Sensing systems for long-term monitoring constitute an important part of the emerging Internet of Things. In this domain, energy harvesting and infrastructure-less communication enable truly autonomous and maintenance-free operation of sensor nodes gathering long-term environmental data. Due to the infrastructure-less nature of the communication, receivers are not always available. The variable energy provided by the environment and the receiver’s mobility lead to non-deterministic node availability. In this work, we study infrastructure-less data transmission schemes to optimize communication when both senders and receivers exhibit intermittent behavior. We rely on the notion of data utility, describing the importance of sensed data to the receiver, to determine an optimal communication scheme. Deriving the communication policy that maximizes the utility of the received data is shown to be a convex optimization problem. The resulting scheme is implemented and validated on a batteryless Bluetooth Low Energy sensor node that communicates to commodity smartphones. Our evaluation demonstrates that the model accurately captures the application scenario with a maximum root-mean-square error of less than 0.016 in data reception probability. The communication scheme’s adaptiveness to variable harvesting conditions is experimentally demonstrated under varying harvesting conditions and is shown to significantly increase the data utility.
{"title":"Harvesting-Aware Optimal Communication Scheme for Infrastructure-Less Sensing","authors":"L. Sigrist, R. Ahmed, Andres Gomez, L. Thiele","doi":"10.1145/3395928","DOIUrl":"https://doi.org/10.1145/3395928","url":null,"abstract":"Sensing systems for long-term monitoring constitute an important part of the emerging Internet of Things. In this domain, energy harvesting and infrastructure-less communication enable truly autonomous and maintenance-free operation of sensor nodes gathering long-term environmental data. Due to the infrastructure-less nature of the communication, receivers are not always available. The variable energy provided by the environment and the receiver’s mobility lead to non-deterministic node availability. In this work, we study infrastructure-less data transmission schemes to optimize communication when both senders and receivers exhibit intermittent behavior. We rely on the notion of data utility, describing the importance of sensed data to the receiver, to determine an optimal communication scheme. Deriving the communication policy that maximizes the utility of the received data is shown to be a convex optimization problem. The resulting scheme is implemented and validated on a batteryless Bluetooth Low Energy sensor node that communicates to commodity smartphones. Our evaluation demonstrates that the model accurately captures the application scenario with a maximum root-mean-square error of less than 0.016 in data reception probability. The communication scheme’s adaptiveness to variable harvesting conditions is experimentally demonstrated under varying harvesting conditions and is shown to significantly increase the data utility.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"60 3 1","pages":"1 - 26"},"PeriodicalIF":2.7,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90118333","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}
Youssef Khazbak, Junpeng Qiu, Tianxiang Tan, Guohong Cao
With the proliferation of IoT cameras, it is possible to use crowdsourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle) on demand. Due to the ubiquity of IoT cameras such as dash mounted and phone cameras, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and, thus, can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homomorphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target’s face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. We also formulate and solve a worker selection problem to maximize the probability of finding the target under some budget constraint. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.
{"title":"TargetFinder","authors":"Youssef Khazbak, Junpeng Qiu, Tianxiang Tan, Guohong Cao","doi":"10.1145/3375878","DOIUrl":"https://doi.org/10.1145/3375878","url":null,"abstract":"With the proliferation of IoT cameras, it is possible to use crowdsourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle) on demand. Due to the ubiquity of IoT cameras such as dash mounted and phone cameras, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and, thus, can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homomorphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target’s face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. We also formulate and solve a worker selection problem to maximize the probability of finding the target under some budget constraint. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"1 1","pages":"1 - 23"},"PeriodicalIF":2.7,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79930833","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}
L. Turchet, J. Pauwels, C. Fischione, György Fazekas
Large online music databases under Creative Commons licenses are rarely recorded by well-known artists, therefore conventional metadata-based search is insufficient in their adaptation to instrument players’ needs. The emerging class of smart musical instruments (SMIs) can address this challenge. Thanks to direct internet connectivity and embedded processing, SMIs can send requests to repositories and reproduce the response for improvisation, composition, or learning purposes. We present a smart guitar prototype that allows retrieving songs from large online music databases using criteria different from conventional music search, which were derived from interviewing 30 guitar players. We investigate three interaction methods coupled with four search criteria (tempo, chords, key and tuning) exploiting intelligent capabilities in the instrument: (i) keywords-based retrieval using an embedded touchscreen; (ii) cloud-computing where recorded content is transmitted to a server that extracts relevant audio features; (iii) edge-computing where the guitar detects audio features and sends the request directly. Overall, the evaluation of these methods with beginner, intermediate, and expert players showed a strong appreciation for the direct connectivity of the instrument with an online database and the approach to the search based on the actual musical content rather than conventional textual criteria, such as song title or artist name.
