IoT-Fog-Cloud Centric Earthquake Monitoring and Prediction

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.

Textile-Based Humidity-Driven Wearable Electroluminescent for Visual Sensing

Miniaturization and integration have become a trend of modern wearable intelligent electronics. But how to visualize sensing information in a single-level device remains a challenge. Here, we present a humidity-driven textile-based electroluminescent (EL) interactive display that allows for both sensing and visualization of humidity changes. Based on an interdigitated EL structure, a transparent humidity sensor layer with high humidity sensitivity was creatively introduced on the top-emitting layer as a bridging electrode. The visualization and sensing of humidity can be attributed to the electrical conductivity difference of the sensor layer, thus leading to the varied lighting emitting of EL devices on the application of given electric fields. Benefiting from the highly sensitive sensor layer and well-designed device structure, a variety of humidity-based behavior can be read immediately, including hand-writing and finger approach. Furthermore, our devices fabricated from textiles have great flexibility, breathability, and skin affinity, which is very suitable for human wearing. More importantly, this humidity-driven textile-based EL interactive display shows great application potential in breathing monitoring and health assessment.

IoT and Deep Learning for Livestock Management

Livestock management is a critical issue for the farming industry as proper management including their health and well-being directly impacts the production. It is difficult for a farmer or shed owner to monitor big herds of cattle manually. This chapter proposes a layered framework that utilizes the power of internet of things (IoT) and deep learning (DL) to real-time livestock monitoring supporting the effective management of cattle. The framework consists of sensor layer where sensor-rich devices or gadgets are used to collect various contextual data related to livestock, data processing layer which deals with various outlier rejections and processing of the data followed by DL approaches to analyze the collected contextual data in detecting sick and on heat animals, and finally, insightful information is sent to shed owner for necessary action. An experimental study conducted is helpful to make wise decisions to increase production cost-effectively. The chapter concludes with the different future aspects that may be further explored by the researchers.

Development of thin-film based sensors for temperature and tool wear monitoring during machining

The development of thin-film sensors for temperature and wear measurement in machining operations is presented in this work. A functional thin-film system, consisting of an Al2O3 insulation layer, a chromium sensor layer structured by photolithography and an Al2O3 wear-protection and insulation layer, is deposited by physical vapor deposition (PVD) processes onto the surface of cemented carbide cutting inserts. First specimen of the sensors are successfully fabricated and tested in laboratory experiments as well as in machining operations to demonstrate their functionality. These tool-integrated sensors can be used as an in-process monitoring device to determine the temperatures on the rake face at or close to the tool-chip contact area and to measure the progress of the flank-wear land width. The knowledge of these important process parameters opens up the possibility to develop new in-process control mechanisms in order to modify and improve the surface integrity of manufactured components. Thereby, their performance and lifetime can be enhanced.

Performances and Biosensing Mechanisms of Interdigitated Capacitive Sensors Based on the Hetero-mixture of SnO2 and In2O3

This study aims to discuss the synthesis and fabrication of SnO2-In2O3-based thick-films and their biosensing applications. The structural characterization of SnO2-In2O3 nanocomposites was performed using X-ray diffraction, Raman spectroscopy and transmission electron microscopy. Furthermore, the screen-printing technology was used in the fabrication of conductive electrodes to form an interdigitated capacitive structure, and the sensor layer based on the mixture of SnO2 and In2O3. Moreover, the sensing performance of the developed structure was tested using Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) bacteria. In addition, the validation of sensing characteristics was performed by electrochemical impedance spectroscopic and self-resonant frequency analysis. Finally, the sensing properties were analyzed for two consecutive days, and changes in both P. aeruginosa and S. aureus pathogens growing media were also studied.

Characterization of Response Frequency for the Pressure-Sensitive Paint Based on Fluorine-Containing Polymer Matrixes Using Oscillating Sound Wave Technique

Five kinds of new homo-polymer and copolymers of methacrylate containing a fluorine ester group were synthesized and used for the binder of pressure-sensitive paint (PSP)to ensure the good compatibility between luminophore (Pt(II) meso-tetra (pentafluorophenyl) porphine (PtTFPP)) and polymer binder. In the work, we were concerned with how the structure of thesepolymers containing fluorine, especially the various ester group structure, affects the response frequency of PSP using oscillating sound wave technique. The results showed that the pressure sensitivities (Sp) of these PSP samples containing different polymers, exhibit some difference. The length of ester chain on the methacrylatepolymer affects the response frequency of PSP sensor layer composed of the polymer. The longer the chain length of the ester group, the higher the response frequency of the PSP sensor layer quenching by oxygen.

