In this work, we study privacy preserving trajectory sensing and query when $n$ mobile entities (e.g., mobile devices or vehicles) move in an environment of $m$ checkpoints (e.g, WiFi or cellular towers). The checkpoints detect the appearances of mobile entities in the proximity, meanwhile, employ the MinHash signatures to record the set of mobile entities passing by. We build on the checkpoints a distributed data structure named the MinHash hierarchy, with which one can efficiently answer queries regarding popular paths and other traffic patterns. The MinHash hierarchy has a total of near linear storage, linear construction cost, and logarithmic update cost. The cost of a popular path query is logarithmic in the number of checkpoints. Further, the MinHash signature provides privacy protection using a model inspired by the differential privacy model.We evaluated our algorithm using a large mobility data set and compared with previous works to demonstrate its utilities and performances.
{"title":"MinHash Hierarchy for Privacy Preserving Trajectory Sensing and Query","authors":"J. Ding, Chien-Chun Ni, Mengyu Zhou, Jie Gao","doi":"10.1145/3055031.3055076","DOIUrl":"https://doi.org/10.1145/3055031.3055076","url":null,"abstract":"In this work, we study privacy preserving trajectory sensing and query when $n$ mobile entities (e.g., mobile devices or vehicles) move in an environment of $m$ checkpoints (e.g, WiFi or cellular towers). The checkpoints detect the appearances of mobile entities in the proximity, meanwhile, employ the MinHash signatures to record the set of mobile entities passing by. We build on the checkpoints a distributed data structure named the MinHash hierarchy, with which one can efficiently answer queries regarding popular paths and other traffic patterns. The MinHash hierarchy has a total of near linear storage, linear construction cost, and logarithmic update cost. The cost of a popular path query is logarithmic in the number of checkpoints. Further, the MinHash signature provides privacy protection using a model inspired by the differential privacy model.We evaluated our algorithm using a large mobility data set and compared with previous works to demonstrate its utilities and performances.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116981843","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}
Despite the advent of numerous Internet-of-Things (IoT) applications, recent research demonstrates potential side-channel vulnerabilities exploiting sensors which are used for event and environment monitoring. In this paper, we propose a new side-channel attack, where a network of distributed non-acoustic sensors can be exploited by an attacker to launch an eavesdropping attack by reconstructing intelligible speech signals. Specifically, we present PitchIn to demonstrate the feasibility of speech reconstruction from non-acoustic sensor data collected offline across networked devices. Unlike speech reconstruction which requires a high sampling frequency (e.g., > 5 KHz), typical applications using non-acoustic sensors do not rely on richly sampled data, presenting a challenge to the speech reconstruction attack. Hence, PitchIn leverages a distributed form of Time Interleaved Analog-Digital-Conversion (TI-ADC) to approximate a high sampling frequency, while maintaining low per-node sampling frequency. We demonstrate how distributed TI-ADC can be used to achieve intelligibility by processing an interleaved signal composed of different sensors across networked devices. We implement PitchIn and evaluate reconstructed speech signal intelligibility via user studies. PitchIn has word recognition accuracy as high as 79%. Though some additional work is required to improve accuracy, our results suggest that eavesdropping using a fusion of non-acoustic sensors is a real and practical threat.