{"title":"Cloud-smart Musical Instrument Interactions","authors":"L. Turchet, J. Pauwels, C. Fischione, György Fazekas","doi":"10.1145/3377881","DOIUrl":"https://doi.org/10.1145/3377881","url":null,"abstract":"Large online music databases under Creative Commons licenses are rarely recorded by well-known artists, therefore conventional metadata-based search is insufficient in their adaptation to instrument players’ needs. The emerging class of smart musical instruments (SMIs) can address this challenge. Thanks to direct internet connectivity and embedded processing, SMIs can send requests to repositories and reproduce the response for improvisation, composition, or learning purposes. We present a smart guitar prototype that allows retrieving songs from large online music databases using criteria different from conventional music search, which were derived from interviewing 30 guitar players. We investigate three interaction methods coupled with four search criteria (tempo, chords, key and tuning) exploiting intelligent capabilities in the instrument: (i) keywords-based retrieval using an embedded touchscreen; (ii) cloud-computing where recorded content is transmitted to a server that extracts relevant audio features; (iii) edge-computing where the guitar detects audio features and sends the request directly. Overall, the evaluation of these methods with beginner, intermediate, and expert players showed a strong appreciation for the direct connectivity of the instrument with an online database and the approach to the search based on the actual musical content rather than conventional textual criteria, such as song title or artist name.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"1 1","pages":"1 - 29"},"PeriodicalIF":2.7,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88024181","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}
Mohamed Abdelaal, S. Sekar, Frank Dürr, K. Rothermel, S. Becker, D. Fritsch
Recently, indoor modeling has gained increased attention, thanks to the immense need for realizing efficient indoor location-based services. Indoor environments differ from outdoor spaces in two aspects: spaces are smaller and there are many structural objects such as walls, doors, and furniture. To model the indoor environments in a proper manner, novel data acquisition concepts and data modeling algorithms have been devised to meet the requirements of indoor spatial applications. In this realm, several research efforts have been exerted. Nevertheless, these efforts mostly suffer either from adopting impractical data acquisition methods or from being limited to 2D modeling. To overcome these limitations, we introduce the MapSense approach, which automatically derives indoor models from 3D point clouds collected by individuals using mobile devices, such as Google Tango, Apple ARKit, and Microsoft HoloLens. To this end, MapSense leverages several computer vision and machine learning algorithms for precisely inferring the structural objects. In MapSense, we mainly focus on improving the modeling accuracy through adopting formal grammars that encode design-time knowledge, i.e., structural information about the building. In addition to modeling accuracy, MapSense considers the energy overhead on the mobile devices via developing a probabilistic quality model through which the mobile devices solely upload high-quality point clouds to the crowd-sensing servers. To demonstrate the performance of MapSense, we implemented a crowd-sensing Android App to collect 3D point clouds from two different buildings by six volunteers. The results showed that MapSense can accurately infer the various structural objects while drastically reducing the energy overhead on the mobile devices.
