Gaetanino Paolone, Danilo Iachetti, Romolo Paesani, Francesco Pilotti, Martina Marinelli, P. D. Felice
The Internet of Things (IoT) is a complex ecosystem of connected devices that exchange data over a wired or wireless network and whose final aim is to provide services either to humans or machines. The IoT has seen rapid development over the past decade. The total number of installed connected devices is expected to grow exponentially in the near future, since more and more domains are looking for IoT solutions. As a consequence, an increasing number of developers are approaching IoT technology for the first time. Unfortunately, the number of IoT-related studies published every year is becoming huge, with the obvious consequence that it would be impossible for anyone to predict the time that could be necessary to find a paper talking about a given problem at hand. This is the reason why IoT-related discussions have become predominant in various practitioners’ forums, which moderate thousands of posts each month. The present paper’s contribution is twofold. First, it aims at providing a holistic overview of the heterogeneous IoT world by taking into account a technology perspective and a business perspective. For each topic taken into account, a tutorial introduction (deliberately devoid of technical content to make this document within the reach of non-technical readers as well) is provided. Then, a table of very recent review papers is given for each topic, as the result of a systematic mapping study.
{"title":"A Holistic Overview of the Internet of Things Ecosystem","authors":"Gaetanino Paolone, Danilo Iachetti, Romolo Paesani, Francesco Pilotti, Martina Marinelli, P. D. Felice","doi":"10.3390/iot3040022","DOIUrl":"https://doi.org/10.3390/iot3040022","url":null,"abstract":"The Internet of Things (IoT) is a complex ecosystem of connected devices that exchange data over a wired or wireless network and whose final aim is to provide services either to humans or machines. The IoT has seen rapid development over the past decade. The total number of installed connected devices is expected to grow exponentially in the near future, since more and more domains are looking for IoT solutions. As a consequence, an increasing number of developers are approaching IoT technology for the first time. Unfortunately, the number of IoT-related studies published every year is becoming huge, with the obvious consequence that it would be impossible for anyone to predict the time that could be necessary to find a paper talking about a given problem at hand. This is the reason why IoT-related discussions have become predominant in various practitioners’ forums, which moderate thousands of posts each month. The present paper’s contribution is twofold. First, it aims at providing a holistic overview of the heterogeneous IoT world by taking into account a technology perspective and a business perspective. For each topic taken into account, a tutorial introduction (deliberately devoid of technical content to make this document within the reach of non-technical readers as well) is provided. Then, a table of very recent review papers is given for each topic, as the result of a systematic mapping study.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86860943","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}
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy and ensure timely feedback to perform reconfiguration of the distribution network. In this context, edge computing is expected to be an enabling technology to ensure decentralized data collection, management, processing and delivery. At the same time, federated learning is an emerging paradigm that fits naturally in such an edge-computing environment, providing an AI-powered and privacy-preserving solution for time-series forecasting. In this paper, we present a performance evaluation of different federated-learning configurations resulting in different privacy levels to the forecast residential energy consumption with data collected by real smart meters. To this aim, different experiments are run using Flower (a popular federated learning framework) and real energy consumption data. Our results allow us to demonstrate the feasibility of such an approach and to study the trade-off between data privacy and the accuracy of the prediction, which characterizes the quality of service of the system for the final users.
{"title":"Performance Evaluation of Federated Learning for Residential Energy Forecasting","authors":"Eugenia Petrangeli, N. Tonellotto, C. Vallati","doi":"10.3390/iot3030021","DOIUrl":"https://doi.org/10.3390/iot3030021","url":null,"abstract":"Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy and ensure timely feedback to perform reconfiguration of the distribution network. In this context, edge computing is expected to be an enabling technology to ensure decentralized data collection, management, processing and delivery. At the same time, federated learning is an emerging paradigm that fits naturally in such an edge-computing environment, providing an AI-powered and privacy-preserving solution for time-series forecasting. In this paper, we present a performance evaluation of different federated-learning configurations resulting in different privacy levels to the forecast residential energy consumption with data collected by real smart meters. To this aim, different experiments are run using Flower (a popular federated learning framework) and real energy consumption data. Our results allow us to demonstrate the feasibility of such an approach and to study the trade-off between data privacy and the accuracy of the prediction, which characterizes the quality of service of the system for the final users.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72668458","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}
Muriel Cabianca, M. L. Clemente, G. Gatto, Carlo Impagliazzo, Lidia Leoni, Martino Masia, Riccardo Piras
This paper presents an exploratory activity with a drone inspection service for environmental control. The aim of the service is to provide technical support to decision-makers in environmental risk management. The proposed service uses IoT for the interaction between a mobile application, a Smart City platform, and an Unmanned Aircraft System (UAS). The mobile application allows the users to report risky situations, such as fire ignition, spills of pollutants in water, or illegal dumping; the user has only to specify the class of the event, while the geographical coordinates are automatically taken from device-integrated GPS. The message sent from the mobile application arrives to a Smart City platform, which shows all the received alerts on a 3D satellite map, to support decision-makers in choosing where a drone inspection is required. From the Smart City platform, the message is sent to the drone service operator; a CSV file defining the itinerary of the drone is automatically built and shown through the platform; the drone starts the mission providing a video, which is used by the decision-makers to understand whether the situation calls for immediate action. An experimental activity in an open field was carried out to validate the whole chain, from the alert to the drone mission, enriched by a Smart City platform to enable a decision-maker to better manage the situation.
{"title":"An Application of IoT in a Drone Inspection Service for Environmental Control","authors":"Muriel Cabianca, M. L. Clemente, G. Gatto, Carlo Impagliazzo, Lidia Leoni, Martino Masia, Riccardo Piras","doi":"10.3390/iot3030020","DOIUrl":"https://doi.org/10.3390/iot3030020","url":null,"abstract":"This paper presents an exploratory activity with a drone inspection service for environmental control. The aim of the service is to provide technical support to decision-makers in environmental risk management. The proposed service uses IoT for the interaction between a mobile application, a Smart City platform, and an Unmanned Aircraft System (UAS). The mobile application allows the users to report risky situations, such as fire ignition, spills of pollutants in water, or illegal dumping; the user has only to specify the class of the event, while the geographical coordinates are automatically taken from device-integrated GPS. The message sent from the mobile application arrives to a Smart City platform, which shows all the received alerts on a 3D satellite map, to support decision-makers in choosing where a drone inspection is required. From the Smart City platform, the message is sent to the drone service operator; a CSV file defining the itinerary of the drone is automatically built and shown through the platform; the drone starts the mission providing a video, which is used by the decision-makers to understand whether the situation calls for immediate action. An experimental activity in an open field was carried out to validate the whole chain, from the alert to the drone mission, enriched by a Smart City platform to enable a decision-maker to better manage the situation.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91341612","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}
The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios.
{"title":"A Survey of Security Architectures for Edge Computing-Based IoT","authors":"Elahe Fazeldehkordi, Tor-Morten Grønli","doi":"10.3390/iot3030019","DOIUrl":"https://doi.org/10.3390/iot3030019","url":null,"abstract":"The Internet of Things (IoT) is an innovative scheme providing massive applications that have become part of our daily lives. The number of IoT and connected devices are growing rapidly. However, transferring the corresponding huge, generated data from these IoT devices to the cloud produces challenges in terms of latency, bandwidth and network resources, data transmission costs, long transmission times leading to higher power consumption of IoT devices, service availability, as well as security and privacy issues. Edge computing (EC) is a promising strategy to overcome these challenges by bringing data processing and storage close to end users and IoT devices. In this paper, we first provide a comprehensive definition of edge computing and similar computing paradigms, including their similarities and differences. Then, we extensively discuss the major security and privacy attacks and threats in the context of EC-based IoT and provide possible countermeasures and solutions. Next, we propose a secure EC-based architecture for IoT applications. Furthermore, an application scenario of edge computing in IoT is introduced, and the advantages/disadvantages of the scenario based on edge computing and cloud computing are discussed. Finally, we discuss the most prominent security and privacy issues that can occur in EC-based IoT scenarios.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80824930","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}
Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.
将物联网(IoT)用于各种应用，如家庭和可穿戴设备、网络应用，甚至自动驾驶车辆，检测异常流量是研究人员保护网络基础设施免受攻击活动影响的问题领域之一。一些网络系统存在缺陷，允许入侵者使用恶意流量获得未经授权的访问。DDoS (Distributed Denial of Service attack)、DoS (Denial of Service attack)和服务扫描(Service scan)等攻击需要一个独特的自动系统，能够在第一时间发现流量异常，避免对系统造成损害。许多自动方法可以检测异常流量。然而，准确性不仅是当前入侵检测系统(IDS)的问题，而且需要提高效率、灵活性和可扩展性，以检测来自各种物联网网络的攻击流量。因此，本研究的重点是使用所提出的集成评估度量(Integrated Evaluation Metrics, IEM)构建一个集成分类器，以确定IDS模型的最佳性能。使用所提出的IEM执行自动排名和最佳选择方法(RBSM)，为集成分类器选择最佳模型，使用机器学习和深度学习技术检测高精度攻击。将三个真实物联网流量数据集合并，以扩展所提出的方法检测来自异构物联网网络的攻击流量的能力。结果表明，该模型在二元分类和多重分类上的准确率分别达到了99.45%和97.81%。
{"title":"Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT","authors":"Rubayyi Alghamdi, M. Bellaiche","doi":"10.3390/iot3020017","DOIUrl":"https://doi.org/10.3390/iot3020017","url":null,"abstract":"Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78750934","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}
Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified. In this study, we thoroughly analyze the impact of heterogeneity in FL and present an overview of the practical problems exerted by the system and statistical heterogeneity. We have extensively investigated state-of-the-art algorithms focusing on their practical use over IoT networks. We have also conducted a comparative analysis of the top available federated algorithms over a heterogeneous dynamic IoT network. Our analysis shows that the existing solutions fail to effectively mitigate the problem, thus highlighting the significance of incorporating both system and statistical heterogeneity in FL system design.
{"title":"On the Performance of Federated Learning Algorithms for IoT","authors":"Mehreen Tahir, M. Ali","doi":"10.3390/iot3020016","DOIUrl":"https://doi.org/10.3390/iot3020016","url":null,"abstract":"Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make it a suitable choice for IoT networks due to its intrinsic distributed infrastructure. However, FL presents a few unique challenges; the most noteworthy is training over largely heterogeneous data samples on IoT devices. The heterogeneity of devices and models in the complex IoT networks greatly influences the FL training process and makes traditional FL unsuitable to be directly deployed, while many recent research works claim to mitigate the negative impact of heterogeneity in FL networks, unfortunately, the effectiveness of these proposed solutions has never been studied and quantified. In this study, we thoroughly analyze the impact of heterogeneity in FL and present an overview of the practical problems exerted by the system and statistical heterogeneity. We have extensively investigated state-of-the-art algorithms focusing on their practical use over IoT networks. We have also conducted a comparative analysis of the top available federated algorithms over a heterogeneous dynamic IoT network. Our analysis shows that the existing solutions fail to effectively mitigate the problem, thus highlighting the significance of incorporating both system and statistical heterogeneity in FL system design.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82276109","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}
During the last decade, the advent of the Internet of Things (IoT) and its quick and pervasive evolution have significantly revolutionized the Information Technology ecosystem [...]
在过去的十年中，物联网(IoT)的出现及其快速而普遍的发展已经显著地改变了信息技术生态系统[…]
{"title":"Editorial “Industrial IoT as IT and OT Convergence: Challenges and Opportunities”","authors":"Carlo Giannelli, Marco Picone","doi":"10.3390/iot3010014","DOIUrl":"https://doi.org/10.3390/iot3010014","url":null,"abstract":"During the last decade, the advent of the Internet of Things (IoT) and its quick and pervasive evolution have significantly revolutionized the Information Technology ecosystem [...]","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82348774","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}
In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.
{"title":"Scheduling UWB Ranging and Backbone Communications in a Pure Wireless Indoor Positioning System","authors":"Maximilien Charlier, R. Koutsiamanis, B. Quoitin","doi":"10.3390/iot3010013","DOIUrl":"https://doi.org/10.3390/iot3010013","url":null,"abstract":"In this paper, we present and evaluate an ultra-wideband (UWB) indoor processing architecture that allows the performing of simultaneous localizations of mobile tags. This architecture relies on a network of low-power fixed anchors that provide forward-ranging measurements to a localization engine responsible for performing trilateration. The communications within this network are orchestrated by UWB-TSCH, an adaptation to the ultra-wideband (UWB) wireless technology of the time-slotted channel-hopping (TSCH) mode of IEEE 802.15.4. As a result of global synchronization, the architecture allows deterministic channel access and low power consumption. Moreover, it makes it possible to communicate concurrently over multiple frequency channels or using orthogonal preamble codes. To schedule communications in such a network, we designed a dedicated centralized scheduler inspired from the traffic aware scheduling algorithm (TASA). By organizing the anchors in multiple cells, the scheduler is able to perform simultaneous localizations and transmissions as long as the corresponding anchors are sufficiently far away to not interfere with each other. In our indoor positioning system (IPS), this is combined with dynamic registration of mobile tags to anchors, easing mobility, as no rescheduling is required. This approach makes our ultra-wideband (UWB) indoor positioning system (IPS) more scalable and reduces deployment costs since it does not require separate networks to perform ranging measurements and to forward them to the localization engine. We further improved our scheduling algorithm with support for multiple sinks and in-network data aggregation. We show, through simulations over large networks containing hundreds of cells, that high positioning rates can be achieved. Notably, we were able to fully schedule a 400-cell/400-tag network in less than 11 s in the worst case, and to create compact schedules which were up to 11 times shorter than otherwise with the use of aggregation, while also bounding queue sizes on anchors to support realistic use situations.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88264007","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}
Internet of Things (IoT) systems are becoming ubiquitous in various cyber–physical infrastructures, including buildings, vehicular traffic, goods transport and delivery, manufacturing, health care, urban farming, etc. Often multiple such IoT subsystems are deployed in the same physical area and designed, deployed, maintained, and perhaps even operated by different vendors or organizations (or “parties”). The collective operational behavior of multiple IoT subsystems can be characterized via (1) a set of operational rules and required safety properties and (2) a collection of IoT-based services or applications that interact with one another and share concurrent access to the devices. In both cases, this collective behavior often leads to situations where their operation may conflict, and the conflict resolution becomes complex due to lack of visibility into or understanding of the cross-subsystem interactions and inability to do cross-subsystem actuations. This article addresses the fundamental problem of detecting and resolving safety property violations. We detail the inherent complexities of the problem, survey the work already performed, and layout the future challenges. We also highlight the significance of detecting/resolving conflicts proactively, i.e., dynamically but with a look-ahead into the future based on the context.
