Browsing by Author "Hossain, Ekram"
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Item Coalition Formation Games for Distributed Cooperation Among Roadside Units in Vehicular Networks(IEEE Journal on Selected Areas in Communications, 12/30/2010) Saad, Walid; Han, Zhu; Hjorungnes, Are; Niyato, Dusit; Hossain, EkramVehicle-to-roadside (V2R) communications enable vehicular networks to support a wide range of applications for enhancing the efficiency of road transportation. While existing work focused on non-cooperative techniques for V2R communications between vehicles and roadside units (RSUs), this paper investigates novel cooperative strategies among the RSUs in a vehicular network. We propose a scheme whereby, through cooperation, the RSUs in a vehicular network can coordinate the classes of data being transmitted through V2R communication links to the vehicles. This scheme improves the diversity of the information circulating in the network while exploiting the underlying content-sharing vehicle-to-vehicle communication network. We model the problem as a coalition formation game with transferable utility and we propose an algorithm for forming coalitions among the RSUs. For coalition formation, each RSU can take an individual decision to join or leave a coalition, depending on its utility which accounts for the generated revenues and the costs for coalition coordination. We show that the RSUs can self-organize into a Nash-stable partition and adapt this partition to environmental changes. Simulation results show that, depending on different scenarios, coalition formation presents a performance improvement, in terms of the average payoff per RSU, ranging between 20.5% and 33.2%, relative to the non-cooperative case.Item Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks(IEEE Journal on Selected Areas in Communications, 2/24/2011) Meng, Jia Jasmine; Yin, Wotao; Li, Husheng; Hossain, Ekram; Han, ZhuSpectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmission requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Item Mean Field Game Theory for Future Heterogeneous and Hierarchical Communication Networks(2020-05) Banez, Reginald A.; Han, Zhu; Pan, Miao; Prasad, Saurabh; Hossain, Ekram; Yin, WotaoAccording to the latest Cisco Annual Internet Report, the current communication network infrastructures are approaching their limits caused by the increasing data traffic, more frequent network usage, and rising number of connected devices. In order to overcome these limitations, enabling technologies such as ultra-dense networks, multi-access edge networks, and massive antenna arrays are proposed as part of the future generation of communication networks. However, in order to analyze, model, and simulate these technologies, an appropriate mathematical framework that can handle large number of interacting entities is necessary. Hence, the application of mean field games (MFGs) to future communication networks is proposed in this dissertation. The first work of this dissertation deals with the modeling of user behavior through belief and opinion evolution in social networks, which is essential in improving the services provided by a network provider. A multiple-population MFG approach is applied to depict the behavior of social network users in a multiple-group setting. Theoretical and experimental simulations using a social evolution dataset suggest the effectiveness of the MFG approach in estimating and predicting the distribution of belief and opinion in social networks. The second work of this dissertation investigates an effective and efficient method for computation offloading in multi-access edge computing networks (MECN). A mean-field-type game (MFTG) approach is utilized to design non-cooperative and cooperative computation offloading algorithms to decrease latency and energy consumption. The results indicate that the proposed MFTG-based algorithms can optimize energy consumption and latency associated with computation offloading. Then, the third work of this dissertation presents a proposed dynamic hierarchical framework for resource allocation in network virtualization. Lastly, this dissertation is concluded with a summary of important results and remarks. Furthermore, future works integrating MFG in unmanned aerial vehicle (UAV) networks, network virtualization, and Internet-of-Things (IoT) are proposed.Item Smart grid sensor data collection, communication, and networking: a tutorial(Wireless Communications and Mobile Computing, 7/23/2012) Kayastha, Nipendra; Niyato, Dusit; Hossain, Ekram; Han, ZhuThe smart grid is an innovative energy network that will improve the conventional electrical grid network to be more reliable, cooperative, responsive, and economical. Within the context of the new capabilities, advanced data sensing, communication, and networking technology will play a significant role in shaping the future of the smart grid. The smart grid will require a flexible and efficient framework to ensure the collection of timely and accurate information from various locations in power grid to provide continuous and reliable operation. This article presents a tutorial on the sensor data collection, communications, and networking issues for the smart grid. First, the applications of data sensing in the smart grid are reviewed. Then, the requirements for data sensing and collection, the corresponding sensors and actuators, and the communication and networking architecture are discussed. The communication technologies and the data communication network architecture and protocols for the smart grid are described. Next, different emerging techniques for data sensing, communications, and sensor data networking are reviewed. The issues related to security of data sensing and communications in the smart grid are then discussed. To this end, the standardization activities and use cases related to data sensing and communications in the smart grid are summarized. Finally, several open issues and challenges are outlined.