Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846753
Manjunath Doddavenkatappa, M. Chan
While high throughput is the key for a number of important applications of sensor networks, performance of the state-of-the-art approach is often poor in practice. This is because if even one of the channels used in its pipeline is bad, the pipeline stalls and throughput degrades significantly. In this paper, we propose a new protocol called P3 (Practical Packet Pipeline) that keeps its packet pipeline flowing despite the quality differences among channels. P3 exploits sender and receiver diversities through synchronous transmissions (constructive interference), involving concurrent transmissions from multiple senders to multiple receivers at every stage of its packet pipeline. To optimize throughput further, P3 uses node grouping to enable the source to transmit in every pipeline cycle, thus fully utilizing the transmission capacity of an underlying radio. Our evaluation results on a 139-node testbed show that P3 achieves an average goodput of 178.5 Kbps while goodput of the state-of-the-art high throughput protocol PIP (Packets In Pipeline) is only 31 Kbps. More interestingly, P3 achieves a minimum goodput of about 149 Kbps, while PIP's goodput reduces to zero in 65% of the cases.
{"title":"P3: A Practical Packet Pipeline using synchronous transmissions for wireless sensor networks","authors":"Manjunath Doddavenkatappa, M. Chan","doi":"10.1109/IPSN.2014.6846753","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846753","url":null,"abstract":"While high throughput is the key for a number of important applications of sensor networks, performance of the state-of-the-art approach is often poor in practice. This is because if even one of the channels used in its pipeline is bad, the pipeline stalls and throughput degrades significantly. In this paper, we propose a new protocol called P3 (Practical Packet Pipeline) that keeps its packet pipeline flowing despite the quality differences among channels. P3 exploits sender and receiver diversities through synchronous transmissions (constructive interference), involving concurrent transmissions from multiple senders to multiple receivers at every stage of its packet pipeline. To optimize throughput further, P3 uses node grouping to enable the source to transmit in every pipeline cycle, thus fully utilizing the transmission capacity of an underlying radio. Our evaluation results on a 139-node testbed show that P3 achieves an average goodput of 178.5 Kbps while goodput of the state-of-the-art high throughput protocol PIP (Packets In Pipeline) is only 31 Kbps. More interestingly, P3 achieves a minimum goodput of about 149 Kbps, while PIP's goodput reduces to zero in 65% of the cases.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130971219","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846738
Ashish Kapoor, Zachary Horvitz, S. Laube, E. Horvitz
We explore the feasibility of using commercial aircraft as sensors for observing weather phenomena at a continental scale. We focus specifically on the problem of wind forecasting and explore the use of machine learning and inference methods to harness air and ground speeds reported by aircraft at different locations and altitudes. We validate the learned predictive model with a field study where we release an instrumented high-altitude balloon and compare the predicted trajectory with the sensed winds. The experiments show the promise of using airplane in flight as a large-scale sensor network. Beyond making predictions, we explore the guidance of sensing with value-of-information analyses, where we consider uncertainties and needs of sets of routes and maximize information value in light of the costs of acquiring data from airplanes. The methods can be used to select ideal subsets of planes to serve as sensors and also to evaluate the value of requesting shifts in trajectories of flights for sensing.
{"title":"Airplanes aloft as a sensor network for wind forecasting","authors":"Ashish Kapoor, Zachary Horvitz, S. Laube, E. Horvitz","doi":"10.1109/IPSN.2014.6846738","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846738","url":null,"abstract":"We explore the feasibility of using commercial aircraft as sensors for observing weather phenomena at a continental scale. We focus specifically on the problem of wind forecasting and explore the use of machine learning and inference methods to harness air and ground speeds reported by aircraft at different locations and altitudes. We validate the learned predictive model with a field study where we release an instrumented high-altitude balloon and compare the predicted trajectory with the sensed winds. The experiments show the promise of using airplane in flight as a large-scale sensor network. Beyond making predictions, we explore the guidance of sensing with value-of-information analyses, where we consider uncertainties and needs of sets of routes and maximize information value in light of the costs of acquiring data from airplanes. The methods can be used to select ideal subsets of planes to serve as sensors and also to evaluate the value of requesting shifts in trajectories of flights for sensing.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129380837","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 poster is aimed at solving the problem of maximizing the energy margin of a solar-powered sensor network at a fixed time horizon, to maximize the network performance during an event to monitor. Using a game theoretic approach, the optimal routing maximizing the energy margin of the network at a given time under solar power forcing can be computed in a decentralized way and solved exactly through dynamic programming with a low overall complexity. We also show that this decentralized algorithm is simple enough to be implemented on practical sensor nodes. Such an algorithm would be very useful whenever the energy margin of a solar-powered sensor network has to be maximized at a specific time.
