ACODS: adaptive computation offloading for drone surveillance system

Jung, Woo‐Sik; Yim, Jinhyuk; Ko, Young-Bae; Singh, Suresh

doi:10.1109/medhocnet.2017.8001647

Cited by 17 publications

(12 citation statements)

References 8 publications

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“…Offloading schemes for UAV systems have been developed recently. In [ 20 ], Jung et al designed a computation offloading system in surveillance drone to a GCS. Adaptive Computation Offloading for Drone Surveillance System (ACODS) architecture is proposed in this work.…”

Section: Related Workmentioning

confidence: 99%

“…Main differences between normal ad-hoc nodes and drone nodes can be seen clearly from the computing power model and mobility model. In this study, the computing power model value from each drone is obtained using a reference computing power value as in [ 20 ]. Moreover, we utilized path-planned mobility model for UAV clusters to match our simulation scenario by modifying the waypoint mobility model on NS-3.…”

Section: Performance Evaluationmentioning

confidence: 99%

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A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

Valentino

Jung

2018

Sensors

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Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones’ tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks

Valentino

Jung

2018

Sensors

Self Cite

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“…Artificial intelligence, navigation strategies, and cryptography have been integrated into UAV communication techniques by various researchers to maintain efficient, reliable, and low-latency communications between nodes of the UAV network [11,12,22,28,29,[32][33][34][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]. However, it is important to consider energy efficiency, as well as the speed of drones for reliable secure communication.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is important to consider energy efficiency, as well as the speed of drones for reliable secure communication. Drones often face issues of inadequate energy and computing resources [25,27,36,38,44,[61][62][63][64][65]. Re-searchers have given insights to optimize solutions for these problems.…”

Section: Introductionmentioning

confidence: 99%

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Communication and networking technologies for UAVs: A survey

Sharma

Vanjani

Paliwal

et al. 2020

Journal of Network and Computer Applications

240

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With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain. But there are many challenges to be tackled, communication being the chief one. This paper aims at providing insights into the latest UAV (Unmanned Aerial Vehicle) communication technologies through investigation of suitable task modules, antennas, resource handling platforms, and network architectures. Additionally, we explore techniques such as machine learning and path planning to enhance existing drone communication methods.Encryption and optimization techniques for ensuring long−lasting and secure communications, as well as for power management, are discussed.Moreover, applications of UAV networks for different contextual uses ranging from navigation to surveillance, URLLC (Ultra-reliable and low−latency communications), edge computing and work related to artificial intelligence are examined. In particular, the intricate interplay between UAV, advanced cellular communication, and internet of things constitutes one of the focal points of this paper. The survey encompasses lessons learned, insights, challenges, open issues, and future directions in UAV communications.

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