IOT-BASED SMART HOME AUTOMATION SYSTEM WITH TOUCHSCREEN INTERFACE AND REAL-TIME CONTROL

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

This work is dedicated to the development of an on-line monitoring system of SF6 decomposition in electrical devices (SF6DcpMS) in order to evaluate the health condition of the devices in real time. The feature decomposition, SO2, and SF6 purity are continuously monitored. Considering the oxidation-reduction reactions of SO2 electrochemical sensor during detection, the methodology of utilizing the air in the electrical device is proposed based on the tests of serving devices. And it is proved via tests that electrochemical sensors can be applied into SF6DcpMS. Besides, infrared detector is employed to detect SF6 purity. Sampling method of SF6 in electrical devices is described. There are three keys in the sampling device design for the on-line monitoring system: -- Sensor working environment: The rating pressure is one atmospheric pressure for sensor working. Therefore, a gas chamber is constructed, in which the suitable environment for sensor is completed via a pressure regulator and a pressure sensor. At the same time, an appropriate control method is scheduled. Gas in the chamber is continuously replaced with that in the electrical device to ensure detection accuracy within an acceptable range. -- SO2 adsorption feature: The monitoring accuracy is seriously influenced by the adsorption feature of SO2. As we known, flowing gas can decrease SO2 adsorption feature. So, a micro bump is designed in the gas chamber, which can ensure the gas flowing at the speed of 200ml/min. -- Sample gas back into electrical devices: In order to avoid the gas cause harm to humans, the gas leakage is not allowed in the on-line monitoring system, and the sample gas must be sent back to the electrical device after detection is accomplished. Sample gas back into electrical devices is realized via a bump and a check valve with adjustable cracking pressure. The first SF6DcpMS in China has been successfully put into service in Zunyi substation, and its performance has been proved out.

The authors designed an interactive telecare system (ITCS) enhanced by Internet of Things (IoT) technology that enables direct communication between patients' medical devices and caregivers' smartphones to improve the quality of care for chronically ill patients. This system can remotely activate hardware components of medical devices in real time to access current information and smartphones via a telecare application. A case study was constructed with 2.5G blood glucose monitors (BGMs) integrated with a cloud platform and with Android and iOS telecare applications. Overseas medical institutions have confirmed the system's potential value in chronic-illness treatment regimens and have provided useful feedback.

Face detection entails the technology that identifies the human face in digital images. Face detection can be used in various applications. One of the major areas of application of face recognition is in security systems. We know that a smart security system is the need of the hour and has become a necessity in today’s time. It is the basic need of humans to be able to protect one’s home and belongings. The security system needs to be smart and efficient. Hence, it avoids intruders and protects your possessions by minimizing break-ins. Installing a home security system helps in protecting your home and valuables from burglars. The security system developed has a camera module that will give access to only authenticated persons to unlock the main door of the house using facial recognition technology. This discrete system is developed using an electronic board called as Raspberry Pi can detect intruders as well as send off an alarm to nearby police stations using SMS or Email. Keywords: Home security; Face Recognition; Internet of Things (IoT); RaspberryPi Microcontroller; Control; Smartphones Cite this Article Ayush Yadav, Shaam Garg, Shivam Jamloki, Aanchal Sadana, Kusum Tharani. Home Security using Facial Recognition. Current Trends in Signal Processing. 2020; 10(2): 5–11p.

Industry 4.0 (I4.0) is a multidisciplinary engineering principle combing the IoT (Internet of things), big data, and cloud computing to cope with the dynamic changing industry. In this paper, the DDS (data distribution service) communication protocol was employed to implement a cloud platform for data acquisition from various sensors on a precision legacy machine tool including an accelerometer and sound, temperature, brightness, and humidity sensors. The sensor signals were acquired using Raspberry Pi as the edge device, then published to the cloud using the DDS application, and stored in the MySQL database. Using the Django web server, the acquired sensor signals could be shown in real time on the webpage via a combination of MQTT and Node-RED. In addition, the motion displacement of the machine tool detected by the encoder could be recorded through the edge device for further performance examination. With the proposed DDS cloud platform, it is demonstrated that a legacy machine can enable sensing and communication abilities such that the development of a smart machine is achievable for future I4.0 application.

The Internet of Things (IoT) contains sets of hundreds of thousands of network-enabled devices communicating with central controlling nodes or information collectors. The correct behaviour of these devices can be monitored by inspecting the traffic that they create. This passive monitoring methodology allows the detection of device failures or security breaches. However, the creation of hundreds of thousands of traffic time series in real time is not achievable without highly optimised algorithms. We herein compare three algorithms for time-series extraction from traffic captured in real time. We demonstrate how a single-core central processing unit (CPU) can extract more than three bidirectional traffic time series for each one of more than 20,000 IoT devices in real time using the algorithm DStries with recursive search. This proposal also enables the fast reconfiguration of the analysis computer when new IoT devices are added to the network.