System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment

In recent years, technological advancements have made a promising impact on the development of autonomous vehicles. The evolution of electric vehicles, development of state-of-the-art sensors, and advances in artificial intelligence have provided necessary tools for the academia and industry to develop the prototypes of autonomous vehicles that enhance the road safety and traffic efficiency. The increase in the deployment of sensors for the autonomous vehicle, make it less cost-effective to be utilized by the consumer. This work focuses on the development of full-stack autonomous vehicle using the limited amount of sensors suite. The architecture aspect of the autonomous vehicle is categorized into four layers that include sensor layer, perception layer, planning layer and control layer. In the sensor layer, the integration of exteroceptive and proprioceptive sensors on the autonomous vehicle are presented. The perception of the environment in term localization and detection using exteroceptive sensors are included in the perception layer. In the planning layer, algorithms for mission and motion planning are illustrated by incorporating the route information, velocity replanning and obstacle avoidance. The control layer constitutes lateral and longitudinal control for the autonomous vehicle. For the verification of the proposed system, the autonomous vehicle is tested in an unconstrained environment. The experimentation results show the efficacy of each module, including localization, object detection, mission and motion planning, obstacle avoidance, velocity replanning, lateral and longitudinal control. Further, in order to demonstrate the experimental validation and the application aspect of the autonomous vehicle, the proposed system is tested as an autonomous taxi service.

Development of Authenticated Key Exchange Protocol for IoT Sensor Layer

An authenticated key exchange for the Internet of Things (IoT) sensor layer is discussed in this paper. This paper presents an enhanced key exchange protocol to provide an authentication scheme and data confidentiality for IoT sensor layer. In our approach, we incorporate an identity-based authentication scheme into the existing key exchange protocol based on Elliptic Curve Diffie Hellman (ECDH). We utilize two communication channels for the process, main channel and auxiliary channel. The main channel is used to exchange key and sensor data and the auxiliary channel is used to exchange the identity information prior to the key exchange process. To provide the data confidentiality, AES encryption algorithm is implemented with a key derived from shared secret key to ensure the Perfect Forward Secrecy. For the evaluations, there are four parameters that are evaluated: the protocol resistance, formal verification of protocol, the protocol security, and performance testing. The protocol resistance was evaluated using security analysis against common security threats on IoT sensor layer. The formal verification of the proposed protocol was evaluated using Scyther, and the protocol security was evaluated using attack scenarios (i.e., authentication and sniffing attack) to prove the authentication and confidentiality. The performance testing was conducted to measure time complexity and memory complexity of the protocol. The experiment results show that the proposed protocol is able to provide an authentication mechanism, data confidentiality, and resilience against common security threats at IoT sensor layers.

Smart Palm: An IoT Framework for Red Palm Weevil Early Detection

Smart agriculture is an evolving trend in the agriculture industry, where sensors are embedded into plants to collect vital data and help in decision-making to ensure a higher quality of crops and prevent pests, disease, and other possible threats. One of the most critical pests of palms is the red palm weevil, which is an insect that causes much damage to palm trees and can devastate vast areas of palm trees. The most challenging problem is that the effect of the weevil is not visible by humans until the palm reaches an advanced infestation state. For this reason, there is a pressing need to use advanced technology for early detection and prevention of infestation propagation. In this project, we have developed an IoT-based smart palm monitoring prototype as a proof-of-concept that (1) allows monitoring palms remotely using smart agriculture sensors, (2) contribute to the early detection of red palm weevil infestation. Users can use web/mobile applications to interact with their palm farms and help them in getting early detection of possible infestations. We used an industrial-level IoT platform to interface between the sensor layer and the user layer. Moreover, we have collected data using accelerometer sensors, and we applied signal processing and statistical techniques to analyze collected data and determine a fingerprint of the infestation.