Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Peyman, Mohammad; Copado-Méndez, Pedro J.; Tordecilla, Rafael D.; Martins, Leandro do C.; Xhafa, Fatos; Juan, Ángel A.

doi:10.3390/en14196309

Cited by 28 publications

(10 citation statements)

References 80 publications

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“…At the end of their study, they propose an agile optimization approach to handle the current raised problems and converge to sustainable smart cities. In another work, Peyman et al [48] review the state-of-the-art of IoT in intelligent transportation systems and identify challenges posed by cloud, fog, and edge computing in ITS. They develop a methodology based on agile optimization algorithms for solving a dynamic ridesharing problem (DRSP) in the context of edge/fog computing.…”

Section: Sustainability and Smart Urban Mobilitymentioning

confidence: 99%

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Ghorbani

Herrera

Ammouriova

et al. 2022

Future Transportation

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Urban logistics consumes a large portion of energy resources worldwide. Thus, optimization algorithms are used to define mobility modes, vehicle fleets, routing plans, and last-mile delivery operations to reduce energy consumption such as metaheuristics. With the emergence of smart cities, new opportunities were defined, such as carsharing and ridesharing. In addition to last-mile delivery, these opportunities form a challenging problem because of the dynamism they possess. New orders or ride requests could be placed or canceled at any time. Further, transportation times might evolve due to traffic conditions. These dynamic changes challenge traditional optimization methods to propose solutions in real-time to large-scale energy-optimization problems. Thus, a more `agile optimization’ approach is required to provide fast solutions to optimization problems when these changes occur. Agile optimization combines biased randomization and parallelism. It provides `good’ solutions compared to solutions found by traditional optimization methods, such as in-team orienteering problems. Additionally, these solutions are found in short wall clock, real-time.

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Section: Sustainability and Smart Urban Mobilitymentioning

confidence: 99%

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Ghorbani

Herrera

Ammouriova

et al. 2022

Future Transportation

Self Cite

View full text Add to dashboard Cite

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“…It was shown that this approach outperformed the solutions provided by its original version. Similarly, Peyman et al [77] provided a comprehensive review of the state of the art of the Internet of Things in intelligent transportation systems (ITSs). In this context, challenges were identified for cloud computing, fog computing, and edge computing.…”

Section: Evolutionarymentioning

confidence: 99%

Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

et al. 2022

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Many real-life applications of the vehicle routing problem (VRP) occur in scenarios subject to uncertainty or dynamic conditions. Thus, for instance, traveling times or customers’ demands might be better modeled as random variables than as deterministic values. Likewise, traffic conditions could evolve over time, synchronization issues should need to be considered, or a real-time re-optimization of the routing plan can be required as new data become available in a highly dynamic environment. Clearly, different solving approaches are needed to efficiently cope with such a diversity of scenarios. After providing an overview of current trends in VRPs, this paper reviews a set of heuristic-based algorithms that have been designed and employed to solve VRPs with the aforementioned properties. These include simheuristics for stochastic VRPs, learnheuristics and discrete-event heuristics for dynamic VRPs, and agile optimization heuristics for VRPs with real-time requirements.

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“…The integration of IoT into EVCS marks a transformative step in the evolution of smart transportation infrastructure [5]. This fusion introduces a level of sophistication and efficiency previously unattainable in traditional EVCS.…”

Section: Introduction To Iot In Vehicle Charging Stationsmentioning

confidence: 99%

“…Comprehensive Performance Analysis: Our model outperforms existing IDS solutions in accuracy and resilience, proven through extensive testing. 5.…”

mentioning

confidence: 99%

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Kilichev,

Turimov,

Kim

2024

Mathematics

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In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

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Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Cited by 28 publications

References 80 publications

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

On the Use of Agile Optimization for Efficient Energy Consumption in Smart Cities’s Transportation and Mobility

Solving Vehicle Routing Problems under Uncertainty and in Dynamic Scenarios: From Simheuristics to Agile Optimization

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

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