Small-scale photovoltaic (PV) systems of up to a few kilowatts capacities are becoming increasingly available and affordable for off-grid installations. However, in our experience with using PV energy in farming in India, we found that many installations had faults or were lying underutilized. Though integrating such systems into IoT applications is now practical, analyzing the system’s performance and utilization requires knowledge of the components and the system design. Off-band infusion of this knowledge into the software applications leads to tight coupling and vertical silos. To address this challenge, we have developed an ontology to describe small-scale PV installations, which enables us to represent subsystems and their components in the form of Web of Things (WoT) Thing Descriptions. We show that our approach results in technical and semantic integration of the PV system into IoT applications, allowing the development of reusable fault detection and optimization programs. This reduces the cost of developing solutions to monitor and optimize the usage of PV systems, thereby bringing benefits to the farming community by improving their livelihood.
{"title":"Every Thing Under the Sun: How Web of Things and Semantic Data Brings Benefit to Small-Scale Photovoltaic Installations","authors":"G. Ramanathan, Srinivas Marella","doi":"10.1145/3567445.3571113","DOIUrl":"https://doi.org/10.1145/3567445.3571113","url":null,"abstract":"Small-scale photovoltaic (PV) systems of up to a few kilowatts capacities are becoming increasingly available and affordable for off-grid installations. However, in our experience with using PV energy in farming in India, we found that many installations had faults or were lying underutilized. Though integrating such systems into IoT applications is now practical, analyzing the system’s performance and utilization requires knowledge of the components and the system design. Off-band infusion of this knowledge into the software applications leads to tight coupling and vertical silos. To address this challenge, we have developed an ontology to describe small-scale PV installations, which enables us to represent subsystems and their components in the form of Web of Things (WoT) Thing Descriptions. We show that our approach results in technical and semantic integration of the PV system into IoT applications, allowing the development of reusable fault detection and optimization programs. This reduces the cost of developing solutions to monitor and optimize the usage of PV systems, thereby bringing benefits to the farming community by improving their livelihood.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122336343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdessalam Elhabbash, Yehia Elkhatib, Georgios Bouloukakis, M. Salama
This paper presents Hetero-Genius, a middleware architecture that enables construction and mediation in Internet of Things (IoT) systems. IoT systems are deployed across physical spaces such as urban parks, residential areas, and highways. The services provided by such IoT deployments are constrained to specific devices and deployment contexts. While existing interoperability solutions enable the “design time” development and deployment of IoT systems, it is often essential to dynamically compose systems that consist of other “small scale” IoT systems. To achieve this, post-deployment composition is needed, i.e., runtime composition of diverse IoT devices and capabilities. Hetero-Genius supports system and service discoverability, as well as automatic composability. We demonstrate this using a real-world Internet of Vehicles (IoV) scenario. Our experimental evaluation shows that developers can save up to 47% of their time when using Hetero-Genius, as well as improve code correctness by 55% on average.
{"title":"A Middleware for Automatic Composition and Mediation in IoT Systems","authors":"Abdessalam Elhabbash, Yehia Elkhatib, Georgios Bouloukakis, M. Salama","doi":"10.1145/3567445.3567451","DOIUrl":"https://doi.org/10.1145/3567445.3567451","url":null,"abstract":"This paper presents Hetero-Genius, a middleware architecture that enables construction and mediation in Internet of Things (IoT) systems. IoT systems are deployed across physical spaces such as urban parks, residential areas, and highways. The services provided by such IoT deployments are constrained to specific devices and deployment contexts. While existing interoperability solutions enable the “design time” development and deployment of IoT systems, it is often essential to dynamically compose systems that consist of other “small scale” IoT systems. To achieve this, post-deployment composition is needed, i.e., runtime composition of diverse IoT devices and capabilities. Hetero-Genius supports system and service discoverability, as well as automatic composability. We demonstrate this using a real-world Internet of Vehicles (IoV) scenario. Our experimental evaluation shows that developers can save up to 47% of their time when using Hetero-Genius, as well as improve code correctness by 55% on average.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134525984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tiago Troccoli, Juho Pirskanen, A. Ometov, J. Nurmi, Ville Kaseva
Direction of arrival (DOA) methods are found in many applications, and in the case of the Internet of Things (IoT), it is used for indoor localization. However, the implementation of DOA in IoT devices poses a real challenge, since they are computationally expensive complex numerical methods that could easily lead to resource starvation, unacceptable execution time, and rapid depletion of batteries of small constrained embedded systems typically found in IoT networks. This paper contributes to alleviating that problem, it presents a fast low-power optimized version of a DOA method called Unitary TLS ESPRIT. The optimization exploits the radio communication system design to avoid two time-consuming executions of eigendecomposition, and instead, it applies two simple Power Method algorithms. The result is a lightweight version of ESPRIT that can attain sub-millisecond execution time. To prove the solution’s viability, we carried out experiments on energy consumption, memory footprint, accuracy, and execution time for three floating-point formats in a commercial constrained embedded IoT device series without any operating system and software layers. Experiments show the solution satisfies the hardware requirements and the floating-point precision fully operated by the Floating-Point Unit is found to be the best option.
