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Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks



Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90\% and an average recall over 95\% in fall detection.
Edge-AI in LoRa-based Health Monitoring:
Fall Detection System with Fog Computing and
LSTM Recurrent Neural Networks
J. Pe˜
na Queralta1, T. N. Gia1, H. Tenhunen2and T. Westerlund1
1Department of Future Technologies, University of Turku, Turku, Finland
2Department of Electronics, KTH Royal Institute of Technology, Stockholm, Sweden
Email: 1{jopequ, tunggi, tovewe},
Abstract—Remote healthcare monitoring has exponentially
grown over the past decade together with the increasing pen-
etration of Internet of Things (IoT) platforms. IoT-based health
systems help to improve the quality of healthcare services
through real-time data acquisition and processing. However,
traditional IoT architectures have some limitations. For instance,
they cannot properly function in areas with poor or unstable
Internet. Low power wide area network (LPWAN) technologies,
including long-range communication protocols such as LoRa,
are a potential candidate to overcome the lacking network
infrastructure. Nevertheless, LPWANs have limited transmission
bandwidth not suitable for high data rate applications such as fall
detection systems or electrocardiography monitoring. Therefore,
data processing and compression are required at the edge of
the network. We propose a system architecture with integrated
artificial intelligence that combines Edge and Fog computing,
LPWAN technology, IoT and deep learning algorithms to perform
health monitoring tasks. In particular, we demonstrate the feasi-
bility and effectiveness of this architecture via a use case of fall
detection using recurrent neural networks. We have implemented
a fall detection system from the sensor node and Edge gateway to
cloud services and end-user applications. The system uses inertial
data as input and achieves an average precision of over 90% and
an average recall over 95% in fall detection.
Index Terms—IoT; Edge Computing; Healthcare Monitoring;
LoRa; LPWAN; RNN; LSTM; Fall Detection;
Health monitoring plays an important role in disease diag-
nosis and treatments. For instance, electrocardiogram (ECG)
monitoring or fall detection systems can help to detect ab-
normalities and send messages to caregivers about the abnor-
malities in real-time. Recently, fall detection systems using
wearable devices are widely used because of several advan-
tages such as light-weight, low-cost, energy efficiency and
non-intrusiveness [1]–[4]. These wearable devices often collect
3-dimensional (3-D) acceleration or 3-D angular velocity or
both of them from a human body. The devices then transmit
the collected data to a gateway which forwards the data to
cloud. However, there are still drawbacks in these systems.
For instance, they cannot function properly in many scenarios
like areas with unstable or lack of a Internet connection.
LoRa is one of the most prominent Low Power Wide Area
Network (LPWAN) technologies [5]. The LoRa modulation
scheme characterizes for enabling long-range and low power
transmissions [6]. LoRa is widely used in many IoT applica-
tions from farming, agriculture monitoring, flood detection to
metering in smart cities [7]–[9]. The potential of LoRa can be
leveraged mostly in areas with poor connectivity or lack of
infrastructure, but also in dense urban environments to reduce
the number of access points and power consumption. Taking
the above considerations into account, Lora seems to be a good
candidate to overcome the limitations of the existing cloud-
based healthcare monitoring systems that rely on traditional
WAN technologies such as Bluetooth or Wi-Fi.
However, Lora cannot support high data rate (i.e., 250 kbps
in theory). In practice, the data rate is much lower such as
a few bytes per message and a few messages per day, due to
strict regulations of Lora duty cycle (i.e., approximate 1% duty
cycle). It is challenging to satisfy both requirements of high
data-rate applications (i.e., fall detection based on wearable
devices) and Lora duty cycle regulations.
