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

Communication privacy and latency perceived by users have become great concerns for delay-sensitive Internet services. Existing anonymous communication systems either provide high anonymity at an expense of prolonged latency (e.g., mix-net), or offer better real-time performance by sacrificing the ability against traffic analysis attacks (e.g., Onion Routing). The emerging Software-Defined Networking (SDN) introduces additional challenges to communication anonymity, due to the existence of a centralized controller that has a global view of the entire network traffic. In this paper, we propose a new anonymous communication scheme for delay-sensitive services under SDN scenarios, which can simultaneously protect communication privacy and reduce the end-to-end latency. A self-adaptive method based on the mix-net framework is designed to dynamically modify the waiting threshold of mix nodes, which helps to reduce the communication latency. In order to preserve the degree of anonymity, the self-adaptive method is incorporated with a random walking strategy for packets forwarding. Both theoretical analysis and experimental results prove that our scheme provides a moderate degree of anonymity and effectively reduces the latency derived from mix-net by up to 50%.

Self-adaptive anonymous communication scheme under SDN architecture

Driver drowsiness is a significant factor in road crashes. However, existing solutions for driver drowsiness detection have major drawbacks of requiring special hardware, constrained recording conditions, and cannot handle the asynchronous and contradictory nature of multiple indicators. In view of this, we propose FDWatch, a novel drowsiness detection system that exploits the low-cost Photoplethysmogram (PPG) sensor and motion sensor integrated into wrist-worn devices. We design a set of novel algorithms to extract multiple drowsiness-related indicators covering major categories of human factors. In particular, we demonstrate that commodity PPG sensors can be utilized to detect yawning behavior; it contributes as an important indicator for drowsiness detection. The core of FDWatch is based on the Dempster-Shafer evidence theory. It considers different indicators as evidence describing the state of the driver from different angles. To make the extracted indicators applicable to Dempster-Shafer evidence theory, we employ backpropagation neural networks to obtain the basic probability assignment. Moreover, we propose a similarity-distance-based method to handle evidence conflicts. Extensive experiments with real-road driving data show that FDWatch can accurately detect driver drowsiness with a missing alarm rate of 3.57% and a false alarm rate of 3.68%.

Towards Reliable Driver Drowsiness Detection Leveraging Wearables

Vehicular networks have been increasingly used for applications like road infrastructure monitoring and traffic jam detection, etc. Data forwarding is a well-known challenging problem in vehicular networks, which suffers from delay and error due to the frequent network disruption and fast topological change. The minimizations of the delivery delay and network cost are both central to data forwarding in vehicular networks. However, previous works usually focus on only one of the two objectives and most of them do not make good use of vehicle trajectory information. In this paper, we formulate the V2V (vehicle to vehicle) data forwarding problem as a novel multi-objective Markov Decision Process (MDP). We exploit the vehicle trajectory information and traffic statistics to estimate the parameters of the MDP (i.e., transition probabilities, rewards). The optimal routing policy is then developed by solving the multi-objective MDP. We conduct extensive simulations on a taxi network in a mega-city, the experimental results validate the effectiveness of our proposed mechanism.

TMODF: Trajectory-based multi-objective optimal data forwarding in vehicular networks

In mobile social delay tolerant networks, many human-carried mobile devices are moving around in a restricted physical space and occasional contact opportunities among these devices are used to deliver data. Routing design in mobile social delay tolerant networks has been a challenging task due to lack of instantaneous end-to-end paths, large transmission delays, and time-varying network topology. In this paper, aiming to precisely measure the social relationships between mobile nodes, we introduce two kinds of time-varying closeness, direct and indirect closeness, where the temporal constraint is considered. Then we propose both single-copy and multi-copy closeness-based routing schemes, which consider direct and indirect social relationships under temporal constraint. Extensive simulations on real-life data traces show the proposed schemes can achieve better performances than existing DTN routing methods.

Closeness-based routing with temporal constraint for mobile social delay tolerant networks

Location sensing in wireless sensor networks (WSNs) is a critical problem when it comes to rescue operation in underground mines. Most of the existing research on node localization uses traditional centroid algorithm-based approach. However, such approaches have higher localization error, which leads to inaccurate node precision. This paper proposes a novel quadrant-based solution on weighted centroid algorithm that uses received signal strength indicator for range calculation and distance improvement by incorporating alternating path loss factor according to the mine environment. It also makes use of four beacon nodes instead of traditional three with weights applied to reflect the impact of each node for the centroid position. The weight factor applied is the inverse of the distance estimated. Simulation results show higher localization accuracy and precision as compared to traditional weighted centroid algorithms.

Fan Li

Papers

Self-adaptive anonymous communication scheme under SDN architecture

Towards Reliable Driver Drowsiness Detection Leveraging Wearables

TMODF: Trajectory-based multi-objective optimal data forwarding in vehicular networks

Closeness-based routing with temporal constraint for mobile social delay tolerant networks

Quadrant-Based Weighted Centroid Algorithm for Localization in Underground Mines