A Novel UAV-Aided Network Architecture Using Wi-Fi Direct

Khan, Muhammad Salman; Hamila, Ridha; Kıranyaz, Serkan; Gabbouj, Moncef

doi:10.1109/access.2019.2916041

Cited by 20 publications

(9 citation statements)

References 55 publications

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“…We have used ns-3 (Network Simulator version 3. It can be used for UAVs networks simulation [9,10]. Using this tool, we have implemented the EP-OLSR routing protocol and compared it with OLSR, DSDV, AODV, and DSR; by using the IEEE 802.11b environment.…”

Section: Methodsmentioning

confidence: 99%

Link Breakage Time Prediction Algorithm for Efficient Power and Routing in Unmanned Aerial Vehicle Communication Networks

Nawaz¹,

Mansoor²

2020

IJACSA

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UAV Communication Networks (UAVCN) comes under the umbrella of Ad hoc Network technology. It has the critical differences with existing wireless networks, which are high mobility, high speed, dynamic updates, and changes in topology due to high movement, which creates the problem of link breakages and affects the routing performance. This problem degrades the performance of UAVCN in terms; it decreases throughput and minimizes the packet delivery ratio. In this paper, we have tried to overcome this problem by considering the received signal power strength (RSPS). We have proposed an algorithm which uses the received signal power strength and time and calculates the link breakage time prediction by using the interpolation method. We have implemented the proposed technique by modifying the OLSR protocol. The extended protocol termed EPOLSR, which efficiently using the signal power strength and time and increasing the performance of UAVCN. The extended protocol implemented by using a research tool network simulator (v3). The metrics received rate, no of received packets, throughput, and packet delivery ratio (PDR) is considered for evaluation. We have examined the proposed EPOLSR with existing routing protocols. It has been observed that the modified protocol performs better concerning all existing evaluated routing approaches.

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Section: Methodsmentioning

confidence: 99%

Link Breakage Time Prediction Algorithm for Efficient Power and Routing in Unmanned Aerial Vehicle Communication Networks

Nawaz¹,

Mansoor²

2020

IJACSA

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“…There are also many other communications technologies that can be used by DL-UAVs, like 3G/4G/5G [106,107], ZigBee (IEEE 802.15.4) [103], Long-Range Wide Area Network (LoRaWAN) [108], Ultra Wide Band (UWB) [109], IEEE 802.11ah [110], or Wi-Fi Direct [111]. Table 3 shows a comparison on the main characteristics of the latest communications technologies for DL-UAVs [4], indicating their frequency band, coverage, data rate, power consumption, and potential applications.…”

Section: Communications Subsystemmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

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“…The Wi-Fi Direct technology has been widely adopted in smartphones and other Wi-Fi enabled devices. However, there are still several open research problems as addressed in [1], [12], [24]- [32]. For instance, the work in [25] proposes multi-hop group-less communication in Wi-Fi Direct.…”

Section: The Wi-fi Direct Technologymentioning

confidence: 99%

Optimal Group Formation in Dense Wi-Fi Direct Networks for Content Distribution

2019

Self Cite

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Wi-Fi Direct enables direct communication between Wi-Fi devices by forming Peer to Peer (P2P) groups. In each P2P group, one device becomes the Group Owner (GO) and serves as an access point (AP) to connect the remaining devices. The group formation in Wi-Fi Direct has two major limitations. Firstly, it is initiated between two P2P devices only. It does not define any mechanism to allow more than two devices to contend for becoming GO. Secondly, it does not include a selection criteria for the GO (to allow vendor-specific implementation). These limitations can significantly reduce the performance of the Wi-Fi Direct networks. Earlier works addressed these issues using heuristic approaches which do not guarantee optimum performance. Furthermore, the selection of multiple GOs (in dense networks) has not been rigorously investigated in the literature. This paper proposes a modified group formation scheme among multiple devices. The proposed scheme formulates the GO selection problem as an optimization problem which is solved using integer programming (IP). The GOs are selected based on link capacities with the objective to maximize the overall network throughput. In multicast applications, the proposed scheme is implemented such that the minimum achievable rate by any device is maximized. The performance of the proposed GO selection scheme is extensively evaluated through realistic simulation performed in ns-3. The results reveal significant performance gains in terms of group formation time and network throughput. For instance, a throughput gain of 19.8% is achieved using a single GO. The gain is further improved by using a higher number of GOs. In multicast applications, a Packet Loss Ratio (PLR) of 2.8% is maintained. Detailed performance evaluation is presented for several scenarios considering different network sizes, number of GOs, and distribution of user's locations. Moreover, a comparison with state-of-the-art schemes is presented to validate the advantages of the proposed scheme.

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A Novel UAV-Aided Network Architecture Using Wi-Fi Direct

Cited by 20 publications

References 55 publications

Link Breakage Time Prediction Algorithm for Efficient Power and Routing in Unmanned Aerial Vehicle Communication Networks

Link Breakage Time Prediction Algorithm for Efficient Power and Routing in Unmanned Aerial Vehicle Communication Networks

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

Optimal Group Formation in Dense Wi-Fi Direct Networks for Content Distribution

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