Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00081
Leonardo Pelonero, Andrea Fornaia, E. Tramontana
As overpopulated cities spread all over the world, recycling has become crucial. Day by day, waste production is rising while resources are becoming limited, making recycling not only a sensible practice but a necessity. In order to be effective, the recycle process needs to start with the efforts of every single citizen, who is, however, often slightly motivated or forced by a punish-based system. In this paper, we propose a reward-based data-centric solution based on both enabling IoT technologies and cloud architectures to promote waste recycling in urban environments. We extend the consolidated rewarding approaches based on the smart bin model by proposing an incentive system that focuses on door-to-door waste collection. Such a solution assists door-to-door garbage collection by using practical and affordable QR-codes and IoT sensors to accumulate and track all the fundamental data related to garbage, such as waste composition (paper, plastics, glass, etc.), bag weight, etc. As recycling is properly handled by citizens, they can be rewarded with an adequate tax relief. The user is given means to monitor his progress by a smartphone app; whereas the waste management company and municipality can monitor their target amounts for each waste category and e.g. provide citizens with incentive to adjust the growth rate of different materials to be collected.
{"title":"From Smart City to Smart Citizen: Rewarding Waste Recycle by Designing a Data-Centric IoT based Garbage Collection Service","authors":"Leonardo Pelonero, Andrea Fornaia, E. Tramontana","doi":"10.1109/SMARTCOMP50058.2020.00081","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00081","url":null,"abstract":"As overpopulated cities spread all over the world, recycling has become crucial. Day by day, waste production is rising while resources are becoming limited, making recycling not only a sensible practice but a necessity. In order to be effective, the recycle process needs to start with the efforts of every single citizen, who is, however, often slightly motivated or forced by a punish-based system. In this paper, we propose a reward-based data-centric solution based on both enabling IoT technologies and cloud architectures to promote waste recycling in urban environments. We extend the consolidated rewarding approaches based on the smart bin model by proposing an incentive system that focuses on door-to-door waste collection. Such a solution assists door-to-door garbage collection by using practical and affordable QR-codes and IoT sensors to accumulate and track all the fundamental data related to garbage, such as waste composition (paper, plastics, glass, etc.), bag weight, etc. As recycling is properly handled by citizens, they can be rewarded with an adequate tax relief. The user is given means to monitor his progress by a smartphone app; whereas the waste management company and municipality can monitor their target amounts for each waste category and e.g. provide citizens with incentive to adjust the growth rate of different materials to be collected.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133782000","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00043
Antonio Bordonaro, A. D. Paola, G. Re, M. Morana
The main goal of Ambient Intelligence (AmI) is to support users in their daily activities by satisfying and anticipating their needs. To achieve such goal, AmI systems rely on physical infrastructures made of heterogenous sensing devices which interact in order to exchange information and perform monitoring tasks. In such a scenario, a full achievement of AmI vision would also require the capability of the system to autonomously check the status of the infrastructure and supervise its maintenance. To this aim, in this paper, we extend some previous works in order to allow the self-management of AmI devices enabling them to directly interact with maintenance service providers. In particular, the combination of smart contracts and blockchains enables AmI systems to autonomously communicate with untrusted entities and complete secure transactions without the brokering of a trusted third party. The proposed approach has been adopted to design a sample AmI application capable of managing requests from faulty devices in a Smart home.