{"title":"MapSense","authors":"Mohamed Abdelaal, S. Sekar, Frank Dürr, K. Rothermel, S. Becker, D. Fritsch","doi":"10.1145/3379342","DOIUrl":"https://doi.org/10.1145/3379342","url":null,"abstract":"Recently, indoor modeling has gained increased attention, thanks to the immense need for realizing efficient indoor location-based services. Indoor environments differ from outdoor spaces in two aspects: spaces are smaller and there are many structural objects such as walls, doors, and furniture. To model the indoor environments in a proper manner, novel data acquisition concepts and data modeling algorithms have been devised to meet the requirements of indoor spatial applications. In this realm, several research efforts have been exerted. Nevertheless, these efforts mostly suffer either from adopting impractical data acquisition methods or from being limited to 2D modeling. To overcome these limitations, we introduce the MapSense approach, which automatically derives indoor models from 3D point clouds collected by individuals using mobile devices, such as Google Tango, Apple ARKit, and Microsoft HoloLens. To this end, MapSense leverages several computer vision and machine learning algorithms for precisely inferring the structural objects. In MapSense, we mainly focus on improving the modeling accuracy through adopting formal grammars that encode design-time knowledge, i.e., structural information about the building. In addition to modeling accuracy, MapSense considers the energy overhead on the mobile devices via developing a probabilistic quality model through which the mobile devices solely upload high-quality point clouds to the crowd-sensing servers. To demonstrate the performance of MapSense, we implemented a crowd-sensing Android App to collect 3D point clouds from two different buildings by six volunteers. The results showed that MapSense can accurately infer the various structural objects while drastically reducing the energy overhead on the mobile devices.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"175 1","pages":"1 - 28"},"PeriodicalIF":2.7,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76653592","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}
The ability to communicate within given delay bounds in noisy RF environments is crucial for Bluetooth Low Energy (BLE) applications used in safety-critical application domains, such as health care and smart cities. In this work, we experimentally study the latency of BLE communications in the presence of radio interference and show that applications may incur long and unpredictable transmission delays. To mitigate this problem, we devise a model capturing the timeliness of connection-based BLE communications in noisy RF channels by expressing the impact of radio interference in terms of the number of connection events necessary to complete a successful data transmission (nCE). We show that this quantity can be estimated using the timing information of commands sent over the host controller interface of common BLE devices, hence without additional communication overhead or energy expenditure. We further show that a BLE device can make use of our BLE timeliness model and recent nCE measurements to adapt its BLE communication parameters at runtime, thereby improving its performance in the presence of dynamic radio interference. We implement such an adaptive scheme on the popular nRF52840 platform and perform an extensive experimental study in multiple indoor environments using three different BLE platforms. Our results show that a BLE application can, indeed, make use of the proposed model and recent nCE measurements to adapt its connection interval at runtime to increase the timeliness of its communications, reducing the number of delayed packets in noisy RF environments by up to a factor of 40.