PurposeAs the number of devices that connect to the Internet of Things (IoT) has grown, privacy and security issues have come up. Because IoT devices collect so much sensitive information, like user names, locations, phone numbers and even how they usually use energy, it is very important to protect users' privacy and security. IoT technology will be hard to use on the client side because IoT-enabled devices do not have clear privacy and security controls.Design/methodology/approachIoT technology would be harder to use on the client side if the IoT did not offer enough well-defined ways to protect users’ privacy and security. The goal of this research is to protect people's privacy in the IoT by using the oppositional artificial flora optimization (EGPKC-OAFA) algorithm to generate the best keys for the ElGamal public key cryptosystem (EGPKC). The EGPKC-OAFA approach puts the most weight on the IEEE 802.15.4 standard for MAC, which is the most important part of the standard. The security field is part of the MAC header of this standard. In addition, the MAC header includes EGPKC, which makes it possible to make authentication keys as quickly as possible.FindingsWith the proliferation of IoT devices, privacy and security have become major concerns in the academic world. Security and privacy are of the utmost importance due to the large amount of personally identifiable information acquired by IoT devices, such as name, location, phone numbers and energy use. Client-side deployment of IoT technologies will be hampered by the absence of well-defined privacy and security solutions afforded by the IoT. The purpose of this research is to present the EGPKC with optimum key generation using the EGPKC-OAFA algorithm for the purpose of protecting individual privacy within the context of the IoT. The EGPKC-OAFA approach is concerned with the MAC standard defined by the IEEE 802.15.4 standard, which includes the security field in its MAC header. Also, the MAC header incorporates EGPKC, which enables the fastest possible authentication key generation. In addition, the best methodology award goes to the OAFA strategy, which successfully implements the optimum EGPKC selection strategy by combining opposition-based (OBL) and standard AFA ideas. The EGPKC-OAFA method has been proved to effectively analyze performance in a number of simulations, with the results of various functions being identified.Originality/valueIn light of the growing prevalence of the IoT, an increasing number of people are becoming anxious about the protection and confidentiality of the personal data that they save online. This is especially true in light of the fact that more and more things are becoming connected to the internet. The IoT is capable of gathering personally identifiable information such as names, addresses and phone numbers, as well as the quantity of energy that is used. It will be challenging for customers to adopt IoT technology because of worries about the security and privacy of the data generated by users. In this work, the EGPKC is paired with adversarial artificial flora, which leads in an increase to the privacy security provided by EGPKC for the IoT (EGPKC-OAFA). The MAC security field that is part of the IEEE 802.15.4 standard is one of the areas that the EGPKC-OAFA protocol places a high focus on. The Authentication Key Generation Protocol Key Agreement, also known as EGPKCA, is used in MAC headers. The abbreviation for this protocol is EGPKCA. The OAFA technique, also known as the combination of OBL and AFA, is the most successful method for selecting EGPKCs. This method is recognized by its acronym, OAFA. It has been shown via a variety of simulations that the EGPKC-OAFA technique is a very useful instrument for carrying out performance analysis.

Internet of Things (IoT) is a new and growing field and has been used in various aspects including safe home, smart and hospitals. The same can be used for fitness tracking as well. This paper proposed a noveldevice for the health analysis of bicycle rider and security of bicycle. The device will help in keeping track of fitness of bicycle rider and make it hassle free. It will measure the heart rate of the rider, distance travelled and the speed of the bicycle to estimate the amount of energy consumed during the ride. The safety of the bicycles is also a great deal. The device uses antitheft technologies like keyless authentication using Bluetooth and accelerometer to detect theft attempt while the bicycle is in thelock position. The system relies on different, yet complimentary technologies like photoplethysmography, Reed switch, accelerometer, stepper motor, all interfaced using serial communication. The system connects to a smartphone with an app using Bluetooth. The mobile application stores and displays the data from the device in real time, perform analytics & provides security.

