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Socially Aware Heterogeneous Wireless Networks.

Kosmides P, Adamopoulou E, Demestichas K, Theologou M, Anagnostou M, Rouskas A - Sensors (Basel) (2015)

Bottom Line: In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure.Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques.By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, Greece. pkosmidis@cn.ntua.gr.

ABSTRACT
The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

No MeSH data available.


K-Means Clustering example with 2 clusters. (a) Initial clusters; (b) Final clusters.
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sensors-15-13705-f004: K-Means Clustering example with 2 clusters. (a) Initial clusters; (b) Final clusters.

Mentions: A simple example of using K-means with two clusters is given in Figure 4. At the initial stage, the data are divided into two non-optimal clusters, while at the final stage the clusters have reached their ending form.


Socially Aware Heterogeneous Wireless Networks.

Kosmides P, Adamopoulou E, Demestichas K, Theologou M, Anagnostou M, Rouskas A - Sensors (Basel) (2015)

K-Means Clustering example with 2 clusters. (a) Initial clusters; (b) Final clusters.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507610&req=5

sensors-15-13705-f004: K-Means Clustering example with 2 clusters. (a) Initial clusters; (b) Final clusters.
Mentions: A simple example of using K-means with two clusters is given in Figure 4. At the initial stage, the data are divided into two non-optimal clusters, while at the final stage the clusters have reached their ending form.

Bottom Line: In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure.Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques.By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, Greece. pkosmidis@cn.ntua.gr.

ABSTRACT
The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.

No MeSH data available.