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The number of Internet-of-Things (IoT) devices has been increasing rapidly every year. Most of these devices have access to important personal data such as health, daily activities, location, and finance. However, these devices have security problems since they have limited processing power and memory to implement complex security measures. Therefore, they possess weak authentication mechanisms and a lack of encryption. Additionally, there are no widely accepted standards for IoT security. Device Identifier Composition Engine (DICE) was proposed as a standard that enables adding a security layer to low-cost microcontrollers with minimal silicon overhead. However, previous studies show that DICE-based attestation is vulnerable to some remote attacks. In this study, we present a novel method called DICEguard to address the security problems of DICE. One of the key innovations of DICEguard is its incorporation of periodic memory forensics (PMF) technique, leveraging a hardware-based hash engine to detect and mitigate potential security breaches resulting from firmware vulnerabilities. DICEguard enhances the overall resilience of IoT devices against attacks by swiftly detecting alterations indicative of malicious activity through periodic calculation and comparison of firmware digests. Furthermore, DICEguard introduces a one-time programmable (OTP) memory component to safeguard critical security parameters, such as public keys used for signature verification, against tampering by adversaries. This ensures the integrity of essential security measures even in the face of sophisticated attacks. We implemented the enhanced DICE architecture using the open-source RISC-V platform Ibex and the mbedTLS library for cryptographic operations. We performed the hash operations required by DICE in a hardware-based manner on a commercial Field Programmable Gate Array (FPGA) platform rather than firmware, which is more vulnerable to attacks. Our test results show that with negligible area overhead to a standard microcontroller system, the proposed method can detect the simulated attacks. [ABSTRACT FROM AUTHOR]

A complete low-power, low-cost and wireless solution for bridge structural health monitoring is presented. This work includes monitoring nodes with modular hardware design and low power consumption based on a control and resource management board called CoreBoard, and a specific board for sensorization called SensorBoard is presented. The firmware is presented as a design of FreeRTOS parallelised tasks that carry out the management of the hardware resources and implement the Random Decrement Technique to minimize the amount of data to be transmitted over the NB-IoT network in a secure way. The presented solution is validated through the characterization of its energy consumption, which guarantees an autonomy higher than 10 years with a daily 8 min monitoring periodicity, and two deployments in a pilot laboratory structure and the Eduardo Torroja bridge in Posadas (Córdoba, Spain). The results are compared with two different calibrated commercial systems, obtaining an error lower than 1.72% in modal analysis frequencies. The architecture and the results obtained place the presented design as a new solution in the state of the art and, thanks to its autonomy, low cost and the graphical device management interface presented, allow its deployment and integration in the current IoT paradigm. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process. [ABSTRACT FROM AUTHOR]

Due to its plug-and-play functionality and wide device support, the universal serial bus (USB) protocol has become one of the most widely used protocols. However, this widespread adoption has introduced a significant security concern: the implicit trust provided to USB devices, which has created a vast array of attack vectors. Malicious USB devices exploit this trust by disguising themselves as benign peripherals and covertly implanting malicious commands into connected host devices. Existing research employs supervised learning models to identify such malicious devices, but our study reveals a weakness in these models when faced with sophisticated data poisoning attacks. We propose, design and implement a sophisticated adversarial data poisoning attack to demonstrate how these models can be manipulated to misclassify an attack device as a benign device. Our method entails generating keystroke data using a microprogrammable keystroke attack device. We develop adversarial attacker by meticulously analyzing the data distribution of data features generated via USB keyboards from benign users. The initial training data is modified by exploiting firmware-level modifications within the attack device. Upon evaluating the models, our findings reveal a significant decrease from 99 to 53% in detection accuracy when an adversarial attacker is employed. This work highlights the critical need to reevaluate the dependability of machine learning-based USB threat detection mechanisms in the face of increasingly sophisticated attack methods. The vulnerabilities demonstrated highlight the importance of developing more robust and resilient detection strategies to protect against the evolution of malicious USB devices. [ABSTRACT FROM AUTHOR]