{"title":"Improving the Timeliness of Bluetooth Low Energy in Dynamic RF Environments","authors":"Michael Spörk, C. Boano, K. Römer","doi":"10.1145/3375836","DOIUrl":"https://doi.org/10.1145/3375836","url":null,"abstract":"The ability to communicate within given delay bounds in noisy RF environments is crucial for Bluetooth Low Energy (BLE) applications used in safety-critical application domains, such as health care and smart cities. In this work, we experimentally study the latency of BLE communications in the presence of radio interference and show that applications may incur long and unpredictable transmission delays. To mitigate this problem, we devise a model capturing the timeliness of connection-based BLE communications in noisy RF channels by expressing the impact of radio interference in terms of the number of connection events necessary to complete a successful data transmission (nCE). We show that this quantity can be estimated using the timing information of commands sent over the host controller interface of common BLE devices, hence without additional communication overhead or energy expenditure. We further show that a BLE device can make use of our BLE timeliness model and recent nCE measurements to adapt its BLE communication parameters at runtime, thereby improving its performance in the presence of dynamic radio interference. We implement such an adaptive scheme on the popular nRF52840 platform and perform an extensive experimental study in multiple indoor environments using three different BLE platforms. Our results show that a BLE application can, indeed, make use of the proposed model and recent nCE measurements to adapt its connection interval at runtime to increase the timeliness of its communications, reducing the number of delayed packets in noisy RF environments by up to a factor of 40.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"74 1","pages":"1 - 32"},"PeriodicalIF":2.7,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82559844","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}
Ashish Gupta, Hari Prabhat Gupta, Bhaskar Biswas, Tanima Dutta
In the era of the Internet of Things (IoT), the sensor-based devices produce the Multivariate Time Series (MTS). A classification approach helps to predict the class label of an incoming MTS. Due to the large dimension and different sampling rate of the sensors in a given MTS, a classifier takes time to predict the class label. Some IoT applications may require early prediction of the class label where the classifier starts the prediction once the minimum number of data points are collected. In this article, we address the problem of early prediction of the class label of an MTS in IoT. This work considers the sensors with different sampling rate to generate the MTS. Each sensor generates a time series (component) of the MTS. We propose a Divide-and-Conquer–based early classification approach for classifying such MTS. The approach constructs an ensemble classifier using a probabilistic classifier and hierarchical clustering. The ensemble classifier employs a Divide-and-Conquer method to handle the different sampling rate components during the prediction of class label. The experimental results show that our approach significantly outperforms the existing approaches on real-world datasets using various evaluation metrics.
{"title":"A Divide-and-Conquer–based Early Classification Approach for Multivariate Time Series with Different Sampling Rate Components in IoT","authors":"Ashish Gupta, Hari Prabhat Gupta, Bhaskar Biswas, Tanima Dutta","doi":"10.1145/3375877","DOIUrl":"https://doi.org/10.1145/3375877","url":null,"abstract":"In the era of the Internet of Things (IoT), the sensor-based devices produce the Multivariate Time Series (MTS). A classification approach helps to predict the class label of an incoming MTS. Due to the large dimension and different sampling rate of the sensors in a given MTS, a classifier takes time to predict the class label. Some IoT applications may require early prediction of the class label where the classifier starts the prediction once the minimum number of data points are collected. In this article, we address the problem of early prediction of the class label of an MTS in IoT. This work considers the sensors with different sampling rate to generate the MTS. Each sensor generates a time series (component) of the MTS. We propose a Divide-and-Conquer–based early classification approach for classifying such MTS. The approach constructs an ensemble classifier using a probabilistic classifier and hierarchical clustering. The ensemble classifier employs a Divide-and-Conquer method to handle the different sampling rate components during the prediction of class label. The experimental results show that our approach significantly outperforms the existing approaches on real-world datasets using various evaluation metrics.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"5 1","pages":"1 - 21"},"PeriodicalIF":2.7,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82709105","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}
Atis Elsts, Xenofon Fafoutis, G. Oikonomou, R. Piechocki, I. Craddock
The emerging Internet of Things has the potential to solve major societal challenges associated with healthcare provision. Low-power wireless protocols for residential Health Internet of Things applications are characterized by high reliability requirements, the need for energy-efficient operation, and the need to operate robustly in diverse environments in the presence of external interference. We enhance and experimentally evaluate the Time-Slotted Channel Hopping protocol from the IEEE 802.15.4 standard to address these challenges. Our contributions are a new schedule and an adaptive channel selection mechanism to increase the performance of time-slotted channel hopping in this domain. Evaluation in a test house shows that the enhanced system is suitable for our e-Health application and compares favorably with state-of-the-art options. The schedule provides higher reliability compared with the minimal scheduling function from the IETF 6TiSCH Working Group and has a better energy-efficiency/reliability tradeoff than the Orchestra scheduler. Results from 29 long-term residential deployments confirm the suitability for the application and show that the system is able to adapt and avoid channels used by WiFi. In these uncontrolled environments, the system achieves 99.96% average reliability for networks that generate 7.5 packets per second on average.
