Indoor Follow Me Drone

Mao, Wenguang; Zhang, Zaiwei; Qiu, Lili; He, Junjia; Cui, Yuchen; Yun, Sungmin

doi:10.1145/3081333.3081362

Cited by 75 publications

(29 citation statements)

References 34 publications

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“…Setting. The recent proliferation of commodity drones has generated an increased interest in the development of aerial sensing and data collection applications [2,4,16,20,33,34], as well as generalpurpose drone orchestration platforms [26,37,40]. An emerging mobility platform is a drones-as-a-service system [21,27,32,46,48], where developers submit apps to a platform that deploys these app tasks on a shared fleet of drones.…”

Section: Case Study: Shared Aerial Sensing Platformmentioning

confidence: 99%

Throughput-fairness tradeoffs in mobility platforms

Balasingam

Gopalakrishnan

Mittal

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness.We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner. CCS CONCEPTS• Computer systems organization → Robotics; Sensor networks; • Computing methodologies → Planning and scheduling; • pplied computing Transportation; • Networks Network A → → resources allocation.

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Section: Case Study: Shared Aerial Sensing Platformmentioning

confidence: 99%

Throughput-fairness tradeoffs in mobility platforms

Balasingam

Gopalakrishnan

Mittal

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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show abstract

“…The accuracy actually does not show much advantage, that is the reason why CAT collaborates FMCW with IMU sensors and Doppler effect. Rabit [43] further employs the distributed FMCW introduced by CAT to video taping and achieves impressive results.…”

Section: Frequency-modulated Continuous Wavementioning

confidence: 99%

Indoor acoustic localization: a survey

Liu

Cheng

Qian

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Applications of localization range from body tracking, gesture capturing, indoor plan construction to mobile health sensing. Technologies such as inertial sensors, radio frequency signals and cameras have been deeply excavated to locate targets. Among all the technologies, the acoustic signal gains enormous favor considering its comparatively high accuracy with common infrastructure and low time latency. Rangebased localization falls into two categories: absolute range and relative range. Different mechanisms, such as Time of Flight, Doppler effect and phase shift, are widely studied to achieve the two genres of localization. The subcategories show distinguishing features but also face diverse challenges. In this survey, we present a comprehensive overview on various indoor localization systems derived from the various mechanisms. We also discuss the remaining issues and the future work.

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“…Tung et al developed a range-free localization technique for room recognition using signal reflections of the surrounding walls and furniture [ 41 ]. Recently, there are attempts to adopt acoustic based signals into drone field, which is one of the fastest emerging fields, including Mao’s work for indoor localization [ 42 ]. The provided is a recent selection of studies using acoustics, and there are more.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks

Jung

Lee

Kim

2018

Sensors

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Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.

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Indoor Follow Me Drone

Cited by 75 publications

References 34 publications

Throughput-fairness tradeoffs in mobility platforms

Throughput-fairness tradeoffs in mobility platforms

Indoor acoustic localization: a survey

Cooperative Computing System for Heavy-Computation and Low-Latency Processing in Wireless Sensor Networks

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