IoT, Volume 2, Issue 4 (December 2021) – 11 articles

This study proposes a security-quality-metrics method tailored for the Internet of things (IoT) and evaluates conformity of the proposed approach with pertinent cybersecurity regulations and guidelines for IoT. Cybersecurity incidents involving IoT devices have recently come to light; consequently, IoT security correspondence has become a necessity. The ISO 25000 series is used for software; however, the concept of security as a quality factor has not been applied to IoT devices. Because software vulnerabilities were not the device vendors’ responsibility as product liability, most vendors did not consider the security capability of IoT devices as part of their quality control. Furthermore, an appropriate IoT security-quality metric for vendors does not exist; instead, vendors have to set their security standards, which lack consistency and are difficult to justify by themselves. To address this problem, the authors propose a universal method for specifying IoT security-quality metrics on a globally accepted scale, inspired by the goal/question/metric (GQM) method. The method enables vendors to verify their products to conform to the requirements of existing baselines and certification programs and to help vendors to tailor their quality requirements to meet the given security requirements. The IoT users would also be able to use these metrics to verify the security quality of IoT devices. Full article

Nowadays, cultural spaces (e.g., museums and archaeological sites) are interested in adding intelligence in their ecosystem by deploying different types of smart applications such as automated environmental monitoring, energy saving, and user experience optimization. Such an ecosystem is better realized through semantics in order to efficiently represent the required knowledge for facilitating interoperability among different application domains, integration of data, and inference of new knowledge as insights into what may have not been observed at first sight. This paper reports on our recent efforts for the engineering of a smart museum (SM) ontology that meets the following objectives: (a) represent knowledge related to trustworthy IoT entities that “live” and are deployed in a SM, i.e., things, sensors, actuators, people, data, and applications; (b) deal with the semantic interoperability and integration of heterogeneous SM applications and data; (c) represent knowledge related to museum visits and visitors toward enhancing their visiting experience; (d) represent knowledge related to smart energy saving; (e) represent knowledge related to the monitoring of environmental conditions in museums; and (f) represent knowledge related to the space and location of exhibits and collections. The paper not only contributes a novel SM ontology, but also presents the updated HCOME methodology for the agile, human-centered, collaborative and iterative engineering of living, reused, and modular ontologies. Full article

The concept of digital twins (DT) has already been discussed some decades ago. Digital representations of physical assets are key components in industrial applications as they are the basis for decision making. What is new is the conceptual approach to consider DT as well-defined software entities themselves that follow the whole lifecycle of their physical counterparts from the engineering, operation up to the discharge, and hence, have their own type description, identity, and lifecycle. This paper elaborates on this idea and argues the need for systematic DT engineering and management. After a conceptual description of DT, the paper proposes a DT lifecycle model and presents methodologies and tools for DT management, also in the context of Industrie 4.0 concepts, such as the asset administration shell (AAS), the international data spaces (IDS), and IEC standards (such as OPC UA and AML). As a tool example for the support of DT engineering and management, the Fraunhofer-advanced AAS tools for digital twins (FA³ST) are presented in more detail. Full article

While a variety of image processing studies have been performed to quantify the potential performance of neural network-based models using high-quality still images, relatively few studies seek to apply those models to a real-time operational context. This paper seeks to extend prior work in neural-network-based mask detection algorithms to a real-time, low-power deployable context that is conducive to immediate installation and use. Particularly relevant in the COVID-19 era with varying rules on mask mandates, this work applies two neural network models to inference of mask detection in both live (mobile) and recorded scenarios. Furthermore, an experimental dataset was collected where individuals were encouraged to use presentation attacks against the algorithm to quantify how perturbations negatively impact model performance. The results from evaluation on the experimental dataset are further investigated to identify the degradation caused by poor lighting and image quality, as well as to test for biases within certain demographics such as gender and ethnicity. In aggregate, this work validates the immediate feasibility of a low-power and low-cost real-time mask recognition system. Full article

