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BenchIoT: Security benchmarks for the Internet of Things.
Abstract not provided.
Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments
Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. Here we used the Grid Workloads Archive dataset to evaluate our proposed approach named TOPREAL. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.
Thread/OpenThread: A Compromise in Low-Power Wireless Multihop Network Architecture for the Internet of Things
Extending an Internet subnet by connecting resource-constrained nodes (e.g., embedded sensors and actuators) over multiple wireless hops is necessary to support the future Internet of Things (IoT). RPL, the IPv6 routing standard for low-power and lossy networks, tried to achieve this goal but has not seen wide adoption in practice. As an alternative, Thread is a recently standardized low-power network protocol for IoT, driven by the Thread group, an industry consortium led by Google/Nest. Here, we provide a comparative analysis of the technical aspects of RPL and Thread based on their specifications, explaining why using Thread, as opposed to RPL, may make sense for the future Internet. Specifically, the fundamental differences between RPL and Thread are their respective scopes and multihop network architectures, which result in Thread's unique design and advantages over RPL. Lastly, we evaluate Thread in an indoor multihop wireless testbed using OpenThread, an official open source implementation of Thread. This work serves as the first analysis of the Thread protocol in academia.
Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput
The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. Here, by reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.
The Lighter Side of Things: The Inevitable Convergence of the Internet of Things and Cybersecurity
By the year 2020 it is estimated that there will be more than 50 billion devices connected to the Internet. These devices not only include traditional electronics such as smartphones and other mobile compute devices, but also eEnabled technologies such as cars, airplanes and smartgrids. The IoT brings with it the promise of efficiency, greater remote management of industrial processes and further opens the doors to world of vehicle autonomy. However, IoT enabled technology will have to operate and contend in the contested domain of cyberspace. This discussion will touch on the impact that cybersecurity has on IoT and the people, processes and technology required to mitigate cyber risks.
Seamless Wireless Communication Platform for Internet of Things Applications
The rapid growth of the Internet of Things (IoT) devices resulted in the proliferation of wireless technologies to cater to their increasing data rate requirements and support multiple applications. However, such ever-increasing wireless technologies present numerous challenges such as incompatible wireless standards, increased energy consumption, and insecure communication. The traditional gateways proposed in the literature suffers from limitations such as computational complexity, resource requirements, increased cost, and device size. We vision an era of seamless wireless communication to alleviate the aforementioned challenges in IoT applications. through three inter-dependent functionalities namely detection and identification of wireless technologies, energy-efficient transmit power control, and secure end-to-end communication. To prove the concept, a new gateway is proposed to achieve these three functionalities with only physical layer measurements so that the different communication protocols in the higher layers can be avoided. Novel schemes are conceptualized for resource-limited seamless IoT applications. Moreover, the conceptual seamless IoT platform is validated through software-based computer simulation and software-defined radio-based testbed implementation. Finally, the preliminary analysis demonstrates that the proposed platform has great potential in advancing seamless IoT applications.
User Guide to the Facility Cybersecurity Framework Internet of Things (IoT) Self-Assessment
The FEMP Facility Cybersecurity Framework (FCF) Internet of Things (IoT) Self-Assessment is a comprehensive tool aimed at illuminating the foggy domains of IoT and Industrial Internet of Things (IIoT) security. The assessment was developed using insights from recognized standards and guidelines to identify, address, and mitigate the challenges posed by the massive surge of interconnected devices. The FCF IoT Self-Assessment was created using the knowledge from established National Institute of Standards and Technology (NIST) publications such as NIST SP 800-53, NIST SP 800-213, and NIST Cybersecurity Framework (CSF). Additionally, integrating NIST SP 800-213A IoT Device Cybersecurity Guidance for the Federal Government ensures federal agencies are equipped with specific IoT security insights. This user guide has been developed to facilitate a thorough understanding of the tool. As users delve into the assessment, the guide offers a clear navigation walkthrough, report generation, and interpretation of the report.