In this paper, we propose an advanced architecture com-
bining Edge computing, Fog computing, LoRa, and IoT-based
technologies. The proposed architecture can inherit the benefits
of these technologies to enhance quality of service. The
proposed architecture can help to overcome the limitations
the existing health monitoring IoT-based systems (e.g., fall
detection or ECG monitoring IoT-based systems) and satisfy
both requirements of high data rate-applications and Lora duty
cycle regulation. We demonstrate the proposed architecture
via a use case of fall detection. In addition, we propose and
implement Edge-AI algorithms based on neural networks at
Edge gateways for improving quality of service. In particular, a
system with the proposed architecture and Edge-AI can detect
human fall cases more accurately and dynamically in different
The remainder of the paper is organized as follows: In
Section II, we present related work in wearable sensors-
based fall detection and implementations of systems relying
on artificial intelligence. Section III introduces the proposed
system architecture; while Section IV describes the imple-
mented prototype and analyses experimental results. Section
V concludes the work.
Many efforts have been devoted to develop fall detection
IoT-based systems [10], [11]. Pivato et al. [12] introduce a
fall detection system which uses a wearable device to collect
and send 3-D acceleration data to a gateway for fall detection.
In [13], authors utilize a smartwatch to collect and send
acceleration to a smartphone which acts as a gateway for
processing data. When a fall case is detected, the smartphone
sends a notification message via 3G/4G to caregivers. Ngu
et al. present a smartwatch-based IoT fall detection system.
The system is able to run Support Vector Machine and Naive
Bayes machine learning algorithms to create the fall model
and detect a fall case with a high level of accuracy. Noury et
al. present an extensive survey of literature on fall detection
[1]. The authors summarize that many proposed methods had
an accuracy of nearly 100%, which is essential in applications
where a person’s life might be at risk. However, in practical
scenarios, this figure decreases dramatically. Therefore, they
defended the necessity of a common framework for accurately
evaluating fall detection systems and protocols.
More recently, researchers have been applying deep learning
techniques for fall detection using both active and passive sen-
sors. In [14], the authors use image processing to detect falls
based on video feeds. They use convolutional neural networks
(CNN) and a fully connected neural network for extracting
features and classifying situations, respectively. Their method
has an accuracy ranging from 90% to 96%. We propose the
use of a wearable device, instead, as the number of scenarios
where it can be used is broader, requires less amount of data to
be analyzed and similar performance can be achieved. Other
authors have explored the use of cameras or radio waves [15],
Fakhrulddin et al. develop a fall detection method for
body sensor networks using CNNs [17]. The authors use data
from two accelerometers as input to the network and obtain
an accuracy varying from 75% to 92% depending on the
dataset used. In our work, we defend that recurrent neural
networks (RNN) are a better alternative to CNN because of the
importance of time as a factor in the decision process. RNNs
have been effectively used by Musci et al. for designing an
accurate fall detection method tested over the SisFall dataset
[18]. The authors developed a method for analyzing data
online in real-time that was executed with the aid of GPUs. In
our work, we focus on similar methods for resource constraint
single-board computers that operate as edge gateways.
In summary, higher accuracy and adaptability to a wider
range of situations can be achieved with deep learning when
compared to threshold-based fall detection or SVM classifica-
tion. In particular, recurrent neural networks show promising
results in fall detection applications. However, previous works
focus on the analysis part or assume that cloud computational
capabilities are available. This requires full sequences of raw
data to be transmitted to the cloud and increases the alert
latency. Instead, we propose the use of lightweight analysis
algorithms that can run on edge gateways. At the same time,
this enables the system to be deployed in rural areas or
scenarios with poor connectivity as the amount of data that is
transmitted over the Internet to cloud servers can be decreased
several orders of magnitude.
We propose a five-layer system architecture consisting of
wearable devices (sensor layer), smart edge gateways (edge
layer), LoRa access points (fog layer) and cloud services
(cloud layer) and end-user terminals (application layer). The
proposed architecture can be used for different health monitor-
ing applications such as cardiovascular or diabetes monitoring.
Sensor nodes in the proposed architecture can collect differ-
ent types of data including e-health (e.g., electroencephalog-
raphy (EEG) electrocardiography (ECG), electromyography
(EMG), and blood pressure) and contextual data such as tem-
perature, humidity, and air quality. The combination of both
e-health and contextual data helps to improve the accuracy of
disease diagnosis and analysis. The collected data is sent via
Bluetooth Low Energy (BLE) to an Edge gateway for data
At Edge-gateways, artificial intelligence algorithms are ap-
plied to enhance quality of service. For instance, the AI-
service can detect a human fall with a high level of accuracy.