{"title":"Fast Real-World Implementation of a Direction of Arrival Method for Constrained Embedded IoT Devices","authors":"Tiago Troccoli, Juho Pirskanen, A. Ometov, J. Nurmi, Ville Kaseva","doi":"10.1145/3567445.3567446","DOIUrl":"https://doi.org/10.1145/3567445.3567446","url":null,"abstract":"Direction of arrival (DOA) methods are found in many applications, and in the case of the Internet of Things (IoT), it is used for indoor localization. However, the implementation of DOA in IoT devices poses a real challenge, since they are computationally expensive complex numerical methods that could easily lead to resource starvation, unacceptable execution time, and rapid depletion of batteries of small constrained embedded systems typically found in IoT networks. This paper contributes to alleviating that problem, it presents a fast low-power optimized version of a DOA method called Unitary TLS ESPRIT. The optimization exploits the radio communication system design to avoid two time-consuming executions of eigendecomposition, and instead, it applies two simple Power Method algorithms. The result is a lightweight version of ESPRIT that can attain sub-millisecond execution time. To prove the solution’s viability, we carried out experiments on energy consumption, memory footprint, accuracy, and execution time for three floating-point formats in a commercial constrained embedded IoT device series without any operating system and software layers. Experiments show the solution satisfies the hardware requirements and the floating-point precision fully operated by the Floating-Point Unit is found to be the best option.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132416845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sigrid Marita Kvamme, Espen Gudmundsen, Tosin Daniel Oyetoyan, D. Cruzes
Data from Internet of Things (IoT) devices has become a critical asset for decision-making. However, IoT devices have security challenges due to their low-resource constraints, heterogeneity, and deployment in hostile environments. Systems consuming IoT data must thus be designed with security measures to detect and prevent data tampering attacks. We develop a data-centric threat modeling method named Data Protection Fortification (DPF) that practitioners can use during planning to assess and mitigate the security risk of using IoT data sources. We use design science to develop and validate DPF on 5 development teams from 3 organizations. Results show that DPF can be used to identify and improve security practices of data sources. Practitioners have a positive attitude towards using DPF and because it is easily understood, it has the potential to become a communication tool for security between developers and stakeholders.
{"title":"Data Protection Fortification: An Agile Approach for Threat Analysis of IoT Data","authors":"Sigrid Marita Kvamme, Espen Gudmundsen, Tosin Daniel Oyetoyan, D. Cruzes","doi":"10.1145/3567445.3569164","DOIUrl":"https://doi.org/10.1145/3567445.3569164","url":null,"abstract":"Data from Internet of Things (IoT) devices has become a critical asset for decision-making. However, IoT devices have security challenges due to their low-resource constraints, heterogeneity, and deployment in hostile environments. Systems consuming IoT data must thus be designed with security measures to detect and prevent data tampering attacks. We develop a data-centric threat modeling method named Data Protection Fortification (DPF) that practitioners can use during planning to assess and mitigate the security risk of using IoT data sources. We use design science to develop and validate DPF on 5 development teams from 3 organizations. Results show that DPF can be used to identify and improve security practices of data sources. Practitioners have a positive attitude towards using DPF and because it is easily understood, it has the potential to become a communication tool for security between developers and stakeholders.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although the medical device industry operates within a stringent regulatory environment, the growing deployment of connected, intelligent medical devices (CIMDs) in the healthcare sector is challenging these established regulatory frameworks. CIMDs come in a variety of forms, from implantables, to specialist IoMT devices deployed at the point-of-care, to AI-based medical devices, and AI as a medical device (AIaMDs). These devices raise several cybersecurity, data management, and algorithmic integrity concerns for patient safety and the delivery of reliable, responsible healthcare. The purpose of this article is to focus on a particular characteristic of CIMDs: their changing risk profile, several times throughout their lifecycle, with limited awareness from users, manufacturers, and regulators. Looking at the implications of these often subtle yet meaningful software modifications for current medical device regulations and for critical stakeholders in the CIMD ecosystem, the article highlights three main challenges to: i) risk assessment, classification and management frameworks that underpin current medical device regulations; ii) current medical device compliance frameworks, especially the post-market surveillance of medical devices; and iii) the detection, categorization, and reporting of compromised devices that might not perform according to their intended purpose. The article brings empirical evidence from a qualitative research study conducted with critical stakeholders in the medical device sector.
{"title":"Risk Assessment and Classification of Medical Device Software for the Internet of Medical Things: Challenges arising from connected, intelligent medical devices","authors":"I. Brass, Andrew Mkwashi","doi":"10.1145/3567445.3571104","DOIUrl":"https://doi.org/10.1145/3567445.3571104","url":null,"abstract":"Although the medical device industry operates within a stringent regulatory environment, the growing deployment of connected, intelligent medical devices (CIMDs) in the healthcare sector is challenging these established regulatory frameworks. CIMDs come in a variety of forms, from implantables, to specialist IoMT devices deployed at the point-of-care, to AI-based medical devices, and AI as a medical device (AIaMDs). These devices raise several cybersecurity, data management, and algorithmic integrity concerns for patient safety and the delivery of reliable, responsible healthcare. The purpose of this article is to focus on a particular characteristic of CIMDs: their changing risk profile, several times throughout their lifecycle, with limited awareness from users, manufacturers, and regulators. Looking at the implications of these often subtle yet meaningful software modifications for current medical device regulations and for critical stakeholders in the CIMD ecosystem, the article highlights three main challenges to: i) risk assessment, classification and management frameworks that underpin current medical device regulations; ii) current medical device compliance frameworks, especially the post-market surveillance of medical devices; and iii) the detection, categorization, and reporting of compromised devices that might not perform according to their intended purpose. The article brings empirical evidence from a qualitative research study conducted with critical stakeholders in the medical device sector.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128677879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 12th International Conference on the Internet of Things","authors":"","doi":"10.1145/3567445","DOIUrl":"https://doi.org/10.1145/3567445","url":null,"abstract":"","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122433843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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