Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation最新文献

{"title":"Demo Abstract: Implementing a COVID-19 Risk Assessment System for User-Informed Travel Planning in Harris County","authors":"A. Cheng, Jude Nwankpa","doi":"10.1145/3576842.3589173","DOIUrl":"https://doi.org/10.1145/3576842.3589173","url":null,"abstract":"The COVID-19 pandemic has forced people worldwide to modify their daily activities, including travel plans. To help individuals make informed decisions about visiting public places, Cheng [2] first proposed a real-time COVID-19 risk assessment system called RT-CIRAM and implemented prototypes for two U.S. metropolitan locations. The system calculates a COVID-19 risk score and categorizes the risk levels into high, medium, and low, recommends the safe travel destination using the users’ location and the specified distance the user is willing to travel, thereby helping users make informed decisions about their travel plans.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547919","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}

{"title":"Poster Abstract: A LoRa-based Energy-Efficient Sensing System for Urban Computing","authors":"Lukas Schulthess, Tiago Salzmann, Christian Vogt, M. Magno","doi":"10.1145/3576842.3589179","DOIUrl":"https://doi.org/10.1145/3576842.3589179","url":null,"abstract":"Public space architecture requires reliable data to optimize city resources and enhance their attractiveness. The feedback on acceptance of these changes by the population currently relies on subjective questionnaires or objective but cost-intensive observations, mobile data usage, or video surveillance, which does not preserve privacy. To collect data on public square utilization and usage anonymously for urban computing, this work presents a low-cost, low-power, and privacy-preserving sensing system based on low-power sensor nodes. It is capable of tracking the usage of public chairs in squares and parks by monitoring environmental noise, chair utilization, chair position, temperature, and humidity while keeping maintenance costs low. The final sensing system’s robust operation is proven by field tests at two public squares in a city, providing real-time measurements over a city-wide LoRaWAN network. The custom-developed sensor node consumes 33.65 mWh per day under worst-case conditions. Depending on the weather condition, the sensor can increase its overall battery lifetime by a minimum of 20 percent and can temporarily reach full self-sustainability which allows data collection over the time period of interest.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129294154","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}

{"title":"Verified Telemetry: A General, Easy to use, Scalable and Robust Fault Detection SDK for IoT Sensors","authors":"Tanmaey Gupta, Shubhankar Handa, A. Nambi","doi":"10.1145/3576842.3582386","DOIUrl":"https://doi.org/10.1145/3576842.3582386","url":null,"abstract":"With the proliferation of IoT sensors, the reliance on sensor telemetry has never been greater. Today numerous applications from smart agriculture to smart buildings and cities, rely on IoT telemetry to provide insights and to take decisions. However, due to the characteristics of these IoT deployments (in the wild, harsh conditions), sensors are prone to failures, leading to the generation of bad/dirty data. Hitherto, data-centric algorithms were used to determine the quality of the sensed data, which has several limitations and relies on additional contextual information or sensor redundancy. Recently, system-centric approaches based on sensor fingerprinting has shown to detect sensor faults reliably without any additional information. However, the sensor fingerprinting approach is validated only for specific sensors, is not robust to real-world conditions, and cannot scale to large-scale deployments. To this end, we propose a general, easy-to-use, scalable, and robust fault detection SDK called Verified Telemetry (VT) SDK. VT SDK builds on the sensor fingerprinting approach and can work with a wide variety of sensors (both analog and digital) and IoT devices with very minimal changes. We propose improved sensor fingerprinting algorithms that are robust to signal variations, sensor circuitry, and real-world conditions. VT SDK is implemented across numerous devices and we show its usage on several practical applications. Finally, VT SDK is made available for the community to address sensor fault detection in IoT deployments (https://aka.ms/verifiedtelemetry).","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"24 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434878","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}

{"title":"Poster Abstract: Light-Weight Real-Time Senior Safety Monitoring using Digital Twins","authors":"Qian Qu, Han Sun, Yu Chen","doi":"10.1145/3576842.3589163","DOIUrl":"https://doi.org/10.1145/3576842.3589163","url":null,"abstract":"The unprecedented increase in the aging population has brought more and more concerns and challenges to meet the compelling need for seniors’ safety protection as they may be at risk of falling, injuries, and medical emergencies. The raising digital healthcare services (DHS) leveraging the Internet of Medical Things (IoMT) are promising to enable solutions. The combination of IoMT and Digital Twins (DT) technologies creates a virtual replica of a physical DHS system. Inspired by many attractive features of DTs, we propose to support real-time senior safety monitoring services utilizing the seniors’ activity and environment data collected by pervasively deployed IoMT sensors and the logical twins created in the virtual space. This poster reports our ongoing effort, introducing the proposed system architecture and some preliminary results that validated the feasibility to serve the design goal for real-time monitoring, instant anomaly detection, and timely alerting.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126581716","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}

