This paper presents Kinesis, a security incident response and prevention system for wireless sensor networks, designed to keep the network functional despite anomalies or attacks and to recover from attacks without significant interruption. Due to the deployment of sensor networks in various critical infrastructures, the applications often impose stringent requirements on data reliability and service availability. Given the failure- and attack-prone nature of sensor networks, it is a pressing concern to enable the sensor networks provide continuous and unobtrusive services. Kinesis is quick and effective in response to incidents, distributed in nature, and dynamic in selecting response actions based on the context. It is lightweight in terms of response policy specification, and communication and energy overhead. A per-node single timer based distributed strategy to select the most effective response executor in a neighborhood makes the system simple and scalable, while achieving proper load distribution and redundant action optimization. We implement Kinesis in TinyOS and measure its performance for various application and network layer incidents. Extensive TOSSIM simulations and testbed experiments show that Kinesis successfully counteracts anomalies/attacks and behaves consistently under various attack scenarios and rates.
{"title":"Kinesis: a security incident response and prevention system for wireless sensor networks","authors":"Salmin Sultana, Daniele Midi, E. Bertino","doi":"10.1145/2668332.2668351","DOIUrl":"https://doi.org/10.1145/2668332.2668351","url":null,"abstract":"This paper presents Kinesis, a security incident response and prevention system for wireless sensor networks, designed to keep the network functional despite anomalies or attacks and to recover from attacks without significant interruption. Due to the deployment of sensor networks in various critical infrastructures, the applications often impose stringent requirements on data reliability and service availability. Given the failure- and attack-prone nature of sensor networks, it is a pressing concern to enable the sensor networks provide continuous and unobtrusive services. Kinesis is quick and effective in response to incidents, distributed in nature, and dynamic in selecting response actions based on the context. It is lightweight in terms of response policy specification, and communication and energy overhead. A per-node single timer based distributed strategy to select the most effective response executor in a neighborhood makes the system simple and scalable, while achieving proper load distribution and redundant action optimization. We implement Kinesis in TinyOS and measure its performance for various application and network layer incidents. Extensive TOSSIM simulations and testbed experiments show that Kinesis successfully counteracts anomalies/attacks and behaves consistently under various attack scenarios and rates.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"37 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":"130491709","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}
Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a "curse" and hence take great efforts to cope with it. Consequently, each sensor only obtains the information about the direct path; this necessitates a networked sensing system (hence higher system complexity and deployment cost) with at least three sensors to actually locate a source. In this paper, we deem multipath a "bless" and thus innovatively exploit the power of it. Essentially, with minor knowledge of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with only one hand-held device. At the same time, the extra information provided by multipath can help to at least partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems. To demonstrate these ideas, we implement a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan offers satisfactory results on both source localization and space scanning.
{"title":"iLocScan: harnessing multipath for simultaneous indoor source localization and space scanning","authors":"Chi Zhang, Feng Li, Jun Luo, Ying He","doi":"10.1145/2668332.2668345","DOIUrl":"https://doi.org/10.1145/2668332.2668345","url":null,"abstract":"Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a \"curse\" and hence take great efforts to cope with it. Consequently, each sensor only obtains the information about the direct path; this necessitates a networked sensing system (hence higher system complexity and deployment cost) with at least three sensors to actually locate a source. In this paper, we deem multipath a \"bless\" and thus innovatively exploit the power of it. Essentially, with minor knowledge of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with only one hand-held device. At the same time, the extra information provided by multipath can help to at least partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems. To demonstrate these ideas, we implement a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan offers satisfactory results on both source localization and space scanning.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"72 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":"134543960","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}
Hessam Mohammadmoradi, O. Gnawali, Nir Rattner, A. Terzis, A. Szalay
Time synchronization is an essential service in many sensor network applications. Harsh environment which causes nodes to fail, go offline, or reboot can challenge many time synchronization protocols. In this work, we first characterize this challenge and use a real time clock in one of the nodes in the network to improve robustness of time synchronization. Our experiments show that our approach improves the robustness of state-of-the-art offline time synchronization protocols.
