AllergyLESS. An Intelligent Recommender System to Reduce Exposition Time to Allergens in Smart-Cities

Garcí­a-Dí­az, José Antonio; Noguera-Arnaldos, José Ángel; Hernández-Alcaraz, María Luisa; Robles-Marín, Isabel María; García‐Sánchez, Francisco; Valencia‐García, Rafael

doi:10.1007/978-3-319-94649-8_8

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…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%

“…To achieve the smart service recommendation task, various methods have been adopted, mainly heuristic based and model based. We distinguish machine learning and multilabel deep learning classification, 11,16 fuzzy logic to handle the decision‐making uncertainty in smart parking, 9 support vector machines (SVM) and Bayesian networks to detect/forecast high concentrations of allergens for route recommendation, 13 SVM to predict riders characteristics, 11 unsupervised natural language processing (NLP) to analyze user‐provided data, 28 in addition to software‐defined networks (SDN), dimensionality reduction, decision tree‐based classification, and convolutional neural networks 7 . Most of these approaches are collaborative filtering‐ and content based, and few ones have adopted a hybrid filtering recommendation (e.g., Reference 14).…”

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 2 more Smart Citations

Cross‐network service recommendation in smart cities

Mezni,

Sellami,

Al‐Rasheed

et al. 2024

Concurrency and Computation

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

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“…A similar work is conducted by Jose Antonio et al targeting the problem of Allergic rhinitis. The authors introduced AllergyLESS, a recommendation system for the citizens suggesting them the walking routes with minimum exposure time to allergens [73]. The quality of air and presence of allergens is measured using wireless pollution posts and open-data sources.…”

Section: Iot and Recommender Systemsmentioning

confidence: 99%

RecIoT: A Deep Insight into IoT-Based Smart Recommender Systems

Sharma

Rani

Nuagah

2022

Wireless Communications and Mobile Computing

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Typically, Internet of Things (IoT) is perceived as the technology for connecting people, devices, vehicles, home appliances, etc., and 212 billion heterogenous devices are presumed to be connected by 2020 under its umbrella. This domain is gaining popularity worldwide because of the wave it has brought in both research and industrial sector. Smart applications of this scenario provide assistance and services to the users in smart homes, smart cities, smart retail, agriculture, healthcare, etc. However, researchers are now exploring the potential of IoT systems in other domains, one of them being recommender systems. In recent years, the focus of researchers has drifted towards utilizing recommender systems to improve offered selections to IoT users. Therefore, the aim of this paper is to study the present practices, approaches, and challenges in the aforementioned domain along with possible solutions. Briefly, the contributions of this paper are as follows: (1) providing an overview of the prevailing scenario in a range of application domains associated with IoT based recommender systems, (2) providing a framework of some real-world applications, (3) outlining key areas for the improvement towards context-aware personalized services in the future research, and (4) providing a bibliometric trend analysis prevalent in the concerned domain.

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“…Most routing studies on exposure optimisation have focused on finding low-exposure routes in respect to air pollution [e.g., 4,5,[11][12][13]. Other studies that optimise routes also consider traffic noise levels or the presence of greenery [6,8,14,15], allergens [16], or extreme environmental conditions [17,18]. Several groups of researchers have developed new routing algorithms or prototype route planners for finding exposure-optimised routes [6,16,17,19].…”

Section: Introductionmentioning

confidence: 99%

The Green Paths Route Planning Software for Exposure-Optimised Active Travel

Helle¹,

Poom²,

Willberg³

et al. 2021

Preprint

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Green Paths is a prototype of route planning software for finding exposure-optimised routes for active travel. It incorporates external data on environmental exposures, including traffic noise levels, air quality, and street-level greenery into the street and paths network produced by the OpenStreetMap project. Written in the Python programming language, the software applies a novel environmental impedance function in the least cost path routing to find exposure-optimised routes. Routes for externally defined origin-destination pairs can be queried via a RESTful API. The API returns alternative routes equipped with rich exposure data. The published version of the software has been applied in population level environmental exposure assessment and in an end-user-oriented web-based route planner application designed for use in the Helsinki Metropolitan Area.

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AllergyLESS. An Intelligent Recommender System to Reduce Exposition Time to Allergens in Smart-Cities

Cited by 7 publications

References 9 publications

Cross‐network service recommendation in smart cities

Cross‐network service recommendation in smart cities

RecIoT: A Deep Insight into IoT-Based Smart Recommender Systems

The Green Paths Route Planning Software for Exposure-Optimised Active Travel

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