Project of an Intelligent Recommender System for Parking Vehicles in Smart Cities

Pankiv, Yuriy; Kunanets, Nataliia; Artemenko, Olga; Веретеннікова, Наталія; Nebesnyi, Ruslan

doi:10.1109/csit52700.2021.9648687

Cited by 11 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recommendation approaches have been proposed in various smart city contexts, including smart health care, 7,25 smart markets, 8 smart transportation, 26,27 smart parking, 9,10 ride sharing, 11 e‐governance, 12 smart mobility, 13 smart tourism services, and attractions 14–16 (see Table 2). To achieve the smart service recommendation task, various methods have been adopted, mainly heuristic based and model based.…”

Section: Related Workmentioning

confidence: 99%

“…For example, functional requirements include resource management, runtime environment, device mobility, service mobility; while nonfunctional requirements include latency, devices heterogeneity, scalability, reliability, context‐awareness, security, interoperability. The above requirements have not been tackled enough in current smart service recommendation solutions, like in References 7–16. In addition, users (e.g., patients, medical staff, tourists, drivers) are often involved in multiple provider information networks simultaneously, to satisfy their diverse and complex needs, which are translated to a combination of smart services.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…POP models have to face many different cases of missing data, for example, a parking lot without any parking records or with partial but insufficient occupancy. Therefore, a unified and scalable model is required, which shall manage not only complete-data cases but also empty-data scenarios effectively and efficiently [25].…”

Section: Emerging Challengesmentioning

confidence: 99%

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Liu

et al. 2022

Mathematics

View full text Add to dashboard Cite

Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102x faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.

show abstract

Enabling Configurable Workflows in Smart Environments with Knowledge-based Process Fragment Reuse

Ayadi,

Mezni

2024

J Grid Computing

View full text Add to dashboard Cite

Project of an Intelligent Recommender System for Parking Vehicles in Smart Cities

Cited by 11 publications

References 5 publications

Cross‐network service recommendation in smart cities

Cross‐network service recommendation in smart cities

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Enabling Configurable Workflows in Smart Environments with Knowledge-based Process Fragment Reuse

Contact Info

Product

Resources

About