Pub Date : 2020-09-01DOI: 10.1109/SMARTCOMP50058.2020.00029
H. Muccini, Karthik Vaidhyanathan
The use of IoT systems is increasing day by day. However, these systems due to their heterogeneity and inherently dynamic nature, face different uncertainties from the context, environment, etc. Such uncertainties can have a big impact on the overall system QoS, especially on energy efficiency and data traffic. This calls for better ways of architecting IoT systems that may self-adapt to keep the desired QoS. This paper presents an approach that leverages the use of machine learning (ML) techniques to perform a proactive adaptation of IoT architectures using self-adaptation patterns. It i) continuously monitors the QoS parameters; ii) forecasts possible deviations from the acceptable QoS parameters; iii) selects the best adaptation pattern based on forecasts using reinforcement learning (RL) techniques; iv) checks the quality of the selected decision using feedback mechanisms; and v) continuously performs the loop of the forecast, adaptation, and feedback. The results of our evaluations show that our approach can provide accurate QoS forecasts and further improve the energy efficiency of the system while maintaining the required data traffic.
{"title":"Leveraging Machine Learning Techniques for Architecting Self-Adaptive IoT Systems","authors":"H. Muccini, Karthik Vaidhyanathan","doi":"10.1109/SMARTCOMP50058.2020.00029","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00029","url":null,"abstract":"The use of IoT systems is increasing day by day. However, these systems due to their heterogeneity and inherently dynamic nature, face different uncertainties from the context, environment, etc. Such uncertainties can have a big impact on the overall system QoS, especially on energy efficiency and data traffic. This calls for better ways of architecting IoT systems that may self-adapt to keep the desired QoS. This paper presents an approach that leverages the use of machine learning (ML) techniques to perform a proactive adaptation of IoT architectures using self-adaptation patterns. It i) continuously monitors the QoS parameters; ii) forecasts possible deviations from the acceptable QoS parameters; iii) selects the best adaptation pattern based on forecasts using reinforcement learning (RL) techniques; iv) checks the quality of the selected decision using feedback mechanisms; and v) continuously performs the loop of the forecast, adaptation, and feedback. The results of our evaluations show that our approach can provide accurate QoS forecasts and further improve the energy efficiency of the system while maintaining the required data traffic.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"56 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":"128389730","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/SMARTCOMP50058.2020.00047
Eura Shin, A. R. Khamesi, Zachary Bahr, S. Silvestri, Denise A. Baker
Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called $K$ -Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.
{"title":"A User-Centered Active Learning Approach for Appliance Recognition","authors":"Eura Shin, A. R. Khamesi, Zachary Bahr, S. Silvestri, Denise A. Baker","doi":"10.1109/SMARTCOMP50058.2020.00047","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00047","url":null,"abstract":"Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called $K$ -Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.","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":"129058928","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/SMARTCOMP50058.2020.00067
Lena Oden, Thorsten Witt
Wearables providing fall detection can provide faster emergency services for elderly, yet privacy concerns limit acceptance of this technology. In this work, we evaluate a machine learning algorithm, called Bosnai, for embedded edge devices to detect falls. The prototype is Arduino based and can be integrated into fabrics for clothes, belts, or other accessories. The fall detection is performed offline on the device. We used data from public datasets of movement and fall events to train a tree-based machine learning model. We evaluated different combinations of prepossessed parameters as input features for the learning algorithm. The learned model is transferred to the microcontroller and can classify the sensor data offline but in real-time. We evaluate the performance of our device by performing intensive test runs with the prototype. The microcontroller is extremely limited in terms of memory capacity and computing performance, which only allows a limited number of features for learning. For this reason, it is especially important to preprocess the raw accelerator data and select the right features for training and inference. Our results show that the best performance (approx. 94.2 % accuracy) is achieved when we choose absolute acceleration and variance as features, with a sampling rate of 20 Hz and a recording window of 3s, as this system is the most robust against external interference.