The growing use of technological devices in everyday life, through the Internet of Things (IoT) paradigm, has enabled the integration of smart objects through embedded computing, wireless communication, and cloud infrastructure. Services such as urban monitoring, smart homes, intelligent transportation systems, and smart agriculture are examples of IoT applications. However, the exponential growth of IoT devices, estimated at 40 billion by 2034, increases cybersecurity risks. Limited hardware resources and minimal security requirements make IoT devices vulnerable to malware infections, botnet formation, and DDoS attacks, harming critical infrastructure and users. Therefore, this work addressed IoT security through the implementation of a honeypot security mechanism. This mechanism is designed to attract, record, and analyze malicious activity. The proposed solution was implemented at the edge of an academic network in Brazil, integrating Cowrie software with a low-cost embedded IoT computing platform, the Raspberry Pi 3. The implemented honeypot enabled the recording and analysis of attack attempts in real time, including executed commands, origins by country and IP address, and most used credentials. The results confirm that the honeypot fulfilled its decoy functions, providing relevant information about malicious activities, attacker profiles, and IoT botnet attacks.

Survival of the Internet of Things (IoT) is due to the spread of smart devices throughout the world. An enormous amount of data from IoT-enabled devices are getting generated in real-time. Real-time data originates from IoT-enabled devices, builds a knowledge database, and magnifies the wisdom in society. E-governance, smart transport, smart waste management, smart water management, smart energy management makes the smart city. IoT technologies like RFID, Sigfox, Zigbee, Lora, RPMA, Ingenu, NB-IoT, GSM, etc., enables city IoT enabled and helped in making a city smart. Data collected through sensors are analyzed using IoT analytics and which contributes to making smart decisions. Smart city implementation helps in enriching the quality of human life.

The fifth generation (5G) mobile network will enable the Internet of Things (IoT) to take a large leap into the age of future computing. As a result of extended connectivity, high speed, and reduced latency services being provided by 5G, IoT has experienced and will continue to undergo a remarkable transition in every field of daily life. Furthermore, fog computing will revolutionize the IoT platforms by decentralizing the operations through the cloud and ensuring sustainability with big data, mobility, and reduced processing lag. 5G is ubiquitous, reliable, scalable, and economic in nature. The features will not only globalize IoT in a broader spectrum, but also make common people interact smartly and efficiently with the environment in real time. In this study, a combined survey is presented on different IoT applications coupled with cloud platforms. Moreover, the capabilities of IoT in the influence of 5G are explored as well as how the IoT platform and services will adopt through 5G are envisaged. Additionally, some open issues triggered by 5G have been introduced to harness the maximum benefit out of this network. Finally, a platform is proposed to implement in the telepresence project based on the investigation and findings.

Chapter 6 - Internet of things-enabled virtual environment for U-health monitoring

Learning about sensor technology and actuator early is important as a step towards knowing and introducing of advanced technologies based on Internet of Things (IoT). The difficulties are how to learn sensor technology and move the actuator with accessing General Purpose Input Output (GPIO) of Raspberry Pi 3 Platforms using programming language syntax which often confusing and difficult to understand. To help people learning IoT by using Raspberry Pi 3 with an interesting Android apps, we believe that this learning module can integrate about the ease and attractiveness of IoT System Editor based on Android apps. This research create a mobile programming apps based on Android which people can build IoT project easily with GUI without program and middleware based on Raspberry Pi to connect between apps and hardware with especially task to manage data communication, data flow, and device driver. Hopefully new developer can develop the IoT application easily by using Android mobile visual programming that combined with Raspberry Pi 3 platform.

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.

In recent years, visual object tracking has become a very active research field which is mainly divided into the correlation filter-based tracking and deep learning (e.g., deep convolutional neural network and Siamese neural network) based tracking. For target tracking algorithms based on deep learning, a large amount of computation is required, usually deployed on expensive graphics cards. However, for the rich monitoring devices in the Internet of Things, it is difficult to capture all the moving targets in each device in real time, so it is necessary to perform hierarchical processing and use tracking based on correlation filtering in insensitive areas to alleviate the local computing pressure. In sensitive areas, upload the video stream to a cloud computing platform with a faster computing speed to perform an algorithm based on deep features. In this paper, we mainly focus on the correlation filter-based tracking. In the correlation filter-based tracking, the discriminative scale space tracker (DSST) is one of the most popular and typical ones which is successfully applied to many application fields. However, there are still some improvements that need to be further studied for DSST. One is that the algorithms do not consider the target rotation on purpose. The other is that it is a very heavy computational load to extract the histogram of oriented gradient (HOG) features from too many patches centered at the target position in order to ensure the scale estimation accuracy. To address these two problems, we introduce the alterable patch number for target scale tracking and the space searching for target rotation tracking into the standard DSST tracking method and propose a visual object multimodality tracker based on correlation filters (MTCF) to simultaneously cope with translation, scale, and rotation in plane for the tracked target and to obtain the target information of position, scale, and attitude angle at the same time. Finally, in Visual Tracker Benchmark data set, the experiments are performed on the proposed algorithms to show their effectiveness in multimodality tracking.