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Ghadah Aldabbagh
Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box. 80221, Jeddah 21589, Saudi Arabia

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Journal article
Published: 22 June 2021 in PeerJ Computer Science
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The development of the Internet of Things (IoT) expands to an ultra-large-scale, which provides numerous services across different domains and environments. The use of middleware eases application development by providing the necessary functional capability. This paper presents a new form of middleware for controlling smart devices installed in an intelligent environment. This new form of middleware functioned seamlessly with any manufacturer API or bespoke controller program. It acts as an all-encompassing top layer of middleware in an intelligent environment control system capable of handling numerous different types of devices simultaneously. This protected de-synchronization of data stored in clone devices. It showed that in this middleware, the clone devices were regularly synchronized with their original master such as locally stored representations were continuously updated with the known true state values.

ACS Style

Daniyal Alghazzawi; Ghadah Aldabbagh; Abdullah Saad Al-Malaise Al-Ghamdi. ScaleUp: middleware for intelligent environments. PeerJ Computer Science 2021, 7, e545 .

AMA Style

Daniyal Alghazzawi, Ghadah Aldabbagh, Abdullah Saad Al-Malaise Al-Ghamdi. ScaleUp: middleware for intelligent environments. PeerJ Computer Science. 2021; 7 ():e545.

Chicago/Turabian Style

Daniyal Alghazzawi; Ghadah Aldabbagh; Abdullah Saad Al-Malaise Al-Ghamdi. 2021. "ScaleUp: middleware for intelligent environments." PeerJ Computer Science 7, no. : e545.

Journal article
Published: 08 June 2021 in Applied Sciences
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The utilization of mobile learning continues to rise and has attracted many organizations, university environments and institutions of higher education all over the world. The cloud storage system consists of several defense issues since data security and privacy have become known as the foremost apprehension for the users. Uploading and storing specific data in the cloud is familiar and widespread, but securing the data is a complicated task. This paper proposes a cloud-based mobile learning system using a hybrid optimal elliptic curve cryptography (HOECC) algorithm comprising public and private keys for data encryption. The proposed approach utilizes an adaptive tunicate slime-mold (ATS) algorithm to generate optimal key value. Thus, the data uploaded in the cloud system are secured with high authentication, data integrity and confidentiality. The study investigation employed a survey consisting of 50 students and the questionnaire was sent to all fifty students. In addition to this, for obtaining secure data transmission in the cloud, various performance measures, namely the encryption time, decryption time and uploading/downloading time were evaluated. The results reveal that the time of both encryption and decryption is less in ATF approach when compared with other techniques.

ACS Style

Ghadah Aldabbagh; Daniyal Alghazzawi; Syed Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. Secure Data Exchange in M-Learning Platform using Adaptive Tunicate Slime-Mold-Based Hybrid Optimal Elliptic Curve Cryptography. Applied Sciences 2021, 11, 5316 .

AMA Style

Ghadah Aldabbagh, Daniyal Alghazzawi, Syed Hasan, Mohammed Alhaddad, Areej Malibari, Li Cheng. Secure Data Exchange in M-Learning Platform using Adaptive Tunicate Slime-Mold-Based Hybrid Optimal Elliptic Curve Cryptography. Applied Sciences. 2021; 11 (12):5316.

Chicago/Turabian Style

Ghadah Aldabbagh; Daniyal Alghazzawi; Syed Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. 2021. "Secure Data Exchange in M-Learning Platform using Adaptive Tunicate Slime-Mold-Based Hybrid Optimal Elliptic Curve Cryptography." Applied Sciences 11, no. 12: 5316.

Journal article
Published: 30 May 2021 in Future Internet
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This paper focuses on the study of IoT network deployments, in both unlicensed and licensed bands, considering LoRaWAN and NB-IoT standards, respectively. The objective is to develop a comprehensive and detailed network planning and coverage dimensioning methodology for assessing key metrics related to the achieved throughput and capacity for specific requirements in order to identify tradeoffs and key issues that are related to the applicability of IoT access technologies for representative use case types. This paper will provide a concise overview of key characteristics of IoT representative IoT access network standards that are considered for being deployed in unlicensed and licensed bands and will present a methodology for modeling the characteristics of both access network technologies in order to assess their coverage and capacity considering different parameters.

ACS Style

Ghadah Aldabbagh; Nikos Dimitriou; Samar Alkhuraiji; Omaimah Bamasag. Radio Coverage and Device Capacity Dimensioning Methodologies for IoT LoRaWAN and NB-IoT Deployments in Urban Environments. Future Internet 2021, 13, 144 .

