Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Combination of Cloud Computing and Internet of Things (IOT) in Medical Monitoring Systems
Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved
A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration
Internet of Things as a Service (iTaaS): Challenges and Solutions for Management of Sensor Data on the Cloud and the Fog
Management of resource constrained devices in the internet of things
IoT-Based Health Monitoring System for Active and Assisted Living
IoT-A and FIWARE: Bridging the Barriers between the Cloud and IoT Systems Design and Implementation
An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare
Internet of Things and Fog Computing
Efficient and Scalable IoT Service Delivery on Cloud
Assessment of the Suitability of Fog Computing in the Context of Internet of Things
The visions, technologies, applications and security issues of Internet of Things
Lightweight Management of Resource-Constrained Sensor Devices in Internet of Things
Overview - Fog Computing and Internet-of-Things (IOT)
A Review on Internet of Things (IoT)
Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices
Interference Aware Scheduling of Sensors in IoT Enabled Health-Care Monitoring System
Rethinking the Internet of Things
Wearables and the Internet of Things for Health: Wearable, Interconnected Devices Promise More Efficient and Comprehensive Health Care
Data Management for Internet of Things: Challenges, Approaches and Opportunities
Integrating Wireless Sensor Networks with Cloud Computing
Multi-layer MPLS Network Design: the Impact of Statistical Multiplexing
Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction
Torus data center network with smart flow control enabled by hybrid optoelectronic routers [Invited]
Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning