Wearables Security 2022 - In the twenty-first century, given the worldwide situation, the first concern of any female is her personal protection. Women Labor Day and night to sustain themselves and their families. These women are more susceptible to attacks and assaults, and their security and safety are paramount issues. This technique proposed several new goods to safeguard women. Among the products that may be employed is a smart jacket for women s safety. The proposed approach also includes features to send alert notification to family members with Geo location live tracking and live camera video streaming placed on the jacket for the emergency attention when women are not secure. This gadget is an appeal to all women to earn the right to a safe and secure planet.

Wearables Security 2022 - In aura and era of the Internet of Things (IoT) and the fourth industrial revolution, modern wearable electronic devices and their communication networks are marching into every corner of modern society and changing every aspect of our daily life. Thus, the progress of digitalization including miniaturization of sensor and wearable technology and its growing importance of physical and psychological wellbeing have a tremendous impact on almost all consumer goods from wearable to nonwearable industries. Different types of signals are used in communication between the devices for wireless transmission of data. such as Radio Frequency, Infrared, and Lightwave Transmissions. Wearable devices are becoming a hot topic in many fields such as medical, fashion, education, etc. Digital dependency of WIoT devices, introduced new security challenges, and vulnerabilities. This research is focused on Fitness Wearable Technology Devices Security and Privacy Vulnerability Analysis and highlights the importance of this topic by revealing the potential security concerns. Fog Computing, Sidera and Blockchain technologies were researched as Security Techniques to enhance security and efficiency while providing access to medical and personal records.

Wearables Security 2022 - Mobile devices such as smartphones are increasingly being used to record personal, delicate, and security information such as images, emails, and payment information due to the growth of wearable computing. It is becoming more vital to employ smartphone sensor-based identification to safeguard this kind of information from unwanted parties. In this study, we propose a sensor-based user identification approach based on individual walking patterns and use the sensors that are pervasively embedded into smartphones to accomplish this. Individuals were identified using a convolutional neural network (CNN). Four data augmentation methods were utilized to produce synthetically more data. These approaches included jittering, scaling, and time-warping. We evaluate the proposed identification model’s accuracy, precision, recall, F1-score, FAR, and FRR utilizing a publicly accessible dataset named the UIWADS dataset. As shown by the experiment findings, the CNN with the timewarping approach operates with very high accuracy in user identification, with the lowest false positive rate of 8.80\% and the most incredible accuracy of 92.7\%.

Wearables Security 2022 - Healthcare has become one of the most important aspects of people s lives, resulting in a surge in medical big data. Healthcare providers are increasingly using Internet of Things (IoT)-based wearable technologies to speed up diagnosis and treatment. In recent years, Through the Internet, billions of sensors, gadgets, and vehicles have been connected. One such example is for the treatment and care of patients, technology—remote patient monitoring—is already commonplace. However, these technologies also offer serious privacy and data security problems. Data transactions are transferred and logged. These medical data security and privacy issues might ensue from a pause in therapy, putting the patient s life in jeopardy. We planned a framework to manage and analyse healthcare large data in a safe manner based on blockchain. Our model s enhanced privacy and security characteristics are based on data sanitization and restoration techniques. The framework shown here make data and transactions more secure.

Wearables Security 2022 - One of the biggest new trends in artificial intelligence is the ability to recognise people s movements and take their actions into account. It can be used in a variety of ways, including for surveillance, security, human-computer interaction, and content-based video retrieval. There have been a number of researchers that have presented vision-based techniques to human activity recognition. Several challenges need to be addressed in the creation of a vision-based human activity recognition system, including illumination variations in human activity recognition, interclass similarity between scenes, the environment and recording setting, and temporal variation. To overcome the above mentioned problem, by capturing or sensing human actions with help of wearable sensors, wearable devices, or IoT devices. Sensor data, particularly one-dimensional time series data, are used in the work of human activity recognition. Using 1D-Convolutional Neural Network (CNN) models, this works aims to propose a new approach for identifying human activities. The Wireless Sensor Data Mining (WISDM) dataset is utilised to train and test the 1D-CNN model in this dissertation. The proposed HAR-CNN model has a 95.2\%of accuracy, which is far higher than that of conventional methods.

Wearables Security 2022 - Interoperability remains one of the biggest challenges facing healthcare organizations today. Despite the advancements made through digital transformation and API that allow increased interoperability, patients still have to contend with a different patient portal for each provider they visit. Several health systems are unable to successfully exchange EHR data. API transfer and consolidate patient information including medical history and treatment records across the disparate healthcare systems. Mobile apps use API to gather data from various medical wearables and add the data to a patient’s health record. However, API exposes application logic and sensitive data information giving patient data a window to the World Wide Web and has thus increasingly become a target for attackers. As the need for tighter API security grows, managing APIs becomes more important than ever. The goal of this paper is to provide an overview and discuss research questions that can aid in understanding and building the knowledge base on API data integration and interoperability.

