An Architecture for IoT-Enabled Smart Transportation Security System: A Geospatial Approach

Zhang, Jun; Wang, Yichuan; Li, Shuyang; Shi, Shuaiyi

doi:10.1109/jiot.2020.3041386

Cited by 54 publications

(23 citation statements)

References 34 publications

(34 reference statements)

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“…The fault diagnosis of subway machinery determines the nature, degree, category, location, cause, development trend of the fault through signal detection, feature extraction, state recognition, and diagnosis decision-making [15]. The content involves mechanical structure, electronic technology, statistical mathematics, and computer science [16].…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

2022

Journal of Sensors

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The application of traditional feature extraction methods in subway mechanical fault diagnosis has attracted the attention of researchers. Based on the 5G intelligent sensor network signal processing theory, this paper constructs a fault diagnosis model for subway machinery and equipment and analyzes the effectiveness of the information redundancy calculation method proposed in this paper by using the on-site vibration data of subway units. The model is obtained by analyzing the entropy characteristics of the vector vibration signal in each bearing section of the subway machinery and equipment. The entropy value quantitatively reflects the vibration complexity of the rotor in the section from different angles and solves the problem of data quantitative analysis. In the simulation process, based on the 5G intelligent sensor network signal and the fuzzy mean clustering information fusion method, in the process of identifying the fault state of the subway unit, a relatively optimistic identification result was obtained. The experimental results show that, no matter whether the Gaussian kernel or the polynomial kernel is selected, the number of kernel principal components whose cumulative contribution rate is greater than 0.85 decreases with the increase of the kernel parameter, and the fault identification support rate is 85%, that is, the diagnosis results of 3 sets of sample data are nonrolling element faults, which significantly improves the performance of the bearing.

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Section: Related Workmentioning

confidence: 99%

Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

2022

Journal of Sensors

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“…For those important nodes, we can enhance the protection by regular patrol in the physical world and consistency checking in the cyber system. For example, if an RSU on a road segment is identified to be critical, then this device should be protected physically [72]. If the attack on this device indeed occurs, the traffic center should spot the anomaly in real-time and block the information sent from this device.…”

Section: Discussionmentioning

confidence: 99%

Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models

Zhu¹,

Feng²,

Pu³

et al. 2021

Preprint

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Real-time traffic prediction models play a pivotal role in smart mobility systems and have been widely used in route guidance, emerging mobility services, and advanced traffic management systems. With the availability of massive traffic data, neural network-based deep learning methods, especially the graph convolutional networks (GCN) have demonstrated outstanding performance in mining spatio-temporal information and achieving high prediction accuracy. Recent studies reveal the vulnerability of GCN under adversarial attacks, while there is a lack of studies to understand the vulnerability issues of the GCNbased traffic prediction models. Given this, this paper proposes a new task -diffusion attack, to study the robustness of GCNbased traffic prediction models. The diffusion attack aims to select and attack a small set of nodes to degrade the performance of the entire prediction model. To conduct the diffusion attack, we propose a novel attack algorithm, which consists of two major components: 1) approximating the gradient of the black-box prediction model with Simultaneous Perturbation Stochastic Approximation (SPSA); 2) adapting the knapsack greedy algorithm to select the attack nodes. The proposed algorithm is examined with three GCN-based traffic prediction models: ST-GCN, T-GCN, and A3T-GCN on two cities. The proposed algorithm demonstrates high efficiency in the adversarial attack tasks under various scenarios, and it can still generate adversarial samples under the drop regularization such as DROPOUT, DROPNODE, and DROPEDGE. The research outcomes could help to improve the robustness of the GCN-based traffic prediction models and better protect the smart mobility systems.

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“…Disaster management services are supported by IoT data streams, which are geospatially annotated to assist big data analytics aiming to provide campus recovery [7]. Geospatial modelling is applied to support safe students' transportation within Smart Campus by exploiting geolocated sensors' infrastructure design to provide a secure IoT-enabled architecture [8].…”

Section: A Geospatial Iotmentioning

confidence: 99%

Challenges and Solutions of Surveillance Systems in IoT-Enabled Smart Campus: A Survey

Anagnostopoulos¹,

Kostakos

Zaslavsky

et al. 2021

IEEE Access

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A Smart Campus is a miniature of a Smart City with a more demanding framework that enables learning, social interaction and creativity. To ensure a Smart Campus uninterruptible secure operation, a key requirement is that daily routines and activities are performed protected in an environment monitored unobtrusively by a robust surveillance system. The various components that compose such an environment, buildings, labs, public spaces, smart lighting, smart parking, or even smart traffic lights, require us to focus on surveillance systems, and recognize which detection activities to establish. In this paper, we perform a comparative assessment in the area of surveillance systems for Smart Campuses. A proposed taxonomy for IoT-enabled Smart Campus unfold five research dimensions: (1) physical infrastructure; (2) enabling technologies; (3) software analytics; (4) system security; and (5) research methodology. By applying this taxonomy and by adopting a weighted scoring model on the surveyed systems, we first present the state-ofthe-art, and then we make a comparative assessment and classify the systems. We extract valuable conclusions and inferences from this classification, providing insights and directions towards required services offered by surveillance systems for Smart Campus.

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An Architecture for IoT-Enabled Smart Transportation Security System: A Geospatial Approach

Cited by 54 publications

References 34 publications

Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

Fault Diagnosis of Subway Mechanical Equipment Based on 5G Intelligent Sensor Network Signal Processing

Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models

Challenges and Solutions of Surveillance Systems in IoT-Enabled Smart Campus: A Survey

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