Socially Aware Heterogeneous Wireless Networks
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 devic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/6/13705 |
| _version_ | 1798005302582312960 |
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| author | Pavlos Kosmides Evgenia Adamopoulou Konstantinos Demestichas Michael Theologou Miltiades Anagnostou Angelos Rouskas |
| author_facet | Pavlos Kosmides Evgenia Adamopoulou Konstantinos Demestichas Michael Theologou Miltiades Anagnostou Angelos Rouskas |
| author_sort | Pavlos Kosmides |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:36:58Z |
| format | Article |
| id | doaj.art-cb123eb09a9141669d1b22d9105862b5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:36:58Z |
| publishDate | 2015-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cb123eb09a9141669d1b22d9105862b52022-12-22T04:23:36ZengMDPI AGSensors1424-82202015-06-01156137051372410.3390/s150613705s150613705Socially Aware Heterogeneous Wireless NetworksPavlos Kosmides0Evgenia Adamopoulou1Konstantinos Demestichas2Michael Theologou3Miltiades Anagnostou4Angelos Rouskas5School of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, GreeceSchool of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, GreeceDepartment of Digital Systems, University of Piraeus, Piraeus 18534, GreeceThe 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.http://www.mdpi.com/1424-8220/15/6/13705heterogeneous wireless networkssoftware defined networkssoftware-based controllerssocial networkslearning algorithmsmobile operator recommendations |
| spellingShingle | Pavlos Kosmides Evgenia Adamopoulou Konstantinos Demestichas Michael Theologou Miltiades Anagnostou Angelos Rouskas Socially Aware Heterogeneous Wireless Networks Sensors heterogeneous wireless networks software defined networks software-based controllers social networks learning algorithms mobile operator recommendations |
| title | Socially Aware Heterogeneous Wireless Networks |
| title_full | Socially Aware Heterogeneous Wireless Networks |
| title_fullStr | Socially Aware Heterogeneous Wireless Networks |
| title_full_unstemmed | Socially Aware Heterogeneous Wireless Networks |
| title_short | Socially Aware Heterogeneous Wireless Networks |
| title_sort | socially aware heterogeneous wireless networks |
| topic | heterogeneous wireless networks software defined networks software-based controllers social networks learning algorithms mobile operator recommendations |
| url | http://www.mdpi.com/1424-8220/15/6/13705 |
| work_keys_str_mv | AT pavloskosmides sociallyawareheterogeneouswirelessnetworks AT evgeniaadamopoulou sociallyawareheterogeneouswirelessnetworks AT konstantinosdemestichas sociallyawareheterogeneouswirelessnetworks AT michaeltheologou sociallyawareheterogeneouswirelessnetworks AT miltiadesanagnostou sociallyawareheterogeneouswirelessnetworks AT angelosrouskas sociallyawareheterogeneouswirelessnetworks |