AMA Style

Ghadah Aldabbagh, Nikos Dimitriou, Samar Alkhuraiji, Omaimah Bamasag. Radio Coverage and Device Capacity Dimensioning Methodologies for IoT LoRaWAN and NB-IoT Deployments in Urban Environments. Future Internet. 2021; 13 (6):144.

Chicago/Turabian Style

Ghadah Aldabbagh; Nikos Dimitriou; Samar Alkhuraiji; Omaimah Bamasag. 2021. "Radio Coverage and Device Capacity Dimensioning Methodologies for IoT LoRaWAN and NB-IoT Deployments in Urban Environments." Future Internet 13, no. 6: 144.

Journal article
Published: 19 May 2021 in Sustainability
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The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice.

ACS Style

Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability 2021, 13, 5691 .

AMA Style

Daniyal Alghazzawi, Syed Hasan, Ghadah Aldabbagh, Mohammed Alhaddad, Areej Malibari, Muhammad Asghar, Hanan Aljuaid. Development of Platform Independent Mobile Learning Tool in Saudi Universities. Sustainability. 2021; 13 (10):5691.

Chicago/Turabian Style

Daniyal Alghazzawi; Syed Hasan; Ghadah Aldabbagh; Mohammed Alhaddad; Areej Malibari; Muhammad Asghar; Hanan Aljuaid. 2021. "Development of Platform Independent Mobile Learning Tool in Saudi Universities." Sustainability 13, no. 10: 5691.

Research article
Published: 05 November 2020 in Complexity
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Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

ACS Style

Ghada Aldabbagh; Daniyal M. AlGhazzawi; Syed Hamid Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network. Complexity 2020, 2020, 1 -13.

AMA Style

Ghada Aldabbagh, Daniyal M. AlGhazzawi, Syed Hamid Hasan, Mohammed Alhaddad, Areej Malibari, Li Cheng. Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network. Complexity. 2020; 2020 ():1-13.

Chicago/Turabian Style

Ghada Aldabbagh; Daniyal M. AlGhazzawi; Syed Hamid Hasan; Mohammed Alhaddad; Areej Malibari; Li Cheng. 2020. "Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network." Complexity 2020, no. : 1-13.

Journal article
Published: 01 January 2019 in Procedia Computer Science
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Densification is one of the 5G networks solutions to increase data rate and network capacity. This technology aims for constructing a large number of small cells in a specific area which may include different wireless technologies (LTE, IEEE 802.11, WiMAX, etc.). Small cells provide shorter distance between the cell access point and the mobile node which will reduce the signal attenuation and noise. This could generate a better connection, which goes along with the expectations of high data rate requests for the year 2020. The downside of the densification is that frequent handoffs will be a challenge for the network nodes. A software-defined network SDN, on the other hand, is another promising solution for 5G networks which controls the network through centralization. Using these two solutions, the handoffs in the network are expected to be frequent and hence a more delay will be added. To reduce such delay, a distributed-mobility-management technique is used in SDN-based networks, where the mobile node takes the handoff decision. This paper has focused on the issue of the distributed-handoff delay and handover failure of mobile nodes in SDN-based IEEE 802.11 network. It proposes a hybrid clustering technique using K-mean and genetic algorithm to cluster the network. The clustering aims to reduce the scanning phase of the mobile node by minimizing the number of access points that need to be scanned for the handoff. The results show lower delays and a reduced number of handoff failure compared to the un-clustered network.

ACS Style

Suzan Basloom; Nadine Akkari; Ghadah Aldabbagh. Reducing Handoff Delay in SDN-based 5G Networks Using AP Clustering. Procedia Computer Science 2019, 163, 198 -208.

AMA Style

Suzan Basloom, Nadine Akkari, Ghadah Aldabbagh. Reducing Handoff Delay in SDN-based 5G Networks Using AP Clustering. Procedia Computer Science. 2019; 163 ():198-208.

Chicago/Turabian Style

Suzan Basloom; Nadine Akkari; Ghadah Aldabbagh. 2019. "Reducing Handoff Delay in SDN-based 5G Networks Using AP Clustering." Procedia Computer Science 163, no. : 198-208.