{"title":"Fall-detection on a wearable micro controller using machine learning algorithms","authors":"Lena Oden, Thorsten Witt","doi":"10.1109/SMARTCOMP50058.2020.00067","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00067","url":null,"abstract":"Wearables providing fall detection can provide faster emergency services for elderly, yet privacy concerns limit acceptance of this technology. In this work, we evaluate a machine learning algorithm, called Bosnai, for embedded edge devices to detect falls. The prototype is Arduino based and can be integrated into fabrics for clothes, belts, or other accessories. The fall detection is performed offline on the device. We used data from public datasets of movement and fall events to train a tree-based machine learning model. We evaluated different combinations of prepossessed parameters as input features for the learning algorithm. The learned model is transferred to the microcontroller and can classify the sensor data offline but in real-time. We evaluate the performance of our device by performing intensive test runs with the prototype. The microcontroller is extremely limited in terms of memory capacity and computing performance, which only allows a limited number of features for learning. For this reason, it is especially important to preprocess the raw accelerator data and select the right features for training and inference. Our results show that the best performance (approx. 94.2 % accuracy) is achieved when we choose absolute acceleration and variance as features, with a sampling rate of 20 Hz and a recording window of 3s, as this system is the most robust against external interference.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"69 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":"122672302","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/SMARTCOMP50058.2020.00046
D. Grimm, Simon Leiner, Martin Sommer, Felix Pistorius, E. Sax
Modern cars are equipped with a wide variety of sensors generating continually growing amounts of data. This data is transmitted via bus systems such as Controller Area Network (CAN) inside of the vehicle to the microcontroller-based Electronic Control Units. By connecting the vehicle to its surroundings using wireless interfaces, this data becomes accessible to the vehicle manufacturer from a distance. Through the opening to the outside, cyber attacks can exploit these interfaces and introduce major risks to the privacy and safety of vehicle users. Hence, suitable methods for vehicle security monitoring such as intrusion detection and logging are needed. In this work, we focus on the logging of network data, since this data is useful for the development of security updates, countermeasures and incident signatures. On this account, we propose a new method to aggregate the data of the CAN bus. The method combines CAN frames into so-called flows. Each flow contains a set of packets that share a certain common attribute (e.g.: frame type and identifier). To integrate security monitoring of vehicle fleets seamlessly into backend server systems, the gathered CAN flow data is stored in an industry standard data format. Additionally, the payload data is included in the flow format using a compression algorithm to leverage deep-packet inspection. The evaluation results with realworld vehicle data indicate that in our case about 40 % reduction of the overall data size is possible with our method compared to industry-standard formats for storing CAN frames. On this account, we propose a new method to aggregate the data of the CAN bus. The method combines CAN frames into so-called flows. Each flow contains a set of packets that share a certain common attribute (e.g.: frame type and identifier). To integrate security monitoring of vehicle fleets seamlessly into backend server systems, the gathered CAN flow data is stored in an industry standard data format. Additionally, the payload data is included in the flow format using a compression algorithm to leverage deep-packet inspection. The evaluation results with realworld vehicle data indicate that in our case about 40 % reduction of the overall data size is possible with our method compared to industry-standard formats for storing CAN frames.