{"title":"TSCH Networks for Health IoT","authors":"Atis Elsts, Xenofon Fafoutis, G. Oikonomou, R. Piechocki, I. Craddock","doi":"10.1145/3366617","DOIUrl":"https://doi.org/10.1145/3366617","url":null,"abstract":"The emerging Internet of Things has the potential to solve major societal challenges associated with healthcare provision. Low-power wireless protocols for residential Health Internet of Things applications are characterized by high reliability requirements, the need for energy-efficient operation, and the need to operate robustly in diverse environments in the presence of external interference. We enhance and experimentally evaluate the Time-Slotted Channel Hopping protocol from the IEEE 802.15.4 standard to address these challenges. Our contributions are a new schedule and an adaptive channel selection mechanism to increase the performance of time-slotted channel hopping in this domain. Evaluation in a test house shows that the enhanced system is suitable for our e-Health application and compares favorably with state-of-the-art options. The schedule provides higher reliability compared with the minimal scheduling function from the IETF 6TiSCH Working Group and has a better energy-efficiency/reliability tradeoff than the Orchestra scheduler. Results from 29 long-term residential deployments confirm the suitability for the application and show that the system is able to adapt and avoid channels used by WiFi. In these uncontrolled environments, the system achieves 99.96% average reliability for networks that generate 7.5 packets per second on average.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"67 1","pages":"1 - 27"},"PeriodicalIF":2.7,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77166661","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}
A. R. Khamesi, S. Silvestri, Denise A. Baker, A. D. Paola
Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances are contingent on the use of others. We define a perceived-value user utility used as an Integer Linear Programming (ILP) problem. We show that the problem is NP-Hard and provide a heuristic method called COndensed DependencY (CODY). We validate our results using synthetic and real datasets, large-scale online experiments, and a real-field experiment at the Missouri University of Science and Technology Solar Village. Simulation results show that our approach achieves near optimal performance and significantly outperforms previously proposed solutions. Results from our online and real-field experiments also show that users largely prefer our solution compared to a previous approach.
{"title":"Perceived-Value-driven Optimization of Energy Consumption in Smart Homes","authors":"A. R. Khamesi, S. Silvestri, Denise A. Baker, A. D. Paola","doi":"10.1145/3375801","DOIUrl":"https://doi.org/10.1145/3375801","url":null,"abstract":"Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances are contingent on the use of others. We define a perceived-value user utility used as an Integer Linear Programming (ILP) problem. We show that the problem is NP-Hard and provide a heuristic method called COndensed DependencY (CODY). We validate our results using synthetic and real datasets, large-scale online experiments, and a real-field experiment at the Missouri University of Science and Technology Solar Village. Simulation results show that our approach achieves near optimal performance and significantly outperforms previously proposed solutions. Results from our online and real-field experiments also show that users largely prefer our solution compared to a previous approach.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"1 1","pages":"1 - 26"},"PeriodicalIF":2.7,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76645834","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}
A. Kiaghadi, Pan Hu, Jeremy Gummeson, Soha Rostaminia, Deepak Ganesan
Recent years have seen exciting developments in the use of RFID tags as sensors to enable a range of applications including home automation, health and wellness, and augmented reality. However, widespread use of RFIDs as sensors requires significant instrumentation to deploy tethered readers, which limits usability in mobile settings. Our solution is WearID, a low-power wrist-worn backscatter reader that bridges this gap and allows ubiquitous sensing of interaction with tagged objects. Our end-to-end design includes innovations in hardware architecture to reduce power consumption and deal with wrist attenuation and blockage, as well as signal processing architecture to reliably detect grasping, touching, and other hand-based interactions. We show via exhaustive characterization that WearID is roughly 6× more power efficient than state-of-art commercial readers, provides 3D coverage of 30 to 50 cm around the wrist despite body blockage, and can be used to reliably detect hand-based interactions. We also open source the design of WearID with the hope that this can enable a range of new and unexplored applications of wearables.
