General Chairs David Simplot-Ryl, IRCICA/LIFL, Univ. Lille 1, France Ivan Stojmenovic, SITE, Univ... more General Chairs David Simplot-Ryl, IRCICA/LIFL, Univ. Lille 1, France Ivan Stojmenovic, SITE, University of Ottawa, Canada ... Program Co-Chairs Vojislav B. Mišić, Ryerson University, Canada Kui Wu, University of Victoria, Canada ... Program Committee Habib M. Ammari, Hofstra University, USA Claudia Campolo, Università Mediterranea di Reggio Calabria, Italy Ling-Jyh Chen, Academia Sinica, Taiwan Yuanzhu Peter Chen, Memorial University of Newfoundland, Canada Junzhao Du, Xidian University, China Hannes Frey, ...
ABSTRACT Automatically recognizing human activities in a body sensor network (BSN) enables many h... more ABSTRACT Automatically recognizing human activities in a body sensor network (BSN) enables many human-centric applications. Many current works recognize human activities through collecting and analyzing sensor readings from on-body sensor nodes. These sensing-based solutions face a dilemma. On one hand, to guarantee data availability and recognition accuracy, sensing-based solutions have to either utilize a high transmission power or involve a packet retransmission mechanism. On the other hand, enhancing the transmission power increases a sensor node's energy overheads and communication range. The enlarged communication range in consequence increases privacy risks. A packet retransmission mechanism complicates on-body sensor nodes' MAC layer and hence increases energy overheads. In contrast to the sensing-based solutions, we build Radio Sense, a prototype system that exploits wireless communication patterns for BSN activity recognition. Using Radio Sense, we benchmark three system parameters (transmission (TX) power, packet sending rate, and smoothing window size) to design algorithms for system parameter selection. The algorithms aim to balance accuracy, latency, and energy overheads. In addition, we investigate the minimal amount of training data needed for reliable performance. We evaluate our Radio Sense system with multiple subjects' data collected over a two-week period and demonstrate that Radio Sense achieves reliable performance in terms of accuracy, latency, and battery lifetime.
IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, 2014
ABSTRACT The increasing availability of inexpensive off-the-shelf 802.11 hardware has made it pos... more ABSTRACT The increasing availability of inexpensive off-the-shelf 802.11 hardware has made it possible to deploy access points (APs) densely to ensure the coverage of complex enterprise environments such as business and college campuses. However, dense AP deployment often leads to increased level of wireless contention, resulting in low system throughput. A promising approach to address this issue is to enable the transmission concurrency of exposed terminals in which two senders lie in the range of one another but do not interfere each other's receiver. However, existing solutions ignore the rate diversity of 802.11 and hence cannot fully exploit concurrent transmission opportunities in a WLAN. In this paper, we present TRACK - Transmission Rate Adaptation for Colliding linKs, a novel protocol for harnessing exposed terminals with a rate adaptation approach in enterprise WLANs. Using measurement-based channel models, TRACK can optimize the bit rates of concurrent links to improve system throughput while maintaining link fairness. Our extensive experiments on a testbed of 17 nodes show that TRACK improves system throughput by up to 67% and 35% over 802.11 CSMA and conventional approaches of harnessing exposed terminals.
IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, 2008
... of Virginia {jl3aq,stankovic}@cs.virginia.edu Intel Corporation {chieh-yih.wan,mark.d ...... more ... of Virginia {jl3aq,stankovic}@cs.virginia.edu Intel Corporation {chieh-yih.wan,mark.d ... in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without ... When the effective bandwidth of the channel degrades due to RF interference or body ...
Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most widespread and des... more Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most widespread and destructive wheat diseases worldwide. Growing resistant cultivars with adult-plant resistance (APR) is an effective approach for the control of the disease. In this study, 540 simple sequence repeat markers were screened to map quantitative trait loci (QTL) for APR to stripe rust in a doubled haploid (DH) population of 137 lines derived from the cross Pingyuan 50 x Mingxian 169. The DH lines were planted in randomized complete blocks with three replicates in Gansu and Sichuan provinces during the 2005-06, 2006-07, and 2007-08 cropping seasons, providing data for four environments. Artificial inoculations were carried out in Gansu and Sichuan with the prevalent Chinese race CYR32. Broad-sense heritability of resistance to stripe rust for maximum disease severity was 0.91, based on the mean value averaged across four environments. Inclusive composite interval mapping detected three QTL for AP...
ABSTRACT In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communic... more ABSTRACT In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.
