Pub Date : 2018-04-17DOI: 10.1109/IoTDI.2018.00041
S. Khare, Hongyang Sun, Kaiwen Zhang, Julien Gascon-Samson, A. Gokhale, X. Koutsoukos
Low latency and scalable data dissemination is a critical requirement for many IoT applications, e.g., smart city applications, which are often built over a publish/subscribe communication paradigm. Ensuring low latency requires effective load balancing of the publish/subscribe topics across the different publishers and subscribers. To that end we present ongoing work on a data-driven approach to learning a latency-aware model of IoT broker loads, and in turn using it to determine broker replication, and balancing topics across them.
{"title":"Poster Abstract: Ensuring Low-Latency and Scalable Data Dissemination for Smart-City Applications","authors":"S. Khare, Hongyang Sun, Kaiwen Zhang, Julien Gascon-Samson, A. Gokhale, X. Koutsoukos","doi":"10.1109/IoTDI.2018.00041","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00041","url":null,"abstract":"Low latency and scalable data dissemination is a critical requirement for many IoT applications, e.g., smart city applications, which are often built over a publish/subscribe communication paradigm. Ensuring low latency requires effective load balancing of the publish/subscribe topics across the different publishers and subscribers. To that end we present ongoing work on a data-driven approach to learning a latency-aware model of IoT broker loads, and in turn using it to determine broker replication, and balancing topics across them.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124650834","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00026
Farley Lai, Marjan Radi, O. Chipara, W. Griswold
Energy-efficiency is a key concern in mobile sensing applications, such as those for tracking social interactions or physical activities. An attractive approach to saving energy is to shape the workload of the system by artificially introducing delays so that the workload would require less energy to process. However, adding delays to save energy may have a detrimental impact on user experience. To address this problem, we present Gratis, a novel paradigm for incorporating workload shaping energy optimizations in mobile sensing applications in an automated manner. Gratis adopts stream programs as a high-level abstraction whose execution is coordinated using an explicit power management policy. We present an expressive coordination language that can specify a broad range of workload-shaping optimizations. A unique property of the proposed power management policies is that they have predictable performance, which can be estimated at compile time, in a computationally efficient manner, from a small number of measurements. We have developed a simulator that can predict the energy with a average error of 7% and delay with a average error of 15%, even when applications have variable workloads. The simulator is scalable: hours of real-world traces can be simulated in a few seconds. Building on the simulator's accuracy and scalability, we have developed tools for configuring power management policies automatically. We have evaluated Gratis by developing two mobile applications and optimizing their energy consumption. For example, an application that tracks social interactions using speaker-identification techniques can run for only 7 hours without energy optimizations. However, when Gratis employs batching, scheduled concurrency, and adaptive sensing, the battery lifetime can be extended to 45 hours when the end-to-end deadline is one minute. These results demonstrate the efficacy of our approach to reduce energy consumption in mobile sensing applications.