Journal article
Published: 01 September 2018 in Computer Networks
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Femto Access Points (FAP) and Device-to-Device (D2D) communications have recently been considered as potential candidates for 5G network densification and cell-edge performance. Mobility and handover management is a major issue in heterogeneous networks (HetNet) offering different access technologies. Recently, many Fuzzy and Multi-Attribute Decision Making (MADM) handover decision algorithms have been proposed to ensure Quality of Service (QoS), reduce the number of handovers and handover blocking probability of mobile users. However, network discovery is still an issue as it increases the total handover delay and drains the battery of the user equipment (UE). In addition, the UE may undergo high interference with other co-channel users after the handover is executed, thus limiting the overall network performance. Currently, the emerging Software Defined Networking (SDN) has been proposed in which one centralized controller can assist in the handover discovery, handover decision, and co-channel interference coordination. SDN-based handover algorithms ensure QoS, Quality of Experience (QoE), reduce delay and interference. In this paper, we will integrate the Fuzzy logic into the SDN to assist in FAPs and D2D discovery, and the decision of candidate networks based on the networks’ QoS parameters. Then, the UE will make the final handover decision by selecting the best network based on the predicted QoE using TOPSIS and AHP algorithms. Frequency reuse and appropriate power control are also applied in order to increase network capacity and reduce interference. Performance results show that the proposed SDN-based Fuzzy MADM handover scheme reduces unnecessary handovers, blocking probability and total handover delay. In addition, throughput is increased as the number of users increases.

ACS Style

Malak Sadik; Nadine Akkari; Ghadah Aldabbagh. SDN-based handover scheme for multi-tier LTE/Femto and D2D networks. Computer Networks 2018, 142, 142 -153.

AMA Style

Malak Sadik, Nadine Akkari, Ghadah Aldabbagh. SDN-based handover scheme for multi-tier LTE/Femto and D2D networks. Computer Networks. 2018; 142 ():142-153.

Chicago/Turabian Style

Malak Sadik; Nadine Akkari; Ghadah Aldabbagh. 2018. "SDN-based handover scheme for multi-tier LTE/Femto and D2D networks." Computer Networks 142, no. : 142-153.

Article
Published: 30 March 2018 in Wireless Networks
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In very crowded areas, a large number of LTE users contained in a single cell will try to access services at the same time causing high load on the Base Station (BS). Some users may be blocked from getting their requested services due to this high load. Using a two-hop relay architecture can help in increasing the system capacity, increasing coverage area, decreasing energy consumption, and reducing the BS load. Clustering techniques can be used to configure the nodes in such two-layer topology. This paper proposes a new algorithm for relay selection based on the Basic Sequential Algorithmic Scheme (BSAS) along with power control protocol. Unlike other capacity improving techniques such as small cells and relay stations this approach does not require additional infrastructure. Instead, users themselves will act as a temporary relay stations. Modifications are implemented to the original BSAS to make it suitable for LTE environment and to improve its performance. The protocol for resource allocation and power control is implemented assuming a multi cell scenario. The algorithm is compared to other relaying and clustering schemes in addition to the conventional LTE. The simulation results show that the proposed algorithm has improved system capacity and energy consumption compared to other existing clustering/relaying schemes.

ACS Style

Maryam Hajjar; Ghadah Aldabbagh; Nikos Dimitriou; Moe Z. Win. Relay selection based clustering techniques for high density LTE networks. Wireless Networks 2018, 25, 2305 -2314.

AMA Style

Maryam Hajjar, Ghadah Aldabbagh, Nikos Dimitriou, Moe Z. Win. Relay selection based clustering techniques for high density LTE networks. Wireless Networks. 2018; 25 (5):2305-2314.

Chicago/Turabian Style

Maryam Hajjar; Ghadah Aldabbagh; Nikos Dimitriou; Moe Z. Win. 2018. "Relay selection based clustering techniques for high density LTE networks." Wireless Networks 25, no. 5: 2305-2314.

Journal article
Published: 16 March 2017 in IEEE Access
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LTE usage is rapidly increasing with the increased demand for high data rate services. LTE cells with high load would be insufficient to handle all users' traffic. This may cause blocking for some users or degrade the quality for others. Several techniques are adopted to increase the capacity of LTE cells. This paper will address specifically the use of some users as relays to others in order to increase the capacity of a specific cell. The result is a two-hop topology network with some users connected directly to the base station (BS) and others using some of the already connected users as relays to access the BS. Different techniques could be used to configure the users in such a topology. The paper proposes a new algorithm for relay selection in a multi-cell scenario based on K-means and selection strategy.