With the increase of IoT devices generating large amounts of user-sensitive data, improper firmware harms users' security and privacy. Latest home appliances are integrated with features to assure compatibility with smart home IoT. However, applying complex security mechanisms to IoT is limited by device hardware capabilities, making them vulnerable to attacks. Such attacks have recently become frequent. To address this issue, we developed a secure verification mechanism for firmware released by the device's manufacturer. We proposed an IoT gateway for secure firmware verification and updating for smart home IoT devices utilizing the IOTA MAM (Masked Authenticated Messaging) protocol and a distributed file system with IPFS (Inter-Planetary File System) protocol. These two communication protocols ensure decentralized communication and firmware file distribution between the IoT device vendor and the IoT end device. The proposed scheme securely shares latest firmware content over IOTA and IPFS networks, performs a secure firmware update on IoT end devices and ensures authenticity and integrity of the firmware. Two types of validation methods were proposed for firmware updating and validation. We implemented the proposed scheme using three entities, Vendor, IoT gateway, and IoT end device. Our system yielded promising results in performing secure automated firmware updates on IoT end devices with very low computational power. The system's functionality was implemented using IOTA's MAM run on Raspberry Pi as an IoT gateway along with an ESP8266 Wi-Fi microcontroller, demonstrating the effectiveness of our approach. Our proposed methodology can be used for secure firmware distribution on home IoT applications. [ABSTRACT FROM AUTHOR]

In this brief, a real-time inversion of petrophysical information in downhole firmware for nuclear magnetic resonance logging while drilling (NMR-LWD) based on a field-programmable gate array (FPGA) is proposed. Firstly, the inverse problem is introduced, the nonnegative least-squares (NNLS) problem is modeled and solved by the accelerated projected gradient descent (APGD) algorithm. Secondly, the efficient architecture for the APGD accelerator based on look-up tables (LUTs) is used in Intel's 10M50 FPGA. Finally, a software cooling method based on a multi-core structure and periodic thermal management technology is deployed. The proposed scheme is applied to NMR-LWD, the effectiveness and feasibility of the proposed scheme are verified experimentally. [ABSTRACT FROM AUTHOR]

The escalating advancement in Software-Defined Vehicles (SDVs) necessitates a formidable strategy for firmware updates, where traditional methods often fall short of guaranteeing absolute integrity. Although decentralization has been explored in studies for firmware integrity verification using blockchain technology, it lacks comprehensive validation in the context of automotive over-the-air (OTA) updates. By recognizing the limitations of current practices and the partial validation of decentralized approaches, such as blockchain, in the automotive sector, our study introduces a novel mechanism for firmware over-the-air (FOTA) updates. This mechanism is grounded in the widely adopted message queuing telemetry transport (MQTT) protocol, integral to the Internet of Things (IoT) domain, and leverages Merkle tree-based blockchain verification to fortify the fidelity and efficiency of firmware updates. Our proposed solution not only prioritizes the stability crucial to automotive OTA updates but also ensures that performance is not compromised. This dual focus on reliability and efficiency represents a significant stride forward in the development of secure, scalable SDV firmware update protocols. [ABSTRACT FROM AUTHOR]

This article discusses the process of establishing an experimental lab alongside computational research. It provides ten simple rules for researchers who are interested in moving into experimental studies, including the importance of collaborating with experimentalists, staying up to date with experimental techniques, finding lab space, keeping computational research ongoing, securing funding, organizing the lab, writing ethics applications, expecting a three-year timeline, documenting experiments, and publishing in experimental journals. The article emphasizes the benefits of combining computational and experimental research and offers practical advice for researchers considering this transition. [Extracted from the article]

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development. [ABSTRACT FROM AUTHOR]

This research paper deals with the enhancement of Internet of Things (IoT) security through federated ensemble learning (FEI) and adversarial machine learning (AMLI) algorithms. The proposed approach strives to enhance the security of IoT devices and networks against numerous cyber threats. To address the increasing threat of IoT, the study will utilize advanced machine learning techniques in order to build more resilient defenses for IoT networks. Using the IoT-23_Combined dataset, the study scrutinized an array of IoT attack types, revealing the distribution and frequency of different attack categories. The FEI and AMLI models were trained and tested to detect and mitigate these IoT attacks, with the evaluation metrics being accuracy, precision, recall, and F1 score. The FEI model surpassed the AMLI model, registering superior performance in all metrics, with an accuracy of 87.94% and an F1 score of 88.44%. Concurrently, the study recommends the adoption of system hardening strategies and real-time response measures, including robust authentication methods, secure firmware updates, and security awareness training programs. The assessment of these measures was based on their implementation complexity, potential impact on system performance, and adaptability to changing threats. Conclusively, the research findings illustrate the potential of machine learning to bolster IoT security, suggesting a path towards more proactive defense mechanisms and the creation of a resilient IoT ecosystem. This study holds relevance for entities seeking to fortify their IoT security systems against an evolving threat landscape. [ABSTRACT FROM AUTHOR]

Embedded devices are pervasive nowadays with the rapid development of the Internet of Things (IoT). This brings significant security issues that make the security analysis of embedded devices important. This paper presents a survey on the security analysis research of embedded devices. First, we analyze the embedded device types and their operating systems. Then, we describe a major dynamic security analysis method for an embedded device, i.e., simulating the firmware of the embedded device and performing fuzzing on the web interface provided by the firmware. Third, we discuss some other issues in embedded security analysis, such as analyzing the attack surface, applying static analysis, and performing large-scale analysis. Based on these analyses, we finally conclude three challenges in the current research and present our insights for future research directions. [ABSTRACT FROM AUTHOR]

The article discusses the development of advanced fly scans using the Mamba software project at the High Energy Photon Source (HEPS) in Beijing, China. The Mamba software aims to provide a reliable and flexible framework for beamline experiments, with a focus on high framerates and data throughputs. The article explores the architectural essentials of fly scans and discusses issues related to motion control, triggering sequences, detectors, and other fields of interest in fly scans. The document discusses the challenges and potential solutions for conducting fly scans with framerates of 10 kHz or higher at HEPS, including limited motor encoder ports, performance limitations of detectors and readout software, and the unsuitability of stepping motors for high-frequency scans. It proposes solutions such as using DeltaTau PowerPMAC for encoder processing and implementing scan fragmentation to address clock drift. The document also discusses the potential for signal distortion in high-frequency data captures and the need for improved hardware/software system architecture for data processing. The source code for the Bubo software is available as part of the open-source edition of Mamba. [Extracted from the article]

The aim of the study is the development of a VTOL (Vertical Takeoff and Landing) bicopter, utilizing Autodesk Inventor and Ultimaker Cura software. Design models were inspired by the V22- Osprey and Samson SA-2 from the Avatar movie. Composite materials (carbon fiber and PLA for 3D printing) were chosen due to their high-performance capabilities, as aerospace technology is in an ongoing development and evolution. The construction of the bicopter employed the FAAT method, and for control, the MATEK F411-WSE was utilized to process data from sensors, with specific firmware and the BetaFLIGHT interface for optimizing drone performance. [ABSTRACT FROM AUTHOR]

LoRa technology has gained popularity as one of the most widely used standards for device interconnection due to its ability to cover long distances and energy efficiency, making it a suitable choice for various Internet of Things (IoT) monitoring and control applications. In this sense, this work presents the development of a visual support tool for creating IoT devices with LoRa and LoRaWAN connectivity. This work significantly advances the state of the art in LoRa technology by introducing a novel visual support tool tailored for creating IoT devices with LoRa and LoRaWAN connectivity. By simplifying the development process and offering compatibility with multiple hardware solutions, this research not only facilitates the integration of LoRaWAN technology within educational settings but also paves the way for rapid prototyping of IoT nodes. The incorporation of block programming for LoRa and LoRaWAN using the Arduinoblocks framework as a graphical environment enhances the capabilities of the tool, positioning it as a comprehensive solution for efficient firmware generation. In addition to the visual tool for firmware generation, multiple compatible hardware solutions enable easy, economical, and stable development, offering a comprehensive hardware and software solution. The hardware proposal is based on an ESP32 microcontroller, known for its power and low cost, in conjunction with an RFM9x module that is based on SX127x LoRa transceivers. Finally, three successfully tested use cases and a discussion are presented. [ABSTRACT FROM AUTHOR]

In view of the security problem of embedded device firmware update, this study proposes a multi-check firmware security update scheme based on hash, symmetric and asymmetric encryption algorithms. In this study, the master key pairs, temporary key pairs, shared key and hash chain are designed to protect firmware update from identity authentication, data encryption, integrity check and other aspects, which can effectively prevent illegal users, firmware tampering, firmware data leakage, replay attack, firmware rollback and other attacks. In this study, the concrete implementation of the security update scheme is carried out. The experiment results show that compared with the ISP(In System Programming) and IAP(In Application Pragramming) technologies without any security protection, the scheme achieves the security protection of the whole process of firmware update at the time cost of about 7% and 11%, it provides a safe and reliable update method for embedded device firmware update. [ABSTRACT FROM AUTHOR]

Although the vigorous development of smart homes brings great convenience to people's lives, smart homes usually suffer from various security threats due to firmware vulnerabilities. Firmware update is a possible solution, but existing methods cannot address the security issues during the update process well. To enable secure firmware updates, a Forward Secure Firmware Update (FSFU) system was realized based on the proposed Puncturable-Ciphertext Policy-Attribute-Based Encryption (P-CP-ABE) scheme. In FSFU, when the service provider delivers the latest firmware, it specifies an access policy and some tags to encrypt the data and appends its signature to achieve both fine-grained access control and authentication. Authorized customers can obtain the latest firmware by decrypting the encrypted data through their private key. In particular, after a successful update, each authorized customer can realize forward security by updating his/her puncturable key, which is an important private key component. In addition, FSFU is further enhanced by outsourcing a part of the parameters and computational tasks. Finally, FSFU was proven to be secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Our proposed FSFU is efficient from both the theoretical analysis and the experimental results. [ABSTRACT FROM AUTHOR]

The pervasiveness of IoT and embedded devices allows the deployment of services that were unthinkable only few years ago. Such devices are typically small, run unattended, possibly on batteries and need to have a low cost of production. As all software systems, this type of devices need to be updated for different reasons, e.g., introducing new features, improving/correcting existing functionalities or fixing security flaws. At the same time, because of their low-complexity, standard software distribution platforms and techniques cannot be used to update the software. In this paper we review the current limitations posed to software distribution systems for embedded/IoT devices, consider challenges that the researchers in this area have been identifying and propose the corresponding solutions. [ABSTRACT FROM AUTHOR]

The paper presents a DSP-based design and control of a three-axis servo motion system that effectively divides control tasks between the master control terminal and four slave drives. The proposed architecture enhances the immediacy of platform positioning and the integration between software and hardware. The paper utilizes a high-accuracy encoder as a reference value for accuracy improvement and integrates firmware via the proposed embedded execution program, thereby reducing the accuracy error of the low-accuracy encoder. The main control core employs a digital signal processor (DSP) for input and output signal reading and writing, digital communication interface processing, and user interface. The slave controller utilizes four digital signal processors to accomplish servo position control in a digital way. The paper employs encoder calibration technology to upgrade the positioning accuracy of the surge axis. Finally, the paper validates the correctness and feasibility of the proposed method through experimental results from a set of adjustable experimental platforms of linear motion stroke and rotary motion stroke. In summary, the paper underscores the integration of hardware and software to attain high-precision and dependable control of the motion system. [ABSTRACT FROM AUTHOR]

