Dronemap Planner: A service-oriented cloud-based management system for the Internet-of-Drones

Zarrad

2020

JSAN

Self Cite

The combination of wireless sensor networks (WSNs) and 3D virtual environments opens a new paradigm for their use in natural disaster management applications. It is important to have a realistic virtual environment based on datasets received from WSNs to prepare a backup rescue scenario with an acceptable response time. This paper describes a complete cloud-based system that collects data from wireless sensor nodes deployed in real environments and then builds a 3D environment in near real-time to reflect the incident detected by sensors (fire, gas leaking, etc.). The system’s purpose is to be used as a training environment for a rescue team to develop various rescue plans before they are applied in real emergency situations. The proposed cloud architecture combines 3D data streaming and sensor data collection to build an efficient network infrastructure that meets the strict network latency requirements for 3D mobile disaster applications. As compared to other existing systems, the proposed system is truly complete. First, it collects data from sensor nodes and then transfers it using an enhanced Routing Protocol for Low-Power and Lossy Networks (RLP). A 3D modular visualizer with a dynamic game engine was also developed in the cloud for near-real time 3D rendering. This is an advantage for highly-complex rendering algorithms and less powerful devices. An Extensible Markup Language (XML) atomic action concept was used to inject 3D scene modifications into the game engine without stopping or restarting the engine. Finally, a multi-objective multiple traveling salesman problem (AHP-MTSP) algorithm is proposed to generate an efficient rescue plan by assigning robots and multiple unmanned aerial vehicles to disaster target locations, while minimizing a set of predefined objectives that depend on the situation. The results demonstrate that immediate feedback obtained from the reconstructed 3D environment can help to investigate what–if scenarios, allowing for the preparation of effective rescue plans with an appropriate management effort.

Section: Optimized Rescue Plan Developmentmentioning

confidence: 99%

A Cloud Based Disaster Management System

Cheikhrouhou

Zarrad

2020

JSAN

Self Cite

“…In [ 37 , 38 ], the authors have proposed Dronemap Planner, a cloud-based management system for the Internet-of-Drones. The objective was to provide a software interface for the access and management of drones over the Internet.…”

Section: Related Workmentioning

confidence: 99%

“…The cloud also offers all the drones and users management capabilities to ensure their connectivity, communication, authentication, and the availability of the services. We have already implemented this functionality in the Dronemap Planner system [ 37 , 38 , 41 ]. The End-User Layer: This layer represents the end-users who are using the DeepBrain system through the Internet.…”

Section: Proposed System: Deepbrain Systemmentioning

confidence: 99%

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

Ammar

Alahdab

et al. 2020

Sensors

Self Cite

Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.

“…The evolution of Graphic Processing Units (GPUs) also significantly contributed to the adoption of CNN in computer vision overcoming the problems of real-time processing of computation intensive tasks through parallelization. In addition, latest trends in cloud robotics [6], [7], [8] have also enabled offloading heavy computations, such as video stream analysis, to the cloud. This allows to process video streams in real-time using advanced deep learning algorithms in the context of surveillance applications.…”

Section: Introductionmentioning

confidence: 99%

Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3

Benjdira

Khursheed

2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS)

et al. 2019

241

112

Unmanned Aerial Vehicles are increasingly being used in surveillance and traffic monitoring thanks to their high mobility and ability to cover areas at different altitudes and locations. One of the major challenges is to use aerial images to accurately detect cars and count-them in real-time for traffic monitoring purposes. Several deep learning techniques were recently proposed based on convolution neural network (CNN) for real-time classification and recognition in computer vision. However, their performance depends on the scenarios where they are used. In this paper, we investigate the performance of two state-of-the art CNN algorithms, namely Faster R-CNN and YOLOv3, in the context of car detection from aerial images. We trained and tested these two models on a large car dataset taken from UAVs. We demonstrated in this paper that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time, although they are comparable in the precision metric.