[Ubicomp'15]SakuraSensor: Quasi-Realtime Cherry-Lined Roads Detection throug...Ubi NAIST
SakuraSensor, a system which senses and shares the information of roads with flowering cherries by leveraging car-mounted smart-phones.
Honorable Mention Award of UbiComp2015.
Distance and Time Based Node Selection for Probabilistic Coverage in People-C...Ubi NAIST
This document proposes algorithms to select mobile sensor nodes for probabilistic coverage in people-centric sensing applications. It formulates the problem of ensuring an area of interest (AOI) is covered with probability α within time T. Two algorithms are presented: Inter-Location Based (ILB) selects nodes far apart, and Inter-Meeting Time Based (IMTB) selects nodes whose expected meeting time is late. Simulation results show IMTB selects fewer nodes than ILB while maintaining coverage, especially for larger AOIs, T, and node populations. Future work includes updating the node selection and extending the selection area.
Energy-Efficient Cooperative Download for Smartphone Users through Contact Ti...Ubi NAIST
The document proposes an energy-efficient cooperative download method for smartphone users. The method uses a server to predict contact times and probabilities between users to construct contact tables. Users then schedule chunk acquisitions from other users and the cellular network based on the tables and chunk rarities. Experimental results show the method reduces cellular usage by 10-28% and battery consumption by 20-30% compared to always using short-range communication, while still completing downloads.
Cost-Efficient Sensor Deployment in Indoor Space with ObstaclesUbi NAIST
The document proposes algorithms for cost-efficient sensor deployment in indoor spaces with obstacles. It formulates the problem of minimizing deployment cost while achieving full coverage and connectivity. A heuristic algorithm is presented that calculates the "per-cost volume" of potential sensor locations and iteratively places sensors to maximize this value. The algorithm is extended to address mobile obstacle coverage by dividing the monitoring area into spherical wedges and ensuring at least one sensor is placed in each wedge. An evaluation demonstrates the approach reduces costs by 45% compared to alternative methods and experiments validate achieving mobile 3-coverage.
Estimating Heart Rate Variation during Walking with SmartphoneUbi NAIST
This document presents a method for estimating heart rate variation during walking using only sensors available on a smartphone. The method constructs a heart rate prediction model using machine learning with inputs of gradient, acceleration amplitude, and estimated oxygen uptake. Evaluation with 18 subjects walking 5 routes showed the method achieved a mean absolute error of less than 7 beats per minute, accurately tracking heart rate variation. Introducing estimated oxygen uptake as a parameter improved accuracy, demonstrating its effectiveness for heart rate prediction.
Gamification-based Incentive Mechanism for Participatory SensingUbi NAIST
1) The document proposes using gamification as an incentive mechanism for participatory sensing to motivate users and reduce costs for clients.
2) An experiment was conducted with 18 users over one month to obtain a participation probability model and found gamification increased probability while difficulty decreased it.
3) A reward minimization problem is formulated to select users and determine reward points to minimize total cost while ensuring sensing requirements are met.
[Ubicomp'15]SakuraSensor: Quasi-Realtime Cherry-Lined Roads Detection throug...Ubi NAIST
SakuraSensor, a system which senses and shares the information of roads with flowering cherries by leveraging car-mounted smart-phones.
Honorable Mention Award of UbiComp2015.
Distance and Time Based Node Selection for Probabilistic Coverage in People-C...Ubi NAIST
This document proposes algorithms to select mobile sensor nodes for probabilistic coverage in people-centric sensing applications. It formulates the problem of ensuring an area of interest (AOI) is covered with probability α within time T. Two algorithms are presented: Inter-Location Based (ILB) selects nodes far apart, and Inter-Meeting Time Based (IMTB) selects nodes whose expected meeting time is late. Simulation results show IMTB selects fewer nodes than ILB while maintaining coverage, especially for larger AOIs, T, and node populations. Future work includes updating the node selection and extending the selection area.
Energy-Efficient Cooperative Download for Smartphone Users through Contact Ti...Ubi NAIST
The document proposes an energy-efficient cooperative download method for smartphone users. The method uses a server to predict contact times and probabilities between users to construct contact tables. Users then schedule chunk acquisitions from other users and the cellular network based on the tables and chunk rarities. Experimental results show the method reduces cellular usage by 10-28% and battery consumption by 20-30% compared to always using short-range communication, while still completing downloads.
Cost-Efficient Sensor Deployment in Indoor Space with ObstaclesUbi NAIST
The document proposes algorithms for cost-efficient sensor deployment in indoor spaces with obstacles. It formulates the problem of minimizing deployment cost while achieving full coverage and connectivity. A heuristic algorithm is presented that calculates the "per-cost volume" of potential sensor locations and iteratively places sensors to maximize this value. The algorithm is extended to address mobile obstacle coverage by dividing the monitoring area into spherical wedges and ensuring at least one sensor is placed in each wedge. An evaluation demonstrates the approach reduces costs by 45% compared to alternative methods and experiments validate achieving mobile 3-coverage.
Estimating Heart Rate Variation during Walking with SmartphoneUbi NAIST
This document presents a method for estimating heart rate variation during walking using only sensors available on a smartphone. The method constructs a heart rate prediction model using machine learning with inputs of gradient, acceleration amplitude, and estimated oxygen uptake. Evaluation with 18 subjects walking 5 routes showed the method achieved a mean absolute error of less than 7 beats per minute, accurately tracking heart rate variation. Introducing estimated oxygen uptake as a parameter improved accuracy, demonstrating its effectiveness for heart rate prediction.
Gamification-based Incentive Mechanism for Participatory SensingUbi NAIST
1) The document proposes using gamification as an incentive mechanism for participatory sensing to motivate users and reduce costs for clients.
2) An experiment was conducted with 18 users over one month to obtain a participation probability model and found gamification increased probability while difficulty decreased it.
3) A reward minimization problem is formulated to select users and determine reward points to minimize total cost while ensuring sensing requirements are met.