{"title":"PitchIn: Eavesdropping via Intelligible Speech Reconstruction Using Non-acoustic Sensor Fusion","authors":"Jun Han, Albert Jin Chung, P. Tague","doi":"10.1145/3055031.3055088","DOIUrl":"https://doi.org/10.1145/3055031.3055088","url":null,"abstract":"Despite the advent of numerous Internet-of-Things (IoT) applications, recent research demonstrates potential side-channel vulnerabilities exploiting sensors which are used for event and environment monitoring. In this paper, we propose a new side-channel attack, where a network of distributed non-acoustic sensors can be exploited by an attacker to launch an eavesdropping attack by reconstructing intelligible speech signals. Specifically, we present PitchIn to demonstrate the feasibility of speech reconstruction from non-acoustic sensor data collected offline across networked devices. Unlike speech reconstruction which requires a high sampling frequency (e.g., > 5 KHz), typical applications using non-acoustic sensors do not rely on richly sampled data, presenting a challenge to the speech reconstruction attack. Hence, PitchIn leverages a distributed form of Time Interleaved Analog-Digital-Conversion (TI-ADC) to approximate a high sampling frequency, while maintaining low per-node sampling frequency. We demonstrate how distributed TI-ADC can be used to achieve intelligibility by processing an interleaved signal composed of different sensors across networked devices. We implement PitchIn and evaluate reconstructed speech signal intelligibility via user studies. PitchIn has word recognition accuracy as high as 79%. Though some additional work is required to improve accuracy, our results suggest that eavesdropping using a fusion of non-acoustic sensors is a real and practical threat.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131002590","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}
Ahmed Saeed, Ahmed Abdelkader, Mouhyemen Khan, A. Neishaboori, Khaled A. Harras, Amr M. Mohamed
Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent visual sensing systems. This potential motivated several research efforts to employ drones as standalone surveillance systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain directions; 3) Occlusion by elements in the environment, including other targets.In this paper, we present Argus, a system that provides visual coverage of wide and oriented targets, using camera-mounted drones, taking into account the challenges stated above. Argus relies on a geometric model that captures both target shapes and coverage constraints. With drones being the scarcest resource in Argus, we study the problem of minimizing the number of drones required to cover a set of such targets and derive a best-possible approximation algorithm. Building upon that, we present a sampling heuristic that performs favorably, while running up to 100x faster compared to the approximation algorithm. We implement a complete prototype of Argus to demonstrate and evaluate the proposed coverage algorithms within a fully autonomous surveillance system. Finally, we evaluate the proposed algorithms via simulations to compare their performance at scale under various conditions.
{"title":"Argus: Realistic Target Coverage by Drones","authors":"Ahmed Saeed, Ahmed Abdelkader, Mouhyemen Khan, A. Neishaboori, Khaled A. Harras, Amr M. Mohamed","doi":"10.1145/3055031.3055078","DOIUrl":"https://doi.org/10.1145/3055031.3055078","url":null,"abstract":"Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent visual sensing systems. This potential motivated several research efforts to employ drones as standalone surveillance systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain directions; 3) Occlusion by elements in the environment, including other targets.In this paper, we present Argus, a system that provides visual coverage of wide and oriented targets, using camera-mounted drones, taking into account the challenges stated above. Argus relies on a geometric model that captures both target shapes and coverage constraints. With drones being the scarcest resource in Argus, we study the problem of minimizing the number of drones required to cover a set of such targets and derive a best-possible approximation algorithm. Building upon that, we present a sampling heuristic that performs favorably, while running up to 100x faster compared to the approximation algorithm. We implement a complete prototype of Argus to demonstrate and evaluate the proposed coverage algorithms within a fully autonomous surveillance system. Finally, we evaluate the proposed algorithms via simulations to compare their performance at scale under various conditions.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582965","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}
In this paper, we are interested in the 3D through-wall imaging of a completely unknown area, using WiFi RSSI and Unmanned Aerial Vehicles (UAVs) that move outside of the area of interest to collect WiFi measurements. It is challenging to estimate a volume represented by an extremely high number of voxels with a small number of measurements. Yet many applications are time-critical and/or limited on resources, precluding extensive measurement collection. In this paper, we then propose an approach based on Markov random field modeling, loopy belief propagation, and sparse signal processing for 3D imaging based on wireless power measurements. Furthermore, we show how to design efficient aerial routes that are informative for 3D imaging. Finally, we design and implement a complete experimental testbed and show high-quality 3D robotic through-wall imaging of unknown areas with less than 4% of measurements.
