Empowering Self-Organized Feature Maps for AI-Enabled Modeling of Fake Task Submissions to Mobile Crowdsensing Platforms

Abstract

Mobile crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of smart devices to improve the sensing capacity and reduce sensing costs in large regions. Due to the ubiquitous nature of MCS services, smart devices require awareness of against misbehaving users that are becoming smarter to clog the resources in such a nondedicated sensing environment. In an MCS setting, the primary goal of a fake sensing task submission is to keep participant devices occupied, such as the battery, sensing, storage, and computing. Since the development of robust sensing campaigns highly depends on the existence of a realistic model of misbehaving users, this article leverages artificial intelligence and introduces a region-based self-organizing feature map (SOFM)-based model on user movement patterns so as to place the fake sensing tasks with the objective of maximum impacted participants and recruits. Uniformly and randomly initialized neurons are designed with fixed and adaptive quantities that are determined based upon the affected area on the covered terrain. Through numerical studies, we show that the impact of the SOFM structures can affect up to 46% of the participants and up to 37% of the recruits under various SOFM topologies. Furthermore, SOFM-based task submission models can increase the energy consumption in recruited devices by up to 39% due to the illegitimate task submission.