Figure - available from: Mobile Information Systems
This content is subject to copyright. Terms and conditions apply.
![Robot Operating System (ROS) [9].](publication/326853689/figure/fig3/AS:1086427823636513@1636035986432/Robot-Operating-System-ROS-9.jpg)
Source publication
![Existing network and robot simulators. (a) NS-3 structure [3]. (b)...](publication/326853689/figure/fig1/AS:1086427823640582@1636035986275/Existing-network-and-robot-simulators-a-NS-3-structure-3-b-Gazebo-8_Q320.jpg)
![Existing network and robot simulators. (a) NS-3 structure [3]. (b)...](publication/326853689/figure/fig2/AS:1086427823636504@1636035986315/Existing-network-and-robot-simulators-a-NS-3-structure-3-b-Gazebo-8_Q320.jpg)
![Robot Operating System (ROS) [9].](publication/326853689/figure/fig3/AS:1086427823636513@1636035986432/Robot-Operating-System-ROS-9_Q320.jpg)
Recently, UAVs (unmanned air vehicles) have been developed with high performance, and hence, the range of system utilizing UAVs has also been widening. UAVs are even considered as connected mobile sensors and are claimed to be the future of IoT (Internet of Things). UAVs’ mission fulfillment is relying on the efficiency and performance of communica...
Citations
... The preservation of clusters is also very important due to the quick changes in infrastructure of FANET. These clusters are used to coordinate efficiently in critical areas such as the response to accidents, military, or medical operations (Ahn et al. 2018). Table 1 summarizes the comparative research works of several researchers. ...
The utilization of drones with their specific features, such as energy efficiency, dynamic structure, and mobility in Flying Ad-hoc Networks (FANET), has found extensive applications in various fields like disaster management, rescue operations, medical services, and military operations. However, the efficiency and effectiveness of FANET communication can be hindered by challenges related to complex and dynamic environments. The need for optimal path selection among drones is crucial to ensure proficient message transmission, energy efficiency, and secure communication. To address these challenges, an Improved Honey-Badger Optimization-Based Communication Approach (IHBO_CA) is proposed and implemented for optimal path selection among drones in FANET. IHBO_CA leverages a combination of a sinusoidal chaotic map and the honey-badger optimization algorithm to enhance the performance of communication within FANETs. The research findings indicate that IHBO_CA offers significant improvements in the efficiency and effectiveness of communication among drones in FANETs. This improvement is crucial in applications where reliable and energy-efficient communication is essential. The implementation of IHBO_CA using MATLAB 2021 showed promising results, highlighting its superiority over other communication protocols, such as OLSR, MP-OLSR, ACO, PSO, and HBA in terms of energy expenditure, overhead, time complexity, packet delivery ratio, and delay.
... Trong [11], Ahn và cộng sự đã phân tích ứng dụng của FANET trong hoạt động cứu trợ thiên tai, phân tích vấn đề bão quảng bá do cơ chế phát ngập lụt gói điều khiển của các giao thức định tuyến, và đề xuất giải pháp phân cụm phân cấp để giải quyết vấn đề này. FANET được tổ chức theo cấu trúc phân cụm, với 2 lớp gồm lớp các nút chủ cụm và lớp các nút biên. ...
... Để thực hiện nhiệm vụ này, hệ thống FANET được triển khai, trong đó các UAV được sử dụng để giám sát một khu vực cụ thể. Các mini-UAV được trang bị camera và cảm biến, chịu trách nhiệm giám sát toàn bộ khu vực và truyền thông tin về trạm mặt đất khi phát hiện mục tiêu [11,12,13]. ...
The use of FANET for search and rescue operations has attracted considerable attention due to its ability to rapidly deploy communication networks in remote and disaster-affected areas. Routing protocols play an important role in ensuring timely and reliable data transmission to the ground control station. However, the high mobility of UAVs makes the efficiency of routing protocols difficult to achieve as expected, depending heavily on the parameter selection for each mobility model and the range of the UAV. In this study, we examined the mobility samples of two mobility models Gauss-Markov and MassMobility, and proposed a solution to deploy FANET for search and rescue operations (named Regioned FANET). Under this solution, a search and rescue area are divided into fixed zones, the UAVs are assigned to operate in each specific area, all the UAVs cooperate to send data to the ground control station. Simulation results on OMNeT++ showed that changing the parameters of the mobility model affects the routing efficiency, using the proposed deployment strategy improves performance in terms of successful packet delivery
rate, end-to-end latency and average throughput compared to normal deployment strategy.