{"title":"Smart Auctions for Autonomic Ambient Intelligence Systems","authors":"Antonio Bordonaro, A. D. Paola, G. Re, M. Morana","doi":"10.1109/SMARTCOMP50058.2020.00043","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00043","url":null,"abstract":"The main goal of Ambient Intelligence (AmI) is to support users in their daily activities by satisfying and anticipating their needs. To achieve such goal, AmI systems rely on physical infrastructures made of heterogenous sensing devices which interact in order to exchange information and perform monitoring tasks. In such a scenario, a full achievement of AmI vision would also require the capability of the system to autonomously check the status of the infrastructure and supervise its maintenance. To this aim, in this paper, we extend some previous works in order to allow the self-management of AmI devices enabling them to directly interact with maintenance service providers. In particular, the combination of smart contracts and blockchains enables AmI systems to autonomously communicate with untrusted entities and complete secure transactions without the brokering of a trusted third party. The proposed approach has been adopted to design a sample AmI application capable of managing requests from faulty devices in a Smart home.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133404804","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00036
Giuseppe Tricomi, Zakaria Benomar, Francesco Aragona, Giovanni Merlino, F. Longo, A. Puliafito
With the widespread emergence of the Internet of Things (IoT), our environment and locations are turning progressively into smart environments ranging from individual houses/offices to schools, factories, and hospitals. Even more interesting, with the rise of Fog/Edge paradigms, the IoT application scope has been extended to provide critical services. By pushing resources such as compute and storage to the network edge, IoT-based services are taking benefits from their proximity to provide better performances. However, albeit an exciting development in and by itself, Edge/Fog computing platforms currently do not provide a convenient level of flexibility and efficiency to support the dynamic composition of services with a data-oriented approach. In this context, the Function-as-a-Service computing paradigm rises as a convenient/suitable paradigm to be adopted in the IoT landscape. For the sake of providing flexible IoT Edge/Fog deployments, this paper introduces a system providing FaaS services based on a distributed IoT infrastructure. Besides, we provide a dashboard based on Node-RED that exploits, in the backend, the FaaS system to make the users able to conceive customized applications using the resources (i.e., sensors and actuators) that the IoT devices can host.
{"title":"A NodeRED-based dashboard to deploy pipelines on top of IoT infrastructure","authors":"Giuseppe Tricomi, Zakaria Benomar, Francesco Aragona, Giovanni Merlino, F. Longo, A. Puliafito","doi":"10.1109/SMARTCOMP50058.2020.00036","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00036","url":null,"abstract":"With the widespread emergence of the Internet of Things (IoT), our environment and locations are turning progressively into smart environments ranging from individual houses/offices to schools, factories, and hospitals. Even more interesting, with the rise of Fog/Edge paradigms, the IoT application scope has been extended to provide critical services. By pushing resources such as compute and storage to the network edge, IoT-based services are taking benefits from their proximity to provide better performances. However, albeit an exciting development in and by itself, Edge/Fog computing platforms currently do not provide a convenient level of flexibility and efficiency to support the dynamic composition of services with a data-oriented approach. In this context, the Function-as-a-Service computing paradigm rises as a convenient/suitable paradigm to be adopted in the IoT landscape. For the sake of providing flexible IoT Edge/Fog deployments, this paper introduces a system providing FaaS services based on a distributed IoT infrastructure. Besides, we provide a dashboard based on Node-RED that exploits, in the backend, the FaaS system to make the users able to conceive customized applications using the resources (i.e., sensors and actuators) that the IoT devices can host.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114390929","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00075
Martina Pappalardo, G. Tanganelli, E. Mingozzi
The Internet of Things (IoT) extends the Internet connectivity into devices and everyday objects. This huge volume of connected devices needs to be managed considering the severe energy, memory, processing, and communication limitations of IoT devices and networks. OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices and related service enablement. In this work we propose the introduction of a LWM2M Proxy in between IoT devices and the management server, in order to control the flow of LWM2M requests sent to IoT devices over a low-power and lossy network, and therefore avoid that device and network resources get overloaded. We evaluate the proposed solution by simulation and show that it strongly improves the performance of LWM2M in terms of service delay as compared to the standard case with no Proxy.
