response' algorithm [3], where n denotes the number of mobile users in crowdsourcing system.

Many Is Better Than All: Efficient Selfish Load Balancing in Mobile Crowdsourcing Systems

With the increase in the number of on-board sensors, vehicles have shown great potential in mobile crowdsensing. To ensure the capability of the vehicular crowdsensing system, it is necessary to inspire sufficient vehicles to participate. However, due to personal interests and privacy protection, this goal is not easy to achieve. In addition, uncertain mobility of vehicles also brings challenges to the design of incentive mechanism. In this paper, we propose an incentive mechanism for nondeterministic vehicular crowdsensing with blockchain (INVCB), which can effectively incentivize vehicles while protecting user privacy. We first propose a framework for nondeterministic vehicular crowdsensing with blockchain and design a series of smart contracts to automate the crowdsensing process. Then, in order to improve the quality of sensing data, we add reputation attribute to each user, and provide an incentive mechanism that considers reputation. Extensive simulation results show the performance of our proposal is reliable and effective.

An Incentive Mechanism for Nondeterministic Vehicular Crowdsensing with Blockchain

The amount of research done in the field of mobile ad hoc networks is extraordinarily large. Evaluation of protocols designed for ad hoc networks is challenging as the cost of node deployment in terms of resources required is high, hence, most of the researchers use simulations for performance evaluation. In this paper we address the pitfalls of simulation studies in ad hoc routing protocols published in recent years. We have conducted a survey to evaluate the current state of simulation studies published in top conference/journals of the communication domain. In majority of the published papers (the way simulation results are reported) we have found design flaws, unrealistic assumptions, are non-reproducible, and statistically invalid results. We also propose a standardizing architecture for automating the reporting and replication process for network simulators. This platform independent architecture alleviates the challenge of simulation parameter reporting and facilitates in designing better network simulation experiments.

Fan Li

Papers

Traffic routing in stochastic network function virtualization networks

HDSpeed: Hybrid Detection of Vehicle Speed via Acoustic Sensing on Smartphones

Many Is Better Than All: Efficient Selfish Load Balancing in Mobile Crowdsourcing Systems

An Incentive Mechanism for Nondeterministic Vehicular Crowdsensing with Blockchain

Simulation Standardization: Current State and Cross-Platform System for Network Simulators