{"title":"Workload Shaping Energy Optimizations with Predictable Performance for Mobile Sensing","authors":"Farley Lai, Marjan Radi, O. Chipara, W. Griswold","doi":"10.1109/IoTDI.2018.00026","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00026","url":null,"abstract":"Energy-efficiency is a key concern in mobile sensing applications, such as those for tracking social interactions or physical activities. An attractive approach to saving energy is to shape the workload of the system by artificially introducing delays so that the workload would require less energy to process. However, adding delays to save energy may have a detrimental impact on user experience. To address this problem, we present Gratis, a novel paradigm for incorporating workload shaping energy optimizations in mobile sensing applications in an automated manner. Gratis adopts stream programs as a high-level abstraction whose execution is coordinated using an explicit power management policy. We present an expressive coordination language that can specify a broad range of workload-shaping optimizations. A unique property of the proposed power management policies is that they have predictable performance, which can be estimated at compile time, in a computationally efficient manner, from a small number of measurements. We have developed a simulator that can predict the energy with a average error of 7% and delay with a average error of 15%, even when applications have variable workloads. The simulator is scalable: hours of real-world traces can be simulated in a few seconds. Building on the simulator's accuracy and scalability, we have developed tools for configuring power management policies automatically. We have evaluated Gratis by developing two mobile applications and optimizing their energy consumption. For example, an application that tracks social interactions using speaker-identification techniques can run for only 7 hours without energy optimizations. However, when Gratis employs batching, scheduled concurrency, and adaptive sensing, the battery lifetime can be extended to 45 hours when the end-to-end deadline is one minute. These results demonstrate the efficacy of our approach to reduce energy consumption in mobile sensing applications.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125222799","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}
Firefighters' safety is a critical problem and a major issue is the lack of reliable indoor firefighter localization. State of the art approaches have failed to provide an automatic, accurate and reliable solution to localize firefighters in harsh environments. This paper presents a novel system to achieve this goal, by combining pedestrian dead reckoning with a recently emerging breadcrumb system. Our solution includes a new collaborative localization algorithm that contains a novel marginalization scheme and can improve the location accuracy of firefighters. We fully implement the algorithm in a complete system and conduct experiments in both an office building and in a simulated firefighting scene that involved a real fire and professional firefighters. Evaluation results from a 400 meter-long trace demonstrate that our approach significantly reduces the average and maximum firefighter location error to 1.4% and 2.7% of the total distance, respectively.
{"title":"An Automatic and Accurate Localization System for Firefighters","authors":"Jinyang Li, Zhiheng Xie, Xiaoshan Sun, Jian Tang, Hengchang Liu, J. Stankovic","doi":"10.1109/IoTDI.2018.00012","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00012","url":null,"abstract":"Firefighters' safety is a critical problem and a major issue is the lack of reliable indoor firefighter localization. State of the art approaches have failed to provide an automatic, accurate and reliable solution to localize firefighters in harsh environments. This paper presents a novel system to achieve this goal, by combining pedestrian dead reckoning with a recently emerging breadcrumb system. Our solution includes a new collaborative localization algorithm that contains a novel marginalization scheme and can improve the location accuracy of firefighters. We fully implement the algorithm in a complete system and conduct experiments in both an office building and in a simulated firefighting scene that involved a real fire and professional firefighters. Evaluation results from a 400 meter-long trace demonstrate that our approach significantly reduces the average and maximum firefighter location error to 1.4% and 2.7% of the total distance, respectively.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125956704","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00044
Andrew Dingman, Gianpaolo Russo, Georg Osterholt, Tyler Uffelman, L. Camp
Guidelines exist for designing less vulnerable "things", but how useful are they in practice? To answer this question we present high-level analysis of the state of best practices in Internet of Things. We examined six sets of best practices, combining them into a single set. We then take that union and examine their applicability to three large-scale events. We evaluate if the best practices had been followed, would these have prevented the large scale abuse of IoT devices.
{"title":"Poster Abstract: Good Advice That Just Doesn't Help","authors":"Andrew Dingman, Gianpaolo Russo, Georg Osterholt, Tyler Uffelman, L. Camp","doi":"10.1109/IoTDI.2018.00044","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00044","url":null,"abstract":"Guidelines exist for designing less vulnerable \"things\", but how useful are they in practice? To answer this question we present high-level analysis of the state of best practices in Internet of Things. We examined six sets of best practices, combining them into a single set. We then take that union and examine their applicability to three large-scale events. We evaluate if the best practices had been followed, would these have prevented the large scale abuse of IoT devices.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129615705","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00023
Hung Nguyen, Radoslav Ivanov, L. T. Phan, O. Sokolsky, James Weimer, Insup Lee
As devices in the Internet of Things (IoT) increase in number and integrate with everyday lives, large amounts of personal information will be generated. With multiple discovered vulnerabilities in current IoT networks, a malicious attacker might be able to get access to and misuse this personal data. Thus, a logger that stores this information securely would make it possible to perform forensic analysis in case of such attacks that target valuable data. In this paper, we propose LogSafe, a scalable, fault-tolerant logger that leverages the use of Intel Software Guard Extensions (SGX) to store logs from IoT devices efficiently and securely. Using the security guarantees of SGX, LogSafe is designed to run on an untrusted cloud infrastructure and satisfies Confidentiality, Integrity, and Availability (CIA) security properties. Finally, we provide an exhaustive evaluation of LogSafe in order to demonstrate that it is capable of handling logs from a large number of IoT devices and at a very high data transmission rate.