{"title":"Enhanced Support of LWM2M in Low Power and Lossy Networks","authors":"Martina Pappalardo, G. Tanganelli, E. Mingozzi","doi":"10.1109/SMARTCOMP50058.2020.00075","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00075","url":null,"abstract":"The Internet of Things (IoT) extends the Internet connectivity into devices and everyday objects. This huge volume of connected devices needs to be managed considering the severe energy, memory, processing, and communication limitations of IoT devices and networks. OMA LightweightM2M (LWM2M) protocol is designed for remote management of constrained devices and related service enablement. In this work we propose the introduction of a LWM2M Proxy in between IoT devices and the management server, in order to control the flow of LWM2M requests sent to IoT devices over a low-power and lossy network, and therefore avoid that device and network resources get overloaded. We evaluate the proposed solution by simulation and show that it strongly improves the performance of LWM2M in terms of service delay as compared to the standard case with no Proxy.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114432329","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00021
Eugenio Balistri, Francesco Casellato, Carlo Giannelli, C. Stefanelli
The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed production lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its operational processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.
{"title":"Blockchain for Increased Cyber-Resiliency of Industrial Edge Environments","authors":"Eugenio Balistri, Francesco Casellato, Carlo Giannelli, C. Stefanelli","doi":"10.1109/SMARTCOMP50058.2020.00021","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00021","url":null,"abstract":"The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed production lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its operational processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102350","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00062
P. Skobelev, V. Larukhin, E. Simonova, O. Goryanin, V. Yalovenko, O. Yalovenko
The paper is devoted to development of a digital twin (DT) of plant. It is built as a smart system based on the knowledge base on macrostages of plant development and multiagent technology that allows for detailed monitoring and control of plant development, recalculation of forecast, namely, assessment of vegetation quality, future yield and timing for onset of next stages. It uses transition rules between stages upon receipt of data from agronomists on the state of plant development, as well as the actual and forecast weather data. The paper proposes a conceptual plant model based on ontologies and multi-agent technology, which is a network of linked states and transition rules that correspond to macrostages of plant development with the possibility of recalculating their parameters. The paper also covers the main principles of agronomist work with the digital twin of plant.
{"title":"Multi-agent approach for developing a digital twin of wheat","authors":"P. Skobelev, V. Larukhin, E. Simonova, O. Goryanin, V. Yalovenko, O. Yalovenko","doi":"10.1109/SMARTCOMP50058.2020.00062","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00062","url":null,"abstract":"The paper is devoted to development of a digital twin (DT) of plant. It is built as a smart system based on the knowledge base on macrostages of plant development and multiagent technology that allows for detailed monitoring and control of plant development, recalculation of forecast, namely, assessment of vegetation quality, future yield and timing for onset of next stages. It uses transition rules between stages upon receipt of data from agronomists on the state of plant development, as well as the actual and forecast weather data. The paper proposes a conceptual plant model based on ontologies and multi-agent technology, which is a network of linked states and transition rules that correspond to macrostages of plant development with the possibility of recalculating their parameters. The paper also covers the main principles of agronomist work with the digital twin of plant.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123010278","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00025
Bipendra Basnyat, Nirmalya Roy, A. Gangopadhyay
Talking to the electronic device and getting the required information at a minimal time has become today's norm. Although AI-powered conversational agents have percolated the commercial market, their use in a communal setting is still evolving. We postulate that the deployments of chatbots in disaster-prone areas can be beneficial to watch, monitor, and warn people during the crisis. Furthermore, the successful implementation of such technology can be life-saving. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques.
{"title":"Towards AI Conversing: FloodBot using Deep Learning Model Stacks","authors":"Bipendra Basnyat, Nirmalya Roy, A. Gangopadhyay","doi":"10.1109/SMARTCOMP50058.2020.00025","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00025","url":null,"abstract":"Talking to the electronic device and getting the required information at a minimal time has become today's norm. Although AI-powered conversational agents have percolated the commercial market, their use in a communal setting is still evolving. We postulate that the deployments of chatbots in disaster-prone areas can be beneficial to watch, monitor, and warn people during the crisis. Furthermore, the successful implementation of such technology can be life-saving. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123769116","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00034
Stepan Mazokha, Fanchen Bao, Jiannan Zhai, J. Hallstrom
Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement. Understanding the causes and effects of changing mobility patterns can help city officials and businesses optimize operations and support economic development. In this paper, we present MobIntel, an alternative to visual surveillance technologies for mobility monitoring. We deployed multiple radio frequency sensors in downtown West Palm Beach and enabled a system for providing valuable metrics concerning pedestrian activity patterns. We discuss several obstacles to accurate trajectory monitoring, such as MAC address randomization and sensor range issues.
