Satisfaction-aware Data Offloading in Surveillance Systems

Apostolopoulos, Pavlos Athanasios; Torres, Marcos Lanzón; Tsiropoulou, Eirini Eleni

doi:10.1145/3349625.3355437

Cited by 22 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[31] proposed the mean-field game (MFG) control theory to achieve fast positioning and low flight consumption, in which two partial differential equations are solved by machine learning methods. [32] treated the UAV swarm as an agent and achieves the optimal navigation with an onpolicy RL method SARSA [33], which is a lightweight policy with linear time complexity.…”

Section: Cooperative Exploration and Path Planingmentioning

confidence: 99%

“…Recent advances in edge computing have significantly improved the computing power of the onboard computer, thus many works have tried to use DRL models to control the multi-UAV navigation in real-world-complexity tasks. Different from RL methods that typically treat the whole UAV swarm as a single agent [32], recent DRL-based works seek to control each UAV in a decentralized manner, hence their performance improves with the progress of multi-agent deep reinforcement learning (MADRL).…”

Section: Drl For Multi-uav Navigationmentioning

confidence: 99%

“…Using DRL to control multi-UAV navigation in a distributed manner, especially using the neural networks to express the communication protocol among UAV as GAT-FANET does, requires more computation expense than RL priors [32] and may raise concerns about computing efficiency.We hereby examine whether the onboard computer could operate the proposed DRL-based policy in real-time.…”

Section: Practicality Of the Drl-based Approachmentioning

confidence: 99%

See 2 more Smart Citations

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Ye¹,

Wang²,

Jiang³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>In this paper, we aim to design a deep reinforcement learning (DRL) based control solution to navigating a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under partial observation, which serves as Mobile Base Stations (MBSs) providing optimal communication coverage for the ground mobile users. To handle the information loss caused by the partial observability, we introduce a novel network architecture named Deep Recurrent Graph Network (DRGN), which could obtain extra spatial information through graph-convolution based inter-UAV communication, and utilize historical features with a recurrent unit. Based on DRGN and maximum-entropy learning, we propose a stochastic DRL policy named Soft Deep Recurrent Graph Network (SDRGN). In SDRGN, a heuristic reward function is elaborated, which is based on the local information of each UAV instead of the global information; thus, SDRGN reduces the training cost and enables distributed online learning. We conducted extensive experiments to design the structure of DRGN and examine the performance of SDRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and three heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of SDRGN.</p>

show abstract

Section: Cooperative Exploration and Path Planingmentioning

confidence: 99%

Section: Drl For Multi-uav Navigationmentioning

confidence: 99%

Section: Practicality Of the Drl-based Approachmentioning

confidence: 99%

See 1 more Smart Citation

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Ye¹,

Wang²,

Jiang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In [15], the authors designed a randomized search heuristic (RSH) algorithm to solve the coverage path planning problem in multi-UAV search and rescue tasks, where the search area is transformed into a graph. The authors of [16] proposed a centralized method to navigate UAVs for crowd surveillance. They regarded the multi-agent system as a single agent and improved its Quality of Service (QoS) by using an on-policy RL algorithm state-action-reward-state-action (SARSA) [17].…”

Section: Related Workmentioning

confidence: 99%

Scalable and Transferable Reinforcement Learning for Multi-Agent Mixed Cooperative–Competitive Environments Based on Hierarchical Graph Attention

Chen

Song

et al. 2022

Entropy

View full text Add to dashboard Cite

Most previous studies on multi-agent systems aim to coordinate agents to achieve a common goal, but the lack of scalability and transferability prevents them from being applied to large-scale multi-agent tasks. To deal with these limitations, we propose a deep reinforcement learning (DRL) based multi-agent coordination control method for mixed cooperative–competitive environments. To improve scalability and transferability when applying in large-scale multi-agent systems, we construct inter-agent communication and use hierarchical graph attention networks (HGAT) to process the local observations of agents and received messages from neighbors. We also adopt the gated recurrent units (GRU) to address the partial observability issue by recording historical information. The simulation results based on a cooperative task and a competitive task not only show the superiority of our method, but also indicate the scalability and transferability of our method in various scale tasks.

show abstract

“…The proposed non-cooperative game obtained efficient-satisfaction-equilibrium and determined the transmission power for connected IoT devices. In a recent study [204], the theory of satisfaction games has been utilized for surveillance systems equipped with MEC capabilities. The novel technique also incorporated fully autonomous aerial systems, and satisfied the service requirements of involved entities.…”

Section: E Green Techniques Combining Mobile Edge Computing and Cachingmentioning

confidence: 99%

A Review on Green Caching Strategies for Next Generation Communication Networks

et al. 2020

View full text Add to dashboard Cite

In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching.

show abstract

Satisfaction-aware Data Offloading in Surveillance Systems

Cited by 22 publications

References 11 publications

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning

Scalable and Transferable Reinforcement Learning for Multi-Agent Mixed Cooperative–Competitive Environments Based on Hierarchical Graph Attention

A Review on Green Caching Strategies for Next Generation Communication Networks

Contact Info

Product

Resources

About