... The security concerns of messages and protections of control signals are obtained by mechanisms based on cryptography and network security [14][15][16]. But, the security threats such as malicious node access control, unauthorized access control, unprompted connection or any other threats require organized and wellversed solutions without unpropitious effects on the overall performance [17][18][19]. The process of identifying threats and preventing them in the IoD infrastructure put forth the varied research drawbacks that are addressed through secured and efficient mechanisms [20]. ...
... In the field of robotics and wireless sensor networks, multiagent systems play a vital role due to their ability in forming large, interconnected networks with coordination among agents that make them an integral part of a variety of robotic and smart city IoT applications [1], [2]. For example, Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to collaborate with other UAVs [3], [4] or with Unmanned Ground Vehicles (UGV) [5] and WSNs [6], [7]. ...
Search and rescue, wildfire monitoring, and flood/hurricane impact assessment are mission-critical services for recent IoT networks. Communication synchronization, dependability, and minimal communication jitter are major simulation and system issues for the time-based physics-based ROS simulator, event-based network-based wireless simulator, and complex dynamics of mobile and heterogeneous IoT devices deployed in actual environments. Simulating a heterogeneous multi-robot system before deployment is difficult due to synchronizing physics (robotics) and network simulators. Due to its master-based architecture, most TCP/IP-based synchronization middlewares use ROS1. A real-time ROS2 architecture with masterless packet discovery synchronizes robotics and wireless network simulations. A velocity-aware Transmission Control Protocol (TCP) technique for ground and aerial robots using Data Distribution Service (DDS) publish-subscribe transport minimizes packet loss, synchronization, transmission, and communication jitters. Gazebo and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP protocols tested our ROS2-based synchronization middleware for packet loss probability and average latency. A thorough ablation research replaced NS-3 with EMANE, a real-time wireless network simulator, and masterless ROS2 with master-based ROS1. Finally, we tested network synchronization and jitter using one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our middleware shows that a large-scale IoT infrastructure with a diverse set of stationary and robotic devices can achieve low-latency communications (12% and 11% reduction in simulation and real) while meeting mission-critical application reliability (10% and 15% packet loss reduction) and high-fidelity requirements.
... In the field of contemporary robotics, multi-agent systems are set to play a vital part. Due to their ability of forming large interconnected networks with coordination among agents make them an integral part in a variety of robotic applications [1], [2]. For example, Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of applications requiring extensive communications, either to collaborate with other UAVs [3] with each other or with Unmanned Ground Vehicles (UGV) [4], [5]. ...
With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is tedious and may result in the wastage of valuable resources. Especially for heterogeneous multi-agent systems in battlefield environments where communication is critical in determining the system's behavior and usability. Due to the necessity of simulators of separate paradigms (co-simulation) to simulate such scenarios before deploying, synchronization between those simulators is vital. Existing works aimed at resolving this issue fall short of addressing diversity among deployed agents. In this work, we propose \textit{SynchroSim}, an integrated co-simulation middleware to simulate a heterogeneous multi-robot system. Here we propose a velocity difference-driven adjustable window size approach with a view to reducing packet loss probability. It takes into account the respective velocities of deployed agents to calculate a suitable window size before transmitting data between them. We consider our algorithm-specific simulator agnostic but for the sake of implementation results, we have used Gazebo as a Physics simulator and NS-3 as a network simulator. Also, we design our algorithm considering the Perception-Action loop inside a closed communication channel, which is one of the essential factors in a contested scenario with the requirement of high fidelity in terms of data transmission. We validate our approach empirically at both the simulation and system level for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our approach achieves a noticeable improvement in terms of reducing packet loss probability ($\approx$11\%), and average packet delay ($\approx$10\%) compared to the fixed window size-based synchronization approach.
... However, accurate simulation of network protocols cannot be accomplished using network simulators or robotic simulators alone. So, in order to achieve accurate simulations of FANETs, researchers combined a network simulator with a robot simulator [191]. ...
When disasters such as floods or earthquakes occur, we may not have a support of regular infrastructure based networks. This proves fatal because people who are trapped can not be easily located by search and rescue team. In such cases, airborne network consisting of miniaturized drones can be extremely beneficial in providing quick and effective coverage of the affected area, in an on-demand manner providing instant insights to rescue teams. While the challenges offered by such networks are plenty, the ongoing research and development shows promise to make such a technology more reliable and effective. In this paper, we discuss various disaster events in which network of drones can play a vital role in offering support to rescue operations. Mainly, the article discusses the protocols proposed by researchers for various layers of protocol stack including physical layer, data link layer, network layer, transport layer, application layer along with clustering protocols, time synchronization protocols and localization protocols. Finally, a brief summary of software simulation platforms and testbeds, along with future trends of Flying Ad-hoc networks have been provided.
