Particle Swarm Optimization for Wireless Charging of Swarming Drones through Ambient Radio Frequencies

Mirzaeinia, Amir; Mirzaeinia, Mehdi; Bradfield, Quincy A.; Bradley, Savannah; Hassanalian, Mostafa

doi:10.2514/6.2019-4463

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…A robot can be sent to collect nasal samples for testing or rendering support to isolated patients, but will need a long battery life and extended capability to execute the entire task properly. In this regard, there has been numerous proposals for wireless charging of drones through laser (Rohan et al, 2019) or ambient radio frequencies (Mirzaeinia et al, 2019). Nevertheless, efficient energy harvesting and optimization techniques need to be deployed in order to charge the drones as they engage in the data collection activities.…”

Section: Expected Challenges and Future Research Directions Challengesmentioning

confidence: 99%

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

et al. 2021

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Social distancing has been regarded as a key method by the authorities worldwide to manage the pandemic of COVID-19. Digital technologies play a crucial role to support the social, professional and economic activities when people are forced to stay locked-down in their homes. Internet of things (IoT) technologies have a track of providing high quality remote health care and automation services which could guarantee social distancing while maintaining health and well-being of populations. In this paper, we propose an end-to-end IoT architecture to support the social distancing in the event of pandemic. The architecture comprises of the major use cases of IoT in relevance with the COVID-19. Furthermore, we also present a short-term and long-term strategy to mange the social distancing methodology using the proposed IoT architecture. The challenges associated with each layer of architecture have been highlighted and design guidelines have been presented to deal with them.

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Section: Expected Challenges and Future Research Directions Challengesmentioning

confidence: 99%

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

et al. 2021

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“…The flight of multiple drones which is inspired by nature is called swarming. Swarming drones can be used to load the network without any burden on network users [31] . They also can be used to scan the network performance.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization

et al. 2019

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Cellular network operators have problems to test their network without affecting their user experience. Testing network performance in a loaded situation is a challenge for the network operator because network performance differs when it has more load on the radio access part. Therefore, in this paper, deploying swarming drones is proposed to load the cellular network and scan/test the network performance more realistically. Besides, manual swarming drone navigation is not efficient enough to detect problematic regions. Hence, particle swarm optimization is proposed to be deployed on swarming drone to find the regions where there are performance issues. Swarming drone communications helps to deploy the particle swarm optimization (PSO) method on them. Loading and testing swarm separation help to have almost non-stochastic received signal level as an objective function. Moreover, there are some situations that more than one network parameter should be used to find a problematic region in the cellular network. It is also proposed to apply multi-objective PSO to find more multi-parameter network optimization at the same time.Cellular networks have been growing rapidly over the last few decades. This technology is exceptionally beloved these days because people can be connected as they move around anyplace. Wireless communication was developed even before cellular networks, but radio communication always suffers from resource limitation (frequency spectrum as the main resource). Resource limitation was the main motivation to propose resource reuse in cellular networks [1] . For example, if a frequency band is used in a certain cell in the cellular network to cover a particular area, it also can be used in another cell which is farther in the distance from the first one. The idea of resource reuse helps to increase the number of users being serviced. This idea of resource reuse is the main difference between cellular network technology and other radio networks. However, this adds to the complexity of fine-tuning of the network to achieve high-performance cells. The reason is that radio interference can decrease the quality of service in these networks. First-generation cellular networks started in the 1980s as analog radio communication. Later on, the 2G network was commercially launched around the

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“…The proposed method has been tested using practical operational data for Tokyo. Another problem that has been faced with EAs is the deployment of CS for UAV: Hu et al 23 faced this problem with GAs and Mirzaeinia et al 24 with Particle Swarm Optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

Niccolai

Bettini

Zich

2021

Int J of Intelligent Sys

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Due to the growing importance of electric vehicles, charging stations (CS) deployment is becoming an important issue in many cities. The aim of this paper is to introduce a novel evolutionary‐based approach for solving the CS deployment problem. This study investigates many aspects of the formulation of this approach, such as the design variables selection and the definition of a feasibility function, to improve both effectiveness and flexibility. In particular, the latter is a key factor compared to many other state‐of‐the‐art approaches: in fact, it can be used with most of the available Evolutionary Algorithms (EAs) and can manage different quality‐of‐service performance parameters. The proposed approach is successfully compared with a greedy optimization on the case study of the City of Milan (Italy) using four different EAs. Two different performance parameters have been defined and used to prove the flexibility of the proposed approach. The results show its very good convergence rate and the quality of the obtained solutions.

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Particle Swarm Optimization for Wireless Charging of Swarming Drones through Ambient Radio Frequencies

Cited by 9 publications

References 24 publications

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic

Loader and tester swarming drones for cellular phone network loading and field test: non-stochastic particle swarm optimization

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

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