IoT-Fog-Cloud Centric Earthquake Monitoring and Prediction

2021 ◽  
Vol 20 (6) ◽  
pp. 1-26
Author(s):  
Kanika Saini ◽  
Sheetal Kalra ◽  
Sandeep K. Sood
Keyword(s):  
Real Time ◽  
Seismic Signal ◽  
Sensor Layer ◽  
Natural Catastrophes ◽  
Specificity And Sensitivity ◽  
Proposed Model ◽  
Earthquake Monitoring ◽  
Artificial Neural Network Ann ◽  
The Internet Of Things ◽  
Selection Of

Earthquakes are among the most inevitable natural catastrophes. The uncertainty about the severity of the earthquake has a profound effect on the burden of disaster and causes massive economic and societal losses. Although unpredictable, it can be expected to ameliorate damage and fatalities, such as monitoring and predicting earthquakes using the Internet of Things (IoT). With the resurgence of the IoT, an emerging innovative approach is to integrate IoT technology with Fog and Cloud Computing to augment the effectiveness and accuracy of earthquake monitoring and prediction. In this study, the integrated IoT-Fog-Cloud layered framework is proposed to predict earthquakes using seismic signal information. The proposed model is composed of three layers: (i) at sensor layer, seismic data are acquired, (ii) fog layer incorporates pre-processing, feature extraction using fast Walsh–Hadamard transform (FWHT), selection of relevant features by applying High Order Spectral Analysis (HOSA) to FWHT coefficients, and seismic event classification by K-means accompanied by real-time alert generation, (iii) at cloud layer, an artificial neural network (ANN) is employed to forecast the magnitude of an earthquake. For performance evaluation, K-means classification algorithm is collated with other well-known classification algorithms from the perspective of accuracy and execution duration. Implementation statistics indicate that with chosen HOS features, we have been able to attain high accuracy, precision, specificity, and sensitivity values of 93.30%, 96.65%, 90.54%, and 92.75%, respectively. In addition, the ANN provides an average correct magnitude prediction of 75%. The findings ensured that the proposed framework has the potency to classify seismic signals and predict earthquakes and could therefore further enhance the detection of seismic activities. Moreover, the generation of real-time alerts further amplifies the effectiveness of the proposed model and makes it more real-time compatible.

scholarly journals Deep Reinforcement Learning by Balancing Offline Monte Carlo and Online Temporal Difference Use Based on Environment Experiences

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1685 ◽  
Author(s):  
Chayoung Kim
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Real Time ◽  
Temporal Difference ◽  
Q Learning ◽  
State Action ◽  
Proposed Model ◽  
Reward Functions ◽  
And Performance ◽  
The Internet Of Things

Owing to the complexity involved in training an agent in a real-time environment, e.g., using the Internet of Things (IoT), reinforcement learning (RL) using a deep neural network, i.e., deep reinforcement learning (DRL) has been widely adopted on an online basis without prior knowledge and complicated reward functions. DRL can handle a symmetrical balance between bias and variance—this indicates that the RL agents are competently trained in real-world applications. The approach of the proposed model considers the combinations of basic RL algorithms with online and offline use based on the empirical balances of bias–variance. Therefore, we exploited the balance between the offline Monte Carlo (MC) technique and online temporal difference (TD) with on-policy (state-action–reward-state-action, Sarsa) and an off-policy (Q-learning) in terms of a DRL. The proposed balance of MC (offline) and TD (online) use, which is simple and applicable without a well-designed reward, is suitable for real-time online learning. We demonstrated that, for a simple control task, the balance between online and offline use without an on- and off-policy shows satisfactory results. However, in complex tasks, the results clearly indicate the effectiveness of the combined method in improving the convergence speed and performance in a deep Q-network.