{"title":"3D Through-Wall Imaging with Unmanned Aerial Vehicles Using WiFi","authors":"Chitra R. Karanam, Y. Mostofi","doi":"10.1145/3055031.3055084","DOIUrl":"https://doi.org/10.1145/3055031.3055084","url":null,"abstract":"In this paper, we are interested in the 3D through-wall imaging of a completely unknown area, using WiFi RSSI and Unmanned Aerial Vehicles (UAVs) that move outside of the area of interest to collect WiFi measurements. It is challenging to estimate a volume represented by an extremely high number of voxels with a small number of measurements. Yet many applications are time-critical and/or limited on resources, precluding extensive measurement collection. In this paper, we then propose an approach based on Markov random field modeling, loopy belief propagation, and sparse signal processing for 3D imaging based on wireless power measurements. Furthermore, we show how to design efficient aerial routes that are informative for 3D imaging. Finally, we design and implement a complete experimental testbed and show high-quality 3D robotic through-wall imaging of unknown areas with less than 4% of measurements.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"490 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116694025","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}
Identifying "who is around" is key in a plethora of smart scenarios. While many solutions exist, they often take a theoretical approach, reasoning about protocol behavior with an abstract model that makes simplifying assumptions about the environment. This approach creates a gap between protocol implementations and the models used during design and analysis. In this paper, we take a system approach to continuous neighbor discovery: starting with the concrete technology of Bluetooth Low Energy (BLE) we build a protocol, called BLEnd, tailored to its constraints. Moreover, we also consider the very real effects of packet collisions, to our knowledge a first in this domain. Our ultimate goal is to directly empower developers with the ability to determine the optimal protocol configuration for their applications; in this respect, the slotless operation of BLEnd offers richer alternatives than state-of-the-art protocols. Developers specify the minimum discovery probability, the target discovery latency, and the maximum expected node density; these are used by an optimizer tool to parameterize the BLEnd implementation towards maximum lifetime. This paper shows that BLEnd not only achieves the user-specified goals, but does so more efficiently than analogous configurations of competing protocols.
{"title":"BLEnd: Practical Continuous Neighbor Discovery for Bluetooth Low Energy","authors":"C. Julien, Chenguang Liu, A. Murphy, G. Picco","doi":"10.1145/3055031.3055086","DOIUrl":"https://doi.org/10.1145/3055031.3055086","url":null,"abstract":"Identifying \"who is around\" is key in a plethora of smart scenarios. While many solutions exist, they often take a theoretical approach, reasoning about protocol behavior with an abstract model that makes simplifying assumptions about the environment. This approach creates a gap between protocol implementations and the models used during design and analysis. In this paper, we take a system approach to continuous neighbor discovery: starting with the concrete technology of Bluetooth Low Energy (BLE) we build a protocol, called BLEnd, tailored to its constraints. Moreover, we also consider the very real effects of packet collisions, to our knowledge a first in this domain. Our ultimate goal is to directly empower developers with the ability to determine the optimal protocol configuration for their applications; in this respect, the slotless operation of BLEnd offers richer alternatives than state-of-the-art protocols. Developers specify the minimum discovery probability, the target discovery latency, and the maximum expected node density; these are used by an optimizer tool to parameterize the BLEnd implementation towards maximum lifetime. This paper shows that BLEnd not only achieves the user-specified goals, but does so more efficiently than analogous configurations of competing protocols.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128123522","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}
Mobile sensor networks are a great source of data. By collecting data with mobile sensor nodes from individuals in a user community, e.g. using their smartphones, we can learn global information such as traffic congestion patterns in the city, location of key community facilities, and locations of gathering places. Can we publish and run queries on mobile sensor network databases without disclosing information about individual nodes?Differential privacy is a strong notion of privacy which guarantees that very little will be learned about individual records in the database, no matter what the attackers already know or wish to learn. Still, there is no practical system applying differential privacy algorithms for clustering points on real databases. This paper describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, we give the first k-means clustering algorithm that is both differentially private, and has an approximation error that depends sub-linearly on the data's dimension d. Previous results introduced errors that are exponential in d.We implemented this algorithm and used it to create differentially private location data from GPS tracks. Specifically our algorithm allows clustering GPS databases generated from mobile nodes, while letting the user control the introduced noise due to privacy. We provide experimental results for the system and algorithms, and compare them to existing techniques. To the best of our knowledge, this is the first practical system that enables differentially private clustering on real data.