Wearables Security 2022 - As it becomes easier to obtain various data from wearable devices, it is known that biometric and behavioral information must be handled with care. On the other hand, data on the surrounding environment, such as outside temperature, is seen as having a weak relationship with the wearer, and data handling is considered to be a chore. We believe that even data with weak relationships have the potential to infer information about the wearer if a large amount of data is acquired. In this paper, we verify whether it is possible to estimate the wearer’s location from time series data of outside air temperature using only the temperature sensor. We calculated the average absolute error between the temperature data from the wearable device and the same time-series data obtained from the Japan Meteorological Agency, and we evaluated the wearer’s position estimation. It was found that the location where the temperature was sampled appeared at the top of the estimation ranking, and that cities near the sampling location were estimated to be at the high ranking. It was also found that the number of data to be used can be a factor that increases the estimation ranking.

Wearables Security 2022 - As 5G is deployed and applied, a large number of mobile devices have been increasingly deployed on the network. Scenarios such as smartphones, smart car, smart transportation, smart wearable devices, and smart industry are increasingly demanding for networks. And the Internet of Things (IoT), as a new and high technology, will play an important role and generate huge economic benefits. However, IoT security also faces many challenges due to the inherent security vulnerabilities in multiple device interactions and the data also needs more accurate processing. Big data and deep learning have been gradually applied in various industries. Therefore, we have summarized and analyzed the use of big data and deep learning technology to solve the hidden dangers of the IoT security under the consideration of some suggestions and thinking for industry applications.

Wearables Security 2022 - Wearable devices are becoming increasingly popular since they are used in a variety of services. A variety of personal data is collected by the smartwatch. Although devices can give convenience to consumers, there are additional security threats that warn of cybersecurity risks, device penetration, and exploiting vulnerabilities. Devices are prone to attack, and hacking might reveal the acquired data. The lack of authentication and location monitoring, as well as Bluetooth issues and security holes, are all problems in these devices. Although there are security recommendations for such devices, consumers are typically unaware of the risks. The goal of this study is to provide awareness regarding cybersecurity to the common people while using the wearable device.

Wearables Security 2022 - In recent years, technological industry has made a large investment in the design of wearable devices. Wearable devices are attractive to a variety of different age groups within the majority of population. The main reasons for such popularity are related to ease of wear and friendly use, affordable prices with competitive products, as well as providing different services. Usually, wearable devices are collecting different kinds of data. Some of these data are sensitive and personal data of the wearer/user. Such data can be attacked, leaked, misused or edited. Therefore, privacy and security issues are among the main important issues to be considered carefully and discussed clearly when wearable devices are designed and used. Presenting unclear privacy and security strategies will lead the user to mistrust wearable technology with its application. In this research, we present our proposed wearable security protocol to create a secure environment of wearable data and their processing. The main idea of our protocol is to secure the identity of people as well as hiding their sensitive and personal data. Meanwhile, it does not affect the quality of data when applying their mining techniques. The protocol can be used for any kind of data with any application while keeping the user’s privacy and security properties. Moreover, it can be easily understood, implemented, and processed, in addition to any update might be needed.

Chaos is an interesting phenomenon for nonlinear systems that emerges due to its complex and unpredictable behavior. With the escalated use of low-powered edge-compute devices, data security at the edge develops the need for security in communication. The characteristic that Chaos synchronizes over time for two different chaotic systems with their own unique initial conditions, is the base for chaos implementation in communication. This paper proposes an encryption architecture suitable for communication of on-chip sensors to provide a POC (proof of concept) with security encrypted on the same chip using different chaotic equations. In communication, encryption is achieved with the help of microcontrollers or software implementations that use more power and have complex hardware implementation. The small IoT devices are expected to be operated on low power and constrained with size. At the same time, these devices are highly vulnerable to security threats, which elevates the need to have low power/size hardware-based security. Since the discovery of chaotic equations, they have been used in various encryption applications. The goal of this research is to take the chaotic implementation to the CMOS level with the sensors on the same chip. The hardware co-simulation is demonstrated on an FPGA board for Chua encryption/decryption architecture. The hardware utilization for Lorenz, SprottD, and Chua on FPGA is achieved with Xilinx System Generation (XSG) toolbox which reveals that Lorenz’s utilization is 9% lesser than Chua’s.