Emerging Threats and Technology Investigation: Industrial Internet of Things - Risk and Mitigation for Nuclear Infrastructure
Industries supporting the global nuclear infrastructure striving for cost savings, expansions in efficiency, and convenience are likely to adopt components (e.g., hardware, software) that comprise the Internet of Things (IoT) and Industrial Internet of Things (IIoT). These devices offer potential improvements along with security challenges. Modern conveniences achieved through application of technology have propagated through society in the form of interconnected devices, from doorbells to microwave ovens, commonly referred to as IoT. IoT devices are often Internet-connected devices that are designed to send data back to a cloud-based server, where a smart phone application then presents device status and control options. Home-based IoT applications carry a different set of risks when compared to a business or security environment, where there is also a history of convenience and interconnection. Industrial settings have long relied on specifically designed Supervisory Control and Data Acquisition (SCADA) systems for process control where IIoT devices are intended to inform business decisions and augment traditional processes. A recent National Institute of Standards and Technology (NIST) report provides a distinction between process control and IIoT in that traditional process control is not replaced by IIoT, but rather IIoT devices are intended to enhance industrial processes through additional monitoring of various sensors and application of data analytics models using artificial intelligence (AI) and machine learning (ML) (Fagan, Marron, et al. 2021) (Ross, et al. 2021).
Investigating Users’ Privacy Concerns of Internet of Things (IoT) Smart Devices
Although the number of smart Internet of Things (IoT) devices has grown in recent years, the public's perception of how effectively these devices secure IoT data has been questioned. Many IoT users do not have a good level of confidence in the security or privacy procedures implemented within IoT smart devices for protecting personal IoT data. Moreover, determining the level of confidence end users have in their smart devices is becoming a major challenge. In this paper, we present a study that focuses on identifying privacy concerns IoT end users have when using IoT smart devices. We investigated multiple smart devices and conducted a survey to identify users’ privacy concerns. Furthermore, we identify five IoT privacy-preserving (IoTPP) control policies that we define and employ in comparing the privacy measures implemented by various popular smart devices. Results from our study show that the over 86% of participants are very or extremely concerned about the security and privacy of their personal data when using smart IoT devices such as Google Nest Hub or Amazon Alexa. In addition, our study shows that a significant number of IoT users may not be aware that their personal data is collected, stored or shared by IoT devices.
Internet of Things (IoT) Products and Applications: Lighting - Manufacturer and End-User Perspectives
It is forecast that the Internet of Things (IoT) market will experience significant growth in the coming years, with many expecting compound annual growth rates generally in the mid-20% (Forbes 2017). For lighting manufacturers, this is a tremendous opportunity to revolutionize their products to address the dwindling sales of lamps, once a major revenue stream, and bring new capabilities and services to their customers through luminaires and other lighting system components. For end-users, lighting products of the future may do more than simply provide light, with a cadre of connected sensors and controls that may help them do everything from exploring energy saving opportunities in other building systems to data-mining occupancy data to optimize use of space or increase sales. The focus of this study is to evaluate where the lighting market is today with respect to integrating IoT into its product offerings and end-user experiences with IoT, and in particular with IoT incorporated into lighting systems. The benefits, in terms of energy savings, of encouraging the adoption of connected lighting systems beyond the current solid state lighting (SSL) path to more aggressive DOE SSL Program Goals can result in an additional 33-percent of the LED energy savings, or 1.4 Quads, in 2035 (Navigant 2016).
The global energy footprint of information and communication technology electronics in connected Internet-of-Things devices
Information and communication technology (ICT) has been critical to the emergence of a new Internet-of-Things (IoT) era, as a rapidly increasing number and variety of IoT electronic devices in a wide range of end-use sectors require massive data communication in terms of volume, velocity/speed, and variety. Additionally, semiconductor technology advancements have played a key role in ICT and unlike the past ICT energy footprint analysis, which focused on the final electronic product, an attempt has been made to examine the global energy impacts for the period 2016–2025 based on the market forecasts of three major IoT IC types, i.e., sensors and actuators, connectivity, and processor ICs distributed among four major IoT device components.Total global IoT semiconductor primary energy demand is projected to increase from 2 EJ in 2016 to 35 EJ by 2025, resulting mainly from a substantial projected increase energy needed in the manufacturing of ICs, with growth in the use of sensors and energy-intensive next fab generation manufacturing. Unlike the trend in manufacturing energy, total global operational energy use is projected to decrease significantly with the development of smaller transistor size, low-power devices, and faster wireless data communication technology. Total operational energy is projected to significantly decrease from 118 TWh/year (1,2 EJ/year) to less than 1 TWh/year (0.003 EJ/year) in spite of a significantly higher anticipated growth in its market size during the 2016–2025 forecast period. Its annual share of total primary energy use is projected to reach an insignificant level of 0.01% by the end of 2025, consistent with the past limited electronic product life cycle analyses besides the analysis focus here limited to ICs in IoT devices.
