Fly, Wake-up, Find: UAV-based Energy-efficient Localization for Distributed Sensor Nodes

Niculescu, Vlad; Palossi, Daniele; Magno, Michele; Benini, Luca

doi:10.1016/j.suscom.2022.100666

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…Data in a WSN are initially gathered by a local node, which then transmits the pertinent data to an infrastructure central to the deployment area but geographically remote from it [8]. Using an unmanned aerial vehicle (UAV) [9] or mobile anchor sensor in place of fixed anchor sensors will reduce the setup costs of WSNs [6].…”

Section: Introduction and Overviewmentioning

confidence: 99%

“…When the UAV approaches the sensor node, it can function as a "smart and ubiquitous" local host and facilitate low-power, short-range data exchange. For instance, this could result in battery-operated sensor nodes for Industry 4.0 or Internet of Things (IoT) scenarios having a much longer lifespan, lasting months or even years [8], [11].…”

Section: Introduction and Overviewmentioning

confidence: 99%

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Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.

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Section: Introduction and Overviewmentioning

confidence: 99%

Section: Introduction and Overviewmentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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“…Leveraging these features, UAVs can make significant contributions to MEC systems and can address the deployment challenges that are associated with terrestrial servers [16]. Despite the deployment of MEC-assisted UAVs, the existing studies still lack optimal resource allocation in terms of UAV and MU constraints [17]. To achieve efficient resource allocation, the existing studies either perform joint optimization of trajectory or the position of the UAV [18].…”

Section: Introductionmentioning

confidence: 99%

JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

Ahmad

Zhang

Khan

et al. 2023

Drones

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Providing robust communication services to mobile users (MUs) is a challenging task due to the dynamicity of MUs. Unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) are used to improve connectivity by allocating resources to MUs more efficiently in a dynamic environment. However, energy consumption and lifetime issues in UAVs severely limit the resources and communication services. In this paper, we propose a dynamic cooperative resource allocation scheme for MEC–UAV-enabled wireless networks called joint optimization of trajectory, altitude, delay, and power (JO-TADP) using anarchic federated learning (AFL) and other learning algorithms to enhance data rate, use rate, and resource allocation efficiency. Initially, the MEC–UAVs are optimally positioned based on the MU density using the beluga whale optimization (BLWO) algorithm. Optimal clustering is performed in terms of splitting and merging using the triple-mode density peak clustering (TM-DPC) algorithm based on user mobility. Moreover, the trajectory, altitude, and hovering time of MEC–UAVs are predicted and optimized using the self-simulated inner attention long short-term memory (SSIA-LSTM) algorithm. Finally, the MUs and MEC–UAVs play auction games based on the classified requests, using an AFL-based cross-scale attention feature pyramid network (CSAFPN) and enhanced deep Q-learning (EDQN) algorithms for dynamic resource allocation. To validate the proposed approach, our system model has been simulated in Network Simulator 3.26 (NS-3.26). The results demonstrate that the proposed work outperforms the existing works in terms of connectivity, energy efficiency, resource allocation, and data rate.

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“…WuRenabled smart lenses [61] can be used to monitor biomarkers in tears, which could find use in the monitoring of worker safety in hazardous industrial environments. • Asset tracking: Asset tracking and localization can be improved using efficient and precise methods using nanodrones, especially in open industrial spaces [62]. The BATS protocol [63] provides a method of tracking wildlife and cattle, which can serve as a basis for improved asset tracking.…”

mentioning

confidence: 99%

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

Rup,

Bajic

2024

Sustainability

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The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

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Fly, Wake-up, Find: UAV-based Energy-efficient Localization for Distributed Sensor Nodes

Cited by 9 publications

References 34 publications

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

JO-TADP: Learning-Based Cooperative Dynamic Resource Allocation for MEC–UAV-Enabled Wireless Network

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

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