Then, the results are sent to a Lora-based access point for
storing in a distributed manner and processing with some
advanced algorithms. Finally, the processed data or results
are sent to cloud servers for final data processing and global
storage. This mechanism helps to reduce latency of sending
a large amount of data via LoRa network. In addition, this
can ensure that the bandwidth can be efficiently utilized
and LoRa duty cycle regulations are satisfied. Furthermore,
Edge-gateways can provide many advanced services such as
distributed storage, security, and localization. These services
altogether with services implemented at Fog-assisted LoRa-
based access points help to improve the quality of healthcare
services. For instance, a Fog-cloud-based push notification
service sends real-time messages to caregivers in case of a
fall or an abnormality. Due to the scope of the paper, Fog
services are not discussed. More detailed information of the
Fog services is discussed in our previous papers [19]–[22].
The proposed architecture can help to reduce the computa-
tional load on the sensor nodes by switching heavy compu-
tational tasks from sensor nodes to Edge-gateways. In case
of fall detection systems, data is processed with advanced
algorithms such as AI-based activity categorization algorithms.
Only results such as generic activity status are sent to the Fog-
assisted access points which then transmit to cloud servers.
End-users can use terminal applications to access results stored
in cloud servers.
In order to test the feasibility of the proposed architecture,
we have implemented the complete system for the use case
of fall detection. Wearable devices equipped with inertial
measurement unit (i.e., MPU9250 3-axis accelerometer, 3-axis
LoRa Access Points
Distributed Storage
Global Storage
Cloud Services
Web/Mobile Application Servers
Bio-signals analysis
Fall detection
Activity status
Fig. 1. Proposed System Architecture
gyroscope, and 3-axis magnetometer) to collect and transmit
the data to an Edge-gateway via Bluetooth Low Energy (BLE).
The sensor node is equipped an AVR 8-bit MCU and supplied
with 3.3 V. The gateways have been implemented using a
Raspberry Pi 3 Model B running Ubuntu Mate Desktop with
a Dragino LoRa shield with communication via SPI. The
LoRa access point is also implemented with a Raspberry Pi
single board computer and a LoRa shield, directly connected
to the Internet. For this experiment, we have used raw LoRa
with customized data format and encryption, instead of using
LoRaWAN as the link a network layer over LoRa. This
allows us to customize further the transmission frequencies
and packet structures. Data from the inertial measurement unit
is analyzed at the edge gateway and then only information
about the status of the patient and instant notifications in case
of a fall are transmitted over LoRa. A PostgreSQL database
is used as a cloud storage solution and the web monitoring
application for end-users is implemented using Django and
Apache on CentOS.
The deep learning algorithms have been trained and eval-
uated with a public dataset. However, we have implemented
the wearable sensor node in a way such that the data format is
equivalent to that of the used dataset. Data is normalized and
preprocessed before the analysis step so that data from dif-
ferent sensors can be used with the same algorithm, enabling
flexibility in the design of the sensor node.
A. Edge-AI: LSTM RNN for Fall Detection
We have implemented a recurrent neural network (RNN)
with long short-term memory layers (LSTM) cell layers. A
recurrent neural network is, essentially, a special case of
densely connected neural networks where time is introduced
in the form of connections across consecutive time steps.
They are particularly useful in applications involving time
series data such as handwriting or speech recognition. Though
RNNs inherently store previous states, in practice they present
vanishing and exploding gradient problems in their raw form.
To reduce the impact of these problems, LSTM cells help to
decrease the vanishing gradient problem allowing longer-term
memory within the neural network [23].
We have used Keras and Tensorflow as its backend to
implement an LSTM RNN using Keras and Tensorflow as its
back-end [24]. We have run several tests and compared the
accuracy of the model with different network structures.
