BEE-DRONES: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors

Trotta, A.; Felice, Marco Di; Perilli, Luca; Scarselli, Eleonora Franchi; Cinotti, Tullio Salmon

doi:10.1016/j.comnet.2020.107425

Cited by 14 publications

(7 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, a decision of the hello interval is defined in (5). Finally, an action in the network regarding the hello interval is performed that is represented by (6). Periodically, the traffic data is directed to the DDPG for finding better link weights and minimizing the end-to-end delay in the network, in accordance to the state vector and the hello messages.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Currently, unmanned aerial vehicles (UAVs) have been used extensively in several applications [1]- [4], in which either human intervention is not required or required at some extent [5]. However, some problems must be avoided such as the energy loss which is considered as one of the major concerns in UAVs networks [6]. In [7], an adaptive hello messaging scheme was proposed for saving energy however, the study focused only on energy consumption without considering the network throughput.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Hello Dissemination Model for FANET Routing Protocols

et al. 2022

View full text Add to dashboard Cite

The routing mechanisms in flying ad-hoc networks (FANETs) using unmanned aerial vehicles (UAVs) have been a challenging issue for many reasons, such as its high speed and different directions of use. In FANETs, the routing protocols send hello messages periodically for the maintenance of routes. However, the hello messages that are sent in the network increase the bandwidth wastage on some occasions and the excessive number of hello messages can also cause the problem of energy loss. Scarce works deal with the problem of excessive hello messages in dynamic UAVs scenarios, and treat several other problems, such as bandwidth and energy wastage simultaneously. Generally, the existing solutions configure the hello interval to an excessive long or short time period originating delay in neighbors discovery. Thus, a selfacting approach is necessary for calculating the exact number of hello messages with the aim to reduce the bandwidth wastage of the network and the energy loss; this approach needs to be low complex in terms of computational resource consumption. In order to solve this problem, an intelligent Hello dissemination model, AI-Hello, based on reinforcement learning algorithms, that adapts the hello message interval scheme is proposed to produce a dense reward structure, and facilitating the network learning. Experimental results, considering FANET dynamic scenarios of high speed range with 40 UAVs, show that the proposed method implemented in two widely adopted routing protocols (AODV and OLSR) saved 30.86% and 27.57% of the energy consumption in comparison to the original AODV and OLSR protocols, respectively. Furthermore, our proposal reached better network performance results in relation to the state-of-the-art methods that are implemented in the same protocols, considering parameters, such as routing overhead, packet delivery ratio, throughput and delay.INDEX TERMS Unmanned aerial vehicles, FANET, hello messages, energy-efficient networking.

show abstract

Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Hello Dissemination Model for FANET Routing Protocols

et al. 2022

View full text Add to dashboard Cite

show abstract

“…A scheme of waking the sensor only when it is going to transmit is presented in Trotta et al [ 9 ], and they propose a framework called BEE-DRONES for large-scale wireless sensor networks. It considers the optimal trajectory together with the lifetime of the sensors.…”

Section: Work Descriptionmentioning

confidence: 99%

“…A new system for agrometereological data collection in areas lacking communication networks [6] A precision adjustable trajectory planning scheme for UAV-based data collection in IoTs [7] A solution for data collection of large-scale outdoor internet of things based on UAV and dynamic clustering [8] A Survey of Key Issues in UAV Data Collection in the Internet of Things [9] BEE-DRONES: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors [10] Data collection using unmanned aerial vehicles for Internet of Things platforms [11] drone-Enabled Internet-of-Things Relay for Environmental Monitoring in Remote Areas Without Public Networks [12] Dynamic Rendezvous Node Estimation for Reliable Data Collection of a drone as a Mobile IoT Gateway [13] Efficient and Reliable Aerial Communication with Wireless Sensors [14] Efficient data collection by mobile sink to detect phenomena in internet of things [15] Environmental Monitoring Using a drone-Enabled Wireless Sensor Network [16] Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments [17] LoRa Communications as an Enabler for Internet of drones towards Large-Scale Livestock Monitoring in Rural Farms [18] Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges [19] Performance Evaluation of 802.11 IoT Devices for Data Collection in the Forest with drones [20] UAV path planning for emergency management in IoT [21] Area Division Cluster-based Algorithm for Data Collection over UAV Networks [22] A Brief Review of the Intelligent Algorithm for Traveling Salesman Problem in UAV Route Planning [23] Age-optimal trajectory planning for UAV-assisted data collection [24] Age-optimal path planning for finite-battery UAV-assisted data dissemination in IoT networks…”

Section: Reference Titlementioning

confidence: 99%

See 1 more Smart Citation

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Goulart

Pinto

Boava

et al. 2022

Sensors

View full text Add to dashboard Cite

The goal of this work is to present a systematic literature mapping (SLM) identifying algorithms for the search for data, determining the best path and types of communication between the local server and the drone, as well as possible simulators to validate proposed solutions. The concept, here considered as IoT Off-Grid, is characterized by being an environment without commercial electrical infrastructure and without communication connected to the internet. IoT equipment generates data to be stored on a local server. It collects these data through a drone that searches each local server for later integration with the commercial internet environment. As a result, we have algorithms to determine the best path based on the TSP—travelling salesman problem. Different types of communication between the drone and the server contain the data, predominantly WiFi 802.11. As a simulator, OMNeT++ stands out.

show abstract

Hybrid energy‐Efficient distributed aided frog leaping dynamic A* with reinforcement learning for enhanced trajectory planning in UAV swarms large‐scale networks

Christal Jebi,

Baulkani,

Femila

2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryUAVs are emerging as a critical asset in the field of data collection from extensive wireless sensor networks (WSNs) on a large scale. UAVs can be used to deploy energy‐efficient nodes or recharge nodes, but it should not compromise the network's coverage and connectivity. This paper proposes a comprehensive approach to optimize UAV trajectories within large‐scale WSNs, utilizing Multi‐Objective Reinforcement Learning (MORL) to balance critical objectives such as coverage, connectivity, and energy efficiency. This research investigates the configuration of a Wireless Sensor Network (WSN) assisted by a pen_spark UAV. In this network, Cluster Heads (CHs) act as central points for collecting data from their assigned sensor nodes. A predefined path is established for the UAV to efficiently gather data from these CHs. The Hybrid Threshold‐sensitive Energy Efficient Network (Hy‐TEEN) encompasses sophisticated algorithms for CH selection, dynamic A* for 3D trajectory planning and leverages reinforcement learning for multi‐objective optimization. The experimental results and analysis demonstrate the effectiveness and efficiency of the proposed approach in improving UAV performance and energy efficiency. The results demonstrate that the proposed methodology's trajectories are capable of achieving a time savings of 3.52% in mission completion when contrasted with conventional baseline methods.

show abstract

BEE-DRONES: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors

Cited by 14 publications

References 42 publications

Intelligent Hello Dissemination Model for FANET Routing Protocols

Intelligent Hello Dissemination Model for FANET Routing Protocols

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Hybrid energy‐Efficient distributed aided frog leaping dynamic A* with reinforcement learning for enhanced trajectory planning in UAV swarms large‐scale networks

Contact Info

Product

Resources

About