Energy Efficient Task Caching and Offloading in UAV-Enabled Crowd Management

Wu, Guang; Liu, Qiang; Xu, Jinfeng; Miao, Yiming; Pustišek, Matevž

doi:10.1109/jsen.2022.3182779

Cited by 16 publications

(5 citation statements)

References 37 publications

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“…Determine the next visited cluster head node according to equation ( 16); 6: end for 7: Calculate the value corresponding to the path found by each ant according to equation ( 15); 8: Update the pheromone according to equation (17) (18); 9: end for 10: Choose the trajectory that minimizes equation (15), denoted as V, the corresponding value is the optimal value of P2.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Then, the pheromone concentration on the path in the system is updated, and the next path finding is performed. Once the maximum number of iterations has been reached, the trajectory with the smallest value of token (15) in all iterations is output. The following is a summary of the precise steps of the algorithm.…”

Section: Ant Colony Algorithm-based Trajectory Optimizationmentioning

confidence: 99%

“…Step 3. When all ants have finished visiting all nodes and return to DC, one round of trajectory planning is completed, and then, the value corresponding to the trajectory explored by each ant is calculated according to equation (15).…”

Section: Ant Colony Algorithm-based Trajectory Optimizationmentioning

confidence: 99%

“…UAVs, as a stable, mobile, and flexible flight device with low cost, can be used for image recognition or vision processing [12][13][14] and as an auxiliary device in wireless communication networks. In [15], a MEC network consisting of IoT devices, UAV base stations, edge clouds, and data centers with energy-efficient UAV support services is proposed, and a GreenUAV-CoCaCo algorithm is proposed to jointly optimize the communication, caching, and computation energy consumption of UAVs. In [16], cache-enabled UAVs are used to provide contextual messaging services to end devices.…”

Section: Introductionmentioning

confidence: 99%

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Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

Zhou,

Zhu

2024

International Journal of Distributed Sensor Networks

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Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Ant Colony Algorithm-based Trajectory Optimizationmentioning

confidence: 99%

Section: Ant Colony Algorithm-based Trajectory Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

Zhou,

Zhu

2024

International Journal of Distributed Sensor Networks

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“…Also, a joint optimization problem of UAV trajectory, user terminal caching placement, and UAV caching placement to maximize the cache utility was presented. Authors in [27] investigated an energy-e cient UAV-enabled MEC network. An algorithm to jointly optimize caching, communications, and computation for the energy e ciency of UAV was proposed.…”

Section: Related Workmentioning

confidence: 99%

A bi-objective optimization model for the UAV caching at amusement parks in fog computing

Darbanian

Nickray

2023

Preprint

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Unmanned Aerial Vehicle (UAV) is designed to provide low-latency and high-quality transmission with a UAV deployed nearby to serve ground users. UAVs can be used as effective flying base stations for a dynamic cache of popular content, tracking user mobility patterns to provide effective services to users. In this paper, we investigate a network consisting of a UAV as a fog node at amusement parks. We calculate the best location of UAV that is entitled geometric median, which is calculated using a Weiszfeld-type algorithm. We define a bi-objective function problem that simultaneously minimizes the caching energy consumption and maximizes the average service success. Then we use the Goal Programming method to solve it because our problem is non-convex. Finally, we found the best number of cache contents in UAV among Pareto front points with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.

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Mobile Edge Computing in Internet of Unmanned Things (IoUT)

Slamnik-Kriještorac

Márquez-Barja

2023

Internet of Things

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Energy Efficient Task Caching and Offloading in UAV-Enabled Crowd Management

Cited by 16 publications

References 37 publications

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

A bi-objective optimization model for the UAV caching at amusement parks in fog computing

Mobile Edge Computing in Internet of Unmanned Things (IoUT)

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