Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments

Ko, Han-Gyu; Ko, In-Young; Lee, Dongman

doi:10.1007/s11042-017-4849-9

Cited by 7 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Localization is an important aspect of GPS-alternative positioning systems and has considerable importance in general communications areas [1]. The development of new radio access standards prompted the exploration of new techniques to improve the location accuracy, which is based on the signals available from the wireless devices that comprise these standards [2][3][4][5][6]. In this communication, the problem of the indoor location, which is based on the signals available in the wireless devices that comprise Wi-Fi and Wi-Max networks within the broadband wireless systems, is presented [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

et al. 2019

View full text Add to dashboard Cite

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

show abstract

Section: Introductionmentioning

confidence: 99%

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In this subsection, we investigate trust metrics for a recommendation in IoT. Reviewed literature in trust evaluation [28][29][30][31][32][33][34][35] proposed various metrics for trust computation in IoT environment and we summarize most pertained ones in Table 1.…”

Section: Trust Evaluation Metricsmentioning

confidence: 99%

“…Ko et al [29] developed a multi-criteria matrix localization and integration (MCMLI) by CF-based algorithms to improve the accuracy of users' preferences prediction by mitigating the effects of data-sparsity. Firstly, MCMLI split a user-item matrix into submatrices (CUIS matrices), by clustering correlated users and items based on their similarity level.…”

Section: Collaborative Filtering (Cf)mentioning

confidence: 99%

Trust-based recommendation systems in Internet of Things: a systematic literature review

Mohammadi

Rahmani

Darwesh

et al. 2019

Hum. Cent. Comput. Inf. Sci.

118

View full text Add to dashboard Cite

During the past decades, in the era of wireless communications and embedded system, the concept of the Internet of Things (IoT) was first presented by Kevin Ashton in the year 1999 [1]. Along with the tremendous growth in the number of sensing devices connected to the Internet, we were a witness of emerging IoT into healthcare, transportation system, smart cities, agriculture, and other enterprises. The IoT benefits cooperation of diverse computing systems such as sensors and smart devices to cloud computers. On the other side, the internet and mobile communication facilitate the spatiotemporal connection between distant people with common experience and values. More recently, technological evolution is introduced by intelligent sensor devices installed in the physical and virtual realm of IoT to act as or on behalf of human beings: the virtual robots [2]. This creates the possibility for physical objects present in a self-organized manner without a central administration and leads to meaningful human-machine interaction in IoT scenario [3].

show abstract

“…al. in [32] introduced a multi-criteria matrix location and integration collaborative filtering method based on the advantages of multi-criteria grading in which users could use to dynamically identify possible tasks and use the IoT. User complexity was solved when various intelligent objects in the environment performed the required tasks.…”

Section: Related Workmentioning

confidence: 99%

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

et al. 2019

View full text Add to dashboard Cite

With the development of the Internet and mobile technologies, the Internet of Things (IoT) era has arrived. Vehicle networking technology can not only facilitate people's travel but also effectively alleviate traffic congestion. The development of fog computing technology provides unlimited possibilities for the Internet of Vehicles (IoV). Intelligent navigator is a very important part of human-computer interaction in IoV. It carries a large number of tasks of recommending content for users. In order to get more accurate recommendation content, we propose a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV. First, the user data are analyzed to establish the association rule mining algorithm. Second, the user interest score is predicted by analyzing the relevance between user interests to recommend personalized service for the user. From the simulation results, we can see that the proposed algorithm can achieve higher recommendation accuracy. INDEX TERMS Content recommendation, association rules, Internet of Vehicles, fog computing.

show abstract

Multi-criteria matrix localization and integration for personalized collaborative filtering in IoT environments

Cited by 7 publications

References 26 publications

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Trust-based recommendation systems in Internet of Things: a systematic literature review

Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

Contact Info

Product

Resources

About