We explore the feasibility of achieving computational imaging using Wi-Fi signals. To achieve this, we leverage multi-path propagation that results in wireless signals bouncing off of objects before arriving at the receiver. These reflections effectively light up the objects, which we use to perform imaging. Our algorithms separate the multi-path reflections from different objects into an image. They can also extract depth information where objects in the same direction, but at different distances to the receiver, can be identified. We implement a prototype wireless receiver using USRP-N210s at 2.4 GHz and demonstrate that it can image objects such as leather couches and metallic shapes in line-of-sight and non-line-of-sight scenarios. We also demonstrate proof-of-concept applications including localization of static humans and objects, without the need for tagging them with RF devices. Our results show that we can localize static human subjects and metallic objects with a median accuracy of 26 and 15 cm respectively. Finally, we discuss the limits of our Wi-Fi based approach to imaging.
{"title":"Feasibility and limits of wi-fi imaging","authors":"D. Huang, R. Nandakumar, Shyamnath Gollakota","doi":"10.1145/2668332.2668344","DOIUrl":"https://doi.org/10.1145/2668332.2668344","url":null,"abstract":"We explore the feasibility of achieving computational imaging using Wi-Fi signals. To achieve this, we leverage multi-path propagation that results in wireless signals bouncing off of objects before arriving at the receiver. These reflections effectively light up the objects, which we use to perform imaging. Our algorithms separate the multi-path reflections from different objects into an image. They can also extract depth information where objects in the same direction, but at different distances to the receiver, can be identified. We implement a prototype wireless receiver using USRP-N210s at 2.4 GHz and demonstrate that it can image objects such as leather couches and metallic shapes in line-of-sight and non-line-of-sight scenarios. We also demonstrate proof-of-concept applications including localization of static humans and objects, without the need for tagging them with RF devices. Our results show that we can localize static human subjects and metallic objects with a median accuracy of 26 and 15 cm respectively. Finally, we discuss the limits of our Wi-Fi based approach to imaging.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"117 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129127496","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. Bhattacharya, D. Culler, Dezhi Hong, K. Whitehouse, Jorge Ortiz
Sensor network research has facilitated advancements in various domains, such as industrial monitoring, environmental sensing, etc., and research challenges have shifted from creating infrastructure to utilizing it. Extracting meaningful information from sensor data, or control applications using the data, depends on the metadata available to interpret it, whether provided by novel networks or legacy instrumentation. Commercial buildings provide a valuable setting for investigating automated metadata acquisition and augmentation, as they typically comprise large sensor networks, but have limited, obscure metadata that are often meaningful only to the facility managers. Moreover, this primitive metadata is imprecise and varies across vendors and deployments. This state-of-the-art is a fundamental barrier to scaling analytics or intelligent control across the building stock, as even the basic steps involve labor intensive manual efforts by highly trained consultants. Writing building applications on its sensor network remains largely intractable as it involves extensive help from an expert in each building's design and operation to identify the sensors of interest and create the associated metadata. This process is repeated for each application development in a particular building, and across different buildings. This results in customized building-specific application queries which are not portable or scalable across buildings. We present a synthesis technique that learns how to transform a building's primitive sensor metadata to a common namespace by using a small number of examples from an expert, such as the building manager. Once the transformation rules are learned for one building, it can be applied across buildings with a similar primitive metadata structure. This common and understandable namespace captures the semantic relationship between sensors, enabling analytics applications that do not require apriori building-specific knowledge. Initial results show that learning the rules to transform 70% of the primitive metadata of two buildings (with completely different metadata structure), comprising 1600 and 2600 sensors, into a common namespace ([1]) took only 21 and 27 examples respectively(Figure 1c). The learned rules were able to transform similar primitive metadata in other buildings as well(Figure 1d), enabling writing of portable applications across these buildings. The techniques developed here may be applicable to the other large legacy sensor networks, such as industrial processing, or urban monitoring.
