UAV-GCS Centralized Data-Oriented Communication Architecture for Crowd Surveillance Applications

Chriki, Amira; Touati, Haifa; Snoussi, Hichem; Kamoun, Farouk

doi:10.1109/iwcmc.2019.8766641

Cited by 25 publications

(5 citation statements)

References 16 publications

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“…In decentralized architectures, we can further distinguish hierarchical organizations from fully distributed ones. Next, we will describe this taxonomy of architectures [7][8][9], which are schematically depicted in Figure 2. In centralized control structures (depicted in Figure 2a), a single platform directly manages the whole swarm, meaning that it performs any calculations needed to execute control tasks.…”

Section: Swarm Management Organizationmentioning

confidence: 99%

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

Arranz,

Carramiñana,

Miguel

et al. 2023

Sensors

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This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications. The swarm is governed by a central swarm controller responsible for distributing different search and tracking tasks among the cooperating UAVs. Each UAV agent is then controlled by a collection of cooperative sub-agents, whose behaviors have been trained using different deep reinforcement learning models, tailored for the different task types proposed by the swarm controller. More specifically, proximal policy optimization (PPO) algorithms were used to train the agents’ behavior. In addition, several metrics to assess the performance of the swarm in this application were defined. The results obtained through simulation show that our system searches the operation area effectively, acquires the targets in a reasonable time, and is capable of tracking them continuously and consistently.

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Section: Swarm Management Organizationmentioning

confidence: 99%

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

Arranz,

Carramiñana,

Miguel

et al. 2023

Sensors

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“…Chriki et al [21] monitored a crowded area using multiple UAVs and presented a centralized data-oriented communication architecture for crowd surveillance. The dataoriented approach prioritizes the message before sending it.…”

Section: Related Workmentioning

confidence: 99%

Improving Response Time for Crowd Management in Hajj

2021

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Flying Adhoc Network (FANET) is a particular type of Mobile Adhoc Network (MANET) that consists of flying drones or unmanned aerial vehicles (UAVs). MANETs are especially useful in rural and remote areas, where the lack of public networks necessitates data delivery through mobile nodes. Additionally, FANETs provide better coverage where there is a lack of roads. Generally, the goal of FANETs is to provide multimedia data to applications such as search and rescue operations, forest fire detection, surveillance and patrol, environmental monitoring, and traffic and urban monitoring. The above applications’ performance and efficiency depend on the quality and timely delivery of these essential data from an area of interest to control centers. This paper presents a Priority-based Routing Framework for Flying Adhoc Networks (PRoFFAN) for the expedited delivery of essential multimedia data to control centers. PRoFFAN reduces the FANET application’s response time by prioritizing the sending and forwarding of critical image data from the UAV to the control center. Our motivation application is crowd management; we believe that having important image features as early as possible will save lives and enhance the crowd’s safety and flow. We integrated PRoFFAN over the RPL routing layer of Contiki-NG’s IPv6 network stack. We used simulations in Cooja to demonstrate the benefit of PRoFFAN over conventional ZigBee.

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“…The coordination between UAVs requires the establishment of a communication network among UAVs. Generally, this kind of network should be coordinated by control stations located on the ground [115]. One of the challenges of UAV networks is to reduce the impact of the mobility of UAVs.…”

Section: A Uav Networkmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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UAV-GCS Centralized Data-Oriented Communication Architecture for Crowd Surveillance Applications

Cited by 25 publications

References 16 publications

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

Improving Response Time for Crowd Management in Hajj

Softwarization of UAV Networks: A Survey of Applications and Future Trends

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