2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)最新文献

{"title":"Distributed Human Trajectory Sensing and Partial Similarity Queries","authors":"Haotian Wang, Jie Gao","doi":"10.1109/IPSN48710.2020.00-43","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00-43","url":null,"abstract":"Advances in wireless communication technology have allowed for the collection of large-scale human motion trajectories by recording the appearance of mobile devices within the neighborhood of wireless base stations. Such city-scale datasets pose new challenges on efficient data collection, analysis and similarity based queries. In this paper, we propose new partial similarity measures, categorized as time-sensitive, order-sensitive and order-insensitive ones, and show with real data that these partial similarity measures are more robust than classical measures and more suitable for generating meaningful query results in near-neighbor type of data mining applications. Further, the power of the partial similarity persists even with significant down-sampling. We presented rigorous analysis of the performance of partial similarity measures with subsampling. Our evaluation using real data shows high recall and precision (≥ 90%) with samples only in the order of 1% of the original data size.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127252049","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":"Road Grade Estimation Using Crowd-Sourced Smartphone Data","authors":"Abhishek Gupta, Shaohan Hu, Weida Zhong, A. Sadek, Lu Su, C. Qiao","doi":"10.1109/IPSN48710.2020.00-25","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00-25","url":null,"abstract":"Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps. Integration of road grade information will widen the scope of digital map’s applications, which is primarily used for navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS), eco-driving, etc. The huge scale and dynamic nature of road networks make sensing road grade a challenging task. Traditional methods oftentimes suffer from limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a cost-effective and scalable road grade estimation framework using sensor data from smartphones. Based on our understanding of the error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and vehicle speed data from OBD-II/smartphone’s GPS to estimate road grade. To improve accuracy and robustness of the system, the estimations of road grade from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of 9km demonstrates the superior performance of our proposed method, achieving 5× improvement on road grade estimation accuracy over baselines, with 90% of errors below 0.3°.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129614668","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":"Demo Abstract: An Internet of Plants System for Micro Gardens","authors":"V. K. Nguyen, Quan Z. Sheng, A. Mahmood, W. Zhang, Minh-Hieu Phan, T. D. Vo","doi":"10.1109/IPSN48710.2020.000-9","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.000-9","url":null,"abstract":"The promising paradigm of the Internet of Things (IoT) has rapidly proliferated in a number of domains, e.g., energy monitoring, transportation, smart cities, healthcare, and the like. Leveraging on the same, in this paper, we proposed an Internet of Plants system to remotely monitor soil moisture level and automate watering control for the plants. The envisaged system not only facilitates in over watering or under watering of the plants but also provides data collection for the gardeners to understand the key features pertinent to healthy growth of plants. Such kind of system also encourages more people to grow their small-scale organic gardens in urban areas, thereby, increasing greenery in modern cities.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121301507","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: Using Deep Learning to Classify The Acceleration Measurement Devices","authors":"Yuezhong Wu, C. Dominguez, Shijia Pan, H. Noh, Mahbub Hassan, Pei Zhang, Wen Hu","doi":"10.1109/IPSN48710.2020.00-11","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00-11","url":null,"abstract":"Recent work has shown that two wearable devices worn on the same user can exploit gait as a secret source to generate a common key for secure pairing. The main threat of using gait comes from side-channel attackers who can use cameras to record the walking user and extract accelerations from the video to pair with legitimate devices. We propose a novel pre-step that uses a CNN-LSTM deep learning model to classify the acceleration measurement devices, i.e., between IMU vs. Camera. We prototype the pre-step and evaluate it using real subjects. Our results show that the proposed pre-step can achieve high classification success rates. The experiments with different cut-off frequencies show that the higher acceleration frequencies appear to contain more distinguishable features to classify camera from IMU.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132932207","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":"PhD Forum Abstract: Towards Deep Learning in Signal Translation for Cross Configurations in device-free WiFi Sensing","authors":"Isura Nirmal","doi":"10.1109/IPSN48710.2020.00011","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00011","url":null,"abstract":"Wireless Sensing has been a topic in contemporary research for years and popularity is still intact as its ubiquitous and non-invasive nature. Human activity recognition using wireless sensing is leveraged by the deep learning advancements. However, maintaining the robustness of the sensing model in sensitive wireless environments is a non-trivial task to achieve. We propose a deep learning based signal translation framework for multiple variations observed in sensing environments such as transceiver variance and configuration of the user who performing the activities. Our approach has two major pipelines, translation pipeline which translates from novel configuration to a reference configuration and an inference pipeline which classifies the activities performed by the user. We further expect to extend this model to more complex multi user interacting sensing environments and to In-Situ setups.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124278032","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 Novel Modeling Involved Security Approach for LoRa Key Generation","authors":"Jiayao Gao, Weitao Xu, S. Kanhere, Sanjay Jha, Wen Hu","doi":"10.1109/IPSN48710.2020.00-23","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00-23","url":null,"abstract":"Taking the advantages of reciprocity and randomness of wireless fading channels, key generation via physical layer is attracting more attention. It becomes a remarkable solution for wireless communication in recent years. However, the feasibility under long-range and low data rate scenarios of narrow band low power wide area network (LPWAN) lacks proper studies. In this poster, we introduce a novel modeling method for Long Range Wide Area Network (LoRaWAN) key generation. The approach combines several signal processing techniques and using measured real-time Received Signal Strength Indicator (RSSI) to improve the applicability of key generation as well as increasing key generation rate (KGR) significantly.