Selective Green Device Discovery for Device-to-Device Communication

The D2D communication is expected to improve devices’ energy-efficiency, which has become a major requirement of the future wireless network. Before the D2D communication can be performed, the device discovery between devices must be done. The previous works usually only assumed one mode of device discovery, i.e. either use network-assisted (with network supervision) or independent (without network supervision) device. Therefore, we propose a selective device discovery for device-to-device (D2D) communication that can utilize both device discovery modes and maintain devices’ energy-efficiency. Different from previous works, our proposed method selects the best device discovery mode to get the best energy-efficiency. Moreover, to further improve the energy-efficiency, our proposed method also deployed in D2D cluster with multiple cluster heads. The proposed method selects the most suitable mode using thresholds (cluster energy consumption and new device acceptance) and cluster energy expectation. Our experiment result indicates that the proposed method provides lowest energy consumption per new accepted device while compared with schemes with full network-assisted and independent device discovery in low numbers of new device arrival (for the number of new devices arrival = 1 ~ 3)

Device Discovery Schemes for Energy-efficient Cluster Head Rotation in D2D

 In this paper, novel device discovery approaches for the Cluster Head Rotation, which is a state-of-the-art method for the Device-to-Device communication, are proposed. The device discovery is the process to detect and to include new devices in the Device-to-Device communication. The proposed device discovery is aimed to attain energy efficiency for the communication devices. We propose two schemes for the device discovery: eNB-assisted and independent device discovery. Compared to previous work, the proposed device discovery is utilizing the cluster head rotation method, to achieve better energy efficiency. In this work, several simulations were performed and discussed for both schemes. In the first simulation, the device energy consumption is examined. After that, the number of devices that get rejected is studied. The device discovery processes in multi cluster head scenario, which is Cluster Head Rotation, are examined in this paper. The result of the simulation shows that eNB-assisted device discovery can provide better energy efficiency. Also, the number of rejected devices of the eNB-assisted device discovery is slightly lower than independent device discovery

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors

Internet of Things and Sensors Networks in 5G Wireless Communications

The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic

Internet of Things and Sensors Networks in 5G Wireless Communications

This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors