Kento Katsumata, Yuka Honda, T. Okoshi, J. Nakazawa
On January 30, 2020, WHO officially declared the outbreak of COVID-19 a Public Health Emergency of International Concern. Japan announced the state of emergency and implemented safety protocols the "Three Cs", a warning guideline addressing to voluntarily avoid potentially COVID-19 hazardous situations such as confined and closed spaces, crowded places and close-contact settings that lead to occurrence of serious clusters. The primary goal of this research is to identify the factors which help to estimate whether the user is in the Three Cs. We propose COVIDGuardian, a system that detects the Three Cs based on data such as CO2, temperature, humidity, and wireless packet log. The results show that estimation of closed space had the highest accuracy followed by close-contact settings and crowded places. The ensemble Random Forest (RF) classifier demonstrates the highest accuracy and F score in detecting closed spaces and crowded spaces. The findings indicated that integrated loudness value, average CO2, average humidity, probe request log, and average RSSI are of critical importance. In addition, when the probe request logs were filtered at three RSSI cutoff points (1m, 3m, and 5m), 1m cut-off points had the highest accuracy and F Score among the Three C models.
{"title":"COVIDGuardian: A Machine Learning approach for detecting the Three Cs","authors":"Kento Katsumata, Yuka Honda, T. Okoshi, J. Nakazawa","doi":"10.1145/3567445.3569166","DOIUrl":"https://doi.org/10.1145/3567445.3569166","url":null,"abstract":"On January 30, 2020, WHO officially declared the outbreak of COVID-19 a Public Health Emergency of International Concern. Japan announced the state of emergency and implemented safety protocols the \"Three Cs\", a warning guideline addressing to voluntarily avoid potentially COVID-19 hazardous situations such as confined and closed spaces, crowded places and close-contact settings that lead to occurrence of serious clusters. The primary goal of this research is to identify the factors which help to estimate whether the user is in the Three Cs. We propose COVIDGuardian, a system that detects the Three Cs based on data such as CO2, temperature, humidity, and wireless packet log. The results show that estimation of closed space had the highest accuracy followed by close-contact settings and crowded places. The ensemble Random Forest (RF) classifier demonstrates the highest accuracy and F score in detecting closed spaces and crowded spaces. The findings indicated that integrated loudness value, average CO2, average humidity, probe request log, and average RSSI are of critical importance. In addition, when the probe request logs were filtered at three RSSI cutoff points (1m, 3m, and 5m), 1m cut-off points had the highest accuracy and F Score among the Three C models.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"9 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":"115537878","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}
Abdel Kader Chabi Sika Boni, Youssef Hablatou, H. Hassan, K. Drira
Autonomous IoT systems require the development of good automation algorithms capable of handling a huge number of IoT devices such as in smart cities. Deep Reinforcement Learning (DRL) is a powerful automation technique that can be used in massive systems thanks to its ability to deal with big state spaces. Moreover, it adapts quickly to changes in the system by reinforcement learning, making the automation algorithm very flexible. However, using DRL relies generally on centralized agent architecture making it more exposed to communication failures. In this paper, we propose a distributed architecture to solve the task offloading problem in autonomous IoT systems where learning is achieved in a master agent while decision making is delegated to IoT devices. This architecture is more resilient as decisions are made locally and interactions between IoT devices and the master agent are less frequent and not blocking. We tested this architecture in the ns3-gym environment and our results show very good resilience of this architecture.
{"title":"Distributed deep reinforcement learning architecture for task offloading in autonomous IoT systems","authors":"Abdel Kader Chabi Sika Boni, Youssef Hablatou, H. Hassan, K. Drira","doi":"10.1145/3567445.3567454","DOIUrl":"https://doi.org/10.1145/3567445.3567454","url":null,"abstract":"Autonomous IoT systems require the development of good automation algorithms capable of handling a huge number of IoT devices such as in smart cities. Deep Reinforcement Learning (DRL) is a powerful automation technique that can be used in massive systems thanks to its ability to deal with big state spaces. Moreover, it adapts quickly to changes in the system by reinforcement learning, making the automation algorithm very flexible. However, using DRL relies generally on centralized agent architecture making it more exposed to communication failures. In this paper, we propose a distributed architecture to solve the task offloading problem in autonomous IoT systems where learning is achieved in a master agent while decision making is delegated to IoT devices. This architecture is more resilient as decisions are made locally and interactions between IoT devices and the master agent are less frequent and not blocking. We tested this architecture in the ns3-gym environment and our results show very good resilience of this architecture.","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":"133476940","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}
Philippe Buschmann, Mostafa H. M. Shorim, Max Helm, Arne Bröring, Georg Carle
To avoid the disadvantages of a cloud-centric infrastructure, next-generation industrial scenarios focus on using distributed edge networks. Task allocation in distributed edge networks with regards to minimizing the energy consumption is NP-hard and requires considerable computational effort to obtain optimal results with conventional algorithms like Integer Linear Programming (ILP). We extend an existing ILP problem including an ILP heuristic for multi-workflow allocation and propose a Particle Swarm Optimization (PSO) and a Deep Reinforcement Learning (DRL) algorithm. PSO and DRL outperform the ILP heuristic with a median optimality gap of and against . DRL has the lowest upper bound for the optimality gap. It performs better than PSO for problem sizes of more than 25 tasks and PSO fails to find a feasible solution for more than 60 tasks. The execution time of DRL is significantly faster with a maximum of 1 s in comparison to PSO with a maximum of 361 s. In conclusion, our experiments indicate that PSO is more suitable for smaller and DRL for larger sized task allocation problems.
{"title":"Task Allocation in Industrial Edge Networks with Particle Swarm Optimization and Deep Reinforcement Learning","authors":"Philippe Buschmann, Mostafa H. M. Shorim, Max Helm, Arne Bröring, Georg Carle","doi":"10.1145/3567445.3571114","DOIUrl":"https://doi.org/10.1145/3567445.3571114","url":null,"abstract":"To avoid the disadvantages of a cloud-centric infrastructure, next-generation industrial scenarios focus on using distributed edge networks. Task allocation in distributed edge networks with regards to minimizing the energy consumption is NP-hard and requires considerable computational effort to obtain optimal results with conventional algorithms like Integer Linear Programming (ILP). We extend an existing ILP problem including an ILP heuristic for multi-workflow allocation and propose a Particle Swarm Optimization (PSO) and a Deep Reinforcement Learning (DRL) algorithm. PSO and DRL outperform the ILP heuristic with a median optimality gap of and against . DRL has the lowest upper bound for the optimality gap. It performs better than PSO for problem sizes of more than 25 tasks and PSO fails to find a feasible solution for more than 60 tasks. The execution time of DRL is significantly faster with a maximum of 1 s in comparison to PSO with a maximum of 361 s. In conclusion, our experiments indicate that PSO is more suitable for smaller and DRL for larger sized task allocation problems.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"42 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":"124865413","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}
M. Jarwar, Jeremy Watson CBE FREng, U. Ani, Stuart W. Chalmers
The Industrial Internet of Things (IIoT) trend presents many significant benefits for improving industrial operations. However, its emergence from the convergence of legacy Industrial Control Systems (ICS) and Information and Communication Technologies (ICT) has introduced newer security issues such as weak or lack of end-to-end security. These challenges have weakened the interest of many critical infrastructure industries in adopting IIoT-enabled systems. Implementing security in IIoT is challenging because this involves many heterogeneous Information Technology (IT) and Operational Technology (OT) devices and complex interactions with humans, and the environments in which these are operated and monitored. This article presents the initial results of the PETRAS Secure Ontologies for Internet of Things Systems (SOfIoTS) project, which consists of key security concepts and a modular design of a base security ontology, which supports security knowledge representation and analysis of IIoT security.
{"title":"Industrial Internet of Things Security Modelling using Ontological Methods","authors":"M. Jarwar, Jeremy Watson CBE FREng, U. Ani, Stuart W. Chalmers","doi":"10.1145/3567445.3571103","DOIUrl":"https://doi.org/10.1145/3567445.3571103","url":null,"abstract":"The Industrial Internet of Things (IIoT) trend presents many significant benefits for improving industrial operations. However, its emergence from the convergence of legacy Industrial Control Systems (ICS) and Information and Communication Technologies (ICT) has introduced newer security issues such as weak or lack of end-to-end security. These challenges have weakened the interest of many critical infrastructure industries in adopting IIoT-enabled systems. Implementing security in IIoT is challenging because this involves many heterogeneous Information Technology (IT) and Operational Technology (OT) devices and complex interactions with humans, and the environments in which these are operated and monitored. This article presents the initial results of the PETRAS Secure Ontologies for Internet of Things Systems (SOfIoTS) project, which consists of key security concepts and a modular design of a base security ontology, which supports security knowledge representation and analysis of IIoT security.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"54 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":"128549456","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}
Ahmed Mohamed Hussain, Nada Abughanam, Savio Sciancalepore, E. Yaacoub, Amr S. Mohamed
The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internet-connected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles’ functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions.