{"title":"Poster abstract: A decentralized routing scheme based on a zero-sum game to optimize energy in solar powered sensor networks","authors":"Ahmad H. Dehwah, H. Tembine, C. Claudel","doi":"10.5555/2602339.2602376","DOIUrl":"https://doi.org/10.5555/2602339.2602376","url":null,"abstract":"This poster is aimed at solving the problem of maximizing the energy margin of a solar-powered sensor network at a fixed time horizon, to maximize the network performance during an event to monitor. Using a game theoretic approach, the optimal routing maximizing the energy margin of the network at a given time under solar power forcing can be computed in a decentralized way and solved exactly through dynamic programming with a low overall complexity. We also show that this decentralized algorithm is simple enough to be implemented on practical sensor nodes. Such an algorithm would be very useful whenever the energy margin of a solar-powered sensor network has to be maximized at a specific time.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122401106","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846767
Frederik Hermans, L. McNamara, T. Voigt, C. Rohner, E. Ngai, P. Gunningberg
Visible light communication over LCD/camera links offers a potential complement to traditional RF communication technology such as WiFi or cellular networks. However, the heterogeneity in receivers (e.g., mobile phone cameras) presents a challenge because the receivers differ widely in resolution, distance to the transmitter (LCD), and other factors, and therefore they differ in channel quality. We are researching a communication scheme in which each receiver can decode as much data from an LCD's transmission as the receiver's channel supports. The core idea is to encode the payload into an image's frequency representation rather than directly into pixels. We have successfully transmitted data using a prototype implementation and are currently investigating appropriate channel models.
{"title":"Poster abstract: Supporting heterogeneous LCD/camera links","authors":"Frederik Hermans, L. McNamara, T. Voigt, C. Rohner, E. Ngai, P. Gunningberg","doi":"10.1109/IPSN.2014.6846767","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846767","url":null,"abstract":"Visible light communication over LCD/camera links offers a potential complement to traditional RF communication technology such as WiFi or cellular networks. However, the heterogeneity in receivers (e.g., mobile phone cameras) presents a challenge because the receivers differ widely in resolution, distance to the transmitter (LCD), and other factors, and therefore they differ in channel quality. We are researching a communication scheme in which each receiver can decode as much data from an LCD's transmission as the receiver's channel supports. The core idea is to encode the payload into an image's frequency representation rather than directly into pixels. We have successfully transmitted data using a prototype implementation and are currently investigating appropriate channel models.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816978","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846760
Chi Zhang, Jun Luo, Jianxin Wu
We present MaWi - a smart phone based scalable indoor localization system. Central to MaWi is a novel framework combining two self-contained but complementary localization techniques: Wi-Fi and Ambient Magnetic Field. Combining the two techniques, MaWi not only achieves a high localization accuracy, but also effectively reduces human labor in building fingerprint databases: to avoid war-driving, MaWi is designed to work with low quality fingerprint databases that can be efficiently built by only one person. Our experiments demonstrate that MaWi, with a fingerprint database as scarce as one data sample at each spot, outperforms the state-of-the-art proposals working on a richer fingerprint database.
