Connectivity and collision constrained opportunistic routing for emergency communication using UAV

P, Sharvari N.; Das, Dibakar; Bapat, Jyotsna; Das, Debabrata

doi:10.1016/j.comnet.2022.109468

Cited by 11 publications

(3 citation statements)

References 48 publications

(61 reference statements)

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“…Literature [22] proposed a multi-UAV distributed collision detection and avoidance method based on DRL in order to realize fully distributed UAV collision detection and avoidance, and simulation experiments corroborated that the method is faster in training compared to the pure DRL method and makes up for the defects of insufficient ranging of two-dimensional light detection. Literature [23] proposes a new multi-hop opportunity 3D router (MO3RD) algorithm for post-disaster data transmission, and simulation experiments confirm the feasibility of the closed-resolution model, which enables UAV operations to satisfy coverage constraints and collision constraints within the threshold distance van between UAVs while maximizing data transmission. Literature [24] explores a new sliding mode position tracking control scheme based on surface-like variables and an attitude synchronization control scheme based on non-singular terminal sliding modes to enable UAV obstacle avoidance without losing trajectory.…”

Section: Introductionmentioning

confidence: 92%

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Li,

Lan,

Liang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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With the development of the times, UAVs are gradually promoted and popularized in military and civil fields, and the future airspace will also face more security risks. This paper combines the graph theory network algorithm to plan the initial path of UAV inspection and completes the dynamic collision avoidance path planning in the process of UAV inspection through the established joint model of UAV inspection sensing and avoidance. At the same time, the ant colony algorithm is introduced to improve the graph theory network algorithm to solve the dynamic collision avoidance optimal path in the process of UAV inspection. On this basis, simulation experiments of path planning design for UAV collision avoidance and kinematic simulation with two-fold priority judgment are carried out, and the kinematic parameters corresponding to the collision avoidance path are selected as the analysis anchor points. The extreme values of horizontal speed, climb speed, trajectory inclination angular rate, and heading angular rate are 31.6 m/s, 3 m/s, 5.7°/s, and 18.4°/s, respectively, which are within the given constraints, and verify the reasonableness and effectiveness of the proposed optimization scheme. The average time of the proposed algorithm is 0.0458s, which is much lower than the corresponding 0.7105s of the original scheme algorithm, through data comparison of simulation experiments and experimental validation. Using a graph theory network algorithm, the proposed optimization scheme is more efficient and stable and has a higher success rate for collision avoidance.

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Section: Introductionmentioning

confidence: 92%

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Li,

Lan,

Liang

et al. 2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…[32][33][34] The primary purpose of UAVs' use in different applications is their serviceability in rough environmental conditions and in the areas where ground nodes (robots or vehicles) cannot reach because of the presence of obstacles. 29,[35][36][37][38][39][40] UAVs embedded with low-price and efficient transceivers, such as IEEE 802.11 and cellular networks, can be engaged in enhancing connectivity in wireless networks. They can benefit the networking domain in numerous ways, such as setting up an efficient system in the sky for…”

Section: Uav Assistance Paradigmmentioning

confidence: 99%

“…In the last few decades, UAVs or drones have been engaged in various applications such as industrial inspections, remote sensing, agriculture, military, disaster relief, and so forth 32–34 . The primary purpose of UAVs' use in different applications is their serviceability in rough environmental conditions and in the areas where ground nodes (robots or vehicles) cannot reach because of the presence of obstacles 29,35–40 . UAVs embedded with low‐price and efficient transceivers, such as IEEE 802.11 and cellular networks, can be engaged in enhancing connectivity in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Network partition detection and recovery with the integration of unmanned aerial vehicle

Zear,

Ranga,

Gola

2024

Concurrency and Computation

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SummaryWireless sensor and actor networks (WSANs) consist of nodes associated in an ad hoc manner to perform sensing tasks for information gathering and acting functions on the basis of gathered information. Connectivity is an essential requirement of large‐scale wireless networks, and WSANs are supposed to stay connected. The nodes in hostile environments are prone to failures such as battery depletion, physical damage, or hardware malfunction. The failure of some nodes, like cut vertex nodes, can partition the network into multiple network segments. Most of the solutions for network partition recovery proposed in the literature depend on the assumption that the network is obstacle‐free. However, an obstacle‐free environment is not possible in real‐life situations. In the last few decades, UAVs or drones have been engaged in various applications such as industrial inspections, remote sensing, agriculture, military, disaster relief, and so forth, UAVs can be employed to strengthen the connections in wireless networks by coordinating with ground nodes since they can render services in rough areas where ground nodes cannot provide services. Thus, our research is based on using UAVs as relay nodes to reconnect the disjoint partitions. This paper proposes two algorithms: Drone assisted partition recovery algorithm (DAPRA) and drone assisted detection and partition recovery algorithm (DADPRA). In both algorithms, partitions are detected by the sink node. In DAPRA sink node determines the failed cut‐vertex node and sends UAV to the location of the failed cut‐vertex node. In DADPRA algorithm, UAV identifies the failed cut‐vertex node and reconnects the disjoint network segments. DAPRA and DADPRA are analyzed according to the state‐of‐the‐art parameters, that is, recovery and detection time, UAV's travel distance, and the total messages transmitted. The proposed algorithms are compared with similar Distributed Partition Detection and Recovery using UAV (DPDRU) approach. The simulation results show the proposed algorithms detect network partitioning in less time as compared to DPDRU approach.

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UAV‐SHUTTLE‐MMM: UAV‐shuttle‐enabled mobility management model in sparsely deployed flying ad hoc networks

Agrawal,

Kapoor

2023

Int J Communication

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SummaryThe reliable surveillance of a large region typically needs a large number of unmanned aerial vehicles (UAVs), which leads to high operational costs in flying ad hoc networks (FANETs). Therefore, the UAV mobility model plays an important role that affecting the performance of FANET. This research paper comes up with a novel UAV‐shuttle‐enabled mobility management model (US‐MMM) that is a crucial attribute for the large and highly sparse network. The proposed mobility model uses UAV shuttles. A relatively lesser number of UAVs are used than comparative approaches to enhance the connectivity among the UAVs in a sparse network. UAV shuttle is the special self‐governing node, which traverses the network and collects the data to deliver to the base station (BS). The main strength of the proposed mobility model is the self‐governing ability of the UAV shuttle, which leads to high connectivity in the network. The UAV shuttle executes the proposed dynamic path planning algorithm and uses the adaptive ranking algorithm to determine its target. The main contribution of this research work is to develop a subnet scan and Number of Neighbor and Distance‐based Adaptive Ranking of Targets (NN‐DART) algorithm for dynamic route planning of UAV shuttle. The proposed mobility model is used with various routing schemes to check the difference in the performance of various routing schemes. The proposed mobility model is simulated in NS‐2.35, and the results have shown an improvement in the packet delivery ratio, routing overhead, and end‐to‐end delay.

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Connectivity and collision constrained opportunistic routing for emergency communication using UAV

Cited by 11 publications

References 48 publications

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Study on dynamic collision avoidance during UAV inspection based on improved graph theory network algorithm

Network partition detection and recovery with the integration of unmanned aerial vehicle

UAV‐SHUTTLE‐MMM: UAV‐shuttle‐enabled mobility management model in sparsely deployed flying ad hoc networks

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