{"title":"Flow-based Aggregation of CAN Frames with Compressed Payload","authors":"D. Grimm, Simon Leiner, Martin Sommer, Felix Pistorius, E. Sax","doi":"10.1109/SMARTCOMP50058.2020.00046","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00046","url":null,"abstract":"Modern cars are equipped with a wide variety of sensors generating continually growing amounts of data. This data is transmitted via bus systems such as Controller Area Network (CAN) inside of the vehicle to the microcontroller-based Electronic Control Units. By connecting the vehicle to its surroundings using wireless interfaces, this data becomes accessible to the vehicle manufacturer from a distance. Through the opening to the outside, cyber attacks can exploit these interfaces and introduce major risks to the privacy and safety of vehicle users. Hence, suitable methods for vehicle security monitoring such as intrusion detection and logging are needed. In this work, we focus on the logging of network data, since this data is useful for the development of security updates, countermeasures and incident signatures. On this account, we propose a new method to aggregate the data of the CAN bus. The method combines CAN frames into so-called flows. Each flow contains a set of packets that share a certain common attribute (e.g.: frame type and identifier). To integrate security monitoring of vehicle fleets seamlessly into backend server systems, the gathered CAN flow data is stored in an industry standard data format. Additionally, the payload data is included in the flow format using a compression algorithm to leverage deep-packet inspection. The evaluation results with realworld vehicle data indicate that in our case about 40 % reduction of the overall data size is possible with our method compared to industry-standard formats for storing CAN frames. On this account, we propose a new method to aggregate the data of the CAN bus. The method combines CAN frames into so-called flows. Each flow contains a set of packets that share a certain common attribute (e.g.: frame type and identifier). To integrate security monitoring of vehicle fleets seamlessly into backend server systems, the gathered CAN flow data is stored in an industry standard data format. Additionally, the payload data is included in the flow format using a compression algorithm to leverage deep-packet inspection. The evaluation results with realworld vehicle data indicate that in our case about 40 % reduction of the overall data size is possible with our method compared to industry-standard formats for storing CAN frames.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"8 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":"127863590","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/SMARTCOMP50058.2020.00079
K. Bakker, R. Knight, J. Leape, Alan K. Mackworth, R. Ng, Max Ritts
This paper presents a meta-review of digital technology applications for dynamic environmental management, which provide contemporaneous signals and incentives to influence resource users' behaviours, thereby generating more spatially and temporally flexible responses to variable ecosystem conditions.
{"title":"Digital Technologies and Dynamic Resource Management","authors":"K. Bakker, R. Knight, J. Leape, Alan K. Mackworth, R. Ng, Max Ritts","doi":"10.1109/SMARTCOMP50058.2020.00079","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00079","url":null,"abstract":"This paper presents a meta-review of digital technology applications for dynamic environmental management, which provide contemporaneous signals and incentives to influence resource users' behaviours, thereby generating more spatially and temporally flexible responses to variable ecosystem conditions.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"37 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":"125309583","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/SMARTCOMP50058.2020.00027
Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, V. Tomaselli, Davide Giacalone, Sajal K. Das
Diagnosis of plant health conditions is gaining significant attention in smart agriculture. Timely recognition of early symptoms of a disease can help avoid the spread of epidemics on the plantations. In this regard, most of the existing solutions use some AI techniques on smart edge devices (IoTs or intelligent Cyber Physical Systems), typically equipped with a hardware like sensors and actuators. However, the resource constraints on such devices like energy (power), memory and computation capability, make the execution of complex operations and AI algorithms (neural network models) for disease detection quite challenging. To this end, compression and quantization techniques offer viable solutions to reduce the memory footprint of neural networks while maximizing performance on the constrained devices. In this paper, we realized a real intelligent CPS on top of which we implemented an AI application, called Deep Leaf running on a microcontroller of the STM32 family, to detect coffee plant diseases with the help of a Quantized Convolutional Neural Network (Q-CNN) model. We present a quantitative analysis of Deep Leaf by comparing five different deep learning models: a 32-bit floating point model, a compressed model, and three different types of quantized models exhibiting differences in terms of accuracy, memory utilization, average inference time, and energy consumption. Experimental results show that the proposed Deep Leaf detector is able to correctly classify the plant health condition with an accuracy of 96%, thus demonstrating the feasibility of our approach on a Smart Edge platform.
