Automated Security Assessment Framework for Wearable BLE-enabled Health Monitoring Devices

The growth of IoT technology, increasing prevalence of embedded devices, and advancements in biomedical technology have led to the emergence of numerous wearable health monitoring devices (WHMDs) in clinical settings and in the community. The majority of these devices are Bluetooth Low Energy (BLE) enabled. Though the advantages offered by BLE-enabled WHMDs in tracking, diagnosing, and intervening with patients are substantial, the risk of cyberattacks on these devices is likely to increase with device complexity and new communication protocols. Furthermore, vendors face risk and financial tradeoffs between speed to market and ensuring device security in all situations. Previous research has explored the security and privacy of such devices by manually testing popular BLE-enabled WHMDs in the market and generally discussed categories of possible attacks, while mostly focused on IP devices. In this work, we propose a new semi-automated framework that can be used to identify and discover both known and unknown vulnerabilities in WHMDs. To demonstrate its implementation, we validate it with a number of commercially available BLE-enabled enabled wearable devices. Our results show that the devices are vulnerable to a number of attacks, including eavesdropping, data manipulation, and denial of service attacks. The proposed framework could therefore be used to evaluate potential devices before adoption into a secure network or, ideally, during the design and implementation of new devices.

Microarchitectural Exploration of STT-MRAM Last-level Cache Parameters for Energy-efficient Devices

As the technology scaling advances, limitations of traditional memories in terms of density and energy become more evident. Modern caches occupy a large part of a CPU physical size and high static leakage poses a limit to the overall efficiency of the systems, including IoT/edge devices. Several alternatives to CMOS SRAM memories have been studied during the past few decades, some of which already represent a viable replacement for different levels of the cache hierarchy. One of the most promising technologies is the spin-transfer torque magnetic RAM (STT-MRAM), due to its small basic cell design, almost absent static current and non-volatility as an added value. However, nothing comes for free, and designers will have to deal with other limitations, such as the higher latencies and dynamic energy consumption for write operations compared to reads. The goal of this work is to explore several microarchitectural parameters that may overcome some of those drawbacks when using STT-MRAM as last-level cache (LLC) in embedded devices. Such parameters include: number of cache banks, number of miss status handling registers (MSHRs) and write buffer entries, presence of hardware prefetchers. We show that an effective tuning of those parameters may virtually remove any performance loss while saving more than 60% of the LLC energy on average. The analysis is then extended comparing the energy results from calibrated technology models with data obtained with freely available tools, highlighting the importance of using accurate models for architectural exploration.

Timed Automaton-Based Quantitative Feasibility Analysis of Symmetric Cipher in Embedded RTOS: A Case Study of AES

The real-time operating system (RTOS) has a wide range of application domains and provides devices with the ability to schedule resources. Because of the restricted resources of embedded devices and the real-time constraints of RTOS, the application of cryptographic algorithms in these devices will affect the running systems. The existing approaches for RTOS ciphers’ evaluation are mainly provided by experimental data performance analysis, which, however, lack a clear judgment on the affected RTOS performance indicators, such as task schedulability, bandwidth, as well as a quantitative prediction of the remaining resources of RTOS. By focusing on task schedulability in RTOS, this paper provides a timed automaton-based quantitative approach to judge the feasibility of ciphers in embedded RTOS. First, a cryptographic algorithm execution overhead estimation model is established. Then, by combining the overhead model with a sensitivity analysis method, we can analyze the feasibility of the cryptographic algorithm. Finally, a task-oriented and timed automaton-based model is built to verify the analysis results. We take AES as a case study and carry out experiments on embedded devices. The experimental results show the effectiveness of our approach, which will provide specific feasibility indicators for the application of cryptographic algorithms in RTOS.

Green House Smart Farming Echosystem

The future of farming has been one of the most talked-about issues on world forums, with the world population increasing yearly there is a special need to develop more efficient ways to grow food and distribute them effectively. This work discusses the design and implementation of a greenhouse smart farming echo system for the cultivation and distribution of plants using mushrooms as a focused product, linking a farm environment to a business market (cultivation processes and supply chain). A greenhouse farm smartly monitored with embedded devices, a control interface for these devices, and a web platform for product distribution and consumer management platform was developed to create a unified smart agricultural echo system. The embedded system has sensors that monitor the levels of light, temperature, soil moisture and humidity and automatically open the tap to water the farm. In addition, the supply chain was designed for the distribution of farm products. The prototype was fabricated and tested. The results showed that both the electronic part and the supply chain are working as proposed.

An Architecture and Reference Implementation for WSN-Based IoT Systems

The Internet of Things and its technologies have evolved quickly in recent years. It became an umbrella term for various technologies, embedded devices, smart objects, and web services. Although it has gained maturity, there is still no clear or common definition of references for creating WSN-based IoT systems. In the awareness that creating an omniscient and ideal architecture that can suit all design requirements is not feasible, modular and scalable architecture that supports adding or subtracting components to fit a lot of requirements of various use cases should be provided as a starting point. This chapter discusses such an architecture and reference implementation. The architecture should cover multiple layers, including the cloud, the gateway, and the edges of the target system, which allows monitoring the environment, managing the data, programming the edge nodes and networking model to establish communication between horizontal and vertical embedded devices. In order to exemplify the proposed architecture and reference implementation, a smart irrigation case study is used.

Animal Activity Recognition From Sensor Data Using Ensemble Learning

Animal activity recognition is an important task to monitor the behavior of animals to know their health condition and psychological state. To provide a solution for this need, this study is aimed to build an internet of things (IoT) system that predicts the activities of animals based on sensor data obtained from embedded devices attached to animals. This chapter especially considers the problem of prediction of goat activity using three types of sensors: accelerometer, gyroscope, and magnetometer. Five possible goat activities are of interest, including stationary, grazing, walking, trotting, and running. The utility of five ensemble learning methods was investigated, including random forest, extremely randomized trees, bagging trees, gradient boosting, and extreme gradient boosting. The results showed that all these methods achieved good performance (>94%) on the datasets. Therefore, this study can be successfully used by professionals such as farmers, vets, and animal behaviorists where animal tracking may be crucial.

Optimized convolutional neural network architectures for efficient on-device vision-based object detection

Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off.

Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones