Commercial Off-the-shelf Hardware Research Articles

HARDWARE SECURITY OVERVIEW OF FOG COMPUTING END DEVICES IN THE INTERNET OF THINGS

The subject of the study is possible means of increasing the hardware security of end devices of fog computing in Internet of Things (IoT) networks, the spread of which is growing rapidly every year and requires a high level of of protection against all types of attacks. The goal of the work is to review available COTS (commercial off-the-shelf) and/or conceptual hardware solutions for protecting low-end devices in Internet of Things networks based on fog technologies. To achieve the goal, the following tasks were solved: the concept of fog computing and the advantages it will bring to IoT networks are presented; cyber threats and hardware attacks on IoT networks are considered; the consequences of using IoT networks based on fog computing are presented; hardware security tools such as TRM, PUF, HSM, etc. are considered. When performing the tasks, such research methods were used as: theoretical analysis of literary sources; comparative analysis of cloud, fog and mobile computing; analysis of existing security hardware. The following results were obtained: fog computing provides most of the advantages of cloud computing by additionally allowing data to be processed on end devices without burdening the central server. Conclusions: hardware security in IoT systems is no less important than software security. This issue is especially important for systems based on fog computing, where data will be processed on the periphery, without being transferred to the cloud. To increase the level of hardware security of fog computing devices, it is suggested to use standard hardware security platforms, such as: Physically Unclonable Functions, Hardware Security Module, System On a Chip, etc. The hardware components of the system using fog computing are less prone to cyber-attacks/hacking/intrusions/manipulation.

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

Tighter NIC/GPU Integration Yields Next-Level Media Processing Performance

As the media industry further consolidates building services based on commercial-off-the-shelf (COTS) hardware, the requirement for increasing performance continues to accelerate. The processing of video resolutions up to ultra-high definition (HD) (UHD) and 8K, higher frame rates, increased bit depth, and high-dynamic-range (HDR) imagery requires tighter integration and optimization between the network interface controller (NIC) and the graphical processing unit (GPU). Combined with simultaneous input and output of multiple streams; this creates the potential for performance bottlenecks that must be unlocked. This article describes how COTS hardware platforms running Graphics Processing Unit (GPU)s alongside ST 2059–2 Precision Time Protocol (PTP) locked Network Interface Cards (NIC)s, which are accurately pacing packets according to ST 2110–21 requirements, may further increase their performance throughput by reducing central processing unit (CPU)-driven data copy overhead and, in doing so, they reduce processing latency and jitter as well as more effectively use GPU resources while freeing up CPU resources.

Trojan Resilient Computing in COTS Processors Under Zero Trust

The commercial off-the-shelf (COTS) component-based ecosystem provides an attractive system design paradigm due to the drastic reduction in development time and cost compared to custom solutions. However, it brings in a growing concern of trustworthiness arising from the possibility of malicious embedded logic or hardware Trojans in COTS components. Existing hardware Trojan countermeasures are typically not applicable to COTS hardware due to the need for zero trust consideration for all supply chain entities, absence of golden models, and lack of observability of internal signals within the component. In this work, we propose a novel approach for runtime Trojan detection and resilience in untrusted COTS processors through judicious modifications in the software. The proposed approach does not rely on any hardware redundancy or architectural modification and hence seamlessly integrates with the COTS-based system design process. Trojan resilience is achieved through the execution of multiple functionally equivalent software variants. We have developed and implemented a solution for compiler-based automatic generation of program variants, metric-guided selection of variants, and their integration in a single executable. To evaluate the proposed approach, we first analyzed the effectiveness of program variants in avoiding the activation of a random pool of Trojans. Then, by implementing several Trojans in an OpenRISC 1000 processor, we analyzed the detectability and resilience under Trojan activation in both single and multiple variants. We also present delay and code size overhead for the automatically generated variants for several programs and discuss future research directions.

Enabling and Scaling of URLLC Verticals on 5G vRAN Running on COTS Hardware

This article discusses an Ultra-Reliable Low-La-tency Communication (URLLC) implementation on a 5G New Radio (NR) virtual radio access Network (vRAN). The vRAN application runs on a commercial off-the-shelf (COTS) test platform, on which 3GPP 5G NR RAN Layer-1 (L1) and Layer-2 (L2) containerized network functions (CNFs) are implemented. To demonstrate capability and scalability, the CNFs are dimensioned to support up to three 100 MHz cells. URLLC and enhanced mobile broadband (eMBB) slices are implemented to partition L1 processing for mixed use cases. This article illustrates an advantage of virtualization in managing L1 processing demand by L1 slice-scaling. It also describes a methodology for optimal cell capacity dimensioning based on connected URLLC users' service level agreements (SLAs) in terms of latency, reliability, and throughput. Using analysis of the test results, the article discusses maintaining a critical time budget of downlink (DL) and uplink (UL) L1 processing pipelines and points out techniques to increase L1 processing capacity to achieve URLLC SLA while serving eMBB users. The article concludes that a 5G NR vRAN application running on COTS hardware enables public/private URLLC verticals including mobile edge computing, cloud gaming, AR/VR, industrial automation, and others.

