Modelling and Simulation of Urban Mobile Agents for Analyzing Mixed Flows in Urban Pedestrian Space

Since the 1990s, complex systems research has been developing agent simulations to explain the phenomena observed in urban spaces. In recent years, agent-based modelling has often been employed to successfully simulate pedestrian behaviour. In such studies, explanations using pedestrian counter flow phases have appeared sporadically. Most state-of-the-art models, however, do not generally consider mobile agents other than pedestrians or counter flows in at least two directions. In this paper, we consider agents such as pedestrians, vehicles, wheelchairs, bicycles and so on in urban pedestrian space (UPS), which we call urban mobile agents (UMAs). The aim of this research is to develop a simulation platform to support urban simulation research. The models of rule-based UMAs that we have been developing are used to analyze the micro-meso behaviours of the mixed flows in UPS. The content of this class of agent includes the pedestrian agent as per the simplified agent simulation of pedestrian flow (sASPF) rules as well as the vehicle agent and bicycle agent in the UPS, including a wheelchair agent in the coming research. Using these models, we explore the following approaches: (a) theoretical analyses of phase transitions such as laminar flow formation or blockade of pedestrian counter flows, with clarification of the relationship between the degree of pedestrian global density and the bias of the diagonal stepping probability, which is the right or left selection probability of avoidance behaviour; (b) the implementation of obstacle avoidance rules in the sASPF pedestrian agent model, and their comparison with published evacuation experiment results, so as to evaluate the performance of the obstacle avoidance function; (c) the development of a vehicle agent model to simulate pedestrian-vehicle mixed flow at a crossroads assuming a disaster scenario; (d) the development of a bicycle agent model by extending sASPF rules; and (e) consideration of a conceptual framework for interaction fields representing heterogeneous agent mixed flows, including vehicle, bicycle, pedestrian and wheelchair agents

Bayesian Estimation-Based Pedestrian Tracking in Microcells

We consider a pedestrian tracking system where sensor nodes are placed only at specific points so that the monitoring region is divided into multiple smaller regions referred to as microcells. In the proposed pedestrian tracking system, sensor nodes composed of pairs of binary sensors can detect pedestrian arrival and departure events. In this paper, we focus on pedestrian tracking in microcells. First, we investigate actual pedestrian trajectories in a microcell on the basis of observations using video sequences, after which we prepare a pedestrian mobility model. Next, we propose a method for pedestrian tracking in microcells based on the developed pedestrian mobility model. In the proposed method, we extend the Bayesian estimation to account for time-series information to estimate the correspondence between pedestrian arrival and departure events. Through simulations, we show that the tracking success ratio of the proposed method is increased by 35.8% compared to a combinatorial optimization-based tracking method

Dealing with Imbalanced Classes in Bot-IoT Dataset

With the rapidly spreading usage of Internet of Things (IoT) devices, a network intrusion detection system (NIDS) plays an important role in detecting and protecting various types of attacks in the IoT network. To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection. This dataset contains imbalanced normal and attack packets because the number of normal packets is much smaller than that of attack ones. The nature of imbalanced data may make it difficult to identify the minority class correctly. In this thesis, to address the class imbalance problem in the Bot-IoT dataset, we propose a binary classification method with synthetic minority over-sampling techniques (SMOTE). The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm. Through numerical results, we demonstrate the proposed classifier's fundamental characteristics and the impact of imbalanced data on its performance

Tracking Pedestrians across Multiple Microcells Based on Successive Bayesian Estimations

We propose a method for tracking multiple pedestrians using a binary sensor network. In our proposed method, sensor nodes are composed of pairs of binary sensors and placed at specific points, referred to as gates, where pedestrians temporarily change their movement characteristics, such as doors, stairs, and elevators, to detect pedestrian arrival and departure events. Tracking pedestrians in each subregion divided by gates, referred to as microcells, is conducted by matching the pedestrian gate arrival and gate departure events using a Bayesian estimation-based method. To improve accuracy of pedestrian tracking, estimated pedestrian velocity and its reliability in a microcell are used for trajectory estimation in the succeeding microcell. Through simulation experiments, we show that the accuracy of pedestrian tracking using our proposed method is improved by up to 35% compared to the conventional method

Scalable Method for Continuous Media Streaming on Peer-to-Peer Networks

With the growth of computing power and the proliferation of broadband access to the Internet, streaming distribution services have widely diffused. Content providers apply the proxy caching technique to accomplish effective streaming distribution services. However, the current proxy mechanism has several problems in achieving scalability to the number of users and flexibility in handling the diversity of users demands. In a peer-to-peer (P2P) network, a host called a "peer" directly communicates and exchanges information and data with other hosts. By using the P2P communication architecture, streaming distribution services can be expected to smoothly react to network conditions and changes in user demands for media-streams. In this thesis, we propose efficient methods to achieve continuous and scalable media streaming service. First, segmentation of media streams is done for efficient use of network bandwidth and storage space. Next, we propose two scalable methods to search a desired media block. Finally, we propose two algorithms to determine an optimum provider peer from the results obtained by the search methods. Through several simulation experiments, we show that the FLS method can perform continuous media play-out while reducing the amount of search traffic to 1/10 compared with full flooding