For training and assessing the accuracy of the models, we
have used the MobiAct Dataset [25]. The dataset contains
acceleration, angular velocity and orientation data. In particu-
lar, we use a subset containing two daily activities (standing,
lying) and four types of falls: forward-lying (fall forward from
standing, using hands for dampening), front-knees-lying (fall
forward from standing, first impact on knees), sideways-lying
(fall sideways from standing, bending legs), and back-sitting-
chair (fall backward while trying to sit on a chair).
We have implemented different RNN models with a variable
number of hidden layers and their sizes. The best results have
been obtained with three hidden layers, two of them LSTM
and one fully connected layer. The input data size is 10 points,
and the output of the network is a single value (probability
of fall occurring in the input data). Therefore, the proposed
model has a total of 5 layers with 2 dropout operations after
the LSTM layers.
B. Results
We have tested the efficiency of five different RNNs and
compared their accuracy in terms of precision (TP/(TP+FP))
and recall (TP/(TP+FN)), where TP,FP are true/false positives,
and TN,FN are true/false negatives.
Figure 2 shows the distribution of the results in the form
of boxplots, with 20 data points for each model. We started
the test with a simple RNN model (M1) with 2 hidden LTSM
layers with 23 neurons each, which allows us to obtain an
average precision of 91.90% ±4.46%, and an average recall
of 62.36% ±2.83%. Even though the precision is good, the
standard deviation in both cases is high and the recall is
too low. More robust results have been obtained with 30
neurons/layer (M2); 10 neurons/layer and an additional fully
M1 M2 M3 M4 M5
Fig. 2. Precision and recall for the five different implemented models.
connected layer of 10 neurons (M3). An improved recall was
obtained when adding a dropout stage of 50% after the two
LSTM layers of M3 (M4). Finally, the best recall was achieved
when using two dropout stages after each of the LSTM laters
(M5), of 30% and 20% respectively. At the same time, the
standard deviation of the results was significantly reduced.
In summary, the best performance was obtained with a
neural network with 3 hidden layers and two dropout stages.
The final precision achieved was of 90.10% ±3%, and a
recall of 95.30% ±0.8%. This shows an improvement in
comparison to our previous work [2], [4], where threshold-
based fall detection was implemented. After the data analysis,
only 10 bytes of data have to be transmitted from the Edge
gateway to the Fog-assisted access point and to the cloud if a
fall is detected, including the unique device ID and the user
status. This reduces the bandwidth usage and allows hundreds
or thousands of devices to use the same LoRa access point
due to the infrequent transmissions. The LoRa transmission
has been tested in an urban environment in Turku, Finland. A
range of over 4km has been achieved with the LoRa access
point over a hill. This long-range has been achieved partly due
to the low buildings in the area. The transmission range can
be significantly extended for a system deployed in rural areas.
We have proposed a system architecture for health monitor-
ing with Edge and Fog computing. We have put an emphasis
on the use of LPWAN technology to enable deployment of
such systems in rural areas. Taking into account the network
capacity in these scenarios, robust data analysis and compres-
sion algorithms are deployed in the edge layer to lower the
size of transmitted data, improving the system latency.
We have shown how high accuracy in a fall detection system
can be achieved by means of an LTSM RNN implemented
to run on the edge gateways. This enables real-time alerts
and notifications, and waives the need for raw data to be
transmitted to cloud servers for online analysis. Furthermore,
when combined with BLE at the sensor node and LoRa
transmission from edge to fog layers, we are able to simplify
the sensor node design and potentially increase its battery life,
while allowing operation in areas with poor connectivity.
Future work will include further improvement of our predic-
tion model and more extensive performance analysis. More-
over, we will evaluate our method with real-time data from the
implemented sensor node, and the system architecture will be
tested as a whole. Finally, we will expand our model to classify
different daily activities apart from fall detection.