TIP4.0: Industrial Internet of Things Platform for Predictive Maintenance.

Industry 4.0, allied with the growth and democratization of Artificial Intelligence (AI) and the advent of IoT, is paving the way for the complete digitization and automation of industrial processes. Maintenance is one of these processes, where the introduction of a predictive approach, as opposed to the traditional techniques, is expected to considerably improve the industry maintenance strategies with gains such as reduced downtime, improved equipment effectiveness, lower maintenance costs, increased return on assets, risk mitigation, and, ultimately, profitable growth. With predictive maintenance, dedicated sensors monitor the critical points of assets. The sensor data then feed into machine learning algorithms that can infer the asset health status and inform operators and decision-makers. With this in mind, in this paper, we present TIP4.0, a platform for predictive maintenance based on a modular software solution for edge computing gateways. TIP4.0 is built around Yocto, which makes it readily available and compliant with Commercial Off-the-Shelf (COTS) or proprietary hardware. TIP4.0 was conceived with an industry mindset with communication interfaces that allow it to serve sensor networks in the shop floor and modular software architecture that allows it to be easily adjusted to new deployment scenarios. To showcase its potential, the TIP4.0 platform was validated over COTS hardware, and we considered a public data-set for the simulation of predictive maintenance scenarios. We used a Convolution Neural Network (CNN) architecture, which provided competitive performance over the state-of-the-art approaches, while being approximately four-times and two-times faster than the uncompressed model inference on the Central Processing Unit (CPU) and Graphical Processing Unit, respectively. These results highlight the capabilities of distributed large-scale edge computing over industrial scenarios.

Evaluation of COTS Hardware Assemblies for use in Risk Averse, Cost Constrained Space‐based Systems

AbstractA need for reduction in cost has driven many aerospace companies to consider using Commercial‐off‐the‐shelf (COTS) hardware products during a space‐based system design and development phase. Newly developed fixed‐price space systems have a difficult challenge to address; they need to optimize their processes and requirements for cost while still adhering to the appropriate level of risk and rigor for their project. For many government‐funded fixed‐price projects, there is still an expectation of compliance to government standards and associated proof, which can be a challenge when evaluating usage of COTS hardware that may contain limited documentation. This paper presents research done to date for COTS usage in both the software and hardware domains, building upon this to provide a framework for evaluation and verification of COTS hardware assemblies used in a risk averse, cost constrained space‐based applications.

On the Use of Affordable COTS Hardware for Network Measurements: Limits and Good Practices

Wireless access technologies are widespread in domestic scenarios, and end users extensively use mobile phones or tablets to browse the Web. Therefore, methods and platforms for the measurement of network key performance indicators must be adapted to the peculiarities of this environment. In this light, the experiments should capture the true conditions of such connections, particularly in terms of the hardware and multi-device interactions that are present in real networks. On the basis of this, this paper presents an evaluation of the capabilities of several affordable commercial off-the-shelf (COTS) devices as network measuring probes, for example, computers-on-module or domestic routers with software measurement tools. Our main goal is to detect the limits of such devices and define a guide of good practices to optimize them. Hence, our work paves the way for the development of fair measurement systems in domestic networks with low expenditures. The obtained experimental results show that these types of devices are suitable as network measuring probes, if they are adequately configured and minimal accuracy losses are assumable.

Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity, and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of the signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity tradeoff for the IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1, and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90%–97% burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.

