scholarly journals Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning Formulation

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3758 ◽  
Author(s):  
Xinbin Li ◽  
Xianglin Xu ◽  
Lei Yan ◽  
Haihong Zhao ◽  
Tongwei Zhang
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Energy Efficient ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Underwater Sensor Networks ◽  
Underwater Glider ◽  
Threshold Policy ◽  
Time Varying Environment ◽  
Autonomous Underwater Glider

The autonomous underwater glider has attracted enormous interest for underwater activities, especially in long-term and large-scale underwater data collection. In this paper, we focus on the application of gliders gathering data from underwater sensor networks over underwater acoustic channels. However, this application suffers from a rapidly time-varying environment and limited energy. To optimize the performance of data collection and maximize the network lifetime, we propose a distributed, energy-efficient sensor scheduling algorithm based on the multi-armed bandit formulation. Besides, we design an indexable threshold policy to tradeoff between the data quality and the collection delay. Moreover, to reduce the computational complexity, we divide the proposed algorithm into off-line computation and on-line scheduling parts. Simulation results indicate that the proposed policy significantly improves the performance of the data collection and reduces the energy consumption. They prove the effectiveness of the threshold, which could reduce the collection delay by at least 10% while guaranteeing the data quality.

Energy Efficient Data Collection in Large-Scale Internet of Things via Computation Offloading

2019 ◽  
Vol 6 (3) ◽  
pp. 4176-4187 ◽  
Author(s):  
Guorui Li ◽  
Jingsha He ◽  
Sancheng Peng ◽  
Weijia Jia ◽  
Cong Wang ◽  
...  
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Large Scale ◽  
Computation Offloading ◽  
Efficient Data ◽  
Efficient Data Collection

scholarly journals Data Quality in Web and App Diaries: A Person-Level Comparison

2021 ◽  
Author(s):  
Stella Chatzitheochari ◽  
Elena Mylona
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Large Scale ◽  
New Technologies ◽  
Mode Selection ◽  
Time Period ◽  
Level Data ◽  
Data Collection Instrument ◽  
Respondent Fatigue ◽  
Over Time

The time-use diary is a complex and burdensome data collection instrument. This can negatively affect data quality, leading to less detailed and/or inaccurate activity reporting as the surveyed time period unfolds. However, it can also be argued that data quality may actually improve over time as respondents become more familiar with the diary instrument format and more interested in the diary task. These competing hypotheses have only been partially tested on data from paper and telephone-administered diaries, which are traditionally used for large-scale data collection. Less is known about self-administered modes that make use of new technologies, despite their increasing popularity among researchers. This research note rectifies this omission by comparing diary quality in self-administered web and app diaries, drawing on data from the Millennium Cohort Study. We construct a person-level data quality typology, using information on missing data, episode changes, and reporting of key daily activity domains. Results show significant mode differences on person-level data quality, after controlling for characteristics known to influence diary mode selection and data quality. App diarists were more likely to return two diaries of inconsistent quality. Both respondent fatigue and improvement of completion over time appear more common among app diarists.

scholarly journals Mode Effects on Data Quality and Measurement in a Mixed-Mode Time-Use Survey

2021 ◽  
Author(s):  
Stella Chatzitheochari ◽  
Elena Mylona
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Time Use ◽  
Mixed Mode ◽  
Large Scale ◽  
New Technologies ◽  
Quality Data ◽  
Mode Effects ◽  
Time Use Survey ◽  
Time Diaries

Recent years have witnessed an increasing interest in the use of new technologies for time-use data collection, driven by their potential to reduce survey administration costs and improve data quality. However, despite the steady growth of studies that employ web and app time diaries, there is little research on their comparability with traditional paper-administered diaries that have long been regarded as the “gold standard” for measurement in time-use research. This paper rectifies this omission by investigating diary mode effects on data quality and measurement, drawing on data from a mixed-mode large-scale time diary study of adolescents in the United Kingdom. After controlling for selection effects, we find that web and app diaries yield higher quality data than paper diaries, which attests to the potential of new technologies in facilitating diary completion. At the same time, our analysis of broad time-use domains does not find substantial mode effects on measurement for the majority of daily activity categories. We conclude by discussing avenues for future methodological research and implications for time-use data collection.

scholarly journals Data Quality in Web and App Diaries: A Person-Level Comparison

2021 ◽  
Author(s):  
Stella Chatzitheochari ◽  
Elena Mylona
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Large Scale ◽  
New Technologies ◽  
Mode Selection ◽  
Time Period ◽  
Level Data ◽  
Data Collection Instrument ◽  
Respondent Fatigue ◽  
Over Time

The time-use diary is a complex and burdensome data collection instrument. This can negatively affect data quality, leading to less detailed and/or inaccurate activity reporting as the surveyed time period unfolds. However, it can also be argued that data quality may actually improve over time as respondents become more familiar with the diary instrument format and more interested in the diary task. These competing hypotheses have only been partially tested on data from paper and telephone-administered diaries, which are traditionally used for large-scale data collection. Less is known about self-administered modes that make use of new technologies, despite their increasing popularity among researchers. This research note rectifies this omission by comparing diary quality in self-administered web and app diaries, drawing on data from the Millennium Cohort Study. We construct a person-level data quality typology, using information on missing data, episode changes, and reporting of key daily activity domains. Results show significant mode differences on person-level data quality, after controlling for characteristics known to influence diary mode selection and data quality. App diarists were more likely to return two diaries of inconsistent quality. Both respondent fatigue and improvement of completion over time appear more common among app diarists.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

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