ACS Style

Maryam Hajjar; Ghadah Aldabbagh; Nikos Dimitriou; Moe Z. Win. Hybrid Clustering Scheme for Relaying in Multi-Cell LTE High User Density Networks. IEEE Access 2017, 5, 4431 -4438.

AMA Style

Maryam Hajjar, Ghadah Aldabbagh, Nikos Dimitriou, Moe Z. Win. Hybrid Clustering Scheme for Relaying in Multi-Cell LTE High User Density Networks. IEEE Access. 2017; 5 ():4431-4438.

Chicago/Turabian Style

Maryam Hajjar; Ghadah Aldabbagh; Nikos Dimitriou; Moe Z. Win. 2017. "Hybrid Clustering Scheme for Relaying in Multi-Cell LTE High User Density Networks." IEEE Access 5, no. : 4431-4438.

Journal article
Published: 01 October 2016 in Computer Networks
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ACS Style

Afraa Khalifah; Nadine Akkari; Ghadah Aldabbagh; Nikos Dimitriou. Hybrid femto/macro rate-based offloading for high user density networks. Computer Networks 2016, 108, 371 -380.

AMA Style

Afraa Khalifah, Nadine Akkari, Ghadah Aldabbagh, Nikos Dimitriou. Hybrid femto/macro rate-based offloading for high user density networks. Computer Networks. 2016; 108 ():371-380.

Chicago/Turabian Style

Afraa Khalifah; Nadine Akkari; Ghadah Aldabbagh; Nikos Dimitriou. 2016. "Hybrid femto/macro rate-based offloading for high user density networks." Computer Networks 108, no. : 371-380.

Journal article
Published: 26 September 2016 in International Journal of Computing and Information Sciences
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ACS Style

Thamary Alruhaili; Ghadah Aldabbagh; Fatma Bouabdallah; Nikos Dimitriou; Moe Win. Performance Evaluation for Wi-Fi Offloading Schemes in LTE Networks. International Journal of Computing and Information Sciences 2016, 12, 121 -131.

AMA Style

Thamary Alruhaili, Ghadah Aldabbagh, Fatma Bouabdallah, Nikos Dimitriou, Moe Win. Performance Evaluation for Wi-Fi Offloading Schemes in LTE Networks. International Journal of Computing and Information Sciences. 2016; 12 (1):121-131.

Chicago/Turabian Style

Thamary Alruhaili; Ghadah Aldabbagh; Fatma Bouabdallah; Nikos Dimitriou; Moe Win. 2016. "Performance Evaluation for Wi-Fi Offloading Schemes in LTE Networks." International Journal of Computing and Information Sciences 12, no. 1: 121-131.

Article
Published: 01 April 2016 in ETRI Journal
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This paper examines the resource gain that can be obtained from the creation of clusters of nodes in densely populated areas. A single node within each such cluster is designated as a “hotspot”; all other nodes then communicate with a destination node, such as a base station, through such hotspots. We propose a semi-distributed algorithm, referred to as coordinated cognitive tethering (CCT), which clusters all nodes and coordinates hotspots to tether over locally available white spaces. CCT performs the following these steps: (a) groups nodes based on a modified k-means clustering algorithm; (b) assigns white-space spectrum to each cluster based on a distributed graph-coloring approach to maximize spectrum reuse, and (c) allocates physical-layer resources to individual users based on local channel information. Unlike small cells (for example, femtocells and WiFi), this approach does not require any additions to existing infrastructure. In addition to providing parallel service to more users than conventional direct communication in cellular networks, simulation results show that CCT can increase the average battery life of devices by 30%, on average.

ACS Style

Haleh Tabrizi; Golnaz Farhadi; John Matthew Cioffi; Ghadah Aldabbagh. Coordinated Cognitive Tethering in Dense Wireless Areas. ETRI Journal 2016, 38, 314 -325.

AMA Style

Haleh Tabrizi, Golnaz Farhadi, John Matthew Cioffi, Ghadah Aldabbagh. Coordinated Cognitive Tethering in Dense Wireless Areas. ETRI Journal. 2016; 38 (2):314-325.

Chicago/Turabian Style

Haleh Tabrizi; Golnaz Farhadi; John Matthew Cioffi; Ghadah Aldabbagh. 2016. "Coordinated Cognitive Tethering in Dense Wireless Areas." ETRI Journal 38, no. 2: 314-325.

