Iot sensor selection for target localization: A reinforcement learning based approach
Internet of things (IoT) is a key enabler for target localization, where IoT-based sensors work
towards identifying target's location in an area of interest (AoI). Appropriate selection of IoT …
towards identifying target's location in an area of interest (AoI). Appropriate selection of IoT …
Smart edge-based driver drowsiness detection in mobile crowdsourcing
Traffic accidents caused by drowsy drivers represent a crucial threat to public safety. Recent
statistics show that drowsy drivers cause an estimated 15.5% of fatal accidents. With the …
statistics show that drowsy drivers cause an estimated 15.5% of fatal accidents. With the …
Challenges and future directions for security and privacy in vehicular fog computing
Cooperative Intelligent Transportation System (CITS) has been introduced recently to
increase road safety, traffic efficiency, and to enable various infotainment and comfort …
increase road safety, traffic efficiency, and to enable various infotainment and comfort …
Smart-3DM: Data-driven decision making using smart edge computing in hetero-crowdsensing environment
Abstract Mobile Edge Computing (MEC) has recently emerged as a promising paradigm for
Mobile Crowdsensing (MCS) environments. In a given Area of Interest (AoI), the sensing …
Mobile Crowdsensing (MCS) environments. In a given Area of Interest (AoI), the sensing …
Smart edge-based fake news detection using pre-trained BERT model
Today, online media applications are an important source of information. People are
creating and sharing more information than ever before around the world. Being provided by …
creating and sharing more information than ever before around the world. Being provided by …
Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data
In this paper, we propose a solution to address the challenges of varying client resource
capabilities in the IoT environment when using the SplitFed architecture for training models …
capabilities in the IoT environment when using the SplitFed architecture for training models …
Fuzzy q-learning-based opportunistic communication for mec-enhanced vehicular crowdsensing
This study focuses on MEC-enhanced, vehicle-based crowdsensing systems that rely on
devices installed on automobiles. We investigate an opportunistic communication paradigm …
devices installed on automobiles. We investigate an opportunistic communication paradigm …
Trust-3dm: trustworthiness-based data-driven decision-making framework using smart edge computing for continuous sensing
Mobile Edge Computing (MEC) has been proposed as an efficient solution for Mobile
crowdsensing (MCS). It allows the parallel collection and processing of data in real time in …
crowdsensing (MCS). It allows the parallel collection and processing of data in real time in …
Abnormal network traffic detection using deep learning models in iot environment
As an emergent technology, the internet of things (IoT) aims to create an interconnected
world of smart devices autonomously communicating via the internet. Integrating …
world of smart devices autonomously communicating via the internet. Integrating …
Distributed versus centralized computing of coverage in mobile crowdsensing
The expected spatial coverage of a crowdsensing platform is an important parameter that
derives from the mobility data of the crowdsensing platform users. We tackle the challenge of …
derives from the mobility data of the crowdsensing platform users. We tackle the challenge of …