{"title":"LILOC: Enabling Precise 3D Localization in Dynamic Indoor Environments using LiDARs","authors":"Darshana Rathnayake, Meera Radhakrishnan, Inseok Hwang, Archan Misra","doi":"10.1145/3576842.3582364","DOIUrl":"https://doi.org/10.1145/3576842.3582364","url":null,"abstract":"We present LiLoc, a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. The key differentiators in our work are: (a) First, unlike traditional localization approaches, our approach is robust to dynamically changing environmental conditions (e.g., varying crowd levels, object placement/layout changes); (b) Second, unlike prior work on visual and 3D SLAM, LiLoc is not dependent on a pre-built static map of the environment and instead works by utilizing dynamically updated point clouds captured from both infrastructural-mounted LiDARs and LiDARs equipped on individual mobile IoT devices. To achieve fine-grained, near real-time location tracking, it employs complex 3D ‘global’ registration among the two point clouds only intermittently to obtain robust spot location estimates and further augments it with repeated simpler ‘local’ registrations to update the trajectory of IoT device continuously. We demonstrate that LiLoc can (a) support accurate location tracking with location and pose estimation error being <=7.4cm and <=3.2° respectively for 84% of the time and the median error increasing only marginally (8%), for correctly estimated trajectories, when the ambient environment is dynamic, (b) achieve a 36% reduction in median location estimation error compared to an approach that uses only quasi-static global point cloud, and (c) obtain spot location estimates with a latency of only 973 msecs.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131961300","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}

{"title":"Poster Abstract: Run-time Dynamic WCET Estimation","authors":"Lia Cagnizi, F. Reghenzani, W. Fornaciari","doi":"10.1145/3576842.3589168","DOIUrl":"https://doi.org/10.1145/3576842.3589168","url":null,"abstract":"To guarantee the timing constraints of real-time IoT devices, engineers need to estimate the Worst-Case Execution Time. Such estimation is always very pessimistic and represents a condition that almost never occurs in practice. In this poster, we present a novel compiler-based approach that instruments the tasks to inform, at run-time, the operating system when non-worst-case branches are taken. The generated slack is then used to take better scheduling decisions.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133407720","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}

{"title":"QuIC-IoT: Model-Driven Short-Term IoT Deployment for Monitoring Physical Phenomena","authors":"Tung-Chun Chang, T. Banerjee, N. Venkatasubramanian, Robert York","doi":"10.1145/3576842.3582381","DOIUrl":"https://doi.org/10.1145/3576842.3582381","url":null,"abstract":"The Internet-of-things ecosystem has been a driving force in the creation of smart communities where a variety of physical phenomena can be monitored continuously, e.g., air quality, traffic conditions on roads, energy consumption in buildings, etc. In this paper, we address how IoT can be quickly and effectively deployed for short-term and sporadic events (e.g., fire spread in a wildland area and flood propagation), where monitoring the evolving event is critical. In particular, we propose QuIC-IoT, a model-driven planning platform that aims to temporarily deploy a custom IoT infrastructure for monitoring short-term events, where phenomena-spread is driven by models that are physics-based. Our driving usecase event is a quasi-planned prescribed fire or RxFire - this is a wildfire resilience technique where intentional small fires are ignited apriori by forestry personnel to destroy fuel and help contain the spread of actual wildfires. Anomalies that may occur during these quasi-planned events must be rapidly captured by the IoT deployment, e.g., escaped RxFires can escalate to catastrophic wildfires under unpredictable conditions of wind, vegetation, etc. QuIC-IoT incorporates domain expert-developed models to guide IoT deployment; the event area is partitioned into subregions and a criticality metric that quantifies the likelihood of anomalies at each location is computed. QuIC-IoT allows us to mix fixed and quasi-mobile IoT devices to flexibly deploy IoT in challenging terrain and as the phenomena (RxBurn) evolves. We evaluate QuIC-IoT in two real-world forest settings (large and small) in Blodgett Forest, CA, USA, with concrete burn plans developed by wildfire experts. Our experimental results reveal that QuIC-IoT enables over 3X improvement in cost-effectiveness and performance (timely detection of anomalies) as compared to baseline IoT deployment algorithms.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125863533","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}