This paper focuses on a block cipher adaptation of the Galois Extension Fields (GEF) combination technique for PRNGs and targets application in the Internet of Things (IoT) space, an area where the combination technique was concluded as a quality stream cipher. Electronic Codebook (ECB) and Cipher Feedback (CFB) variations of the cryptographic algorithm are discussed. Both modes offer computationally efficient, scalable cryptographic algorithms for use over a simple combination technique like XOR. The cryptographic algorithm relies on the use of quality PRNGs, but adds an additional layer of security while preserving maximal entropy and near-uniform distributions. The use of matrices with entries drawn from a Galois field extends this technique to block size chunks of plaintext, increasing diffusion, while only requiring linear operations that are quick to perform. The process of calculating the inverse differs only in using the modular inverse of the determinant, but this can be expedited by a look-up table. We validate this GEF block cipher with the NIST test suite. Additional statistical tests indicate the condensed plaintext results in a near-uniform distributed ciphertext across the entire field. The block cipher implemented on an MSP430 offers a faster, more power-efficient alternative to the Advanced Encryption Standard (AES) system. This cryptosystem is a secure, scalable option for IoT devices that must be mindful of time and power consumption. Full article

Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard. It has proven to be one of the most reliable options when it comes to industrial applications. TSCH offers a degree of high flexibility and can be tailored to the requirements of specific applications. Several performance aspects of TSCH have been investigated so far, such as the energy consumption, reliability, scalability and many more. However, mobility in TSCH networks remains an aspect that has not been thoroughly explored. In this paper, we examine how TSCH performs under mobility situations. We define two mobile scenarios: one where autonomous agriculture vehicles move on a predefined trail, and a warehouse logistics scenario, where autonomous robots/vehicles and workers move randomly. We examine how different TSCH scheduling approaches perform on these mobility patterns and when a different number of nodes are operating. The results show that the current TSCH scheduling approaches are not able to handle mobile scenarios efficiently. Moreover, the results provide insights on how TSCH scheduling can be improved for mobile applications. Full article

This paper proposes a policy management framework which we call the SANIJO framework. This framework comprises three different types of policy rules that are applicable to smart devices for managing their multiuser–multidevice interactions in IoT collectives, from a socio-ethical perspective. We developed a policy language to help regulate and manage the interaction behaviors of smart internet-connected devices that are being deployed at an increasing rate around the world. The policy rules are classified into Authorization, Obligation, and Prohibition rules and are prototyped in the SANIJO system. We implemented our framework as a collection of mobile apps (running on smartphones) and a robot app (running on the robot). We then illustrate its operation based on an aged care center scenario. Full article

The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage. Full article

The Fourth Industrial Revolution has led to the adoption of novel technologies and methodologies in factories, making these more efficient and productive. Among the new services which are changing industry, there are those based on machine learning algorithms, which enable machines to learn from their past observations and hence possibly forecast future states. Specifically, predictive maintenance represents the opportunity to understand in advance possible machine outages due to broken parts and schedule the necessary maintenance operations. However, in real scenarios predictive maintenance struggles to be adopted due to a multitude of variables and the heavy customization it requires. In this work, we propose a novel framework for predictive maintenance, which is trained online to recognize new issues reported by the operators. Our framework, tested on different scenarios and with a varying number and several kinds of sensors, shows recall levels above 0.85, demonstrating its effectiveness and adaptability. Full article

CultivData proposes the convergence of technologies, such as IoT, big data, HPC, open data and artificial intelligence, to apply HPDA (High Performance Data Analytics) to the cultivation of agricultural data and improve the efficiency and effectiveness of farms. An information system has been developed as an IT platform for the cultivation of open data to extract knowledge and to support the decision making of stakeholders in the agricultural sector, so that it is possible to improve product quality and farm productivity. The resulting system integrates access to data provided by IoT devices that sensorize farms and public and open data sources (Open Data). The platform was designed to make precision agriculture a reality and to be useful not only to farmers, but also to agricultural decision-makers who plan species and crops based on data such as available water; expected weather; prices and market demands, and so forth. In addition, the platform provides to agricultural producers access to historical climate data; climate forecasts to anticipate times of drought or disasters; pest situations or monitoring of their plantations with sensorization and orthophotographs. Full article

This paper assesses the relative performance of the MQTT protocol in comparison to the Secure Remote Update Protocol (SRUP) in a number of simulated real-world conditions, and describes an experiment that has been conducted to measure the processing delay associated with the use of the more secure protocol. Experimental measurements for power consumption of the devices and the size of comparable TCP packets were also made. Analysis shows that the use of the SRUP protocol added an additional processing delay of between 42.92 ms and 51.60 ms—depending on the specific hardware in use. There was also shown to be a 55.47% increase in power consumption when running the secure SRUP protocol, compared with an MQTT implementation. Full article