{"title":"Quantitative Analysis of Deep Leaf: a Plant Disease Detector on the Smart Edge","authors":"Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, V. Tomaselli, Davide Giacalone, Sajal K. Das","doi":"10.1109/SMARTCOMP50058.2020.00027","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00027","url":null,"abstract":"Diagnosis of plant health conditions is gaining significant attention in smart agriculture. Timely recognition of early symptoms of a disease can help avoid the spread of epidemics on the plantations. In this regard, most of the existing solutions use some AI techniques on smart edge devices (IoTs or intelligent Cyber Physical Systems), typically equipped with a hardware like sensors and actuators. However, the resource constraints on such devices like energy (power), memory and computation capability, make the execution of complex operations and AI algorithms (neural network models) for disease detection quite challenging. To this end, compression and quantization techniques offer viable solutions to reduce the memory footprint of neural networks while maximizing performance on the constrained devices. In this paper, we realized a real intelligent CPS on top of which we implemented an AI application, called Deep Leaf running on a microcontroller of the STM32 family, to detect coffee plant diseases with the help of a Quantized Convolutional Neural Network (Q-CNN) model. We present a quantitative analysis of Deep Leaf by comparing five different deep learning models: a 32-bit floating point model, a compressed model, and three different types of quantized models exhibiting differences in terms of accuracy, memory utilization, average inference time, and energy consumption. Experimental results show that the proposed Deep Leaf detector is able to correctly classify the plant health condition with an accuracy of 96%, thus demonstrating the feasibility of our approach on a Smart Edge platform.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"22 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":"131324573","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/SMARTCOMP50058.2020.00092
Kotaro Chinen, Hiroaki Anada
We consider the security definition of decentralized multi-authority anonymous authentication schemes (DMA-AAuth) which was proposed by Anada-Arita at ICICS2018. The security is against causing-misauthentication attack, and we modify it to capture a threat of corrupted key-issuing authorities. Then we prove that the concrete scheme proposed by Anada at CANDAR'19 is secure under the new definition. Next, we evaluate efficiency of the concrete scheme by implementation. We use the C programming language with the TEPLA library.
{"title":"Security Reconsideration and Efficiency Evaluation of Decentralized Multi-authority Anonymous Authentication Scheme","authors":"Kotaro Chinen, Hiroaki Anada","doi":"10.1109/SMARTCOMP50058.2020.00092","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00092","url":null,"abstract":"We consider the security definition of decentralized multi-authority anonymous authentication schemes (DMA-AAuth) which was proposed by Anada-Arita at ICICS2018. The security is against causing-misauthentication attack, and we modify it to capture a threat of corrupted key-issuing authorities. Then we prove that the concrete scheme proposed by Anada at CANDAR'19 is secure under the new definition. Next, we evaluate efficiency of the concrete scheme by implementation. We use the C programming language with the TEPLA library.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"10 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":"132174490","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/SMARTCOMP50058.2020.00091
Antonio Biscotti, Carlo Giannelli, Cedric Franck Ngatcha Keyi, R. Lazzarini, Assunta Sardone, C. Stefanelli, Giovanni Virgilli
In modern society, food safety is becoming more and more important. The adoption of appropriate practices, such as the ones defined in the HACCP system, during food production, handling, preparation, and storage can reasonably guarantee food safety. However, it is not easy to apply HACCP methodologies in an automatic form, thus hindering its use in industrial machines. To solve this problem, the paper presents a novel solution adopting Internet of Things (IoT) and Blockchain technologies in the ice cream production process to automate the enforcement of HACCP directives. The new Carpigiani ice cream making machines exploit IoT for the automation of data gathering (in particular the temperature, that is of particular concern for dairy products) and a Blockchain solution for a tamper-proof and non-repudiable distributed storage of HACCP sensitive production data.
{"title":"Internet of Things and Blockchain Technologies for Food Safety Systems","authors":"Antonio Biscotti, Carlo Giannelli, Cedric Franck Ngatcha Keyi, R. Lazzarini, Assunta Sardone, C. Stefanelli, Giovanni Virgilli","doi":"10.1109/SMARTCOMP50058.2020.00091","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00091","url":null,"abstract":"In modern society, food safety is becoming more and more important. The adoption of appropriate practices, such as the ones defined in the HACCP system, during food production, handling, preparation, and storage can reasonably guarantee food safety. However, it is not easy to apply HACCP methodologies in an automatic form, thus hindering its use in industrial machines. To solve this problem, the paper presents a novel solution adopting Internet of Things (IoT) and Blockchain technologies in the ice cream production process to automate the enforcement of HACCP directives. The new Carpigiani ice cream making machines exploit IoT for the automation of data gathering (in particular the temperature, that is of particular concern for dairy products) and a Blockchain solution for a tamper-proof and non-repudiable distributed storage of HACCP sensitive production data.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"15 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":"132866218","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/smartcomp50058.2020.00019
Sajal K. Das, H. Yamana, M. Conti, A. Dubey, K. Yasumoto
Smart computing aiming to at improve human quality of life and experience represents the next wave of computing. Key technologies contributing to the realization of smart and connected communities include sensing, IoT, mobile and pervasive computing, cyber-physical-social systems, big data, machine learning, data analytics, social and cognitive computing. Smart computing helps solve a wide variety of societal challenges related to transportation, energy, healthcare, finance, disaster management, and so on. At the core of these systems, critical issues are security, privacy, reliability, resiliency, and robustness.
