Open AccessPrivacy-preserving Trust-Aware Group-based Framework in Mobile crowdsensing
Author(s) -
Bayan Hashr Saeed Alamri,
Muhammad Mostafa Monowar,
Suhair Alshehri
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3232401
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In practical mobile crowdsensing (MCS) systems, many cooperative sensing tasks require a group of reliable participants to perform collaboratively. In this article, we address the problem of group formulation in MCS, which aims to recruit highly trusted participants and form a high-reputation group. We propose a novel Privacy-preserving Trust-Aware Group Formation (PTAGF) framework that ensures trust and privacy between the group members. This framework consists of three mechanisms; the member trust assessment mechanism, the group forming mechanism, and the two-layer privacy-preserving mechanism. Furthermore, we prove that the group forming problem is NP-hard, and thus propose a heuristic-based Trust-Aware Group Formulation (TAGF) algorithm. A theoretical analysis is provided, which demonstrates that the proposed framework achieves privacy and security. Finally, we experimentally evaluate the performance of PTAGF on a real-world dataset against two state-of-the-art approaches. The results show that PTAGF outperforms these approaches in terms of trustworthiness in group selection. Moreover, it achieves reasonable task coverage and running time with different communities size, group sizes, and task scales.