Browsing by Author "Long, Chengnian"

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Mitigate the Obstructing Effect of Vehicles on the Propagation of VANETs Safety-Related Information
(2018-07-01)
Huang, Rui
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Wu, Jing
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Long, Chengnian
;
Zhu, Yanmin
;
Li, Bo
As physical obstacles, vehicles have significant impacts on the efficient propagations of safety-related information in vehicular ad hoc networks (VANETs) by frequently obstructing the LOS links between transmitters and receivers. Obstructing effect will diminish the effective coverage of safety-related information and incur severe impact on road safety. However, this impact has not been addressed effectively. In this paper, we first present the definition of broadcast efficiency as the metric for quantizing the obstructing effect. We investigate the optimization issue for mitigating the impact of obstructing effect on the propagation of safety-related information and propose a graph theory based optimization algorithm. For a distributed implementation of this optimization in VANETs, we further propose the maximum broadcast efficiency relaying (MBER) algorithm. MBER maximizes the effective coverage of information, meanwhile, meets delay and reliability constraints by incorporating broadcast efficiency and propagation distance in relay contention. Simulation results demonstrate that MBER promotes the effective coverage of safety-related information in VANETs with varying vehicular distribution.
Mitigate the Obstructing Effect of Vehicles on the Propagation of VANETs Safety-related Information
(2017-01-01)
Huang, Rui
;
Wu, Jing
;
Long, Chengnian
;
Zhu, Yanmin
;
Lin, Yi-Bing
As physical obstacles, vehicles have a significant impact on the efficient propagation of safety-related information in vehicular ad hoc networks (VANETs) by frequently obstructing the LOS link between the source and receivers. Obstructing effect of vehicles will diminish the effective coverage of broadcast as part of vehicles can not decode the broadcast successfully and incur severe impact on road safety. However, this impact has not been addressed by existing protocols. In this paper, we first present the definition of broadcast efficiency (BE) as the metric for measuring the obstructing effect and conclude the optimization issue of propagation. We further propose a distributed algorithm for mitigating this impact that can be embedded in VANETs. This algorithm maximizes the effective coverage of the broadcast of safety-related information and meanwhile satisfies the constraints on both link state and delay by selecting relay according to BE and referential propagation distance. Simulation results demonstrate that the proposed algorithm promotes the efficiency of propagation in dynamic multilane VANETs with varying vehicular distribution.
SPRCA: Distributed Multisource Information Propagation in Multichannel VANETs
(2017-12-01)
Huang, Rui
;
Wu, Jing
;
Long, Chengnian
;
Zhu, Yanmin
;
Li, Bo
Information dissemination in VANETs mainly focuses on, e.g., the propagation of cooperative awareness messages and decentralized environmental notification messages on the control channel. As vehicles advance toward higher automation levels, VANETs should support more complex traffic services, which generates the demand for the propagation of Internet Protocol (IP)-based information and service data in multichannel environment as specified by IEEE Standard for Wireless Access in Vehicular Environments (WAVE). Multisource propagation in the multichannel environments will incur multisource relay confliction (MRC) which leads to higher latency and lower information propagation speed (IPS). However, the problem of how to mitigate the impact of MRC and maintain high IPS has not been studied sufficiently. In this paper, we propose success probability based relay contention algorithm (SPRCA) for mitigating the impact of MRC under WAVE channel environments. SPRCA presents a vital metric, success probability, for each vehicle to determine whether to join the relay candidate set of certain source and execute relay contention algorithm. SPRCA explores both real time location information of vehicles and stochastic properties of vehicular distribution. We construct a theoretical model for analyzing the performance of SPRCA with a stochastic geometry-based approach and derive the properties of the performance of SPRCA. We further demonstrate the network performance and the practicality in dynamic VANETs of SPRCA through simulations with realistic traffic flow data.
The Convergence of IoE and Blockchain: Security Challenges
(2019-09-01)
Wei, Lijun
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Wu, Jing
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Long, Chengnian
;
Lin, Yi-Bing
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交大名義發表
To build a large-scale distributed Internet of Things (IoT), a feasible prototype for Internet of Everything, blockchain can provide strong support with its excellent characteristics such as traceability and openness. Despite that the blockchain technology ideally enhances the reliability and security of IoT systems, emerging new security challenges remain to be resolved. This article details the security vulnerabilities in the convergence of blockchain and IoT as well as corresponding feasible solutions.
Ubiquitous and Low Power Vehicles Speed Monitoring for Intelligent Transport Systems
(2020-06-01)
Choy, Jose Luis Calderon
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Wu, Jing
;
Long, Chengnian
;
Lin, Yi-Bing
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資訊工程學系
This paper presents a high scalability real-time intelligent traffic monitoring system, based on Radio Frequency Identification (RFID). The main features of this system are low cost, low power consumption, traffic monitoring, and connectivity. The system's architecture includes an RFID reader, a passive tag, and a Raspberry Pi. Our solution collects vehicle information from the labels and stores the data into a database by employing only one antenna. The main challenge is that the RFID module is not robust enough to recognize the information in the vehicle's RFID tag on each information query, while the label is in the reading zone. This instability does not allow us to know precisely when and where a vehicle enters or leaves the sensing zone. What is more, the high random error in the power signal and the complexity of its characteristic curve pattern add difficulty to the speed calculation, when we reduce the number of antennas to one. For this reason, an innovative approach has been designed, using customized modular neural network (MNN). This method fits the collected data (power signal vs time) affected by acute random noise, to the characteristic correspondence function among the signal power and the position of the terminal, which domains are dimensionally different. As a result, we can estimate the vehicle speeds and obtain the whole vehicle information. Under this novel method, we are able to reduce the hardware, in comparison with previous approaches, making it cheaper and decreasing power consumption.