{"title":"Automated metadata transformation for a-priori deployed sensor networks","authors":"A. Bhattacharya, D. Culler, Dezhi Hong, K. Whitehouse, Jorge Ortiz","doi":"10.1145/2668332.2668369","DOIUrl":"https://doi.org/10.1145/2668332.2668369","url":null,"abstract":"Sensor network research has facilitated advancements in various domains, such as industrial monitoring, environmental sensing, etc., and research challenges have shifted from creating infrastructure to utilizing it. Extracting meaningful information from sensor data, or control applications using the data, depends on the metadata available to interpret it, whether provided by novel networks or legacy instrumentation. Commercial buildings provide a valuable setting for investigating automated metadata acquisition and augmentation, as they typically comprise large sensor networks, but have limited, obscure metadata that are often meaningful only to the facility managers. Moreover, this primitive metadata is imprecise and varies across vendors and deployments. This state-of-the-art is a fundamental barrier to scaling analytics or intelligent control across the building stock, as even the basic steps involve labor intensive manual efforts by highly trained consultants. Writing building applications on its sensor network remains largely intractable as it involves extensive help from an expert in each building's design and operation to identify the sensors of interest and create the associated metadata. This process is repeated for each application development in a particular building, and across different buildings. This results in customized building-specific application queries which are not portable or scalable across buildings. We present a synthesis technique that learns how to transform a building's primitive sensor metadata to a common namespace by using a small number of examples from an expert, such as the building manager. Once the transformation rules are learned for one building, it can be applied across buildings with a similar primitive metadata structure. This common and understandable namespace captures the semantic relationship between sensors, enabling analytics applications that do not require apriori building-specific knowledge. Initial results show that learning the rules to transform 70% of the primitive metadata of two buildings (with completely different metadata structure), comprising 1600 and 2600 sensors, into a common namespace ([1]) took only 21 and 27 examples respectively(Figure 1c). The learned rules were able to transform similar primitive metadata in other buildings as well(Figure 1d), enabling writing of portable applications across these buildings. The techniques developed here may be applicable to the other large legacy sensor networks, such as industrial processing, or urban monitoring.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132448303","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}
Bernhard Buchli, Felix Sutton, J. Beutel, L. Thiele
In this work we consider a real-world environmental monitoring scenario that requires uninterrupted system operation over time periods on the order of multiple years. To achieve this goal, we propose a novel approach to dynamically adjust the system's performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved. We first consider the annual dynamics of the energy source to design an appropriate power subsystem (i.e., solar panel size and energy store capacity), and then dynamically compute the long-term sustainable performance level at runtime. We show through trace-driven simulations using eleven years of real-world data that our approach outperforms existing predictive, e.g., EWMA, WCMA, and reactive, e.g., ENO-MAX, approaches in terms of average performance level by up to 177%, while reducing duty-cycle variance by up to three orders of magnitude. We further demonstrate the benefits of the dynamic power management scheme using a wireless sensor system deployed for environmental monitoring in a remote, high-alpine environment as a case study. A performance evaluation over two years reveals that the dynamic power management scheme achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.
{"title":"Dynamic power management for long-term energy neutral operation of solar energy harvesting systems","authors":"Bernhard Buchli, Felix Sutton, J. Beutel, L. Thiele","doi":"10.1145/2668332.2668333","DOIUrl":"https://doi.org/10.1145/2668332.2668333","url":null,"abstract":"In this work we consider a real-world environmental monitoring scenario that requires uninterrupted system operation over time periods on the order of multiple years. To achieve this goal, we propose a novel approach to dynamically adjust the system's performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved. We first consider the annual dynamics of the energy source to design an appropriate power subsystem (i.e., solar panel size and energy store capacity), and then dynamically compute the long-term sustainable performance level at runtime. We show through trace-driven simulations using eleven years of real-world data that our approach outperforms existing predictive, e.g., EWMA, WCMA, and reactive, e.g., ENO-MAX, approaches in terms of average performance level by up to 177%, while reducing duty-cycle variance by up to three orders of magnitude. We further demonstrate the benefits of the dynamic power management scheme using a wireless sensor system deployed for environmental monitoring in a remote, high-alpine environment as a case study. A performance evaluation over two years reveals that the dynamic power management scheme achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134410716","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}
Addisu Z. Taddese, P. Völgyesi, Á. Lédeczi, M. Beccani, E. Susilo, P. Valdastri
Minimally invasive surgical techniques are becoming popular due to their enhanced patient benefits. Less invasive procedures can be achieved with the use of wireless Medical Capsule Robots (MCRs). MCRs are low powered and small in size and can be used for physiological parameter monitoring, therapy delivery, and biopsy sampling. Designing MCRs from the ground up is a costly and time consuming process. In this work, we present a flexible modular architecture to facilitate the design of MCRs and propose using TinyOS as the operating system. To assess the architecture and validate the feasibility of TinyOS, we implement a closed-loop control of a sensor-actuator system and compare the results with a traditional MCR built based on an 8051 microcontroller (MCU) programmed in plain C. Similar performances from the two approaches lead us to conclude that TinyOS is a valid option to implement a modular architecture for designing MCRs.