Internet of Things cybersecurity is gaining attention as the number of devices installed in IoT environments is exponentially increasing while the number of attacks successfully addressed to these devices are also proliferating. Security concerns have, however, been mainly addressed to service availability and information integrity and confidentiality. Code integrity, on the other hand, is not receiving proper attention, mainly because of the limited resources of these devices, thus preventing the implementation of advanced protection mechanisms. This situation calls for further research on how traditional mechanisms for code integrity can be adapted to IoT devices. This work presents a mechanism for code integrity in IoT devices based on a virtual-machine approach. A proof-of-concept virtual machine is presented, specially designed for providing code integrity during firmware updates. The proposed approach has been experimentally validated in terms of resource consumption among the most-widespread micro-controller units. The obtained results demonstrate the feasibility of this robust mechanism for code integrity. [ABSTRACT FROM AUTHOR]

Recently, the Internet of things (IoT) has become extensively used in our daily lives. This technology offers a new vision of the future internet where devices are interconnected and can communicate together. A big number of these devices complicates the firmware update and makes it more expensive since each node must be updated locally. Nevertheless, there are many cases where devices cannot change their location to upgrade the firmware locally due to an unreachable location or dangerous place. Therefore, it is necessary to remotely update the firmware of devices. In this study, the authors propose an efficient implementation of a low cost and secure framework of upgrading the firmware employing the latter On‐The‐Air programming technique. The authors present a proof of concept for a ubiquitous system applying wireless programmer. Our design offers a remote broadcasting of image code without disturbing the main functionality of nodes. The authors validated the performance of our design on a real network based on STM32 micro‐controllers. The results showed the reduction of the network time‐off, enabling a continuous operation of the ecosystem. [ABSTRACT FROM AUTHOR]

An Open Brain–Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing. [ABSTRACT FROM AUTHOR]

In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors' knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns. [ABSTRACT FROM AUTHOR]

Introduction/purpose: This paper provides an overview of research on computer system vulnerabilities caused by compromised electromagnetic radiation by wireless keyboards. Wireless devices that use event-triggered communication have been shown to have critical privacy issues due to the inherent leakage associated with radio frequency emissions. Wireless connectivity technology is a source of signal emanation that must be protected in terms of performance and security. Methods: Wireless device vulnerabilities and side-channel attacks are observed, along with electromagnetic emission of radio waves. Results: The findings highlight a specific wireless keyboard's security and encryption flaws. The results of penetration testing reveal vulnerabilities of targeted wireless keyboards in terms of outdated firmware, encryption, wireless reliability, and connection strength. Conclusion: Wireless keyboards have security flaws that disrupt radio communication, giving a malicious user complete access to the computer to which the keyboard is connected. An attacker can steal sensitive data by observing how the system works using compromised electromagnetic emissions. [ABSTRACT FROM AUTHOR]

The increase in renewable energy sources (RESs) in distribution grids is a major driver for achieving green energy goals worldwide. However, RES power inverters affect power quality, increase power losses, and, in certain cases, may cause power interruptions due to harmonics, deterioration of the rate of change of frequency, and inability to rapidly react in grid faults. Today, phasor measurement units (PMUs) are the ultimate tools for real-time monitoring of distribution grids' health, and they enable several data-driven added-value services such as fast and automated fault detection, isolation, and recovery; state estimation; power quality monitoring; dynamic events analysis, etc. The present paper proposes an open hardware and software PMU platform, which is low cost, high performance, expandable, and, in general, suitable for research and innovation activities. The system is based on two processor modules (a digital signal processor from Texas Instruments TMS320c5517, and a microprocessor System-in-Package from Octavo Systems OSD3358), two local databases of 64 Gbytes each, GPS module, 5G modem interface, as well as analog and signal conditioning circuits to interface three-phase power voltage and current signals. The entire hardware design, schematics, and instrumentation components, as well as all firmware and software functions are completely open source. Pilot operation of the prototype design has been installed in three medium-/low-voltage substations in Cyprus, as well as twelve substations in Spain and Italy. [ABSTRACT FROM AUTHOR]