{"title":"MobIntel: Sensing and Analytics Infrastructure for Urban Mobility Intelligence","authors":"Stepan Mazokha, Fanchen Bao, Jiannan Zhai, J. Hallstrom","doi":"10.1109/SMARTCOMP50058.2020.00034","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00034","url":null,"abstract":"Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement. Understanding the causes and effects of changing mobility patterns can help city officials and businesses optimize operations and support economic development. In this paper, we present MobIntel, an alternative to visual surveillance technologies for mobility monitoring. We deployed multiple radio frequency sensors in downtown West Palm Beach and enabled a system for providing valuable metrics concerning pedestrian activity patterns. We discuss several obstacles to accurate trajectory monitoring, such as MAC address randomization and sensor range issues.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129760914","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}
Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00070
Giuseppe Tricomi, D. Giosa, Giovanni Merlino, O. Romeo, F. Longo
Nowadays, the study of nucleic acids (DNA/RNA) has become a digital science thanks to the advent of modern massive parallel sequencing technologies, better known with the acronym NGS standing for next-generation sequencing, and to the availability of a vast amount of genetic data easily accessible from publicly available databases. Due to the quantity and complexity of such data, its processing requires strong computer science knowledge and skills. This background includes topics such as programming and scripting languages, command-line interfaces, low-level data management tools, which are not always part of the toolbox of molecular biologists and geneticists. The need to adapt to entirely new IT tools and workflows slow down even the more experienced researchers, thus dedicated and customizable GUIs would be much more preferable and conducive. In this paper, we tackle this issue by proposing a preliminary architecture for a framework providing the following benefits: i) it supports the post-NGS analysis process definition phase (commonly called pipeline definition) via a graphical dashboard designed with NodeRED; ii) it automatically deploys the workflows on top of a cluster of computational resources, according to the Function-as-a-Service paradigm, i.e., treating each step of the pipeline as a function to be executed within Linux-based containers, pre-configured with all the necessary dependencies; iii) it runs such containers taking care automatically of resource load balancing. Finally, the framework is thought to include human feedback in the loop, thanks to the availability of a smart notification system, allowing the researcher to monitor the workflows and make any decision needed for its continuation.
{"title":"Toward a Function-as-a-Service Framework for Genomic Analysis","authors":"Giuseppe Tricomi, D. Giosa, Giovanni Merlino, O. Romeo, F. Longo","doi":"10.1109/SMARTCOMP50058.2020.00070","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00070","url":null,"abstract":"Nowadays, the study of nucleic acids (DNA/RNA) has become a digital science thanks to the advent of modern massive parallel sequencing technologies, better known with the acronym NGS standing for next-generation sequencing, and to the availability of a vast amount of genetic data easily accessible from publicly available databases. Due to the quantity and complexity of such data, its processing requires strong computer science knowledge and skills. This background includes topics such as programming and scripting languages, command-line interfaces, low-level data management tools, which are not always part of the toolbox of molecular biologists and geneticists. The need to adapt to entirely new IT tools and workflows slow down even the more experienced researchers, thus dedicated and customizable GUIs would be much more preferable and conducive. In this paper, we tackle this issue by proposing a preliminary architecture for a framework providing the following benefits: i) it supports the post-NGS analysis process definition phase (commonly called pipeline definition) via a graphical dashboard designed with NodeRED; ii) it automatically deploys the workflows on top of a cluster of computational resources, according to the Function-as-a-Service paradigm, i.e., treating each step of the pipeline as a function to be executed within Linux-based containers, pre-configured with all the necessary dependencies; iii) it runs such containers taking care automatically of resource load balancing. Finally, the framework is thought to include human feedback in the loop, thanks to the availability of a smart notification system, allowing the researcher to monitor the workflows and make any decision needed for its continuation.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125312891","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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