{"title":"LogSafe: Secure and Scalable Data Logger for IoT Devices","authors":"Hung Nguyen, Radoslav Ivanov, L. T. Phan, O. Sokolsky, James Weimer, Insup Lee","doi":"10.1109/IoTDI.2018.00023","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00023","url":null,"abstract":"As devices in the Internet of Things (IoT) increase in number and integrate with everyday lives, large amounts of personal information will be generated. With multiple discovered vulnerabilities in current IoT networks, a malicious attacker might be able to get access to and misuse this personal data. Thus, a logger that stores this information securely would make it possible to perform forensic analysis in case of such attacks that target valuable data. In this paper, we propose LogSafe, a scalable, fault-tolerant logger that leverages the use of Intel Software Guard Extensions (SGX) to store logs from IoT devices efficiently and securely. Using the security guarantees of SGX, LogSafe is designed to run on an untrusted cloud infrastructure and satisfies Confidentiality, Integrity, and Availability (CIA) security properties. Finally, we provide an exhaustive evaluation of LogSafe in order to demonstrate that it is capable of handling logs from a large number of IoT devices and at a very high data transmission rate.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124534560","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00014
Md Abdullah Al Hafiz Khan, Nirmalya Roy
The success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.
{"title":"UnTran: Recognizing Unseen Activities with Unlabeled Data Using Transfer Learning","authors":"Md Abdullah Al Hafiz Khan, Nirmalya Roy","doi":"10.1109/IoTDI.2018.00014","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00014","url":null,"abstract":"The success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"51 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130184660","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00042
Joshua Streiff, O. Kenny, Sanchari Das, Andrew Leeth, L. Camp
The Internet of Things (IoT) can affect the physical safety of a child in addition to their digital safety. Digital safety concerns include what is being recorded and who is monitoring them. IoT devices, like the Fisher Price Smart Toys are designed to play with children of ages 3-8 and entertain them with various activities. This has expanded digital exposure into children's spaces. These toys contain a variety of communication technologies that users are ill-prepared to understand, a myriad of sensors collecting private data, including video, and often rely on inadequate security tools and methodology. This intersection of poor security, invasive sensor data, and proximity to children may put children at risks both online and in-person. In examining the Fisher Price Bear, our researchers were able to both verify that security tools have been implemented to fix network security failures previously found in the toy, but also discovered a security flaw which allows root access to the smart toy, allowing full access to the nose camera and other sensors. Preliminary results are presented in how the operating system can be modified in order to install software so that a modified bear can be controlled remotely. Mitigation education is presented as a critical instrument for self-protection of parents and children in a smart toy environment.