General Chairs David Simplot-Ryl, IRCICA/LIFL, Univ. Lille 1, France Ivan Stojmenovic, SITE, Univ... more General Chairs David Simplot-Ryl, IRCICA/LIFL, Univ. Lille 1, France Ivan Stojmenovic, SITE, University of Ottawa, Canada ... Program Co-Chairs Vojislav B. Mišić, Ryerson University, Canada Kui Wu, University of Victoria, Canada ... Program Committee Habib M. Ammari, Hofstra University, USA Claudia Campolo, Università Mediterranea di Reggio Calabria, Italy Ling-Jyh Chen, Academia Sinica, Taiwan Yuanzhu Peter Chen, Memorial University of Newfoundland, Canada Junzhao Du, Xidian University, China Hannes Frey, ...
ABSTRACT Automatically recognizing human activities in a body sensor network (BSN) enables many h... more ABSTRACT Automatically recognizing human activities in a body sensor network (BSN) enables many human-centric applications. Many current works recognize human activities through collecting and analyzing sensor readings from on-body sensor nodes. These sensing-based solutions face a dilemma. On one hand, to guarantee data availability and recognition accuracy, sensing-based solutions have to either utilize a high transmission power or involve a packet retransmission mechanism. On the other hand, enhancing the transmission power increases a sensor node's energy overheads and communication range. The enlarged communication range in consequence increases privacy risks. A packet retransmission mechanism complicates on-body sensor nodes' MAC layer and hence increases energy overheads. In contrast to the sensing-based solutions, we build Radio Sense, a prototype system that exploits wireless communication patterns for BSN activity recognition. Using Radio Sense, we benchmark three system parameters (transmission (TX) power, packet sending rate, and smoothing window size) to design algorithms for system parameter selection. The algorithms aim to balance accuracy, latency, and energy overheads. In addition, we investigate the minimal amount of training data needed for reliable performance. We evaluate our Radio Sense system with multiple subjects' data collected over a two-week period and demonstrate that Radio Sense achieves reliable performance in terms of accuracy, latency, and battery lifetime.
IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, 2014
ABSTRACT The increasing availability of inexpensive off-the-shelf 802.11 hardware has made it pos... more ABSTRACT The increasing availability of inexpensive off-the-shelf 802.11 hardware has made it possible to deploy access points (APs) densely to ensure the coverage of complex enterprise environments such as business and college campuses. However, dense AP deployment often leads to increased level of wireless contention, resulting in low system throughput. A promising approach to address this issue is to enable the transmission concurrency of exposed terminals in which two senders lie in the range of one another but do not interfere each other's receiver. However, existing solutions ignore the rate diversity of 802.11 and hence cannot fully exploit concurrent transmission opportunities in a WLAN. In this paper, we present TRACK - Transmission Rate Adaptation for Colliding linKs, a novel protocol for harnessing exposed terminals with a rate adaptation approach in enterprise WLANs. Using measurement-based channel models, TRACK can optimize the bit rates of concurrent links to improve system throughput while maintaining link fairness. Our extensive experiments on a testbed of 17 nodes show that TRACK improves system throughput by up to 67% and 35% over 802.11 CSMA and conventional approaches of harnessing exposed terminals.
IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, 2008
... of Virginia {jl3aq,stankovic}@cs.virginia.edu Intel Corporation {chieh-yih.wan,mark.d ...... more ... of Virginia {jl3aq,stankovic}@cs.virginia.edu Intel Corporation {chieh-yih.wan,mark.d ... in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without ... When the effective bandwidth of the channel degrades due to RF interference or body ...
Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most widespread and des... more Stripe rust, caused by Puccinia striiformis f. sp. tritici, is one of the most widespread and destructive wheat diseases worldwide. Growing resistant cultivars with adult-plant resistance (APR) is an effective approach for the control of the disease. In this study, 540 simple sequence repeat markers were screened to map quantitative trait loci (QTL) for APR to stripe rust in a doubled haploid (DH) population of 137 lines derived from the cross Pingyuan 50 x Mingxian 169. The DH lines were planted in randomized complete blocks with three replicates in Gansu and Sichuan provinces during the 2005-06, 2006-07, and 2007-08 cropping seasons, providing data for four environments. Artificial inoculations were carried out in Gansu and Sichuan with the prevalent Chinese race CYR32. Broad-sense heritability of resistance to stripe rust for maximum disease severity was 0.91, based on the mean value averaged across four environments. Inclusive composite interval mapping detected three QTL for AP...
ABSTRACT In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communic... more ABSTRACT In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.
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Papers by Gang Zhou