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Edge healthcare system is recognized as an acceptable paradigm for resolving this problem. The IoMT is divided into two sub-networks - intraWBANs and beyond-WBANs - based on the physical bonds of WBANs. Given the features of the healthcare systems, medical emergency, AoI and power depreciation are the prices of MUs. Intra-WBANs, a cooperative game shapes the wireless channel resource allocation problem. The Nash negotiation solution is used to get the unique optimum point in Pareto. MUs are regarded reasonable and perhaps egoistic in non-WBANs. Another non-cooperative activity is therefore developed to reduce overall system costs. The assessments of the performance of the system-wide cost and of the number of MUs gaining from edge computer systems are done to illustrate the success of our solution. Finally, for further effort, numerous barriers to research and open questions are highlighted.
By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.
The healthcare industry is developing rapidly, and innovations are now considered as the significant game-changer. IoT (Internet of Things) is shaping the healthcare industry in a new form with promising advances in testing, monitoring processes. Monitoring the health issues of the patients, organizing the treatment initiatives, and empowering the physicians it is providing superlative measures. The invention of the IoT through internet based artificial intelligence is determining the bright future of the medical field. Whether IoT is diagnosing the disease, or analyzing the past history of a certain disease the implementation of artificial intelligence is great. Here in this study the roles of internet based artificial intelligence are illustrated. Furthermore, it has described the current working features in health monitoring. Key aim of this study is to analyze this new innovative implementation in health monitoring. The article is developed including secondary qualitative analysis. Data collection, diagnosing health issues, and in monitoring the preventive care of IoT is compared with the traditional way of heath monitoring. Many experts see that artificial intelligence is more able than the conventional method to work in a more organized way. This study targets to analyze both the advantages, and disadvantages of implementation of artificial intelligence. Various components are addressed along with the gap to predict the increasing use of it in the near future. Comparing with the traditional; ways in giving better service experience is discussed. Including both the gaps, and benefits this study would be beneficial to give a better and effective understanding about the chosen topic.
Data mining is the science of extracting information or ‘knowledge’ from data. It is a task commonly executed on cloud computing resources, personal computers and laptops. However, what about smartphones? Despite the fact that these ubiquitous mobile devices now offer levels of hardware and performance approaching that of laptops, locally executed model-training using data mining methods on smartphones is still notably rare. On-device model-training offers a number of advantages. It largely mitigates issues of data security and privacy, since no data is required to leave the device. It also ensures a self-contained, fully-portable data mining solution requiring no cloud computing or network resources and able to operate in any location. In this paper, we focus on the intersection of smartphones and data mining. We investigate the growth in smartphone performance, survey smartphone usage models in previous research and look at recent developments in locally-executed data mining on smartphones.
In remote monitoring edge-based IoT applications, high latency caused by the mobility of a sensor device can cause serious consequences such as inaccurate analysis and low quality of services. Therefore, it is required to have mobility support approaches that help reduce latency while maintaining a connection, high quality of service, and energy efficiency. However, the number of mobility support approaches for high data rate IoT applications using Bluetooth Low Energy (BLE) is limited and they have some disadvantages. For example. they have not been designed for edge-based applications where local computation occurs frequently. Many of them have not been implemented and tested in daily working environments with actual mobility cases. They have not comprehensively analyzed the mobility latency and energy consumption of sensor devices. Hence, this paper presents three possible mobility support approaches including passive and active handover mechanisms for edge-based IoT applications using high data rate BLE5. These approaches based on passive and active handover mechanisms are implemented and tested in an office environment. The results of latency and power consumption of a sensor device via many experiments are measured and analyzed. The results show that the presented mobility support approaches maintain the connection during mobility with a latency of around 900ms for many cases. The results also show that using BLE5’s LE 2M physical layer consumes less power than using LE 1M physical layer. Specifically, it can reduce energy consumption when sending or receiving larger data sizes at faster rates.