{"title":"Conflict Detection and Resolution in IoT Systems: A Survey","authors":"P. Pradeep, K. Kant","doi":"10.3390/iot3010012","DOIUrl":"https://doi.org/10.3390/iot3010012","url":null,"abstract":"Internet of Things (IoT) systems are becoming ubiquitous in various cyber–physical infrastructures, including buildings, vehicular traffic, goods transport and delivery, manufacturing, health care, urban farming, etc. Often multiple such IoT subsystems are deployed in the same physical area and designed, deployed, maintained, and perhaps even operated by different vendors or organizations (or “parties”). The collective operational behavior of multiple IoT subsystems can be characterized via (1) a set of operational rules and required safety properties and (2) a collection of IoT-based services or applications that interact with one another and share concurrent access to the devices. In both cases, this collective behavior often leads to situations where their operation may conflict, and the conflict resolution becomes complex due to lack of visibility into or understanding of the cross-subsystem interactions and inability to do cross-subsystem actuations. This article addresses the fundamental problem of detecting and resolving safety property violations. We detail the inherent complexities of the problem, survey the work already performed, and layout the future challenges. We also highlight the significance of detecting/resolving conflicts proactively, i.e., dynamically but with a look-ahead into the future based on the context.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86453243","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}
The Internet of Things (IoT) brings Internet connectivity to devices and everyday objects. This huge volume of connected devices has to be managed taking into account the severe energy, memory, processing, and communication constraints of IoT devices and networks. In this context, the OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices, and related service enablement, through a management server usually deployed in a distant cloud data center. Following the Edge Computing paradigm, we propose in this work the introduction of a LWM2M Proxy that is deployed at the network edge, in between IoT devices and management servers. On one hand, the LWM2M Proxy improves various LWM2M management procedures whereas, on the other hand, it enables the support of QoS-aware services provided by IoT devices by allowing the implementation of advanced policies to efficiently use network, computing, and storage (i.e., cache) resources at the edge, thus providing benefits in terms of reduced and more predictable end-to-end latency. We evaluate the proposed solution both by simulation and experimentally, showing that it can strongly improve the LWM2M performance and the QoS of the system.
{"title":"An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services","authors":"Martina Pappalardo, A. Virdis, E. Mingozzi","doi":"10.3390/iot3010011","DOIUrl":"https://doi.org/10.3390/iot3010011","url":null,"abstract":"The Internet of Things (IoT) brings Internet connectivity to devices and everyday objects. This huge volume of connected devices has to be managed taking into account the severe energy, memory, processing, and communication constraints of IoT devices and networks. In this context, the OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices, and related service enablement, through a management server usually deployed in a distant cloud data center. Following the Edge Computing paradigm, we propose in this work the introduction of a LWM2M Proxy that is deployed at the network edge, in between IoT devices and management servers. On one hand, the LWM2M Proxy improves various LWM2M management procedures whereas, on the other hand, it enables the support of QoS-aware services provided by IoT devices by allowing the implementation of advanced policies to efficiently use network, computing, and storage (i.e., cache) resources at the edge, thus providing benefits in terms of reduced and more predictable end-to-end latency. We evaluate the proposed solution both by simulation and experimentally, showing that it can strongly improve the LWM2M performance and the QoS of the system.","PeriodicalId":6745,"journal":{"name":"2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)","volume":"193 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77680398","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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