{"title":"Robust time synchronization in wireless sensor networks using real time clock","authors":"Hessam Mohammadmoradi, O. Gnawali, Nir Rattner, A. Terzis, A. Szalay","doi":"10.1145/2668332.2668365","DOIUrl":"https://doi.org/10.1145/2668332.2668365","url":null,"abstract":"Time synchronization is an essential service in many sensor network applications. Harsh environment which causes nodes to fail, go offline, or reboot can challenge many time synchronization protocols. In this work, we first characterize this challenge and use a real time clock in one of the nodes in the network to improve robustness of time synchronization. Our experiments show that our approach improves the robustness of state-of-the-art offline time synchronization protocols.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"33 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":"127838214","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}
Ravel is a software framework for developing sensor network applications that follow the eMbedded-Gateway-Cloud architecture. Developers describe a Ravel application as a data processing pipeline in terms of two abstractions: models and transforms between models. This pipeline generates code for controllers that can compile to and run on any element of the architecture, from embedded devices to cloud servers. Developers also specify views, that represent the data set on a particular device. Therefore, each device type is a space where data flows via transform. The framework automatically handles moving data between spaces using appropriate network protocols. Compile-time tools verify that the code, once modified by the developer, still follows application specification as defined by the data pipeline.
{"title":"Ravel a framework for embedded-gateway-cloud applications","authors":"Laurynas Riliskis, P. Levis","doi":"10.1145/2668332.2668356","DOIUrl":"https://doi.org/10.1145/2668332.2668356","url":null,"abstract":"Ravel is a software framework for developing sensor network applications that follow the eMbedded-Gateway-Cloud architecture. Developers describe a Ravel application as a data processing pipeline in terms of two abstractions: models and transforms between models. This pipeline generates code for controllers that can compile to and run on any element of the architecture, from embedded devices to cloud servers. Developers also specify views, that represent the data set on a particular device. Therefore, each device type is a space where data flows via transform. The framework automatically handles moving data between spaces using appropriate network protocols. Compile-time tools verify that the code, once modified by the developer, still follows application specification as defined by the data pipeline.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"10 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":"133385529","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 demo presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. We implement the proposed scheme and deploy the sensor network in a large urban reservoir of 2.5km * 3.0km to monitor the field wind distribution. We show the in-field deployment procedure of the wind field measurement system and demonstrate the performance of the data collection protocol by a small testbed on site.
{"title":"From rateless to distanceless: enabling sparse sensor network deployment in large areas","authors":"Wan Du, Zhenjiang Li, J. C. Liando, Mo Li","doi":"10.1145/2668332.2668372","DOIUrl":"https://doi.org/10.1145/2668332.2668372","url":null,"abstract":"This demo presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. We implement the proposed scheme and deploy the sensor network in a large urban reservoir of 2.5km * 3.0km to monitor the field wind distribution. We show the in-field deployment procedure of the wind field measurement system and demonstrate the performance of the data collection protocol by a small testbed on site.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"5 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":"131289787","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}
Swarnava Dey, P. Datta, A. Mukherjee, H. Paul, A. Basu
Smartphones are currently being used to develop diverse range of applications (apps) involving sensors. These apps generally acquire and analyze sensor data and are usually implemented as background services. The importance values of Android processes are in a hierarchy of foreground, visible, background etc. in decreasing order of importance. Whenever a new process arrives, it may necessitate removal of old and less important processes for reclaiming memory. Current smartphones do not provide any options through which user's idea of priority can override that of the system defaults. In this work we present an implementation that enables the user to obtain alerts on system load and recommendations to proactively kill a set of processes to reclaim system memory. This enables user selected background process to be spared from the standard android policy of process termination, in lieu of foreground apps, relatively unimportant from user perspective, during that period. We show that manual reclaiming of memory based on recommendations from our app, reduces the automatic killing and measurement lag experienced by a sensor analytics app under test. This work is redundant if processing power and main memory of a smartphone is always surplus than required for its normal usage.
