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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.


Misclassification percentage per sub-area, (a) during morning periods; (b) during noon periods; (c) during afternoon periods; (d) during night periods.
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sensors-15-13705-f009: Misclassification percentage per sub-area, (a) during morning periods; (b) during noon periods; (c) during afternoon periods; (d) during night periods.

Mentions: In Figure 9, we present the misclassification percentage gained by each learning algorithm examined (PNN, SVM, MLP) for 11 sub-areas of the created map (Figure 5b), under four different time periods (morning, noon, afternoon, night). As observed, PNN provides lower misclassification percentage compared to the other two methods. MLP produces higher misclassifications, while SVM performs better and in some occasions the misclassification percentage is similar to the one produced by PNN.


Socially Aware Heterogeneous Wireless Networks.

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

Misclassification percentage per sub-area, (a) during morning periods; (b) during noon periods; (c) during afternoon periods; (d) during night periods.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-13705-f009: Misclassification percentage per sub-area, (a) during morning periods; (b) during noon periods; (c) during afternoon periods; (d) during night periods.
Mentions: In Figure 9, we present the misclassification percentage gained by each learning algorithm examined (PNN, SVM, MLP) for 11 sub-areas of the created map (Figure 5b), under four different time periods (morning, noon, afternoon, night). As observed, PNN provides lower misclassification percentage compared to the other two methods. MLP produces higher misclassifications, while SVM performs better and in some occasions the misclassification percentage is similar to the one produced by PNN.

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.