... An optimization is also used to perform high speed packet transmission inside a shorter time and minimum energy expenditure. The trustworthy movable IoT systems are preferably developed on behalf of tragedy liberation actions to perform on time transaction and improved the PDR and reduced the delay [21]. The basic concerns of MANET namely high potency depletion, short stability and high mobility are reduced by using clustering with virtual links. ...
Unmanned Aerial Vehicles (UAVs) are well appropriate devices for wireless communication in Flying Ad-hoc Networks deployed in several applications like disaster and rescue management. The challenging issues of UAVs such as tiny flight time and infertile routing in view of constrained battery ability and maximum movement are abridged by utilizing the Salp Swarm Optimization based Clustering Algorithm (SSOCA). The proficiency of SSOCA is analyzed in terms of the packet delivery ratio, cluster existence period, total clusters, throughput, delay, cluster construction time, and energy consumption and consequences clarify the preferable adeptness of SSOCA against several previous clustering approaches such as MOPSO, CLPSO, CACONET, and CAVDO.
... The constant broadcasting of messages by multiple drones introduces the issue of "broadcast storms," where the network becomes saturated with messages as multiple drones broadcast their messages simultaneously. This can be avoided through techniques that use time-division access, which consists of synchronizing the internal clocks of each drone and allocating time slots during which each drone is allowed to broadcast messages [27]. This eliminates packet collisions as multiple drones are not allowed to transmit messages during the same time slot. ...
With unmanned aerial vehicles (UAVs), also known as drones, becoming readily available and affordable, applications for these devices have grown immensely. One type of application is the use of drones to fly over large areas and detect desired entities. For example, a swarm of drones could detect marine creatures near the surface of the ocean and provide users the location and type of animal found. However, even with the reduction in cost of drone technology, such applications result costly due to the use of custom hardware with built-in advanced capabilities. Therefore, the focus of this thesis is to compile an easily customizable, low-cost drone design with the necessary hardware for autonomous behavior, swarm coordination, and on-board object detection capabilities. Additionally, this thesis outlines the necessary network architecture to handle the interconnection and bandwidth requirements of the drone swarm. The drone on-board system uses a PixHawk 4 flight controller to handle flight mechanics, a Raspberry Pi 4 as a companion computer for general-purpose computing power, and a NVIDIA Jetson Nano Developer Kit to perform object detection in real-time. The implemented network follows the 802.11s standard for multi-hop communications with the HWMP routing protocol. This topology allows drones to forward packets through the network, significantly extending the flight range of the swarm. Our experiments show that the selected hardware and implemented network can provide direct point-to-point communications at a range of up to 1000 feet, with extended range possible through message forwarding. The network also provides sufficient bandwidth for bandwidth intensive data such as live video streams. With an expected flight time of about 17 minutes, the proposed design offers a low-cost drone swarm solution for mid-range aerial surveillance applications.
... Smart wearable devices (i.e., IoT devices) include sensors, actuators, and cameras, for smart environments [11,12]. Thus, drones are equipped with onboard IoT devices connected to IoT devices to perform complex tasks effectively and efficiently [13]. In case of a disaster, drone technology is equipped with IoT devices to capture a map or high-resolution image and sense its surroundings. ...
Disasters, either manmade or natural, call for rapid and timely actions. Due to disaster, all of the communication infrastructures are destroyed, and there is no way for connection between people in disaster and others outside the disaster range. Drone technology is the critical technology for delivering communication services and guiding people and monitoring the unwanted effects of a disaster. The collaboration of advanced technologies can reduce life losses, save people’s lives, and manage the disaster crisis. The network performance of collaboration between the Internet of Things (IoT) and drone edge intelligence can help gather and process data, extend the wireless coverage area, deliver medical emergencies, provide real-time information about the emergency, and gather data from areas that are impossible for humans to reach. In this paper, we focus on the network performance for efficient collaboration of drone edge intelligence and smart wearable devices for disaster management. We focus mainly on network connectivity parameters for improving real-time data sharing between the drone edge intelligence and smart wearable devices. The relevant parameters that are considered in this study include delay, throughput, and the load from drone edge intelligence. It is further shown that network performance can have significant improvement when the abovementioned parameters are correctly optimised, and the improved performance can significantly improve the guiding/coordinating of search and rescue (SAR) teams effectively and efficiently.
... The diversity and capabilities of IoT devices grow exponentially day by day which allows people to use them in different application areas. Tracking the status and location of patients and health care devices in hospitals [5], automation of activities and increasing the quality and efficiency of products in agriculture [6], tracking a mobile object in indoor or outdoor environments [7], controlling the objects in smart homes [8], automation of fabrication in factories [9], fast and efficient rescue systems [10], real-time monitoring systems of critical infrastructures [11], and providing ad-hoc or mobile communication platforms [12] are a few samples of IoT applications. ...