{"title":"Jammer Localization in the Internet of Vehicles: Scenarios, Experiments, and Evaluation","authors":"Ahmed Mohamed Hussain, Nada Abughanam, Savio Sciancalepore, E. Yaacoub, Amr S. Mohamed","doi":"10.1145/3567445.3567463","DOIUrl":"https://doi.org/10.1145/3567445.3567463","url":null,"abstract":"The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internet-connected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles’ functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"56 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":"126983677","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}
Henrik Dyrberg Egemose, Brodie W. Hobson, M. Ouf, M. Kjærgaard
Estimation of occupant count in commercial and institutional buildings enables energy experts to make better decisions on which buildings to prioritize for green upgrading and retrofitting from a large building portfolio. A cheap easy-to-install solution will enable energy experts to obtain occupancy estimation by easily scaling to large building portfolios. This paper presents a method for estimating occupancy based on sparse coverage of low-cost IoT sensors. The method is tested on 2 datasets, one academic building in Denmark (DK) and one academic building in Canada (CAN). The datasets contain PIR, CO2 measurements, and electric energy data together with ground truth occupancy counts. We show that 20% sensor coverage is comparable to full sensor coverage (60%) with an NRMSE of 0.142 (DK) and 0.174 (CAN) for 20% sensor coverage and an NRMSE of 0.129 (DK) and 0.163 (CAN) for full sensor coverage. Results show that with less sensor coverage, sensor placement becomes more important and that even with 20% it is possible to get as good of an accuracy as full coverage. The occupant count is used for key performance indicators of the buildings’ energy usage which shows higher energy use per occupant at low occupancy.
{"title":"Occupancy Estimation Using Sparse Sensor Coverage","authors":"Henrik Dyrberg Egemose, Brodie W. Hobson, M. Ouf, M. Kjærgaard","doi":"10.1145/3567445.3567449","DOIUrl":"https://doi.org/10.1145/3567445.3567449","url":null,"abstract":"Estimation of occupant count in commercial and institutional buildings enables energy experts to make better decisions on which buildings to prioritize for green upgrading and retrofitting from a large building portfolio. A cheap easy-to-install solution will enable energy experts to obtain occupancy estimation by easily scaling to large building portfolios. This paper presents a method for estimating occupancy based on sparse coverage of low-cost IoT sensors. The method is tested on 2 datasets, one academic building in Denmark (DK) and one academic building in Canada (CAN). The datasets contain PIR, CO2 measurements, and electric energy data together with ground truth occupancy counts. We show that 20% sensor coverage is comparable to full sensor coverage (60%) with an NRMSE of 0.142 (DK) and 0.174 (CAN) for 20% sensor coverage and an NRMSE of 0.129 (DK) and 0.163 (CAN) for full sensor coverage. Results show that with less sensor coverage, sensor placement becomes more important and that even with 20% it is possible to get as good of an accuracy as full coverage. The occupant count is used for key performance indicators of the buildings’ energy usage which shows higher energy use per occupant at low occupancy.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"103 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":"125590874","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}
Jan Strecker, Kimberly García, K. Bektaş, S. Mayer, G. Ramanathan
To enable people to interact more efficiently with virtual and physical services in their surroundings, it would be beneficial if information could more fluently be passed across digital and non-digital spaces. To this end, we propose to combine semantic technologies with Optical Character Recognition on an Augmented Reality (AR) interface to enable the semantic integration of (written) information located in our everyday environments with Internet of Things devices. We hence present SOCRAR, a system that is able to detect written information from a user’s physical environment while contextualizing this data through a semantic backend. The SOCRAR system enables in-band semantic translation on an AR interface, permits semantic filtering and selection of appropriate device interfaces, and provides cognitive offloading by enabling users to store information for later use. We demonstrate the feasibility of SOCRAR through the implementation of three concrete scenarios.
{"title":"SOCRAR: Semantic OCR through Augmented Reality","authors":"Jan Strecker, Kimberly García, K. Bektaş, S. Mayer, G. Ramanathan","doi":"10.1145/3567445.3567453","DOIUrl":"https://doi.org/10.1145/3567445.3567453","url":null,"abstract":"To enable people to interact more efficiently with virtual and physical services in their surroundings, it would be beneficial if information could more fluently be passed across digital and non-digital spaces. To this end, we propose to combine semantic technologies with Optical Character Recognition on an Augmented Reality (AR) interface to enable the semantic integration of (written) information located in our everyday environments with Internet of Things devices. We hence present SOCRAR, a system that is able to detect written information from a user’s physical environment while contextualizing this data through a semantic backend. The SOCRAR system enables in-band semantic translation on an AR interface, permits semantic filtering and selection of appropriate device interfaces, and provides cognitive offloading by enabling users to store information for later use. We demonstrate the feasibility of SOCRAR through the implementation of three concrete scenarios.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"70 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":"133618601","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 undergoing remarkable technological innovation, it is expected uncountable number of IoT devices will be installed everywhere and enrich our daily life in near future. There is a technical challenge that is the physically accurate data does not always match the “experience” of people, because the “perception” of people will be easily biased by various stimulations from surrounding environments. This paper presents a concept of Internet of Perception-aware Things (IoPT), which aims to fill the gap in perception between IoT and human. Through the case study targeting subjective crowdedness, we have confirmed perception data have huge deviations though there are correlations between sensor data and perception data, and perception will be biased due to the environmental conditions.