scholarly journals An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

2021 ◽  
Vol 13 (21) ◽  
pp. 11893
Author(s):  
Abdul Rauf Bhatti ◽  
Ahmed Bilal Awan ◽  
Walied Alharbi ◽  
Zainal Salam ◽  
Abdullah S. Bin Humayd ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Energy System ◽  
Weather Data ◽  
Training Performance ◽  
Proposed Model ◽  
Artificial Neural Network Ann

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

scholarly journals Towards a New Model to Secure IoT-based Smart Home Mobile Agents using Blockchain Technology

2020 ◽  
Vol 10 (2) ◽  
pp. 5441-5447
Author(s):  
B. E. Sabir ◽  
M. Youssfi ◽  
O. Bouattane ◽  
H. Allali
Keyword(s):  
Mobile Agents ◽  
Smart Home ◽  
Daily Lives ◽  
Security Issues ◽  
Blockchain Technology ◽  
Network Load ◽  
Proposed Model ◽  
Agent Migration ◽  
The Internet Of Things ◽  
Selection Of

The Internet of Things (IoT) is becoming an indispensable part of the actual Internet and continues to extend deeper into the daily lives of people, offering distributed and critical services. Mobile agents are widely used in the context of IoT and due to the possibility of transmitting their execution status from one device to another in an IoT network, they offer many advantages such as reducing network load, encapsulating protocols, exceeding network latency, etc. Also, Blockchain Technology is growing rapidly allowing for the addition of an approved security layer in many areas. Security issues related to mobile agent migration can be resolved with the use of Blockchain. This paper aims to demonstrate how Blockchain Technology can be used to secure mobile agents in the context of the IoT using Ethereum and a Smart Contract. The transactions within the Blockchain are used to detect the malevolent mobile agents that could infiltrate the IoT systems. The proposed model aims to provide a secure migration of mobile agents to ensure security and protect the IoT applications against malevolent agents. The case of a smart home with multiple applications is applied to verify the proposed solution. The model presented in this paper could be extended to a wider selection of IoT systems outside of the smart home.

scholarly journals IoT-based Earthquake Prediction Using Fog and Cloud Computing

2021 ◽  
Author(s):  
Kanika Saini ◽  
Sheetal Kalra
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Earthquake Prediction ◽  
Fog Computing ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Loss Of Life ◽  
Life Threatening ◽  
Monitoring And Forecasting ◽  
The Internet Of Things

Earthquakes are severe, unexpected, life-threatening catastrophes that affect all kind of living beings. It frequently results in the loss of life and property. Predicting earthquake is the most important aspect of this field. With the golden age of the Internet of Things (IoT), an innovative new idea is to couple IoT technology with cloud and fog computing to improve the potency and accuracy of earthquake monitoring and forecasting. The embedded IoT-Fog-Cloud layered structure is adopted in this research to predict earthquakes using seismic signal data. This model transfers sensed seismic signals to fog for analysis of seismic data. At fog, Fast Walsh Hadamard transform is used to extract time and frequency domain features and PCA is employed to reduce the dimensionality of feature sets. Random Forest algorithm has been used to classify seismic signals into two different events, viz., earthquake and non-earthquake accompanied by the real-time warnings. When compared to other classification models, implementation findings indicate that the Random Forest classifier achieves high values of specificity, sensitivity, precision, and accuracy with average values of 88.50%, 90.25%, 89.50%, and 92.66%. Hence make this framework more real-time compliant for earthquake prediction.

COMPARATIVE STUDY OF SPECIFICITY AND SENSITIVITY FOR IHHNV DETECTION BETWEEN REAL-TIME PCR AND LAMP METHODS

2010 ◽  
Vol 36 (5) ◽  
pp. 984-989
Author(s):  
Zhe ZHAO ◽  
Chun-Hua REN ◽  
Xiao JIANG ◽  
Lü-Ping ZHANG ◽  
Juan FENG ◽  
...  
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Real Time Pcr ◽  
Specificity And Sensitivity

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

Network Selection in Wireless Heterogeneous Environment Based on Available Bandwidth Estimation