{"title":"Facilitating continued run of sensor data analytics services using user driven proactive memory reclamation scheme","authors":"Swarnava Dey, P. Datta, A. Mukherjee, H. Paul, A. Basu","doi":"10.1145/2668332.2668362","DOIUrl":"https://doi.org/10.1145/2668332.2668362","url":null,"abstract":"Smartphones are currently being used to develop diverse range of applications (apps) involving sensors. These apps generally acquire and analyze sensor data and are usually implemented as background services. The importance values of Android processes are in a hierarchy of foreground, visible, background etc. in decreasing order of importance. Whenever a new process arrives, it may necessitate removal of old and less important processes for reclaiming memory. Current smartphones do not provide any options through which user's idea of priority can override that of the system defaults. In this work we present an implementation that enables the user to obtain alerts on system load and recommendations to proactively kill a set of processes to reclaim system memory. This enables user selected background process to be spared from the standard android policy of process termination, in lieu of foreground apps, relatively unimportant from user perspective, during that period. We show that manual reclaiming of memory based on recommendations from our app, reduces the automatic killing and measurement lag experienced by a sensor analytics app under test. This work is redundant if processing power and main memory of a smartphone is always surplus than required for its normal usage.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"75 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":"130385056","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}
Petko Georgiev, N. Lane, Kiran Rachuri, C. Mascolo
The rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor among these is the microphone which enables, for instance, the detection of valence in speech, or the identification of speakers. Deploying multiple of these applications on a mobile device to continuously monitor the audio environment allows for the acquisition of a diverse range of sound-related contextual inferences. However, the cumulative processing burden critically impacts the phone battery. To address this problem, we propose DSP.Ear -- an integrated sensing system that takes advantage of the latest low-power DSP co-processor technology in commodity mobile devices to enable the continuous and simultaneous operation of multiple established algorithms that perform complex audio inferences. The system extracts emotions from voice, estimates the number of people in a room, identifies the speakers, and detects commonly found ambient sounds, while critically incurring little overhead to the device battery. This is achieved through a series of pipeline optimizations that allow the computation to remain largely on the DSP. Through detailed evaluation of our prototype implementation we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3 to 7 times increase in the battery lifetime compared to a solution that uses only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more power efficient than a naïve DSP solution without optimizations. We further analyze a large-scale dataset from 1320 Android users to show that in about 80-90% of the daily usage instances DSP.Ear is able to sustain a full day of operation (even in the presence of other smartphone workloads) with a single battery charge.
{"title":"DSP.Ear: leveraging co-processor support for continuous audio sensing on smartphones","authors":"Petko Georgiev, N. Lane, Kiran Rachuri, C. Mascolo","doi":"10.1145/2668332.2668349","DOIUrl":"https://doi.org/10.1145/2668332.2668349","url":null,"abstract":"The rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor among these is the microphone which enables, for instance, the detection of valence in speech, or the identification of speakers. Deploying multiple of these applications on a mobile device to continuously monitor the audio environment allows for the acquisition of a diverse range of sound-related contextual inferences. However, the cumulative processing burden critically impacts the phone battery. To address this problem, we propose DSP.Ear -- an integrated sensing system that takes advantage of the latest low-power DSP co-processor technology in commodity mobile devices to enable the continuous and simultaneous operation of multiple established algorithms that perform complex audio inferences. The system extracts emotions from voice, estimates the number of people in a room, identifies the speakers, and detects commonly found ambient sounds, while critically incurring little overhead to the device battery. This is achieved through a series of pipeline optimizations that allow the computation to remain largely on the DSP. Through detailed evaluation of our prototype implementation we show that, by exploiting a smartphone's co-processor, DSP.Ear achieves a 3 to 7 times increase in the battery lifetime compared to a solution that uses only the phone's main processor. In addition, DSP.Ear is 2 to 3 times more power efficient than a naïve DSP solution without optimizations. We further analyze a large-scale dataset from 1320 Android users to show that in about 80-90% of the daily usage instances DSP.Ear is able to sustain a full day of operation (even in the presence of other smartphone workloads) with a single battery charge.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123128372","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}
While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, can open doors for the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.
{"title":"Mining users' significant driving routes with low-power sensors","authors":"S. Nawaz, C. Mascolo","doi":"10.1145/2668332.2668348","DOIUrl":"https://doi.org/10.1145/2668332.2668348","url":null,"abstract":"While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, can open doors for the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131497889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","authors":"Ákos Lédecz, P. Dutta, Chenyang Lu","doi":"10.1145/2668332","DOIUrl":"https://doi.org/10.1145/2668332","url":null,"abstract":"","PeriodicalId":223777,"journal":{"name":"Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129180597","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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