{"title":"Poster abstract: MaWi: A hybrid Magnetic and Wi-Fi system for scalable indoor localization","authors":"Chi Zhang, Jun Luo, Jianxin Wu","doi":"10.1109/IPSN.2014.6846760","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846760","url":null,"abstract":"We present MaWi - a smart phone based scalable indoor localization system. Central to MaWi is a novel framework combining two self-contained but complementary localization techniques: Wi-Fi and Ambient Magnetic Field. Combining the two techniques, MaWi not only achieves a high localization accuracy, but also effectively reduces human labor in building fingerprint databases: to avoid war-driving, MaWi is designed to work with low quality fingerprint databases that can be efficiently built by only one person. Our experiments demonstrate that MaWi, with a fingerprint database as scarce as one data sample at each spot, outperforms the state-of-the-art proposals working on a richer fingerprint database.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036537","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846739
Dong Wang, Md. Tanvir Al Amin, Shen Li, T. Abdelzaher, Lance M. Kaplan, Siyu Gu, Chenji Pan, Hengchang Liu, C. Aggarwal, R. Ganti, Xinlei Wang, P. Mohapatra, B. Szymanski, H. Le
The explosive growth in social network content suggests that the largest “sensor network” yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a “sensor network” for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.
{"title":"Using humans as sensors: An estimation-theoretic perspective","authors":"Dong Wang, Md. Tanvir Al Amin, Shen Li, T. Abdelzaher, Lance M. Kaplan, Siyu Gu, Chenji Pan, Hengchang Liu, C. Aggarwal, R. Ganti, Xinlei Wang, P. Mohapatra, B. Szymanski, H. Le","doi":"10.1109/IPSN.2014.6846739","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846739","url":null,"abstract":"The explosive growth in social network content suggests that the largest “sensor network” yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a “sensor network” for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668056","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846755
S. Achleitner, Ankur Kamthe, Tao Liu, Alberto Cerpa
There is high interest in up-scaling capacities of renewable energy sources such as wind and solar. However, variability and uncertainty in power output is a major concern and forecasting is, therefore, a top priority. Advancements in forecasting can potentially limit the impact of fluctuations in solar power generation, specifically in cloudy days when the variability and dynamics are the largest. We propose SIPS, Solar Irradiance Prediction System, a novel sensing infrastructure using wireless sensor networks (WSNs) to enable sensing of solar irradiance for solar power generation forecasting. In this paper, we report the findings of a deployment of a hierarchical WSN system consisting of 19 TelosB nodes equipped with solar irradiance sensors, and 5 MicaZ nodes equipped with GPS boards, deployed in the vicinity of a 1 MW solar array. We evaluate different irradiance sensor types and the performance of different novel prediction methods using SIPS' data and show that the spatial-temporal cross-correlations between sensor node readings and solar array output power exists and can be exploited to improve prediction accuracy. Using this data for short-term solar forecasting for cloudy days with very high dynamics in solar output power generation - the worst case scenario for prediction-, we get an average of 97.24% accuracy in our prediction for short time horizon forecasting and 240% reduction of predicted normalized root mean square error (NRMSE) compared to state-of-the-art methods that do not use SIPS data.
{"title":"SIPS: Solar Irradiance Prediction System","authors":"S. Achleitner, Ankur Kamthe, Tao Liu, Alberto Cerpa","doi":"10.1109/IPSN.2014.6846755","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846755","url":null,"abstract":"There is high interest in up-scaling capacities of renewable energy sources such as wind and solar. However, variability and uncertainty in power output is a major concern and forecasting is, therefore, a top priority. Advancements in forecasting can potentially limit the impact of fluctuations in solar power generation, specifically in cloudy days when the variability and dynamics are the largest. We propose SIPS, Solar Irradiance Prediction System, a novel sensing infrastructure using wireless sensor networks (WSNs) to enable sensing of solar irradiance for solar power generation forecasting. In this paper, we report the findings of a deployment of a hierarchical WSN system consisting of 19 TelosB nodes equipped with solar irradiance sensors, and 5 MicaZ nodes equipped with GPS boards, deployed in the vicinity of a 1 MW solar array. We evaluate different irradiance sensor types and the performance of different novel prediction methods using SIPS' data and show that the spatial-temporal cross-correlations between sensor node readings and solar array output power exists and can be exploited to improve prediction accuracy. Using this data for short-term solar forecasting for cloudy days with very high dynamics in solar output power generation - the worst case scenario for prediction-, we get an average of 97.24% accuracy in our prediction for short time horizon forecasting and 240% reduction of predicted normalized root mean square error (NRMSE) compared to state-of-the-art methods that do not use SIPS data.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125896739","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846744