{"title":"A modular software architecture for miniature capsule robots based on TinyOS","authors":"Addisu Z. Taddese, P. Völgyesi, Á. Lédeczi, M. Beccani, E. Susilo, P. Valdastri","doi":"10.1145/2668332.2668363","DOIUrl":"https://doi.org/10.1145/2668332.2668363","url":null,"abstract":"Minimally invasive surgical techniques are becoming popular due to their enhanced patient benefits. Less invasive procedures can be achieved with the use of wireless Medical Capsule Robots (MCRs). MCRs are low powered and small in size and can be used for physiological parameter monitoring, therapy delivery, and biopsy sampling. Designing MCRs from the ground up is a costly and time consuming process. In this work, we present a flexible modular architecture to facilitate the design of MCRs and propose using TinyOS as the operating system. To assess the architecture and validate the feasibility of TinyOS, we implement a closed-loop control of a sensor-actuator system and compare the results with a traditional MCR built based on an 8051 microcontroller (MCU) programmed in plain C. Similar performances from the two approaches lead us to conclude that TinyOS is a valid option to implement a modular architecture for designing MCRs.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535631","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}
Currently, researchers study face-to-face interactions using wearable sensors and smartphones which provide 2 to 5 m proximity sensing every 20 to 300 s. However, studying interaction distance, which is known to impact disease spread, communication behavior, and other phenomenon, has proven challenging. Smartphones are limited by their inaccurate and/or impractical ranging capabilities, while wearable sensors are limited by their need for infrastructure nodes, bulkiness, and/or inaccurate ranging. To address these challenges, we present Opo, a 14 cm2, 11.4 g "lapel pin" built from commercial components. Opo sensors range neighbors every 2 s up to 2 m away with 5% average error, all while requiring zero infrastructure and improving upon current wearable sensors' accuracy and power usage. The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19 μA when no neighbors are present. This enables Opo sensors to discover and range neighbors without the need for infrastructure nodes and slow or power-hungry RF discovery protocols. Thus, Opo is able to sense interaction distance with high accuracy (5 cm) and temporal fidelity (2 s) on a limited power budget.
{"title":"Opo: a wearable sensor for capturing high-fidelity face-to-face interactions","authors":"Will Huang, Ye-Sheng Kuo, P. Pannuto, P. Dutta","doi":"10.1145/2668332.2668338","DOIUrl":"https://doi.org/10.1145/2668332.2668338","url":null,"abstract":"Currently, researchers study face-to-face interactions using wearable sensors and smartphones which provide 2 to 5 m proximity sensing every 20 to 300 s. However, studying interaction distance, which is known to impact disease spread, communication behavior, and other phenomenon, has proven challenging. Smartphones are limited by their inaccurate and/or impractical ranging capabilities, while wearable sensors are limited by their need for infrastructure nodes, bulkiness, and/or inaccurate ranging. To address these challenges, we present Opo, a 14 cm2, 11.4 g \"lapel pin\" built from commercial components. Opo sensors range neighbors every 2 s up to 2 m away with 5% average error, all while requiring zero infrastructure and improving upon current wearable sensors' accuracy and power usage. The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19 μA when no neighbors are present. This enables Opo sensors to discover and range neighbors without the need for infrastructure nodes and slow or power-hungry RF discovery protocols. Thus, Opo is able to sense interaction distance with high accuracy (5 cm) and temporal fidelity (2 s) on a limited power budget.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124683624","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}
Yurong Jiang, Hang Qiu, M. McCartney, William G. J. Halfond, F. Bai, Donald K. Grimm, R. Govindan
Automotive apps can improve efficiency, safety, comfort, and longevity of vehicular use. These apps achieve their goals by continuously monitoring sensors in a vehicle, and combining them with information from cloud databases in order to detect events that are used to trigger actions (e.g., alerting a driver, turning on fog lights, screening calls). However, modern vehicles have several hundred sensors that describe the low level dynamics of vehicular subsystems, these sensors can be combined in complex ways together with cloud information. Moreover, these sensor processing algorithms may incur significant costs in acquiring sensor and cloud information. In this paper, we propose a programming framework called CARLOG to simplify the task of programming these event detection algorithms. CARLOG uses Datalog to express sensor processing algorithms, but incorporates novel query optimization methods that can be used to minimize bandwidth usage, energy or latency, without sacrificing correctness of query execution. Experimental results on a prototype show that CARLOG can reduce latency by nearly two orders of magnitude relative to an unoptimized Datalog engine.
