A Clustering-Based Device-to-Device Communication to Support Diverse Applications
Amirshahram Hematian, Wei Yu, Chao Lu, David W. Griffith, Nada T. Golmie
The expanding coverage and capacity of cellular networks through broadband wireless infrastructure has limitations in accommodating the expanding strain and capacity required by diverse applications (public safety, smart grid, transportation, healthcare systems, smart cities, social networking, etc.). In this paper, we address the issue of how to leverage Wi-Fi Direct (as an outband solution) to enable the device-to-device (D2D) communication, which can not only offload massive data traffic from the LTE-based cellular network, but also support the communications of Internet-of-Things(IoT) applications. Particularly, we develop a clustering-based scheme that automatically finds the best candidates to remain connected to the LTE network while the rest of the devices can be disconnected directly from the LTE-based cellular network. By doing so, we can reduce the signal interference, increase the average throughput and spectral efficiency of the network, and also reduce unnecessary data traffic that can be transmitted locally by D2D communications instead of going through the LTE- based cellular network. Devices in established clusters can indirectly communicate with the LTE network via the cluster head, which can be dynamically selected and remains connected to the LTE network directly. Using the real-world cellular data collected from a public database related to the deployment of LTE networks, and Google geo-coding APIs to locate the real-world smart meters, we show the effectiveness of our proposed scheme based on an extensive study of two scenarios: the first is in traffic offloading in the cellular network, and the second is in IoT applications, specifically, smart grid communications.
International Conference on Research in Adaptive and Convergent Systems (RACS '16)