{"title":"Poster Abstract: Who's Watching Your Child? Exploring Home Security Risks with Smart Toy Bears","authors":"Joshua Streiff, O. Kenny, Sanchari Das, Andrew Leeth, L. Camp","doi":"10.1109/IoTDI.2018.00042","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00042","url":null,"abstract":"The Internet of Things (IoT) can affect the physical safety of a child in addition to their digital safety. Digital safety concerns include what is being recorded and who is monitoring them. IoT devices, like the Fisher Price Smart Toys are designed to play with children of ages 3-8 and entertain them with various activities. This has expanded digital exposure into children's spaces. These toys contain a variety of communication technologies that users are ill-prepared to understand, a myriad of sensors collecting private data, including video, and often rely on inadequate security tools and methodology. This intersection of poor security, invasive sensor data, and proximity to children may put children at risks both online and in-person. In examining the Fisher Price Bear, our researchers were able to both verify that security tools have been implemented to fix network security failures previously found in the toy, but also discovered a security flaw which allows root access to the smart toy, allowing full access to the nose camera and other sensors. Preliminary results are presented in how the operating system can be modified in order to install software so that a modified bear can be controlled remotely. Mitigation education is presented as a critical instrument for self-protection of parents and children in a smart toy environment.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"21 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132532202","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00043
Basman M. Hasan Alhafidh, Amar I. Daood, W. Allen
There is a strong interest in IoT-based systems that monitor and control smart home environments by accurately predicting the needs of the human occupants. Past research has focused on the accuracy of prediction of a user's future action. However, much of that work uses synthetic datasets which do not always reflect the real-world interactions that occur between an individual and the home environment. In addition, a focus on prediction accuracy often comes at the cost of slower processing time. This paper focuses on the prediction of future human actions in an intelligent environment with the goal of achieving both high accuracy and real-time performance. We performed experiments using the MavPad dataset, which was gathered from a fully-instrumented home environment, and compared several different machine learning algorithms that included both single and ensemble classifiers. The results show that using a Support Vector Machine approach achieved the best results when using a group of sensors within a local zone around the user and the Random Forest classifier achieved higher performance when using sensors that are distributed across the entire home environment.
{"title":"Poster Abstract: Comparison of Classifiers for Prediction of Human Actions in a Smart Home","authors":"Basman M. Hasan Alhafidh, Amar I. Daood, W. Allen","doi":"10.1109/IoTDI.2018.00043","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00043","url":null,"abstract":"There is a strong interest in IoT-based systems that monitor and control smart home environments by accurately predicting the needs of the human occupants. Past research has focused on the accuracy of prediction of a user's future action. However, much of that work uses synthetic datasets which do not always reflect the real-world interactions that occur between an individual and the home environment. In addition, a focus on prediction accuracy often comes at the cost of slower processing time. This paper focuses on the prediction of future human actions in an intelligent environment with the goal of achieving both high accuracy and real-time performance. We performed experiments using the MavPad dataset, which was gathered from a fully-instrumented home environment, and compared several different machine learning algorithms that included both single and ensemble classifiers. The results show that using a Support Vector Machine approach achieved the best results when using a group of sensors within a local zone around the user and the Random Forest classifier achieved higher performance when using sensors that are distributed across the entire home environment.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128246597","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00035
Chuan Li, Hongwei Zhang, J. Rao, L. Wang, G. Yin
Predictable inter-vehicle communication reliability is a basis for the paradigm shift from the traditional singlevehicle-oriented safety and efficiency control to networked vehicle control. The lack of predictable interference control in existing mechanisms of inter-vehicle communications, however, makes them incapable of ensuring predictable communication reliability. For predictable interference control, we propose the Cyber-Physical Scheduling (CPS) framework that leverages the PRK interference model and addresses the challenges of vehicle mobility to PRK-based scheduling. In particular, for lightweight control signaling and effective interference relation estimation, CPS leverages the physical locations of vehicles to define the gPRK interference model as a geometric approximation of the PRK model; for effective use of the gPRK model, CPS leverages cyber-physical structures of vehicle traffic flows, particularly, the spatiotemporal interference correlation as well as the macroand micro-scopic vehicle dynamics. Through experimental analysis with high-fidelity ns-3 and SUMO simulation, we observe that CPS enables predictable reliability while achieving high throughput and low delay in communication. To the best of our knowledge, CPS is the first field-deployable method that ensures predictable interference control and thus reliability in inter-vehicle communications.