{"title":"CARLOG: a platform for flexible and efficient automotive sensing","authors":"Yurong Jiang, Hang Qiu, M. McCartney, William G. J. Halfond, F. Bai, Donald K. Grimm, R. Govindan","doi":"10.1145/2668332.2668350","DOIUrl":"https://doi.org/10.1145/2668332.2668350","url":null,"abstract":"Automotive apps can improve efficiency, safety, comfort, and longevity of vehicular use. These apps achieve their goals by continuously monitoring sensors in a vehicle, and combining them with information from cloud databases in order to detect events that are used to trigger actions (e.g., alerting a driver, turning on fog lights, screening calls). However, modern vehicles have several hundred sensors that describe the low level dynamics of vehicular subsystems, these sensors can be combined in complex ways together with cloud information. Moreover, these sensor processing algorithms may incur significant costs in acquiring sensor and cloud information. In this paper, we propose a programming framework called CARLOG to simplify the task of programming these event detection algorithms. CARLOG uses Datalog to express sensor processing algorithms, but incorporates novel query optimization methods that can be used to minimize bandwidth usage, energy or latency, without sacrificing correctness of query execution. Experimental results on a prototype show that CARLOG can reduce latency by nearly two orders of magnitude relative to an unoptimized Datalog engine.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"630 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132960994","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}
This paper describes the components used to build a sensor/actuator network to control a model railroad layout. Control of the model railroad is accomplished using a network of Digi International XBee modules. Information generated by sensors and actions sent to the layout are coordinated using the open source JMRI software package on a general purpose computer connected to the XBee network.
本文介绍了用于建立传感器/执行器网络来控制模型铁路布局的组件。模型铁路的控制是使用Digi International XBee模块网络完成的。传感器生成的信息和发送到布局的动作使用连接到XBee网络的通用计算机上的开源JMRI软件包进行协调。
{"title":"Wireless sensor/actuator network for model railroad control","authors":"Paul Bender","doi":"10.1145/2668332.2668370","DOIUrl":"https://doi.org/10.1145/2668332.2668370","url":null,"abstract":"This paper describes the components used to build a sensor/actuator network to control a model railroad layout. Control of the model railroad is accomplished using a network of Digi International XBee modules. Information generated by sensors and actions sent to the layout are coordinated using the open source JMRI software package on a general purpose computer connected to the XBee network.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114484244","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 poster, we present a novel approach to study and reveal network protocol information from radio activities instrumentation in wireless sensor network. Recent studies have analyzed radio activities; however, most of these studies focus on estimating energy consumption, since radio chip usually dominates the energy consumption of nodes. In our work, we analyze radio activities with a different purpose, which aims to reveal network protocols and application workloads by an analysis of fine-grained low level radio activities on the nodes. We design a feature called Radio Awake Length Counter and use it to classify and reveal network activity. Results from experiments on a real world testbed indicate that our approach can achieve up to 97% accuracy to identify the routing protocols, average 85% accuracy to distinguish application workloads.