{"title":"Message from General Chairs and TPC Chairs","authors":"Sajal K. Das, H. Yamana, M. Conti, A. Dubey, K. Yasumoto","doi":"10.1109/smartcomp50058.2020.00019","DOIUrl":"https://doi.org/10.1109/smartcomp50058.2020.00019","url":null,"abstract":"Smart computing aiming to at improve human quality of life and experience represents the next wave of computing. Key technologies contributing to the realization of smart and connected communities include sensing, IoT, mobile and pervasive computing, cyber-physical-social systems, big data, machine learning, data analytics, social and cognitive computing. Smart computing helps solve a wide variety of societal challenges related to transportation, energy, healthcare, finance, disaster management, and so on. At the core of these systems, critical issues are security, privacy, reliability, resiliency, and robustness.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"62 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":"132456647","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/SMARTCOMP50058.2020.00030
Klement Streit, C. Schmitt, Carlo Giannelli
Already available WiFi direct and upcoming 5G Device-to-Device (D2D) communication mechanisms are paving the way for the development of Mobile Ad-hoc Networks (MANET) applications. This trend involves the cooperation of nearby mobile nodes in charge of dispatching messages. In addition, the consolidation of the Fog paradigm enables innovative scenarios characterized by the interaction of MANET and Edge nodes. For instance, tourists visiting a city form a MANET to share pictures while the municipality provides Internet connectivity via Edge devices. However, it is required to address specific issues stemming from the collaborative nature of D2D communication, ranging from limited node capabilities providing multi-hop networks to unreliable connectivity due to node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility.
{"title":"SDN-Based Regulated Flow Routing in MANETs","authors":"Klement Streit, C. Schmitt, Carlo Giannelli","doi":"10.1109/SMARTCOMP50058.2020.00030","DOIUrl":"https://doi.org/10.1109/SMARTCOMP50058.2020.00030","url":null,"abstract":"Already available WiFi direct and upcoming 5G Device-to-Device (D2D) communication mechanisms are paving the way for the development of Mobile Ad-hoc Networks (MANET) applications. This trend involves the cooperation of nearby mobile nodes in charge of dispatching messages. In addition, the consolidation of the Fog paradigm enables innovative scenarios characterized by the interaction of MANET and Edge nodes. For instance, tourists visiting a city form a MANET to share pictures while the municipality provides Internet connectivity via Edge devices. However, it is required to address specific issues stemming from the collaborative nature of D2D communication, ranging from limited node capabilities providing multi-hop networks to unreliable connectivity due to node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility. This paper presents the Reliable and Dynamic Routing Technique (RaDRT) solution, adopting the Software Defined Networking (SDN) approach to regulate routing of traffic flows in such Edge-MANET environments. To this purpose, RaDRT originally exploits the joint combination of three primary guidelines: 1) SDN to monitor/manage the state of the mobile network also considering different Quality of Service (QoS) requirements of concurrently running applications, 2) dynamic management of service priority to tune if and how packets are forwarded in a fine-grained per-flow differentiated manner, and 3) joined mobile/fixed solution to maximize the overall QoS also evaluating path reliability based on node mobility.","PeriodicalId":346827,"journal":{"name":"2020 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"24 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":"133255537","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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