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Blood glucose plays an important role in maintaining body's activities. For example, brain only uses glucose as its energy source. However, when blood glucose level is abnormal, it causes some serious consequences. For instance, low-blood glucose phenomenon referred to as hypoglycemia can cause heart repolarization and induce cardiac arrhythmia causing sudden cardiac deaths. Diabetes, which can be viewed as a high-blood glucose level for a long period of time, is a dangerous disease as it can directly or indirectly cause heart attack, stroke, heart failure, and other vicious diseases. A solution for reducing the serious consequences caused by diabetes and hypoglycemia is to continuously monitor blood glucose level for real-time responses such as adjusting insulin levels from the insulin pump. Nonetheless, it is a misstep when merely monitoring blood glucose without considering other signals or data such as Electrocardiography (ECG) and activity status since they have close relationships. When hypoglycemia occurs, a fall can easily occur especially in case of people over 65 years old. Fall's consequences are more hazardous when a fall is not detected. Therefore, we present a Fog-based system for remote health monitoring and fall detection. Through the system, both e-health signals such as glucose, ECG, body temperature and contextual data such as room temperature, humidity, and air quality can be monitored remotely in real-time. By leveraging Fog computing at the edge of the network, the system offers many advanced services such as ECG feature extraction, security, and local distributed storage. Results show that the system works accurately and the wearable sensor node is energy efficient. Even though the node is equipped with many types of sensors, it can operate in a secure way for up to 157 h per a single charge when applying a 1000 mAh Lithium battery.
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With the rise in the elderly population, the importance of care services for the elderly is also increasing. Among the care services, sudden fall detection is one of the most important services that the elderly need. The hip joints are prone to damage when they fall, and most of such injuries can lead to very severe consequences. In recent times, researches on fall detection have been very active. Fall detection by attaching an acceleration sensor to the waist of a person is popular and the detection rate is very high. However, when the fall is detected from a sensor attached to the wrist, which is more convenient as compared to the waist attachment, the detection accuracy is lower. To overcome the problem, in this article, we propose a system that distinguishes falls from the acceleration sensor attached to the wrist using an artificial neural network–based deep learning method. With the proposed method, we could detect the falls with a 100% accuracy in an experiment.
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Handover mechanism for mobility support in a remote real-time streaming IoT system was proposed in this paper. The handover mechanism serves to keep the connection between sensor nodes and a gateway with a low latency. The handover mechanism also attentively considers oscillating nodes which often occur in many streaming IoT systems. By leveraging the strategic position of smart gateways and Fog computing in a real-time streaming IoT system, sensor nodes’ loads were alleviated whereas advanced services, like push notification and local data storage, were provided. The paper discussed and analyzed metrics for the handover mechanism based on Wi-Fi. In addition, a complete remote real-time health monitoring IoT system was implemented for experiments. The results from evaluating our mobility handover mechanism for mobility support shows that the latency of switching from one gateway to another is 10% - 50% less than other state-of-the-art mobility support systems. The results show that the proposed handover mechanism is a very promising approach for mobility support in both Fog computing and IoT systems.
Conference Paper
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Low-Power Wide-Area Network (LPWAN) is an emerging network technology for Internet of Things (IoT) which offers long-range and wide-area communication at low-power. It thus overcomes the range limits and scalability challenges associated with traditional short range wireless sensor networks. Due to their escalating demand, LPWANs are gaining momentum, with multiple competing technologies currently being developed. Despite their promise, existing LPWAN technologies raise a number of challenges in terms of spectrum limitation, coexistence, mobility, scalability, coverage, security, and application-specific requirements which make their adoption challenging. In this paper, we identify the key opportunities of LPWAN, highlight the challenges, and show potential directions of the future research on LPWAN.
Conference Paper
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According to the World Health Organization, around 28-35% of people aged 65 and older fall each year. This number increases to around 32-42% for people over 70 years old. For this reason, this research targets the exploration of the role of Convolutional Neural Networks(CNN) in human fall detection. There are a number of current solutions related to fall detection; however, remain low detection accuracy. Although CNN has proven a powerful technique for image recognition problems, and the CNN library in Matlab was designed to work with either images or matrices, this research explored how to apply CNN to streaming sensor data, collected from Body Sensor Networks (BSN), in order to improve the fall detection accuracy. The idea of this research is that given the stream data sets as input, we converted them into images before applying CNN. The final accuracy result achieved is, to the best of our knowledge, the highest compared to other proposed methods: 92.3%.