{"title":"Understanding radio activity signature of wireless sensor network protocols","authors":"Dong Han, O. Gnawali, Abhishek B. Sharma","doi":"10.1145/2668332.2668368","DOIUrl":"https://doi.org/10.1145/2668332.2668368","url":null,"abstract":"In this poster, we present a novel approach to study and reveal network protocol information from radio activities instrumentation in wireless sensor network. Recent studies have analyzed radio activities; however, most of these studies focus on estimating energy consumption, since radio chip usually dominates the energy consumption of nodes. In our work, we analyze radio activities with a different purpose, which aims to reveal network protocols and application workloads by an analysis of fine-grained low level radio activities on the nodes. We design a feature called Radio Awake Length Counter and use it to classify and reveal network activity. Results from experiments on a real world testbed indicate that our approach can achieve up to 97% accuracy to identify the routing protocols, average 85% accuracy to distinguish application workloads.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124891669","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 Yottos, an event driven operating system for wireless embedded devices that reduces energy consumption by coalescing tasks into workloads with similar resource requirements thereby reducing time and energy consumed from power cycling peripherals. With Yottos we target a different group of programmers than the ones well-versed in embedded C, TinyOS and Contiki, namely web and app developers who on one hand are familiar with event driven programming in the form of user interaction events, but on the other have no experience with low-level hardware access. We trade off memory footprint (both data and code) in support of a programming environment more akin to iOS and Android development by supporting Objective-C and C/C++.
{"title":"Yottos operating system connecting low-power devices with high-level programming","authors":"Marcus Chang, James Crosby, Hugo J. M. Vincent","doi":"10.1145/2668332.2668360","DOIUrl":"https://doi.org/10.1145/2668332.2668360","url":null,"abstract":"We present Yottos, an event driven operating system for wireless embedded devices that reduces energy consumption by coalescing tasks into workloads with similar resource requirements thereby reducing time and energy consumed from power cycling peripherals. With Yottos we target a different group of programmers than the ones well-versed in embedded C, TinyOS and Contiki, namely web and app developers who on one hand are familiar with event driven programming in the form of user interaction events, but on the other have no experience with low-level hardware access. We trade off memory footprint (both data and code) in support of a programming environment more akin to iOS and Android development by supporting Objective-C and C/C++.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129556437","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}
Analyzing the power consumption of smartphones is difficult because of the complex interplay between soft- and hardware. Currently, researchers rely on mainly two options: external measurement tools, which are precise but constrain the mobility of the device and require the annotation of power traces; or modelling methods, which allow mobility and consider explicitly the state of events, but have less accuracy and lower sampling rates than external tools. We address the challenges of mobile power analysis with a novel power metering toolkit, called NEAT, which comprises a coin-sized power measurement board that fits inside a typical smartphone, and analysis software that automatically fuses the event logs taken from the phone with the obtained power trace. The combination of high-fidelity power measurements and detailed information about the state of the phone's hardware and software components allows for fine-grained analysis of complex and short-lived energy patterns. We equipped smartphones with NEAT and conducted various experiments to highlight (i) its accuracy with respect to model-based approaches, showing errors upwards of 20%; (ii) its ability to gather accurate and well annotated user-data "in the wild", which would be hard to do with current external meters; and (iii) the importance of having fine-granular and expressive traces by resolving kernel energy bugs.
{"title":"NEAT: a novel energy analysis toolkit for free-roaming smartphones","authors":"N. Brouwers, Marco Zúñiga, K. Langendoen","doi":"10.1145/2668332.2668337","DOIUrl":"https://doi.org/10.1145/2668332.2668337","url":null,"abstract":"Analyzing the power consumption of smartphones is difficult because of the complex interplay between soft- and hardware. Currently, researchers rely on mainly two options: external measurement tools, which are precise but constrain the mobility of the device and require the annotation of power traces; or modelling methods, which allow mobility and consider explicitly the state of events, but have less accuracy and lower sampling rates than external tools. We address the challenges of mobile power analysis with a novel power metering toolkit, called NEAT, which comprises a coin-sized power measurement board that fits inside a typical smartphone, and analysis software that automatically fuses the event logs taken from the phone with the obtained power trace. The combination of high-fidelity power measurements and detailed information about the state of the phone's hardware and software components allows for fine-grained analysis of complex and short-lived energy patterns. We equipped smartphones with NEAT and conducted various experiments to highlight (i) its accuracy with respect to model-based approaches, showing errors upwards of 20%; (ii) its ability to gather accurate and well annotated user-data \"in the wild\", which would be hard to do with current external meters; and (iii) the importance of having fine-granular and expressive traces by resolving kernel energy bugs.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115569220","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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