C. Boano, Marco Zúñiga, James Brown, U. Roedig, C. Keppitiyagama, K. Römer
Temperature has a strong impact on the operations of all electrical and electronic components. In wireless sensor nodes, temperature variations can lead to loss of synchronization, degradation of the link quality, or early battery depletion, and can therefore affect key network metrics such as throughput, delay, and lifetime. Considering that most outdoor deployments are exposed to strong temperature variations across time and space, a deep understanding of how temperature affects network protocols is fundamental to comprehend flaws in their design and to improve their performance. Existing testbed infrastructures, however, do not allow to systematically study the impact of temperature on wireless sensor networks. In this paper we present TempLab, an extension for wireless sensor network testbeds that allows to control the on-board temperature of sensor nodes and to study the effects of temperature variations on the network performance in a precise and repeatable fashion. TempLab can accurately reproduce traces recorded in outdoor environments with fine granularity, while minimizing the hardware costs and configuration overhead. We use TempLab to analyse the detrimental effects of temperature variations (i) on processing performance, (ii) on a tree routing protocol, and (iii) on CSMA-based MAC protocols, deriving insights that would have not been revealed using existing testbed installations.
{"title":"TempLab: A testbed infrastructure to study the impact of temperature on wireless sensor networks","authors":"C. Boano, Marco Zúñiga, James Brown, U. Roedig, C. Keppitiyagama, K. Römer","doi":"10.1109/IPSN.2014.6846744","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846744","url":null,"abstract":"Temperature has a strong impact on the operations of all electrical and electronic components. In wireless sensor nodes, temperature variations can lead to loss of synchronization, degradation of the link quality, or early battery depletion, and can therefore affect key network metrics such as throughput, delay, and lifetime. Considering that most outdoor deployments are exposed to strong temperature variations across time and space, a deep understanding of how temperature affects network protocols is fundamental to comprehend flaws in their design and to improve their performance. Existing testbed infrastructures, however, do not allow to systematically study the impact of temperature on wireless sensor networks. In this paper we present TempLab, an extension for wireless sensor network testbeds that allows to control the on-board temperature of sensor nodes and to study the effects of temperature variations on the network performance in a precise and repeatable fashion. TempLab can accurately reproduce traces recorded in outdoor environments with fine granularity, while minimizing the hardware costs and configuration overhead. We use TempLab to analyse the detrimental effects of temperature variations (i) on processing performance, (ii) on a tree routing protocol, and (iii) on CSMA-based MAC protocols, deriving insights that would have not been revealed using existing testbed installations.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125423453","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846764
Yixin Zheng, Linglong Li, Lin Zhang
PiMiair.org is a participatory indoor air quality data sharing project we launched in January 2014. Over 200 PiMi air boxes, a low-cost indoor air quality monitor, were given out to volunteer users across China. The PiMi air boxes measure the approximate indoor particulate matter concentration, and the ambient temperate and humidity. When a user accesses the PiMi air box for his personal air quality data on his smartphone, the data is relayed to the backend PiMi cloud server for analysis. Accumulating large amount of indoor air quality data under different circumstances, the PiMi cloud server is able to use statistical learning methodologies to detect point of interests (POIs) in the data series, and asks users to label their activities or events at the POIs. Together with the user-reported physicality information on the indoor environments, PiMiair.org is able to quantitatively evaluate the impacts of the environment physicality and human behaviors on the indoor air quality, and mine the knowledges on how to alleviate indoor air pollution. We believe that by sharing these knowledge among the community, healthier breathing environments could be nurtured for the well-being of the public.