A Software-Defined Ultrasonic Networking Framework for Wearable Devices

Wearable medical devices with wireless capabilities have become the cornerstone of many revolutionary digital health applications that promise to predict and treat major diseases by acquiring and processing physiological information. Existing wireless wearable devices are connected through radio frequency electromagnetic wave carriers based on standards, such as Bluetooth or Wi-Fi. However, these solutions tend to almost blindly scale down traditional wireless technologies to the body environment, with little or no attention to the peculiar characteristics of the human body and the severe privacy and security requirements of patients. We contend that this is not the only possible approach, and we introduce U-Wear, the first networking framework for wearable medical devices based on ultrasonic communications. U-Wear encloses a set of physical, data link, and network layer functionalities that can flexibly adapt to application and system requirements to efficiently distribute information between ultrasonic wearable devices. U-Wear also offers reconfiguration functionalities at the application layer to provide a flexible platform to develop medical applications. We design two prototypes that implement U-Wear and operate in the near-ultrasonic frequency range using commercial-off-the-shelf (COTS) speakers and microphones. Despite the limited bandwidth, i.e., about $2~{kHz}$ , and COTS hardware components not optimized for operating at high frequency, our prototypes: 1) achieve data rates up to $2.76~{kbit/s}$ with bit-error-rate lower than $10^{-5}$ using a transmission power of $13~{dBm}$ ( $20~{mW}$ ); 2) enable multiple nodes to share the medium; and 3) implement reconfigurable processing to extract medical parameters from sensors with high accuracy.

A Pathway to Small Satellite Market Growth

The United States is experiencing a renaissance in interest in space due to the advent of new lowercost small spacecraft. New launch entrants, such as Interorbital Systems, promise to lower launch cost levels. Many developers also benefit from free-to-developer launch services from NASA or the ESA. Unfortunately, existing commercial off the shelf (COTS) CubeSat hardware is priced based on amortization of design costs across low-sales volume. A lack of trained staff in any one of the numerous disciplines required for spacecraft design or other resources required for in-house development restricts entry into the small satellite industry to those who can afford expensive COTS hardware or pay for significant design expenses. With entry-level satellite hardware still priced in the six-figure range, limited market growth is expected even as the average CubeSat launch cost continues to decline. A new archetype could lower barriers to entry for building small satellites. A free, public-domain architecture for building a small satellite could allow low-cost, in-house satellite development. Under this paradigm, the expenses for initiating a small satellite program are limited to component and launch vehicle costs. The proposed framework allows for broad access to small satellite hardware, greatly increasing the size of the small satellite developer community. In the context of the small satellite market, freely offering plans to construct an entry-level satellite will court new non-traditional actors into building space hardware for launch on commercial and government small satellite launchers. The low-cost, high flight rate possible with the next generation of launch systems affords operators the freedom to experiment and innovate in a risktolerant environment. Successfully demonstrating products and services utilizing low-risk, publicdomain plans will stimulate demand for mature and more capable flight systems in the retail marketplace. If technical schools, community colleges, universities, small businesses and even amateurs can enter into the small satellite ecosystem, at an affordable entrance price, a positive spiral of increasing demand and decreasing cost may be created over time. A free, public domain satellite architecture may, thus, open the door to sustained growth and commercial opportunity for the small satellite industry.

Design and development of volatile analysis system for analog field test of lunar exploration mission

The recent evidence of water in the lunar crater Cabeus from the LCROSS mission (Colaprete et al., 2010) provides confirmation of a valuable resource on the lunar surface. To understand this resource and the impact it can have on future exploration, further information is needed on the distribution and availability of the water ice. The Lunar Advanced Volatile Analysis (LAVA) subsystem is a part of the Regolith & Environment Science and Oxygen & Lunar Volatile Extraction (RESOLVE) payload, designed to provide ground truth to the volatile distribution near the permanently shadowed regions on the lunar surface. The payload is designed to drill and extract a regolith core sample, heat the regolith to drive off the volatiles, and identify and quantify the volatile resources. The LAVA subsystem is specifically responsible for processing and analyzing the volatile gas sample from the lunar regolith sample. The main objective of this paper is to provide insight into the operations and hardware for volatile analysis developed and deployed at the 2012 RESOLVE Field Test on the slopes of Mauna Kea. The vision of employing Commercial Off the Shelf (COTS) and modified COTS hardware to lower the cost for mission-enabling field tests will be highlighted. This paper will discuss how the LAVA subsystem hardware supported several high level RESOLVE mission objectives to demonstrate the challenging lunar mission concept proposed.

My view: Automation vendors seek pastures new

Industrial automation is becoming an increasingly commoditised business. With the ever more widespread adoption of COTS (commercial off the shelf) hardware, software and communications technologies - Windows, Intel, and Ethernet to name but three - it's becoming harder than ever for automation vendors to differentiate themselves from their competitors, to identify areas where they can seek genuine competitive advantage and, most important, to make serious money. That's not to deny that opportunities still exist to innovate in the mainstream automation business, but the fact still remains that advances in technology tend to offer only marginal improvements in performance, both over a particular vendor's previous generation of equipment and over the current offerings of its competitors. Time was when factory and process automation vendors sold instruments and control systems. They still do, but an increasing proportion of their revenues are coming from outside their traditional areas of interest.