Journal article
Published: 28 December 2015 in ETRI Journal
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ACS Style

Haleh Tabrizi; Golnaz Farhadi; John Cioffi; Ghadah Aldabbagh. Coordinated Cognitive Tethering in Dense Wireless Areas. ETRI Journal 2015, 1 .

AMA Style

Haleh Tabrizi, Golnaz Farhadi, John Cioffi, Ghadah Aldabbagh. Coordinated Cognitive Tethering in Dense Wireless Areas. ETRI Journal. 2015; ():1.

Chicago/Turabian Style

Haleh Tabrizi; Golnaz Farhadi; John Cioffi; Ghadah Aldabbagh. 2015. "Coordinated Cognitive Tethering in Dense Wireless Areas." ETRI Journal , no. : 1.

Journal article
Published: 04 March 2015 in Wireless Networks
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With advances in technology, network operators may need to set up a dynamic spectrum access overlay in heterogeneous networks (HetNets) to increase network coverage, spectrum efficiency, and the capacity of these networks. Uses of TV white space (TVWS) and long term evolution (LTE) are the combination of a new research direction to meet the increasing user demands in the domain of wireless cellular networks. Without the consideration of traffic flow, a network may operate with serious congestion problems that degrade the system performance. Congestion problems can be resolved by either reducing traffic flow or increasing the bandwidth provision. This paper has proposed Distributed dynamic load balancing (DDLB) cellular-based TVWS and LTE technique, such that a cellular-based device can operate on both TVWS and LTE by simply switching its frequency of operation when necessary. The objective of this paper is to resolve the congestion problems in a HetNet through dynamically constructing new clusters to increase the system bandwidth. The simulation results show that the proposed technique solved the bottleneck problem, reduced transmission control overhead and power consumption, and increased the average throughput and load balancing index.

ACS Style

Ghadah Aldabbagh; Sheikh Tahir Bakhsh; Nadine Akkari; Sabeen Tahir; Sana Khan; John Cioffi. Distributed dynamic load balancing in a heterogeneous network using LTE and TV white spaces. Wireless Networks 2015, 21, 2413 -2424.

AMA Style

Ghadah Aldabbagh, Sheikh Tahir Bakhsh, Nadine Akkari, Sabeen Tahir, Sana Khan, John Cioffi. Distributed dynamic load balancing in a heterogeneous network using LTE and TV white spaces. Wireless Networks. 2015; 21 (7):2413-2424.

Chicago/Turabian Style

Ghadah Aldabbagh; Sheikh Tahir Bakhsh; Nadine Akkari; Sabeen Tahir; Sana Khan; John Cioffi. 2015. "Distributed dynamic load balancing in a heterogeneous network using LTE and TV white spaces." Wireless Networks 21, no. 7: 2413-2424.

Journal article
Published: 03 October 2014 in IEEE Transactions on Vehicular Technology
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Coordinated tethering over a white-space spectrum is investigated herein to increase mobile broadband spectrum efficiency in densely populated areas. This paper proposes an algorithm for operator-controlled tethering over white spaces. The proposed approach does not add to the existing infrastructure but instead allows the individual nodes to act as “hotspots” and to tether data to and from other nodes. The proposed algorithm iteratively clusters the nodes into hotspots and slaves and allocates resources to maximize spectrum utility. The proposed method's dynamic characteristics allow cellular systems to hierarchically evolve in dense areas as necessary. A signaling framework for node-to-node and base-station-to-node communication that enables such operator-controlled tethering is also presented. Simulation results show that given a fixed amount of network resources, the proposed algorithm can significantly increase the number of supported users.

ACS Style

Haleh Tabrizi; Golnaz Farhadi; John M. Cioffi; Ghadah Aldabbagh; Ghadah Aldabagh. Coordinated Tethering over White-Spaces. IEEE Transactions on Vehicular Technology 2014, 64, 1 -1.

AMA Style

Haleh Tabrizi, Golnaz Farhadi, John M. Cioffi, Ghadah Aldabbagh, Ghadah Aldabagh. Coordinated Tethering over White-Spaces. IEEE Transactions on Vehicular Technology. 2014; 64 (9):1-1.

Chicago/Turabian Style

Haleh Tabrizi; Golnaz Farhadi; John M. Cioffi; Ghadah Aldabbagh; Ghadah Aldabagh. 2014. "Coordinated Tethering over White-Spaces." IEEE Transactions on Vehicular Technology 64, no. 9: 1-1.