A Right Transfer Access Control Model of Internet of Things Based on Smart Contract
Sensor nodes play a crucial role in the promotion of development of Internet of Things (IoT). Through this transaction, RO defines access control policies in script form based on ABAC's access control model to grant access right. The identity of all users in the model is identified by address. This paper builds a more flexible right transfer access control model by means of combining the Attribute-Based Access Control model (ABAC) and blockchain technology. Owing to the characteristics of ABAC’s attributes and right association, the massive problems of some sensor nodes can be solved. At the same time, for the sake of addressing the dynamic problems such as node access and right transfer, right transfer contract (TS) and access control contract (CS) are employed on the chain to ensure efficient and safe transmission of rights. To solve on-chain storage problems and ensure transparency of the operation, the idea of Rollup in Ethereum expansion is used to upload the final state of protocol policy and right exchange to the chain. Any user can know the policy and current right transfer status at any time. Finally, comparative and security analysis show that the model presented here can solve IoT devices’ massive and dynamic problems more effectively and it is more secure than the traditional models.
A 5.8 GHz 1.77mW AFSK-OFDM CMOS Backscatter Transmitter for Low Power Internet of Things Applications
This paper presents an AFSK-OFDM (amplitude frequency shift keying-orthogonal frequency division multiplexing) based backscatter transmitter for Internet-of-Things (IoT) applications. The transmitter uses an array of 4 low-power direct digital frequency synthesizers (DDFSs) and DACs to transmit data using both frequency and amplitude symbols on 4 different OFDM subcarriers. The backscatter modulator is demonstrated within a 5.8 GHz wireless link using an 8-symbol OFDM library at a range of 2.7m and shown to consume 1.77 mW when operated at a symbol rate of 4MS/s (corresponds to 12 Mb/s). The modulator chip occupies 0.45mm² of silicon area.
SQL and NoSQL Databases for Cyber Physical Production Systems in Internet of Things for Manufacturing (IoTfM)
Abstract In this paper, the design and performance differences between Relational Database Management Systems (RDBMS) and NoSQL Database Systems are examined, with attention to their applicability for real-world Internet of Things for manufacturing (IoTfM) data. While previous work has extensively compared SQL and NoSQL for both generalized and IoT uses, this work specifically examines the tradeoffs and performance differences for manufacturing applications by using a high-fidelity data set collected from a large US manufacturing firm. Growing an IoT system beyond the pilot stage requires scalable data storage; this work seeks to determine the impact of selected database systems on data write performance at scale. Payload size and message frequency were used as the primary characteristics to maintain model fidelity in simulated clients. As the number of simulated asset clients grow, the data write latency was calculated to determine how both database systems’ performance were affected. To isolate the RDBMS and NoSQL differences, a cloud environment was created using Amazon Web Services (AWS) with two identical data ingestion pipelines: writing data to an RDMBS (1) using AWS Aurora MySQL, and (2) using AWS DynamoDB NoSQL. The findings may provide guidance for further experimentation in large-scale manufacturing IoT implementations.
The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks
Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.
Automated fault detection and diagnosis deployment Internet of Things solution for building energy system
The building sector is the highest energy consumer and ranks first in terms of carbon emissions among all sectors. To address these issues, decarbonization and electrification in the building energy sector are two critical missions of the new US administration. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50–52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostic (AFDD) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. Here, this study reviews existing IoT solutions developed for building energy–related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.
Internet of Things Data Characterization Process: Pattern of Life Behavioral Data Study
The HoneyBee™ TARDIS LDRD team completed a data scoping study that identified the initial processes and procedures to baseline the normal and expected behaviors during operability and interoperability of Internet of Things (IoT) device networks. This research is the initial step in developing a process (or methodology) to inform a much broader information framework incorporating machine learning to determine device pattern-of-life which enables the detection of abnormal IoT behaviors on an individual device, as well as in the context of a larger network.