{"title":"Coresets for Differentially Private K-Means Clustering and Applications to Privacy in Mobile Sensor Networks","authors":"Dan Feldman, C. Xiang, Ruihao Zhu, D. Rus","doi":"10.1145/3055031.3055090","DOIUrl":"https://doi.org/10.1145/3055031.3055090","url":null,"abstract":"Mobile sensor networks are a great source of data. By collecting data with mobile sensor nodes from individuals in a user community, e.g. using their smartphones, we can learn global information such as traffic congestion patterns in the city, location of key community facilities, and locations of gathering places. Can we publish and run queries on mobile sensor network databases without disclosing information about individual nodes?Differential privacy is a strong notion of privacy which guarantees that very little will be learned about individual records in the database, no matter what the attackers already know or wish to learn. Still, there is no practical system applying differential privacy algorithms for clustering points on real databases. This paper describes the construction of small coresets for computing k-means clustering of a set of points while preserving differential privacy. As a result, we give the first k-means clustering algorithm that is both differentially private, and has an approximation error that depends sub-linearly on the data's dimension d. Previous results introduced errors that are exponential in d.We implemented this algorithm and used it to create differentially private location data from GPS tracks. Specifically our algorithm allows clustering GPS databases generated from mobile nodes, while letting the user control the introduced noise due to privacy. We provide experimental results for the system and algorithms, and compare them to existing techniques. To the best of our knowledge, this is the first practical system that enables differentially private clustering on real data.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934846","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}
Wearable technologies play a central role in human-centered Internet-of-Things applications. Wearables leverage machine learning algorithms to detect events of interest such as physical activities and medical complications. These algorithms, however, need to be retrained upon any changes in configuration of the system, such as addition/ removal of a sensor to/ from the network or displacement/ misplacement/ mis-orientation of the physical sensors on the body. We challenge this retraining model by stimulating the vision of autonomous learning with the goal of eliminating the labor-intensive, time-consuming, and highly expensive process of collecting labeled training data in dynamic environments. We propose an approach for autonomous retraining of the machine learning algorithms in real-time without need for any new labeled training data. We focus on a dynamic setting where new sensors are added to the system and worn on various body locations. We capture the inherent correlation between observations made by a static sensor view for which trained algorithms exist and the new dynamic sensor views for which an algorithm needs to be developed. By applying our real-time dynamic-view autonomous learning approach, we achieve an average accuracy of 81.1% in activity recognition using three experimental datasets. This amount of accuracy represents more than 13.8% improvement in the accuracy due to the automatic labeling of the sensor data in the newly added sensor. This performance is only 11.2% lower than the experimental upper bound where labeled training data are collected with the new sensor.
{"title":"Synchronous Dynamic View Learning: A Framework for Autonomous Training of Activity Recognition Models Using Wearable Sensors","authors":"Seyed Ali Rokni, Hassan Ghasemzadeh","doi":"10.1145/3055031.3055087","DOIUrl":"https://doi.org/10.1145/3055031.3055087","url":null,"abstract":"Wearable technologies play a central role in human-centered Internet-of-Things applications. Wearables leverage machine learning algorithms to detect events of interest such as physical activities and medical complications. These algorithms, however, need to be retrained upon any changes in configuration of the system, such as addition/ removal of a sensor to/ from the network or displacement/ misplacement/ mis-orientation of the physical sensors on the body. We challenge this retraining model by stimulating the vision of autonomous learning with the goal of eliminating the labor-intensive, time-consuming, and highly expensive process of collecting labeled training data in dynamic environments. We propose an approach for autonomous retraining of the machine learning algorithms in real-time without need for any new labeled training data. We focus on a dynamic setting where new sensors are added to the system and worn on various body locations. We capture the inherent correlation between observations made by a static sensor view for which trained algorithms exist and the new dynamic sensor views for which an algorithm needs to be developed. By applying our real-time dynamic-view autonomous learning approach, we achieve an average accuracy of 81.1% in activity recognition using three experimental datasets. This amount of accuracy represents more than 13.8% improvement in the accuracy due to the automatic labeling of the sensor data in the newly added sensor. This performance is only 11.2% lower than the experimental upper bound where labeled training data are collected with the new sensor.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122500229","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}
We present code instrumentation strategies to allow transiently-powered embedded sensing devices efficiently checkpoint the system’s state before energy is exhausted. Our solution, called HarvOS, operates at compile-time with limited developer intervention based on the control-flow graph of a program, while adapting to varying levels of remaining energy and possible program executions at run-time. In addition, the underlying design rationale allows the system to spare the energy-intensive probing of the energy buffer whenever possible. Compared to existing approaches, our evaluation indicates that HarvOS allows transiently-powered devices to complete a given workload with 68% fewer checkpoints, on average. Moreover, our performance in the number of required checkpoints rests only 19% far from that of an “oracle” that represents an ideal solution, yet unfeasible in practice, that knows exactly the last point in time when to checkpoint.