2019 ◽  
Vol 12 ◽  
Author(s):  
Kiran Ahuja ◽  
Brahmjit Singh ◽  
Rajesh Khanna
Keyword(s):  
Real Time ◽  
Estimation Error ◽  
Network Selection ◽  
Bandwidth Estimation ◽  
Heterogeneous Environment ◽  
Bootstrap Approximation ◽  
Available Bandwidth ◽  
Internet Connection ◽  
Available Bandwidth Estimation ◽  
Selection Of

Background: With the availability of multiple options in wireless network simultaneously, Always Best Connected (ABC) requires dynamic selection of the best network and access technologies. Objective: In this paper, a novel dynamic access network selection algorithm based on the real time is proposed. The available bandwidth (ABW) of each network is required to be estimated to solve the network selection problem. Method: Proposed algorithm estimates available bandwidth by taking averages, peaks, low points and bootstrap approximation for network selection. It monitors real-time internet connection and resolves the selection issue in internet connection. The proposed algorithm is capable of adapting to prevailing network conditions in heterogeneous environment of 2G, 3G and WLAN networks without user intervention. It is implemented in temporal and spatial domains to check its robustness. Estimation error, overhead, estimation time with the varying size of traffic and reliability are used as the performance metrics. Results: Through numerical results, it is shown that the proposed algorithm’s ABW estimation based on bootstrap approximation gives improved performance in terms of estimation error (less than 20%), overhead (varies from 0.03% to 83%) and reliability (approx. 99%) with respect to existing techniques. Conclusion: Our proposed methodology of network selection criterion estimates the available bandwidth by taking averages, peaks, and low points and bootstrap approximation method (standard deviation) for the selection of network in the wireless heterogeneous environment. It monitors real-time internet connection and resolves internet connections selection issue. All the real-time usage and test results demonstrate the productivity and adequacy of available bandwidth estimation with bootstrap approximation as a practical solution for consistent correspondence among heterogeneous wireless networks by precise network selection for multimedia services.

NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

2018 ◽  
pp. 73-78
Author(s):  
Yu. V. Morozov ◽  
M. A. Rajfeld ◽  
A. A. Spektor
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Simulation Procedure ◽  
Proposed Model ◽  
Seismic Location ◽  
Passive Seismic ◽  
System Characteristics ◽  
Primary Pulse

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

scholarly journals Evaluation of Optimal Vibrotactile Feedback for Force-Controlled Upper Limb Myoelectric Prostheses

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5209 ◽  
Author(s):  
Andrea Gonzalez-Rodriguez ◽  
Jose L. Ramon ◽  
Vicente Morell ◽  
Gabriel J. Garcia ◽  
Jorge Pomares ◽  
...  
Keyword(s):  
Real Time ◽  
Upper Limb ◽  
Closed Loop ◽  
Fine Tuning ◽  
Vibrotactile Feedback ◽  
Loop Control ◽  
Myoelectric Prostheses ◽  
Amplitude Level ◽  
Real Time Application ◽  
Selection Of

The main goal of this study is to evaluate how to optimally select the best vibrotactile pattern to be used in a closed loop control of upper limb myoelectric prostheses as a feedback of the exerted force. To that end, we assessed both the selection of actuation patterns and the effects of the selection of frequency and amplitude parameters to discriminate between different feedback levels. A single vibrotactile actuator has been used to deliver the vibrations to subjects participating in the experiments. The results show no difference between pattern shapes in terms of feedback perception. Similarly, changes in amplitude level do not reflect significant improvement compared to changes in frequency. However, decreasing the number of feedback levels increases the accuracy of feedback perception and subject-specific variations are high for particular participants, showing that a fine-tuning of the parameters is necessary in a real-time application to upper limb prosthetics. In future works, the effects of training, location, and number of actuators will be assessed. This optimized selection will be tested in a real-time proportional myocontrol of a prosthetic hand.

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