The advent of energy communities will revolutionize the energy market. However, exploiting their full potential requires innovations in the structure and management of low-voltage grids. End users shall be aggregated within microgrids, where their physical interaction is possible and coordinated operation of power sources and energy storage systems can be achieved. Moreover, meshed network topologies will enable multiple paths for the power flow. The combination of smart control and meshed networks can dramatically improve microgrid performance in terms of power quality, efficiency, and resilience to transients and faults. Ubiquitous control of the power flow becomes possible, as well as active fault clearing and isolation of subgrids without tripping circuit breakers. This paper proposes a control approach that pursues such goals without requiring modification of control and communication hardware implemented in commercial inverters. Instead, a revision of control firmware, integrated with local measurements, allows retrofitting existing plants to improve microgrid operation. Further improvements may derive from the installation of community power sources and energy storage systems, which can extend microgrid operation to pursue demand response and islanding. The potential of the proposed control methods is demonstrated by simulation considering a standard microgrid under different operating conditions. [ABSTRACT FROM AUTHOR]

Fire disasters are one the most challenging accidents that can take place in any urban buildings like houses, offices, hospitals, colleges and industries. These accidents which the world faces now, have never been more frequent and fatal, leading to innumerable loses, damage of expensive equipment and unparalleled human lives. The concrete landscapes are threatened by fire disasters, which have prolifically outnumbered in the last decade, both in intensity and frequency. Thus, to minimize the impact of fire disasters, adoption of well planned, intelligent and robust fire detection technology harnessing the niches of machine learning is necessary for early warning and coordinated prevention and response approach. In this research a novel hybrid ensemble technology based machine algorithm using maximum averaging voting classifier has been designed for fire detection in buildings. The proposed model uses feature engineering pre-processing techniques followed by a synergistic integration of four classifiers namely, logistic regression, support vector machine (SVM), Decision tree and Naive Bayes classifier to yield better prediction and improved robustness. A database from NIST has been chosen to validate the research under different fire scenarios. Results indicate an improved classification accuracy of the proposed ensemble technique as compared to reported literatures. After validating the algorithm, the firmware has been implemented on a laboratory developed prototype of smart multi sensor, embedded fire detection node. The designed smart hardware is successfully able to transmit the sensed data wirelessly onto the cloud platform for further data analytics in real time with high precision and reduced root mean square error (MAE). [ABSTRACT FROM AUTHOR]

Building a reliable communication network is a challenging task using the standard Ethernet as the medium for control and data acquisition networks. Such sensor-actuator networks could be distributed across multiple locations across a building. Packet retransmissions and losses could be critical when Ethernet is used for closed-loop controls and measurements. There could be multiple design challenges to build such networks such as total-traffic, compact node design, bandwidth limitations, packet retransmissions, delays, drops etc. In this paper, we report how we implemented a reliable http-based automation network of large interconnected microcontroller-based nodes with a careful design of hardware, firmware components and a new application-layer faulty-node-filter-algorithm. It analysed some of the top causes of poor network performance within such control networks using a popular network protocol-analyser "Wireshark". Though Ethernet seemed the most future promising, it has the single drawback of nondeterministic packet transfers. Additional research is carried out to prove that IEEE 802.3 standard Ethernet could be used in applications like our slow-cryogenics-control systems of linear Accelerators to build an error-free, near deterministic control-application. This approach allowed the continued use of conventional Ethernet networking components. even for the control networks and the same communication medium. The tested system is distributed within a private LAN of more than 50 embedded servers each of which is built out of on-chip TCP/IP stacks implemented on ARM processors as nodes, for a successfully implemented Cryogenic control network of Superconducting linear Accelerator at Inter University Accelerator Centre, New Delhi, India. [ABSTRACT FROM AUTHOR]

