相关论文: Detecting Irregular Patterns in IoT Streaming Data…
Falling, especially in the elderly, is a critical issue to care for and surveil. There have been many studies focusing on fall detection. However, from our survey, there is still no research indicating the prior-fall activities, which we…
The increasing shortage of nursing staff and the acute risk of falls in nursing homes pose significant challenges for the healthcare system. This study presents the development of an automated fall detection system integrated into care…
In the Internet of Things (IoT) devices are exposed to various kinds of attacks when connected to the Internet. An attack detection mechanism that understands the limitations of these severely resource-constrained devices is necessary. This…
The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and…
Falls among seniors are a major public health issue. Existing solutions using wearable sensors, ambient sensors, and RGB-based vision systems face challenges in reliability, user compliance, and practicality. Studies indicate that…
Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted living (AAL) uses recent technologies such as 5G networks and…
In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detection of these irregularities is crucial for ensuring service…
In recent years a growing interest on action recognition is observed, including detection of fall accident for the elderly. However, despite many efforts undertaken, the existing technology is not widely used by elderly, mainly because of…
Smart home IoT systems and devices are susceptible to attacks and malfunctions. As a result, users' concerns about their security and safety issues arise along with the prevalence of smart home deployments. In a smart home, various…
The rapid proliferation of Internet of Things (IoT) devices introduces significant security challenges due to limited visibility and weak device-level guarantees. Accurate and timely identification of devices is essential for enforcing…
The Medical Internet of Things, a recent technological advancement in medicine, is incredibly helpful in providing real-time monitoring of health metrics. This paper presents a non-invasive IoT system that tracks patients' emotions,…
The rapid development of IoT applications and their use in various fields of everyday life has resulted in an escalated number of different possible cyber-threats, and has consequently raised the need of securing IoT devices. Collecting…
Internet of Things (IoT) is a whole new ecosystem comprised of heterogeneous connected devices -i.e. computers, laptops, smart-phones and tablets as well as embedded devices and sensors-that communicate to deliver capabilities making our…
Detecting unintended falls is essential for ambient intelligence and healthcare of elderly people living alone. In recent years, deep convolutional nets are widely used in human action analysis, based on which a number of fall detection…
Given real-time sensor data streams obtained from machines, how can we continuously predict when a machine failure will occur? This work aims to continuously forecast the timing of future events by analyzing multi-sensor data streams. A key…
A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning…
Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks,…
In this work, we present OCLADS, a novel communication framework with continual learning (CL) for Internet of Things (IoT) anomaly detection (AD) when operating in non-stationary environments. As the statistical properties of the observed…
Internet of Things (IoT) sensors are ubiquitous technologies deployed across smart cities, industrial sites, and healthcare systems. They continuously generate time series data that enable advanced analytics and automation in industries.…
This paper presents the detection of DDoS attacks in IoT networks using machine learning models. Their rapid growth has made them highly susceptible to various forms of cyberattacks, many of whose security procedures are implemented in an…