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116179770","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: Data Communication using Switchable Privacy Glass","authors":"Changshuo Hu, Dong Ma, Mahbub Hassan, Wen Hu","doi":"10.1109/IPSN48710.2020.00-18","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00-18","url":null,"abstract":"Switchable privacy glass can electronically change its state between opaque and transparent. In this work, we propose to exploit the electronic configurability of switchable glass to modulate natural light, which can be demodulated by a nearby receiver with light sensing capability to realise data communication over natural light. A key advantage is that no energy is used to generate light, as it simply modulates the existing light in the nature. We demonstrate that the proposed data communication using switchable glass modulation can achieve 33.33 bits per second communication with a bit rate below 1% under a wide range of ambient luminance.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124016969","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":"SolarFinder: Automatic Detection of Solar Photovoltaic Arrays","authors":"Qi Li, Yuzhou Feng, Yuyang Leng, Dong Chen","doi":"10.1109/IPSN48710.2020.00024","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00024","url":null,"abstract":"Smart cities, utilities, third-parties, and government agencies are having pressure on managing stochastic power generation from distributed rooftop solar photovoltaic (PV) arrays, such as predicting and reacting to the variations in electric grid. Recently, there is a rising interest to identify solar PV arrays automatically and passively. Traditional approaches such as online assessment and utilities interconnection filings are time consuming and costly, and limited in geospatial resolution, and thus do not scale up to every location. Significant recent work focuses on using aerial imagery to train machine learning or deep learning models to automatically detect solar PV arrays. Unfortunately, these approaches typically require Very High Resolution (VHR) images and human handcrafted solar PV array templates for training, which have a minimum cost of $15 per km2 and are not always available at every location.To address the problem, we design a new system—SolarFinder that can automatically detect distributed solar PV arrays in a given geospatial region without any extra cost. SolarFinder first automatically fetches regular resolution satellite images within the region using publicly-available imagery APIs. Then, SolarFinder leverages multi-dimensional K-means algorithm to automatically segment solar arrays on rooftop images. Eventually, SolarFinder employs hybrid linear regression approach that integrates support vector machine (SVM) modeling with a deep convolutional neural networks (CNNs) approach to accurately identify solar PV arrays and characterize each solar deployment. We evaluate SolarFinder using 269,632 satellite images that include 1,143,636 contours from 13 geospatial regions in U.S. We find that pre-trained SolarFinder yields a Matthews Correlation Coefficient (MCC) of 0.17, which is 3 times better than the most recent pre-trained CNNs approach and the same as a re-trained CNNs approach.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128709196","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":"IPSN 2020 Index","authors":"","doi":"10.1109/ipsn48710.2020.00068","DOIUrl":"https://doi.org/10.1109/ipsn48710.2020.00068","url":null,"abstract":"","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127117877","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":"OFFICE: Optimization Framework For Improved Comfort & Efficiency","authors":"Daniel A. Winkler, Ashish Yadav, C. Chitu, Alberto Cerpa","doi":"10.1109/IPSN48710.2020.00030","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00030","url":null,"abstract":"Buildings are responsible for a significant portion of energy consumption in the US, accounting for more than 40% of US primary energy consumption. Heating, ventilation and air-conditioning (HVAC) accounts for nearly 50% of that use. Conditioning buildings is important since people spend 87% of their time in the place they live (residential) and the place they work (commercial). Despite this massive expense, many users are dissatisfied with the thermal conditions in buildings. Savings made in HVAC systems, therefore, have a major impact on energy consumption and cost, together with the reduction of greenhouse emissions for the nation. Equally critical is to provide thermal quality of service to their users, so people are comfortable in the place they reside and work.In this paper, we explore the tradeoff between commercial building HVAC energy consumption and the quality of thermal conditioning provided to users. We argue that optimal HVAC control cannot be achieved due to lack of critical information, namely where the users are inside the building, what do they want with respect to thermal comfort and how each zone responds to thermal changes. In this work, we present OFFICE, a model predictive control (MPC) framework for smart building HVAC control. The framework has several components that help to address the current HVAC control systems shortcomings, including (a) occupancy sensing in real-time, (b) occupancy prediction models based on historical occupancy data, (c) human-in-the-loop comfort feedback, (d) data-driven thermodynamic building models, and (e) weather forecasting data. All these components provide the necessary input to our model predictive control optimization framework that minimizes monetary costs in energy use while maintaining quality comfort bounds for the building’s users based on real-time user’s feedback. We developed a large system that involves all the above components, replacing the Building Management System control algorithms, taking over full control of the HVAC system. We tested OFFICE in a real LEED Gold certified university building with over 20 workers performing their daily tasks for 4 weeks, and we showed that we could obtain monetary costs savings of more than 10% while at the same time reducing the users’ dissatisfaction levels with thermal comfort from 25% to 0% dissatisfaction, significantly improving the quality of thermal service provided to the building’s users.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132279748","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}