Industrial assets often feature multiple sensing devices to keep track of their status by monitoring certain physical parameters. These readings can be analyzed with machine learning (ML) tools to identify potential failures through anomaly detection, allowing operators to take appropriate corrective actions. Typically, these analyses are conducted on servers located in data centers or the cloud. However, this approach increases system complexity and is susceptible to failure in cases where connectivity is unavailable. Furthermore, this communication restriction limits the approach's applicability in extreme industrial environments where operating conditions affect communication and access to the system. This paper proposes and evaluates an end-to-end adaptable and configurable anomaly detection system that uses the Internet of Things (IoT), edge computing, and Tiny-MLOps methodologies in an extreme industrial environment such as submersible pumps. The system runs on an IoT sensing Kit, based on an ESP32 microcontroller and MicroPython firmware, located near the data source. The processing pipeline on the sensing device collects data, trains an anomaly detection model, and alerts an external gateway in the event of an anomaly. The anomaly detection model uses the isolation forest algorithm, which can be trained on the microcontroller in just 1.2 to 6.4 s and detect an anomaly in less than 16 milliseconds with an ensemble of 50 trees and 80 KB of RAM. Additionally, the system employs blockchain technology to provide a transparent and irrefutable repository of anomalies. [ABSTRACT FROM AUTHOR]

We describe the newest generation of the SLAC Microresonator RF (SMuRF) electronics, a warm digital control and readout system for microwave-frequency resonator-based cryogenic detector and multiplexer systems, such as microwave superconducting quantum interference device multiplexers (μmux) or microwave kinetic inductance detectors. Ultra-sensitive measurements in particle physics and astronomy increasingly rely on large arrays of cryogenic sensors, which in turn necessitate highly multiplexed readout and accompanying room-temperature electronics. Microwave-frequency resonators are a popular tool for cryogenic multiplexing, with the potential to multiplex thousands of detector channels on one readout line. The SMuRF system provides the capability for reading out up to 3328 channels across a 4–8 GHz bandwidth. Notably, the SMuRF system is unique in its implementation of a closed-loop tone-tracking algorithm that minimizes RF power transmitted to the cold amplifier, substantially relaxing system linearity requirements and effective noise from intermodulation products. Here, we present a description of the hardware, firmware, and software systems of the SMuRF electronics, comparing achieved performance with science-driven design requirements. In particular, we focus on the case of large-channel-count, low-bandwidth applications, but the system has been easily reconfigured for high-bandwidth applications. The system described here has been successfully deployed in lab settings and field sites around the world and is baselined for use on upcoming large-scale observatories. [ABSTRACT FROM AUTHOR]

The Programmable Logic Controller (PLC) is located at the junction of the virtual network and physical reality in the Industrial Control System (ICS), which is vulnerable to attacks due to its weak security. Specifically, firmware tampering attacks take the firmware under the PLC operating system as the primary attack target. The firmware provides the bridge between PLC's hardware and software, which means tampering against the firmware can be more destructive and harmful than other attacks. However, existing defense and forensics methods against firmware tampering attacks are asymmetrical, which directly leads to the proliferation of such attacks and the difficulty of forensic tracing. How to accurately, quickly, and efficiently conduct forensics for such attacks is an urgent problem. In this paper, we designed and implemented a reliable detection method based on Joint Test Action Group (JTAG) and memory comparison—Aye, which can detect mainstream firmware tampering attacks reliably. To determine the effectiveness and reliability of Aye, we selected a widely used PLC to observe Aye's performance in defense and forensics by simulating the two latest PLC firmware tampering attack methods. The experimental results show that Aye can effectively defend against firmware tampering attacks, helping improve the efficiency and accuracy of such attack detection and forensics. [ABSTRACT FROM AUTHOR]