{"title":"Poster abstract: PiMi air community: Getting fresher indoor air by sharing data and know-hows","authors":"Yixin Zheng, Linglong Li, Lin Zhang","doi":"10.1109/IPSN.2014.6846764","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846764","url":null,"abstract":"PiMiair.org is a participatory indoor air quality data sharing project we launched in January 2014. Over 200 PiMi air boxes, a low-cost indoor air quality monitor, were given out to volunteer users across China. The PiMi air boxes measure the approximate indoor particulate matter concentration, and the ambient temperate and humidity. When a user accesses the PiMi air box for his personal air quality data on his smartphone, the data is relayed to the backend PiMi cloud server for analysis. Accumulating large amount of indoor air quality data under different circumstances, the PiMi cloud server is able to use statistical learning methodologies to detect point of interests (POIs) in the data series, and asks users to label their activities or events at the POIs. Together with the user-reported physicality information on the indoor environments, PiMiair.org is able to quantitatively evaluate the impacts of the environment physicality and human behaviors on the indoor air quality, and mine the knowledges on how to alleviate indoor air pollution. We believe that by sharing these knowledge among the community, healthier breathing environments could be nurtured for the well-being of the public.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124270957","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}
Pub Date : 2014-04-15DOI: 10.1109/IPSN.2014.6846740
Iyswarya Narayanan, Arunchandar Vasan, V. Sarangan, A. Sivasubramaniam
Leak localization is a major issue faced by water utilities worldwide. Leaks are ideally detected and localized by a network-wide metering infrastructure. However, in many utilities, in-network metering is minimally present at just the inlets of subnetworks called District Metering Area (DMA). We consider the problem of leak localization using data from a single flow meter placed at the inlet of a DMA. We use standard time-series based modeling to detect if a current meter reading is a leak or not, and if so, to estimate the excess flow. Conventional approaches use an a-priori fully calibrated hydraulic model to map the excess flow back to a set of candidate leak locations. However, obtaining an accurate hydraulic model is expensive and hence, beyond the reach of many water utilities. We present an alternate approach that exploits the network structure and static properties in a novel way. Specifically, we extend the use of centrality metrics to infrastructure domains and use these metrics to map from the excess leak flow to the candidate leak location(s). We evaluate our approach on benchmark water utility network topologies as well as on real data obtained from an European water utility. On benchmark topologies, the localization obtained by our method is comparable to that obtained from a complete hydraulic model. On a real-world network, we were able to localize two out of the three leaks whose data we had access to. Of these two cases, we find that the actual leak location was in the candidate set identified by our approach; further, the approach pruned as much as 78% of the DMA locations, indicating a high degree of localization.
{"title":"One meter to find them all-water network leak localization using a single flow meter","authors":"Iyswarya Narayanan, Arunchandar Vasan, V. Sarangan, A. Sivasubramaniam","doi":"10.1109/IPSN.2014.6846740","DOIUrl":"https://doi.org/10.1109/IPSN.2014.6846740","url":null,"abstract":"Leak localization is a major issue faced by water utilities worldwide. Leaks are ideally detected and localized by a network-wide metering infrastructure. However, in many utilities, in-network metering is minimally present at just the inlets of subnetworks called District Metering Area (DMA). We consider the problem of leak localization using data from a single flow meter placed at the inlet of a DMA. We use standard time-series based modeling to detect if a current meter reading is a leak or not, and if so, to estimate the excess flow. Conventional approaches use an a-priori fully calibrated hydraulic model to map the excess flow back to a set of candidate leak locations. However, obtaining an accurate hydraulic model is expensive and hence, beyond the reach of many water utilities. We present an alternate approach that exploits the network structure and static properties in a novel way. Specifically, we extend the use of centrality metrics to infrastructure domains and use these metrics to map from the excess leak flow to the candidate leak location(s). We evaluate our approach on benchmark water utility network topologies as well as on real data obtained from an European water utility. On benchmark topologies, the localization obtained by our method is comparable to that obtained from a complete hydraulic model. On a real-world network, we were able to localize two out of the three leaks whose data we had access to. Of these two cases, we find that the actual leak location was in the candidate set identified by our approach; further, the approach pruned as much as 78% of the DMA locations, indicating a high degree of localization.","PeriodicalId":297218,"journal":{"name":"IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104597","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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