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Found 23 papers, 1 papers with code
Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services
We propose a novel framework for crowdsourced images to determine the likelihood of an image being fake.
Positional Encoding-based Resident Identification in Multi-resident Smart Homes
We propose a novel resident identification framework to identify residents in a multi-occupant smart environment.
CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities
However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale.
Energy Loss Prediction in IoT Energy Services
We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment.
Monitoring Efficiency of IoT Wireless Charging
Crowdsourcing wireless energy is a novel and convenient solution to charge nearby IoT devices.
DIY-IPS: Towards an Off-the-Shelf Accurate Indoor Positioning System
We present DIY-IPS - Do It Yourself - Indoor Positioning System, an open-source real-time indoor positioning mobile application.
IMAP: Individual huMAn mobility Patterns visualizing platform
In addition, the rigid mobility models may result in missing the hidden regularities in users' records.
A Deep Reinforcement Learning Approach for Composing Moving IoT Services
We develop a novel framework for efficiently and effectively discovering crowdsourced services that move in close proximity to a user over a period of time.
Occupancy Estimation from Thermal Images
We propose a non-intrusive, and privacy-preserving occupancy estimation system for smart environments.
Conflict Detection in IoT-based Smart Homes
Conflicts may arise during interactions between the resident and IoT services in smart homes.
Reputation Bootstrapping for Composite Services using CP-nets
We consider the history of invocation among component services to determine reputation-interdependence in a composition.
Drone-as-a-Service Composition Under Uncertainty
Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts.
Sequential Learning-based IaaS Composition
We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking.
A CP-Net based Qualitative Composition Approach for an IaaS Provider
The IaaS provider's and consumers' qualitative preferences are captured using CP-Nets.
Long-term IaaS Provider Selection using Short-term Trial Experience
We propose a novel approach to select privacy-sensitive IaaS providers for a long-term period.
Cryptography and Security Distributed, Parallel, and Cluster Computing
Layer-based Composite Reputation Bootstrapping
We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services.
Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction
We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes.
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home
We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities.
Service mining for Internet of Things
A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up.
Cognitive Amplifier for Internet of Things
We present a Cognitive Amplifier framework to augment things part of an IoT, with cognitive capabilities for the purpose of improving life convenience.
A Conflict Detection Framework for IoT Services in Multi-resident Smart Homes
A novel IoT conflict model is proposed considering the functional and non-functional properties of IoT services.
Computers and Society Distributed, Parallel, and Cluster Computing
Social-Sensor Composition for Tapestry Scenes
The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events.
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally.
