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A Survey on Internet of Things From Industrial Market Perspective
TLDR
This survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm and provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.
Experience-driven Networking: A Deep Reinforcement Learning based Approach
TLDR
A novel experience-driven approach that can learn to well control a communication network from its own experience rather than an accurate mathematical model, just as a human learns a new skill (such as driving, swimming, etc).
Mobile Cloud Computing: A Survey, State of Art and Future Directions
TLDR
The applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias is illustrated, and research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale are identified.
Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things
TLDR
A context-aware sensor search, selection, and ranking model, called CASSARAM, is proposed to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities.
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach
TLDR
The proposed DRL-EC3 maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity, and makes decisions under the guidance of two powerful deep neural networks.
QoI-Aware Multitask-Oriented Dynamic Participant Selection With Budget Constraints
TLDR
Real and extensive trace-based simulations show that the proposed dynamic participant selection strategy can achieve far better QoI satisfactions for all tasks than selecting participants randomly or through the reversed-auction-based approaches.
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
TLDR
This paper surveys over one hundred IoT smart solutions in the marketplace and examines them closely in order to identify the technologies used, functionalities, and applications, and suggests a number of potentially significant research directions.
Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning
TLDR
A decentralized deep reinforcement learning (DRL) based framework to control each UAV in a distributed manner to maximize the temporal average coverage score achieved by all UAVs in a task, maximize the geographical fairness of all considered point-of-interests (PoIs), and minimize the total energy consumptions.
Energy-Efficient Location and Activity-Aware On-Demand Mobile Distributed Sensing Platform for Sensing as a Service in IoT Clouds
TLDR
The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.
Context-Awareness for Mobile Sensing: A Survey and Future Directions
TLDR
This paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions and points out the challenges faced and enlightens them by proposing possible solutions.
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