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Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
A review of machine learning-based human activity recognition for diverse applications
Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …
computer science. Due to the articulated nature of human motion, it is not trivial to detect …
Learning-based adaptive sensor selection framework for multi-sensing WSN
Wireless sensor nodes equipped with multiple sensors often have limited energy availability.
To optimize the energy sustainability of such sensor hubs, in this paper a novel adaptive …
To optimize the energy sustainability of such sensor hubs, in this paper a novel adaptive …
Swear: Sensing using wearables. generalized human crowdsensing on smartwatches
In this work, we present SWear, a generalized human crowdsensing platform to collect
human behavior data on smartwatches. It uses the stand-alone capabilities of smartwatches …
human behavior data on smartwatches. It uses the stand-alone capabilities of smartwatches …
Leveraging mobile sensing and machine learning for personalized mental health care
Mental illness is widespread in our society, yet remains difficult to treat due to challenges
such as stigma and overburdened health care systems. New paradigms are needed for …
such as stigma and overburdened health care systems. New paradigms are needed for …
Leveraging LSTM and Reinforcement Learning for Adaptive Sensing in CIoT Nodes
Wireless sensor networks (WSNs) offer a broad range of applications in the Consumer
Internet of Things (CIoT). The sensor nodes in a WSN are equipped with an array of sensors …
Internet of Things (CIoT). The sensor nodes in a WSN are equipped with an array of sensors …
A Delaunay triangulation-based improved energy-aware clustering algorithm for WSNs
Energy efficiency is an important constraint in Wireless Sensor Networks (WSNs). In this
paper, we propose a Delaunay triangulation-based approach for the efficient participation of …
paper, we propose a Delaunay triangulation-based approach for the efficient participation of …
Mobile sensing: Leveraging machine learning for efficient human behavior modeling
EK Barrett, CM Fard, HN Katinas… - 2020 Systems and …, 2020 - ieeexplore.ieee.org
Smartphones can collect millions of data points from each of its users daily, contributing to a
significant change in how the healthcare community approaches health monitoring. This …
significant change in how the healthcare community approaches health monitoring. This …
Designing adaptive passive personal mobile sensing methods using reinforcement learning framework
Smartphone embedded sensors have created unprecedented opportunities to study human
behavior in natural conditions through continuous mobile sensing. However, continuous …
behavior in natural conditions through continuous mobile sensing. However, continuous …
[PDF][PDF] 群智感知需求不确定任务的资源分配方法
姚秋言， 赵丹 - 计算机与数字工程, 2024 - jsj.journal.cssc709.net
摘要对群智感知任务类型中的突发任务资源分配问题进行研究. 首先分析突发任务的特点,
建立突发任务需求不确定的多阶段随机规划模型, 并使用三个指标: 效率, 效力和公**来衡量资源 …
建立突发任务需求不确定的多阶段随机规划模型, 并使用三个指标: 效率, 效力和公**来衡量资源 …