{"title":"HarvOS: Efficient Code Instrumentation for Transiently-Powered Embedded Sensing","authors":"Naveed Anwar Bhatti, L. Mottola","doi":"10.1145/3055031.3055082","DOIUrl":"https://doi.org/10.1145/3055031.3055082","url":null,"abstract":"We present code instrumentation strategies to allow transiently-powered embedded sensing devices efficiently checkpoint the system’s state before energy is exhausted. Our solution, called HarvOS, operates at compile-time with limited developer intervention based on the control-flow graph of a program, while adapting to varying levels of remaining energy and possible program executions at run-time. In addition, the underlying design rationale allows the system to spare the energy-intensive probing of the energy buffer whenever possible. Compared to existing approaches, our evaluation indicates that HarvOS allows transiently-powered devices to complete a given workload with 68% fewer checkpoints, on average. Moreover, our performance in the number of required checkpoints rests only 19% far from that of an “oracle” that represents an ideal solution, yet unfeasible in practice, that knows exactly the last point in time when to checkpoint.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131971977","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}
GPS is used for outdoor localization in a large variety of applications. Current receivers consume too much power for energy-constrained situations like continuous location tracking on small wearable devices. Mainly, this is due to the large amount of GPS signal that has to be decoded to compute the first position fix. While Coarse-Time Navigation (CTN) can reduce the necessary signal to a few milliseconds, it is not robust to noise. Collective Detection (CD) of satellites can mitigate noise to some degree, but the basic method is computationally expensive.We show how CD can be solved optimally and efficiently.Furthermore, we improve the accuracy of CD by exploiting the shape of the likelihood function.All our results are based on real-world signal observations and we achieve localization accuracies of less than 25 meters using a single millisecond of signal.When using 10 consecutive millisecond samples the accuracy improves to less than 10 meters.
{"title":"Fast and Robust GPS Fix Using One Millisecond of Data","authors":"Pascal Bissig, M. Eichelberger, Roger Wattenhofer","doi":"10.1145/3055031.3055083","DOIUrl":"https://doi.org/10.1145/3055031.3055083","url":null,"abstract":"GPS is used for outdoor localization in a large variety of applications. Current receivers consume too much power for energy-constrained situations like continuous location tracking on small wearable devices. Mainly, this is due to the large amount of GPS signal that has to be decoded to compute the first position fix. While Coarse-Time Navigation (CTN) can reduce the necessary signal to a few milliseconds, it is not robust to noise. Collective Detection (CD) of satellites can mitigate noise to some degree, but the basic method is computationally expensive.We show how CD can be solved optimally and efficiently.Furthermore, we improve the accuracy of CD by exploiting the shape of the likelihood function.All our results are based on real-world signal observations and we achieve localization accuracies of less than 25 meters using a single millisecond of signal.When using 10 consecutive millisecond samples the accuracy improves to less than 10 meters.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365512","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}
Wanli Xue, Chengwen Luo, Guohao Lan, R. Rana, Wen Hu, A. Seneviratne
Internet ofThings (IoT) is flourishing and has penetrated deeply into people’s daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud.The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption.e performance of Kryptein is also measured on off -the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.
{"title":"Kryptein: A Compressive-Sensing-Based Encryption Scheme for the Internet of Things","authors":"Wanli Xue, Chengwen Luo, Guohao Lan, R. Rana, Wen Hu, A. Seneviratne","doi":"10.1145/3055031.3055079","DOIUrl":"https://doi.org/10.1145/3055031.3055079","url":null,"abstract":"Internet ofThings (IoT) is flourishing and has penetrated deeply into people’s daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud.The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption.e performance of Kryptein is also measured on off -the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124664269","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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