Multiparadigm scheduling for distributed real-time embedded computing

Increasingly complex requirements, coupled with tighter economic and organizational constraints, are making it hard to build complex distributed real-time embedded (DRE) systems entirely from scratch. Therefore, the proportion of DRE systems made up of commercial-off-the-shelf (COTS) hardware and software is increasing significantly. There are relatively few systematic empirical studies, however, that illustrate how suitable COTS-based hardware and software have become for mission-critical DRE systems. This paper provides the following contributions to the study of real-time quality-of-service (QoS) assurance and performance in COTS-based DRE systems: it presents evidence that flexible configuration of COTS middleware mechanisms, and the operating system (OS) settings they use, allows DRE systems to meet critical QoS requirements over a wider range of load and jitter conditions than statically configured systems; it shows that in addition to making critical QoS assurances, noncritical QoS performance can be improved through flexible support for alternative scheduling strategies; and it presents an empirical study of three canonical scheduling strategies; specifically the conditions that predict success of a strategy for a production-quality DRE avionics mission computing system. Our results show that applying a flexible scheduling framework to COTS hardware, OSs, and middleware improves real-time QoS assurance and performance for mission-critical DRE systems.

Proof of commercial off-the-shelf hardware scalability in an on-line clinical PACS.

A frequently cited reason for not using commercial off-the-shelf (COTS) hardware in picture archiving and communications systems (PACS) is lack of capacity, reliability, and scalability. This report describes the evaluation the scalability of COTS hardware in a clinical PACS by migrating from an active server-passive backup server environment to an active-active cluster server arrangement.

Distance Learning over ATM/SONET: The Distance Learning Environment Demonstration

A new movement in learning and communicating is underway. High bandwidth data link services are being installed throughout the country in support of evolving learning and communication needs. Early adopters are pushing the infrastructure and networks to bring high quality interactive services to the desktop. “New applications such as distance learning and interactive Internet capabilities are capturing the hearts and minds of decision-makers. A growing number of businesses and institutions are facing the challenge of finding smart, affordable network solutions” [1]. Most distance learning multimedia programs remain instructor-led mediated events. The infrastructure technology is expensive to install and most applications, especially distance learning applications, do not exploit student-centric applications. Distance learning systems integrators are quick to offer high cost instructor-centric solutions, however, the educational community is slowly moving toward low cost self-directed education-on-demand. Studies, such as the Distance Learning Environment Demonstration (DLED), offer a base study solution for legacy Ethernet local area networks (LANs), school-centric courseware management, and student-centric courses. Using commercial-off-the-shelf (COTS) PC-based hardware and software, computer-based training (CBT) courseware was either re-purposed or developed new in support of the study. Students logged into a centralized server via asynchronous transfer mode/synchronous optical network (ATM/SONET) to take different interactive multimedia courses. Individual student information was tracked and stored on the server and accessible only by the administration. The centralized courseware simplified the task of software maintenance and gave instructors and administrators direct control of the courses. The performance metrics indicated that low cost high performance COTS hardware and software can be used to develop high fidelity multimedia interactive courseware distributed over a wide area network (WAN). This study would suggest that distance learning over a high bandwidth ATM/SONET WAN was a viable solution for a remote student population.

Faster, better, cheaper-NASA visualizes the solar system

Pathfinder is the first of many planned Program missions by NASA. The Discovery program is based on the concept of using industry spacecraft comprised of near commodity components built from generic parts that cost orders of magnitude less than the custom built equipment of earlier phases of space exploration. Daniel Goldin, administrator of NASA, has given his agency a mandate to explore the solar system faster, better, and cheaper than in the past. How do you build an industry spacecraft? First, use as much COTS (commercial off the shelf) hardware and software as possible. That eliminates the need for NASA to build every part and write all the software. Instead of building a custom computer, at a cost of millions, now NASA finds a commercial system (on Pathfinder, an IBM RS6000) and modifies it as needed. And the computer will probably run some version of a commercial operating system, saving thousands or millions more dollars in development costs. Other COTS components include the Motorola modem used to communicate between the rover and lander, and the real time operating system used in the lander. Second, make everything as small and light as possible. Launch costs-the cost of a rocket and fuel-still account for 30 to 40 percent of the total cost of a mission. Every gram of weight shaved off a payload reduces the cost of launch or adds to the number of scientific instruments that can go on board. Image processing equipment and methods used in Pathfinder are discussed.