Cooperative Navigation of UAVs in GNSS-Denied Area With Colored RSSI Measurements

Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

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“…Available sensor nodes are also equipped with this facility. Thus, the RSSI calculation ability for the UAV is a legitimate assumption [58].…”

Section: Rssi Calculation Abilitymentioning

confidence: 99%

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Nazib

Moh

2021

IEEE Access

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“…Hence, the UAV rotation and translation are unobservable [ 97 ]. In addition, the presence of noise, both white and colored, degrade the positional accuracy of the UAV when using RSSI method [ 98 , 99 , 100 ]. White noise is a type of noise whose power spectrum is flat at different frequency bands and spreads uniformly.…”

Section: Recent Advancement On Uav Tracking Antennamentioning

confidence: 99%

The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review

Kelechi¹,

Alsharif

Oluwole³

et al. 2021

Sensors

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Unmanned aerial vehicle (UAV) antenna tracking system is an electromechanical component designed to track and steer the signal beams from the ground control station (GCS) to the airborne platform for optimum signal alignment. In a tracking system, an antenna continuously tracks a moving target and records their position. A UAV tracking antenna system is susceptible to signal loss if omnidirectional antenna is deployed as the preferred design. Therefore, to achieve longer UAV distance communication, there is a need for directional high gain antenna. From design principle, directional antennas are known to focus their signal energy in a particular direction viewed from their radiation pattern which is concentrated in a particular azimuth direction. Unfortunately, a directional antenna is limited by angle, thus, it must always be directed to the target. The other limitation of a UAV mechanical beam steering system is that the system is expensive to maintain and with low reliability. To solve this problem, we are proposing the use of MIMO technology as a readily available technology for UAV beyond line of sight technology. Although UAV antenna tracking is domiciled in the mechanical beam steering arrangement, this study shows that this native technology could be usurped by MIMO beam forming.

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“…The receiving sensor nodes form a list of its neighboring nodes

based on RSSI values. Both the UAV and deployed sensors are capable of calculating the RSSI, as it is trivial for a WSN device to calculate the RSSI, which is a legitimate assumption [ 41 ]. Moreover, the proposed system assumes a low-altitude UAV, the sensors are clustered together and UAV fine-tunes the position for data collection.…”

Section: System Modelmentioning

confidence: 99%

Trajectory Design for UAV-Based Data Collection Using Clustering Model in Smart Farming

Qayyum

Trabelsi

Malik

et al. 2021

Sensors

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Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to the nearest base station. In the second phase, the BS finds the optimally available fog node based on efficiency, response rate, and availability to send workload for processing. The proposed framework is implemented in OMNeT++ and compared with existing work in terms of energy and network delay.

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Cooperative Navigation of UAVs in GNSS-Denied Area With Colored RSSI Measurements

Cited by 16 publications

References 54 publications

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review

Trajectory Design for UAV-Based Data Collection Using Clustering Model in Smart Farming

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