BioMCS 2.0: A Distributed, Energy-Aware Fog-Based Framework for Data Forwarding in Mobile Crowdsensing
Mobile crowdsensing (MCS) paradigm enables users equipped with energy-constrained smart devices to participate in sensing and reporting of assigned tasks. To achieve seamless communication as well as effective energy and resource management, we leveraged the fog computing platform to propose a centralized, energy-efficient and robust data collection framework, called bioMCS, based on the topological properties of a biological network called transcriptional regulatory network. However, since MCS platforms may potentially entail a high number of mobile users and massive volumes of data traffic, we extend the current work under the name bioMCS 2.0 to conceive a distributed energy-aware data forwarding mechanism where the fog devices function as task data relay nodes. bioMCS 2.0 combines energy-awareness, abundance of subgraphs (called motifs) in the fog network and proximity to the base station to perform efficient task sensing and forwarding in a dynamic scenario where fog devices are both energy constrained and mobile. It also ensures quality of information by accepting task data from reliable smart devices. Extensive simulation on the map of New York City and realistic mobility models suggests that bioMCS 2.0 exhibits comparable performance in terms of data delivery, latency and energy efficiency in comparison with both random next hop (fog node) selection as well as centralized forwarding technique that rely on global network knowledge.
S. Roy et al., "BioMCS 2.0: A Distributed, Energy-Aware Fog-Based Framework for Data Forwarding in Mobile Crowdsensing," Pervasive and Mobile Computing, vol. 73, Elsevier, Jun 2021.
The definitive version is available at https://doi.org/10.1016/j.pmcj.2021.101381
Center for High Performance Computing Research
Keywords and Phrases
Distributed data forwarding; Feed forward loops; Fog computing; Mobile crowdsensing; Motifs
International Standard Serial Number (ISSN)
Article - Journal
© 2021 Elsevier, All rights reserved.
01 Jun 2021