{"title":"IoPT: A Concept of Internet of Perception-aware Things","authors":"Yuki Matsuda","doi":"10.1145/3567445.3571108","DOIUrl":"https://doi.org/10.1145/3567445.3571108","url":null,"abstract":"The Internet of Things (IoT) is undergoing remarkable technological innovation, it is expected uncountable number of IoT devices will be installed everywhere and enrich our daily life in near future. There is a technical challenge that is the physically accurate data does not always match the “experience” of people, because the “perception” of people will be easily biased by various stimulations from surrounding environments. This paper presents a concept of Internet of Perception-aware Things (IoPT), which aims to fill the gap in perception between IoT and human. Through the case study targeting subjective crowdedness, we have confirmed perception data have huge deviations though there are correlations between sensor data and perception data, and perception will be biased due to the environmental conditions.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"5 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":"122478688","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}
Georgios Bouloukakis, Chrysostomos Zeginis, N. Papadakis, Panagiotis‐Ioannis Zervakis, D. Plexousakis, K. Magoutis
This paper presents a model-based approach to facilitate the development of IoT applications in Transportation Systems. Existing public transportation services are provided by relying on standard data models such as GTFS. However, such models are limited in representing IoT-based infrastructures and the locations that IoT devices cover (e.g., bus seating areas). We introduce a context-aware publish/subscribe IoT platform that supports synchronous data requests, asynchronous notifications and analytics applications. Data requests are created using the system’s context, which in our case is based on a transport bus system. Both static and dynamic context properties are modeled by extending the NGSI smart data models. We then introduce a GTFS-to-NGSI mapping tool to enable the enhancement of existing GTFS-based transportation systems with IoT capabilities. We develop a prototype of our platform and we demonstrate the applicability of our approach using open data from the Roma Mobilità bus transportation system.
{"title":"Enabling IoT-enhanced Transportation Systems using the NGSI Protocol","authors":"Georgios Bouloukakis, Chrysostomos Zeginis, N. Papadakis, Panagiotis‐Ioannis Zervakis, D. Plexousakis, K. Magoutis","doi":"10.1145/3567445.3567460","DOIUrl":"https://doi.org/10.1145/3567445.3567460","url":null,"abstract":"This paper presents a model-based approach to facilitate the development of IoT applications in Transportation Systems. Existing public transportation services are provided by relying on standard data models such as GTFS. However, such models are limited in representing IoT-based infrastructures and the locations that IoT devices cover (e.g., bus seating areas). We introduce a context-aware publish/subscribe IoT platform that supports synchronous data requests, asynchronous notifications and analytics applications. Data requests are created using the system’s context, which in our case is based on a transport bus system. Both static and dynamic context properties are modeled by extending the NGSI smart data models. We then introduce a GTFS-to-NGSI mapping tool to enable the enhancement of existing GTFS-based transportation systems with IoT capabilities. We develop a prototype of our platform and we demonstrate the applicability of our approach using open data from the Roma Mobilità bus transportation system.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"244 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":"121458687","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}
Arslan Musaddiq, Neda Maleki, Francis Palma, David Mozart, Tobias Olsson, Mustafa Omareen, F. Ahlgren
The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.
{"title":"Internet of Things for Wetland Conservation using Helium Network: Experience and Analysis","authors":"Arslan Musaddiq, Neda Maleki, Francis Palma, David Mozart, Tobias Olsson, Mustafa Omareen, F. Ahlgren","doi":"10.1145/3567445.3569167","DOIUrl":"https://doi.org/10.1145/3567445.3569167","url":null,"abstract":"The Internet of Things (IoT), as a new paradigm of connected things or objects to the Internet, allows us to monitor the environment by collecting data in a wide spatial and temporal window. Especially the utilization of IoT has increased significantly since the development of the Long Range Wide Area Network (LoRaWAN). However, deploying LoRa gateways, maintaining network infrastructure, operational cost, and quality of service are challenging. Helium has emerged as one of the largest networks in terms of coverage for IoT devices to solve such problems. Helium is decentralized, cryptocurrency incentives-based network infrastructure replacing traditional service providers. However, due to network incentives, currently, it contains more hotspots compared to active users. This paper presents our experience and analysis of deploying IoT devices for real-world applications using the Helium network. We present experiences from the IoT device’s deployment for wetland conservation in southern Sweden.","PeriodicalId":152960,"journal":{"name":"Proceedings of the 12th International Conference on the Internet of Things","volume":"84 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":"124893455","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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