EDGO: UAV-based effective data gathering scheme for wireless sensor networks with obstacles

Raj, P. V. Pravija; Khedr, Ahmed M.; Aghbari, Zaher Al

doi:10.1007/s11276-022-02983-1

Cited by 13 publications

(5 citation statements)

References 40 publications

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“…UAV data collection considering obstacles: There are some studies on UAV-aided data collection in WSNs where the researchers considered obstacles when planning the UAV path. For example, in [16], the authors considered a single UAV to collect data while avoiding obstacles. They combined various evolutionary procedures to produce a noncollision path for the UAV.…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, when the unit vector is in the x-axis direction and the y-axis vector value of the other vector is less than zero, then to find the actual rotation angle, the angle obtained from the equation should be subtracted from 360 • . Otherwise, the angle calculated from the equation in line 15 is the correct rotation angle (line numbers [16][17][18][19][20][21][22]. Finally, for each current visiting point P c , select the next point with the smallest angle and update P c (line numbers [23][24].…”

Section: Obstacle Avoidance Algorithm (Vraoa)mentioning

confidence: 99%

“…However, these studies did not consider the existence of obstacles in the paths of UAVs, whereas there can be many obstacles (e.g., buildings, mountains, big trees, and no-fly zone areas) in a UAV's trajectory that need to be avoided when it flies from one sensor to another. Although some studies considered obstacles in the UAV trajectories [16][17][18][19][20][21], they did not consider the UAV path as the Hamiltonian path, which is impractical because UAVs are battery-constrained devices and need to return to the base station (BS) to recharge their batteries after a given period of data collection.…”

Section: Introductionmentioning

confidence: 99%

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OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

2022

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Unmanned aerial vehicles (UAVs) are used widely for data collection in wireless sensor networks (WSNs). UAVs visit the sensors to collect the data. UAV-aided data collection is a challenging problem because different paths of a UAV, i.e., visiting orders of sensors, affect energy consumption and data delivery times. The problem becomes more difficult when there are obstacles in the path of the UAV. Thus, the UAV needs to take a detour to avoid them, resulting in different travel distances and times. Therefore, this study formulated the obstacle-aware path planning problem of UAVs, i.e., the obstacle-constrained distance minimization (OCDM) problem, as an integer linear programming problem (ILP) to minimize the total traveling distances of all UAVs while considering the UAVs’ flight time constraints. First, a possible detour-points-selection algorithm called vector rotation-angle-based obstacle avoidance (VRAOA) is proposed to find the detour points around each obstacle in the environment. Then, a genetic algorithm with VRAOA (GA w/VRAOA)is developed to find the trajectories of the UAVs, using the VRAOA and Dijkstra algorithm to find a detour path if there is an obstacle between any two sensors. Finally, simulations were performed for algorithm variants, where GA w/VRAOA outperformed others.

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Section: Related Workmentioning

confidence: 99%

Section: Obstacle Avoidance Algorithm (Vraoa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

2022

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“…For example, the LoS blockage probability of UAV links in urban deployment is presented in [ 20 ]. Effective data acquisition for wireless sensors with obstacles is presented in [ 21 ]. A detailed overview of channel model literature is shown next, followed by an illustration of the objectives and contributions of this paper.…”

Section: Introductionmentioning

confidence: 99%

Customized Millimeter Wave Channel Model for Enhancement of Next-Generation UAV-Aided Internet of Things Networks

Altheeb,

Elshafiey,

Altamimi

et al. 2024

Sensors

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The success of next-generation Internet of Things (IoT) applications could be boosted with state-of-the-art communication technologies, including the operation of millimeter-wave (mmWave) bands and the implementation of three-dimensional (3D) networks. With some access points (APs) mounted on unmanned aerial vehicles (UAVs), the probability of line-of-sight (LoS) connectivity to IoT nodes could be augmented to address the high path loss at mmWave bands. Nevertheless, system optimization is essential to maintaining reliable communication in 3D IoT networks, particularly in dense urban areas with elevated buildings. This research adopts the implementation of a geometry-based stochastic channel model. The model customizes the standard clustered delay line (CDL) channel profile based on the environmental geometry of the site to obtain realistic performance and optimize system design. Simulation validation is conducted based on the actual maps of highly dense urban areas to demonstrate that the proposed approach is comprehensive. The results reveal that the use of standard channel models in the analysis introduces errors in the channel quality indicator (CQI) that can exceed 50% due to the effect of the environmental geometry on the channel profile. The results also quantify accuracy improvements in the wireless channel and network performance in terms of the CQI and downlink (DL) throughput.

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“…It saves costs on installation and maintenance since it uses inexpensive wireless devices [ 20 ]. The IoT combines a number of technologies, including big data, WSN, and cloud computing [ 21 , 22 ]. Sensing, collecting, storing, and transmitting data to the Base Station (BS) are the main tasks of the IoT elements [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

Osamy

Khedr

Alwasel

et al. 2023

Sensors

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Wireless Sensor Networks (WSNs) have been successfully utilized for developing various collaborative and intelligent applications that can provide comfortable and smart-economic life. This is because the majority of applications that employ WSNs for data sensing and monitoring purposes are in open practical environments, where security is often the first priority. In particular, the security and efficacy of WSNs are universal and inevitable issues. One of the most effective methods for increasing the lifetime of WSNs is clustering. In cluster-based WSNs, Cluster Heads (CHs) play a critical role; however, if the CHs are compromised, the gathered data loses its trustworthiness. Hence, trust-aware clustering techniques are crucial in a WSN to improve node-to-node communication as well as to enhance network security. In this work, a trust-enabled data-gathering technique based on the Sparrow Search Algorithm (SSA) for WSN-based applications, called DGTTSSA, is introduced. In DGTTSSA, the swarm-based SSA optimization algorithm is modified and adapted to develop a trust-aware CH selection method. A fitness function is created based on the nodes’ remaining energy and trust values in order to choose more efficient and trustworthy CHs. Moreover, predefined energy and trust threshold values are taken into account and are dynamically adjusted to accommodate the changes in the network. The proposed DGTTSSA and the state-of-the-art algorithms are evaluated in terms of the Stability and Instability Period, Reliability, CHs Average Trust Value, Average Residual Energy, and Network Lifetime. The simulation results indicate that DGTTSSA selects the most trustworthy nodes as CHs and offers a significantly longer network lifetime than previous efforts in the literature. Moreover, DGTTSSA improves the instability period compared to LEACH-TM, ETCHS, eeTMFGA, and E-LEACH up to 90%, 80%, 79%, 92%, respectively, when BS is located at the center, up to 84%, 71%, 47%, 73%, respectively, when BS is located at the corner, and up to 81%, 58%, 39%, 25%, respectively, when BS is located outside the network.

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EDGO: UAV-based effective data gathering scheme for wireless sensor networks with obstacles

Cited by 13 publications

References 40 publications

OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles

Customized Millimeter Wave Channel Model for Enhancement of Next-Generation UAV-Aided Internet of Things Networks

DGTTSSA: Data Gathering Technique Based on Trust and Sparrow Search Algorithm for WSNs

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