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

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PhD Forum Abstract: Dynamic Inference on IoT Network Traffic using Programmable Telemetry and Machine Learning 摘要:基于可编程遥测和机器学习的物联网网络流量动态推断

2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2020-04-01 DOI: 10.1109/IPSN48710.2020.00006

Arman Pashamokhtari

{"title":"PhD Forum Abstract: Dynamic Inference on IoT Network Traffic using Programmable Telemetry and Machine Learning","authors":"Arman Pashamokhtari","doi":"10.1109/IPSN48710.2020.00006","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00006","url":null,"abstract":"IoT networks continue to expand in various domains, from smart homes and campuses to smart cities and critical infrastructures. Due to the lack of appropriate security measures embedded in IoT devices, they are increasingly becoming the target of sophisticated cyber-attacks. Existing machine learning-based methods for monitoring and securing IoT networks are fragile, expensive, and inflexible, and hence unable to cost-effectively cope with the dynamic and complex nature of attacks. For my PhD thesis, the aim is to develop a robust, accurate, scalable, and cost-effective network monitoring solution for securing large IoT systems. To do so, we would require to dynamically measure and analyze certain portions of network traffic. Therefore, we employ programmable networking techniques to dynamically and selectively acquire necessary telemetry data (packets and/or flows of connected devices) fed to a collection of learning-based models (each specialized in certain protocols with specific granularity) to classify devices, monitor their activity, and detect malicious behaviors. Our preliminary results show that the signature analysis of selected signaling packets from 26 real IoT devices yields a reasonably accurate inference by progressively collecting telemetry data at manageable processing costs.","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":"116892500","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}

引用次数: 5

MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing MDLdroid:一种用于个人移动传感的移动深度学习的ChainSGD-reduce方法

2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2020-02-07 DOI: 10.1109/IPSN48710.2020.00-45

Yu Zhang, Tao Gu, Xi Zhang

{"title":"MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing","authors":"Yu Zhang, Tao Gu, Xi Zhang","doi":"10.1109/IPSN48710.2020.00-45","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.00-45","url":null,"abstract":"Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model inference and update. Since data collection is costly in reality, Google’s Federated Learning offers not only complete data privacy but also better model robustness based on multiple user data. However, personal mobile sensing applications are mostly user-specific and highly affected by environment. As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning. In addition, deploying Federated Learning on a local server, e.g., edge server, may quickly reach the bottleneck due to resource constraint and serious failure by attacks. Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, we propose a ChainSGD-reduce approach which includes a novel chain-directed Synchronous Stochastic Gradient Descent algorithm to effectively reduce overhead among multiple devices. We also design an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Our evaluations show that our model training on off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device training, and 1.5x faster than the master-slave approach.","PeriodicalId":439751,"journal":{"name":"2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115509","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}

引用次数: 15

Demo Abstract: RF Soil Moisture Sensing via Radar Backscatter Tags 摘要:基于雷达反向散射标签的射频土壤湿度传感

2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Pub Date : 2019-12-28 DOI: 10.1109/IPSN48710.2020.000-4

Colleen Josephson, Bradley Barnhart, S. Katti, Keith Winstein, Ranveer Chandra

{"title":"Demo Abstract: RF Soil Moisture Sensing via Radar Backscatter Tags","authors":"Colleen Josephson, Bradley Barnhart, S. Katti, Keith Winstein, Ranveer Chandra","doi":"10.1109/IPSN48710.2020.000-4","DOIUrl":"https://doi.org/10.1109/IPSN48710.2020.000-4","url":null,"abstract":"We present a sensing system that determines soil moisture via RF using backscatter tags paired with a commodity ultra-wideband RF transceiver. Despite decades of research confirming the benefits, soil moisture sensors are still not widely adopted on working farms for three key reasons: the high cost of sensors, the difficulty of deploying and maintaining these sensors, and the lack of reliable internet access in rural areas. We seek to address some of these obstacles by designing a low-cost soil moisture sensing system that uses a hybrid approach of pairing completely wireless backscatter tags with a mobile reader.(p)(/p)We designed and built a backscatter tag prototype and tested our system both in laboratory and in situ at an organic farm field. Our backscatter tag has a projected battery lifetime of up to 15 years on 4xAA batteries, and can operate at a depth of at least 30cm and up to 75cm. It achieves an average accuracy within 0.01-0.03cm3/cm3 of the ground truth with a 90th percentile of 0.034cm3/cm3, which is comparable to state-of-the-art commercial soil sensors, at an order of magnitude lower cost.","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":"2019-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131194771","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}

引用次数: 9

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