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By 2020, more than 50 billion devices will be connected through radio communications. In conjunction with the rapid growth of the Internet of Things (IoT) market, low power wide area networks (LPWAN) have become a popular low-rate long-range radio communication technology. Sigfox, LoRa, and NB-IoT are the three leading LPWAN technologies that compete for large-scale IoT deployment. This paper provides a comprehensive and comparative study of these technologies, which serve as efficient solutions to connect smart, autonomous, and heterogeneous devices. We show that Sigfox and LoRa are advantageous in terms of battery lifetime, capacity, and cost. Meanwhile, NB-IoT offers benefits in terms of latency and quality of service. In addition, we analyze the IoT success factors of these LPWAN technologies, and we consider application scenarios and explain which technology is the best fit for each of these scenarios.
This chapter exploits fog computing in health‐monitoring Internet‐of‐Things (IoT) systems for enhancing the quality of healthcare service. It shows an overview of the architecture of an IoT‐based system with fog computing. Fog computing services locating in a fog layer of smart gateways are diversified for serving IoT applications. The chapter discusses the fog computing services in smart e‐health gateways. The health‐monitoring IoT system consists of several wearable sensor nodes, smart gateways with fog services, cloud servers, and terminals. The chapter discusses detailed implementations of these components. It provides a case study, experimental results, and evaluation related to heart rate variability (HRV) analysis. The chapter presents the related applications in fog computing and discusses future research directions. Fog computing demonstrates that it is one of the most suitable candidates for augmenting IoT systems in healthcare and other domains.
Falls are the top reason for fatal and non-fatal injuries among seniors. Existing solutions are based on wearable fall-alert sensors, but medical research has shown that they are ineffective, mostly because seniors do not wear them. These revelations have led to new passive sensors that infer falls by analyzing Radio Frequency (RF) signals in homes. Seniors can go about their lives as usual without the need to wear any device. While passive monitoring has made major advances, current approaches still cannot deal with the complexities of real-world scenarios. They typically train and test their classifiers on the same people in the same environments, and cannot generalize to new people or new environments. Further, they cannot separate motions from different people and can easily miss a fall in the presence of other motions. To overcome these limitations, we introduce Aryokee, an RF-based fall detection system that uses convolutional neural networks governed by a state machine. Aryokee works with new people and environments unseen in the training set. It also separates different sources of motion to increase robustness. Results from testing Aryokee with over 140 people performing 40 types of activities in 57 different environments show a recall of 94% and a precision of 92% in detecting falls.
Falls can cause serious traumas such as brain injuries and bone fractures, especially among elderly people. Fear of falling might reduce physical activities resulting in declining social interactions and eventually causing depression. To lessen the effects of a fall, timely delivery of medical treatment can play a vital role. In a similar scenario, an IoT-based wearable system can pave the most promising way to mitigate serious consequences of a fall while providing the convenience of usage. However, to deliver sufficient degree of monitoring and reliability, wearable devices working at the core of fall detection systems are required to work for a prolonged period of time. In this work, we focus on energy efficiency of a wearable sensor node in an Internet-of-Things (IoT) based fall detection system. We propose the design of a tiny, lightweight, flexible and energy efficient wearable device. We investigate different parameters (e.g. sampling rate, communication bus interface, transmission protocol, and transmission rate) impacting on energy consumption of the wearable device. In addition, we provide a comprehensive analysis of energy consumption of the wearable in different configurations and operating conditions. Furthermore, we provide hints (hardware and software) for system designers implementing the optimal wearable device for IoT-based fall detection systems in terms of energy efficiency and high quality of service. The results clearly indicate that the proposed sensor node is novel and energy efficient. In a critical condition, the wearable device can be used continuously for 76 hours with a 1000 mAh li-ion battery.