{"title":"Cyber-Physical Scheduling for Predictable Reliability of Inter-Vehicle Communications","authors":"Chuan Li, Hongwei Zhang, J. Rao, L. Wang, G. Yin","doi":"10.1109/IoTDI.2018.00035","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00035","url":null,"abstract":"Predictable inter-vehicle communication reliability is a basis for the paradigm shift from the traditional singlevehicle-oriented safety and efficiency control to networked vehicle control. The lack of predictable interference control in existing mechanisms of inter-vehicle communications, however, makes them incapable of ensuring predictable communication reliability. For predictable interference control, we propose the Cyber-Physical Scheduling (CPS) framework that leverages the PRK interference model and addresses the challenges of vehicle mobility to PRK-based scheduling. In particular, for lightweight control signaling and effective interference relation estimation, CPS leverages the physical locations of vehicles to define the gPRK interference model as a geometric approximation of the PRK model; for effective use of the gPRK model, CPS leverages cyber-physical structures of vehicle traffic flows, particularly, the spatiotemporal interference correlation as well as the macroand micro-scopic vehicle dynamics. Through experimental analysis with high-fidelity ns-3 and SUMO simulation, we observe that CPS enables predictable reliability while achieving high throughput and low delay in communication. To the best of our knowledge, CPS is the first field-deployable method that ensures predictable interference control and thus reliability in inter-vehicle communications.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126331244","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 : 2018-04-17DOI: 10.1109/IoTDI.2018.00017
Kyle E. Benson, Guoxi Wang, N. Venkatasubramanian, Young-Jin Kim
Internet of Things (IoT) deployments rely on data exchange middleware to manage communications between constrained devices and cloud resources that provide analytics, data storage, and serve user applications. In this paper, we propose the Resilient IoT Data Exchange (Ride) middleware that enables resilient operation of IoT applications despite prevalent network failures and congestion. It leverages programmable Software-Defined Networking (SDN)-enabled infrastructure along with both localized edge and cloud services. The two-phase Ride middleware extends existing publish-subscribe oriented IoT data exchanges according to application-specified resilience requirements and without IoT device client modifications. The first phase, Ride-C, improves IoT data collection by gathering network-awareness via a novel resource-aware adaptive probing mechanism and dynamically redirecting IoT data flows across multiple public and local (edge) cloud data exchange connections. The second phase, Ride-D, uses this information to disseminate time-critical alerts via an intelligent network-aware resilient multicast mechanism. Results from our prototype smart campus testbed implementation, Mininet-based emulated experiments, and larger-scale simulations show that Ride enables network awareness for greater cloud connection up-times, timely fail-over to edge services, and more resilient local alert dissemination.
{"title":"Ride: A Resilient IoT Data Exchange Middleware Leveraging SDN and Edge Cloud Resources","authors":"Kyle E. Benson, Guoxi Wang, N. Venkatasubramanian, Young-Jin Kim","doi":"10.1109/IoTDI.2018.00017","DOIUrl":"https://doi.org/10.1109/IoTDI.2018.00017","url":null,"abstract":"Internet of Things (IoT) deployments rely on data exchange middleware to manage communications between constrained devices and cloud resources that provide analytics, data storage, and serve user applications. In this paper, we propose the Resilient IoT Data Exchange (Ride) middleware that enables resilient operation of IoT applications despite prevalent network failures and congestion. It leverages programmable Software-Defined Networking (SDN)-enabled infrastructure along with both localized edge and cloud services. The two-phase Ride middleware extends existing publish-subscribe oriented IoT data exchanges according to application-specified resilience requirements and without IoT device client modifications. The first phase, Ride-C, improves IoT data collection by gathering network-awareness via a novel resource-aware adaptive probing mechanism and dynamically redirecting IoT data flows across multiple public and local (edge) cloud data exchange connections. The second phase, Ride-D, uses this information to disseminate time-critical alerts via an intelligent network-aware resilient multicast mechanism. Results from our prototype smart campus testbed implementation, Mininet-based emulated experiments, and larger-scale simulations show that Ride enables network awareness for greater cloud connection up-times, timely fail-over to edge services, and more resilient local alert dissemination.","PeriodicalId":149725,"journal":{"name":"2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114858218","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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