{"title":"Continuous Measurement of Interactions with the Physical World with a Wrist-Worn Backscatter Reader","authors":"A. Kiaghadi, Pan Hu, Jeremy Gummeson, Soha Rostaminia, Deepak Ganesan","doi":"10.1145/3375800","DOIUrl":"https://doi.org/10.1145/3375800","url":null,"abstract":"Recent years have seen exciting developments in the use of RFID tags as sensors to enable a range of applications including home automation, health and wellness, and augmented reality. However, widespread use of RFIDs as sensors requires significant instrumentation to deploy tethered readers, which limits usability in mobile settings. Our solution is WearID, a low-power wrist-worn backscatter reader that bridges this gap and allows ubiquitous sensing of interaction with tagged objects. Our end-to-end design includes innovations in hardware architecture to reduce power consumption and deal with wrist attenuation and blockage, as well as signal processing architecture to reliably detect grasping, touching, and other hand-based interactions. We show via exhaustive characterization that WearID is roughly 6× more power efficient than state-of-art commercial readers, provides 3D coverage of 30 to 50 cm around the wrist despite body blockage, and can be used to reliably detect hand-based interactions. We also open source the design of WearID with the hope that this can enable a range of new and unexplored applications of wearables.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"9 1","pages":"1 - 22"},"PeriodicalIF":2.7,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90421328","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}
Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the observed signals in continuous form are common tasks for a variety of near real-time processing IoT devices, such as in smart homes, body-area, and environmental sensing applications. However, these systems are likely low-cost resource-constrained embedded systems, equipped with compact memory space, whereby the ability to store the full information state of continuous signals is limited. Hence, in this article,* we develop solutions of efficient timely processing embedded systems for online classification and tracking of continuous signals with compact memory space. Particularly, we focus on the application of smart plugs that are capable of timely classification of appliance types and tracking of appliance behavior in a standalone manner. We implemented a smart plug prototype using low-cost Arduino platform with small amount of memory space to demonstrate the following timely processing operations: (1) learning and classifying the patterns associated with the continuous power consumption signals and (2) tracking the occurrences of signal patterns using small local memory space. Furthermore, our system designs are also sufficiently generic for timely monitoring and tracking applications in other resource-constrained IoT devices.
{"title":"Efficient Online Classification and Tracking on Resource-constrained IoT Devices","authors":"Muhammad Aftab, S. Chau, P. Shenoy","doi":"10.1145/3392051","DOIUrl":"https://doi.org/10.1145/3392051","url":null,"abstract":"Timely processing has been increasingly required on smart IoT devices, which leads to directly implementing information processing tasks on an IoT device for bandwidth savings and privacy assurance. Particularly, monitoring and tracking the observed signals in continuous form are common tasks for a variety of near real-time processing IoT devices, such as in smart homes, body-area, and environmental sensing applications. However, these systems are likely low-cost resource-constrained embedded systems, equipped with compact memory space, whereby the ability to store the full information state of continuous signals is limited. Hence, in this article,* we develop solutions of efficient timely processing embedded systems for online classification and tracking of continuous signals with compact memory space. Particularly, we focus on the application of smart plugs that are capable of timely classification of appliance types and tracking of appliance behavior in a standalone manner. We implemented a smart plug prototype using low-cost Arduino platform with small amount of memory space to demonstrate the following timely processing operations: (1) learning and classifying the patterns associated with the continuous power consumption signals and (2) tracking the occurrences of signal patterns using small local memory space. Furthermore, our system designs are also sufficiently generic for timely monitoring and tracking applications in other resource-constrained IoT devices.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":"23 1","pages":"1 - 29"},"PeriodicalIF":2.7,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87326198","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：