Browsing by Author "Mao, Wencan"

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Capacity Planning for Vehicular Fog Computing
(2023) Mao, Wencan
School of Electrical Engineering | Doctoral dissertation (article-based)
The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. Fog computing shortens the network latency by moving computation close to the location where the data is generated. Vehicular fog computing (VFC) proposes to complement stationary fog nodes co-located with cellular base stations (i.e., CFNs) with mobile ones carried by vehicles (i.e., VFNs) in a cost-efficient way. Previous works on VFC have mainly focused on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, the uncertainty in the computational demand, and the trade-off between the quality of service (QoS) and cost expenditure. This dissertation focuses on capacity planning for VFC. The objective of capacity planning is to maximize the techno-economic performance of VFC in terms of profit and QoS. To address the spatial-temporal dynamics of vehicular traffic, this dissertation presents a capacity planning solution for VFC that jointly decide the location and number of CFNs together with the route and schedule of VFNs carried by buses. Such a long-term planning solution is supposed to be updated seasonally according to the traffic pattern and bus timetables. To address the uncertainty in the computational resource demand, this dissertation presents two capacity planning solutions for VFC that dynamically schedule the routes of VFNs carried by taxis in an on-demand manner. Such a short-term planning solution is supposed to be updated within minutes or even seconds. To evaluate the techno-economic performance of our capacity planning solutions, an open-source simulator was developed that takes real-world data as inputs and simulates the VFC scenarios in urban environments. The results of this dissertation can contribute to the development of edge and fog computing, the Internet of Vehicles (IoV), and intelligent transportation systems (ITS).
Data-driven Capacity Planning for Vehicular Fog Computing
(2022-08-01) Mao, Wencan; Akgul, Özgür; Mehrabidavoodabadi, Abbas; Cho, Byung; Xiao, Yu; Ylä-Jääski, Antti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities (a.k.a fog nodes). In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of fog nodes. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile fog nodes and create more savings in operational costs in the long term.
On-demand Vehicular Fog Computing for Beyond 5G Networks
(2023-12-01) Mao, Wencan; Akgul, Ozgur Umut; Cho, Byungjin; Xiao, Yu; Yla-Jaaski, Antti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Emerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles, namely vehicular fog nodes (VFNs), have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatiotemporal variations of demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios are built on the assumption of certain spatiotemporal patterns of vehicular traffic. They consider long-term capacity planning (e.g., updated every season) but leave the adaptation to temporary changes or unexpected variations out of scope. These solutions typically result in high computational costs and thus are not suitable for short-term capacity planning, which requires low-latency responses. To reduce time complexity, we propose an integer linear programming (ILP)-based framework called on-demand capacity planning (ODCP) to implement two-phase planning through optimizing the routing strategies of VFNs, with the aim of maximizing the profit and quality of service (QoS). More specifically, ODCP first predicts the traffic flow and resource demand using seasonal autoregressive integrated moving average (SARIMA) and estimates the revenue using an economic model defined by service level agreement (SLA). With the estimated workload and revenue, the first phase (i.e., global planning) decides the ratio of tasks that can be served at the city scale and assigns VFNs to each region. The second phase (i.e., regional planning) assigns the VFNs to users within the same region and schedules the routes of VFNs based on the mobility of users. Experimental results show that the proposed solution achieves a higher performance in terms of profit and QoS than the existing single-phase capacity planning solutions. We also find that a large number of VFNs, a small region size, high penalty costs, and low travel and rental costs lead to high service rates, whereas a large region size and low travel, rental, and penalty costs lead to high profits.
UAV-assisted integrated sensing and communication for emergency rescue activities based on transfer deep reinforcement learning
(2024) Liu, Yaxi; Mao, Wencan; Li, Xulong; Huangfu, Wei; Ji, Yusheng; Xiao, Yu
A4 Artikkeli konferenssijulkaisussa
Joint task scheduling and resource allocation for unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) in emergency rescue activities has become an essential and challenging problem. However, the existing works have only considered such a problem for standalone UAV networks without considering the cooperation between UAVs and ground base stations (BSs), nor have they considered the uncertainty in terms of the availability of BSs due to damage/reconstruction in disaster events. In this paper, we consider a novel post-disaster UAV-assisted ISAC system where the UAVs are used to supplement the networking capacity of out-of-service ground BSs while using their radio signals for sensing. We apply transfer learning with deep reinforcement learning (DRL) to learn task scheduling and resource allocation strategies that can rapidly adapt to uncertainty in the environment. Experimental results show that the proposed algorithm outperforms the state-of-the-art in both communication and sensing performance and convergence speed. Moreover, the transfer learning-based DRL shows faster convergence and better robustness when the availability of BSs suddenly changes.
VFogSim: A Data-Driven Platform for Simulating Vehicular Fog Computing Environment
(2023-09-01) Akgul, Ozgur Umut; Mao, Wencan; Cho, Byungjin; Xiao, Yu
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of emerging vehicle applications, such as cooperative and autonomous driving. Vehicular fog computing (VFC) is a cost-efficient deployment option that complements stationary fog nodes with mobile ones carried by moving vehicles. To plan the deployment and manage the VFC resources in the real world, it is essential to consider the spatiotemporal variations in both demand and supply of fog computing capacity and the tradeoffs between achievable quality-of-services and potential deployment and operating costs. The existing edge/fog computing simulators, such as IFogSim, IoTSim, and EdgeCloudSim, cannot provide a realistic technoeconomic investigation to analyze the implications of VFC deployment options due to the simplified network models in use, the lack of support for fog node mobility, and limited testing scenarios. In this article, we propose an open-source simulator VFogSim that allows real-world data as input for simulating the supply and demand of VFC in urban areas. It follows a modular design to evaluate the performance and cost efficiency of deployment scenarios under various vehicular traffic models, and the effectiveness of the diverse network and computation schedulers and prioritization mechanisms under user-defined scenarios. To the best of our knowledge, our platform is the first one that supports the mobility of fog nodes and provides realistic modeling of vehicle-to-everything in 5G and beyond networks in the urban environment. Furthermore, we validate the accuracy of the platform using a real-world 5G measurement and demonstrate the functionality of the platform taking VFC capacity planning as an example.
Vision-based vehicle detection and tracking in intelligent transportation system
(2019-06-17) Mao, Wencan
Insinööritieteiden korkeakoulu | Master's thesis
This thesis aims to realize vision-based vehicle detection and tracking in the Intelligent Transportation System. First, it introduces the methods for vehicle detection and tracking. Next, it establishes the sensor fusion framework of the system, including dynamic model and sensor model. Then, it simulates the traffic scene at a crossroad by a driving simulator, where the research target is one single car, and the traffic scene is ideal. YOLO Neural Network is applied to the image sequence for vehicle detection. Kalman filter method, extended Kalman filter method, and particle filter method are utilized and compared for vehicle tracking. The Following part is the practical experiment where there are multiple vehicles at the same time, and the traffic scene is in real life with various interference factors. YOLO Neural Network combined with OpenCV is adopted to realize real-time vehicle detection. Kalman filter and extended Kalman filter are applied for vehicle tracking; an identification algorithm is proposed to solve the occlusion of the vehicles. The effects of process noise as well as measurement noise are analysed using variable-controlling approach. Additionally, perspective transformation is illustrated and implemented to transfer the coordinates from the image plane to the ground plane. If the vision-based vehicle detection and tracking can be realized and popularized in daily lives, vehicle information can be shared among infrastructures, vehicles, and users, so as to build interactions inside the Intelligent Transportation System.