Research Article
A New Energy Efficient Big Data Dissemination Approach Using the Opportunistic D2D Communications
@INPROCEEDINGS{10.1007/978-3-319-94965-9_16, author={Ambreen Memon and William Liu and Adnan Al-Anbuky}, title={A New Energy Efficient Big Data Dissemination Approach Using the Opportunistic D2D Communications}, proceedings={Smart Grid and Innovative Frontiers in Telecommunications. Third International Conference, SmartGIFT 2018, Auckland, New Zealand, April 23-24, 2018, Proceedings}, proceedings_a={SMARTGIFT}, year={2018}, month={7}, keywords={Big data Opportunistic routing Delay tolerant network Energy efficient data dissemination Similarity analysis}, doi={10.1007/978-3-319-94965-9_16} }- Ambreen Memon
William Liu
Adnan Al-Anbuky
Year: 2018
A New Energy Efficient Big Data Dissemination Approach Using the Opportunistic D2D Communications
SMARTGIFT
Springer
DOI: 10.1007/978-3-319-94965-9_16
Abstract
The emerging cyber-physical paradigm endeavours to unite all the physical objects embedded with electronics, software, sensors, and network connectivity to allow more direct interactions and information sharing between the physical and cyber worlds. While these massively connected devices and their associated communications can exponentially increase the data generation, transmission, and processing which consume a huge amount of energy and finally end up with harming the environment seriously. In this paper, we propose a solution for energy efficient data dissemination by using the opportunistic device-to-device (D2D) communications. Each sender can decide either use network infrastructure or through encountering the end-users according to the quality of service (QoS) requirements of each data demand and also the mobility behaviors of the users. These decisions are based on the time and location- traces of daily mobility routines and related activities of users and their social relationship. The case study, based on the similarity analysis of the mobility traces, has confirmed the rich opportunities for encountering among people, thus the proposed approach has great promises to reduce the energy consumption of big data dissemination.