{"title":"LOIS: Low-cost Packet Header Protection for IoT Devices","authors":"Minmei Wang, Shouqian Shi, Xiaoxue Zhang, Song Han, Chen Qian","doi":"10.1145/3576842.3582380","DOIUrl":"https://doi.org/10.1145/3576842.3582380","url":null,"abstract":"The widely deployed IoT devices in various applications, such as smart homes and smart factories, pose new privacy concerns. IoT devices typically capture users’ activities or collect information from their surroundings and then send the information to remote cloud servers, exposing private information to passive adversaries by looking at the packet headers. Thus, in an enhanced IoT security protocol, protecting privacy also requires hiding packet headers and other traffic metadata. This work presents the LOIS framework, a packet-level packet header protector based on efficient one-time keystreams. LOIS allows IoT devices to efficiently hide the IP and port information in packet headers while allowing the cloud to recover the original packet headers. Besides, LOIS can easily integrate with existing IoT traffic padding algorithms to hide traffic patterns. We implement LOIS on commodity servers running in a public cloud. Our experimental results show that LOIS only introduces moderate overhead. For example, results show that LOIS only incurs about 250–365 ns end-to-end latency on average for the upload traffic, which is 80%–90% less than that of IPsec.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740028","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}

{"title":"SolarDetector: Automatic Solar PV Array Identification using Big Satellite Imagery Data","authors":"Qi Li, Sander Schott, Dong Chen","doi":"10.1145/3576842.3582384","DOIUrl":"https://doi.org/10.1145/3576842.3582384","url":null,"abstract":"Due to the intermittent nature of solar energy, it has been increasingly challenging for the utilities, third-parties, and government agencies to integrate distributed energy resources generated by rooftop solar photovoltaic (PV) arrays into smart grids. Recently, there is a rising interest in automatically collecting solar installation information in a geospatial region that are necessary to manage this stochastic green energy, including the quantity and locations of solar PV deployments, and their profiling information. Most recent work focuses on using big aerial or satellite imagery data to train machine learning or deep learning models to automatically detect solar PV arrays. Unfortunately, these approaches are suffering low detection accuracy due to the insufficient sample and feature learning when building their models, and the separation of rooftop object segmentation and identification during their detection process. In addition, most recent approaches cannot report accurate multi-panel detection results. To address these problems, we design a new approach—SolarDetector that can automatically detect and profile distributed solar photovoltaic arrays in a given geospatial region without any extra cost. SolarDetector first leverages data augmentation techniques and Generative adversarial networks (GANs) to automatically learn accurate features for rooftop objects. Then, SolarDetector employs Mask R-CNN algorithm to accurately identify rooftop solar arrays and also learn the detailed installation information for each solar array simultaneously. In addition, SolarDetector could also integrate with large-scale data processing engine—Apache Spark and graphics processing units (GPUs) to further improve its training cost. We evaluate SolarDetector using 263,430 public satellite images from 11 geospatial regions in the U.S. We find that pre-trained SolarDetector yields an average MCC of 0.76 to detect solar PV arrays over two big datasets, which is ∼ 50% better than the most notable approach—SolarFinder. In addition, unlike prior work, we show that SolarDetector can also accurately report the profiling information for the detected rooftop objects.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130887914","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}

{"title":"FaceTouch: Practical Face Touch Detection with a Multimodal Wearable System for Epidemiological Surveillance","authors":"Li Liu, Zhichao Cao, Tianxing Li","doi":"10.1145/3576842.3582368","DOIUrl":"https://doi.org/10.1145/3576842.3582368","url":null,"abstract":"In this paper, we propose FaceTouch, a low-power and versatile method that enables accurate face touch detection with a multimodal wearable system. FaceTouch consists of two sensing components, an inertial sensor on the wrist and a novel vibration sensor on the finger. We leverage the wrist inertial sensor to detect the face-touch gesture that the hand moves towards the face area. To achieve this goal in a computation-efficient manner, we develop a cascading classification model including three classifiers to filter out irrelevant gestures to significantly extend the battery life while keeping a high recall. Once a face-touch gesture is triggered, we activate the vibration sensor to detect touch events. We implement FaceTouch using commercial off-the-shelf hardware components and evaluate its performance with various user activities and false-positive behaviors. FaceTouch achieves 93.5% F-1 score of face touch detection. The entire system only consumes 60.89 μ W power on average in normal daily usage and 209.15 μ W in extremely heavy usage, which is several magnitudes lower than the state-of-the-art systems, and FaceTouch can continuously detect face-touch events for 79 – 273 days using a small 400 mWh battery depending on usage.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134167569","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}