Mobile gateway for Wireless Sensor Networks utilizing drones

Deaconu, Ioan-Dragoș; Voinescu, Andrei

doi:10.1109/roedunet-renam.2014.6955319

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…This exhibits the real-time monitoring and efficient energy consumption. Chun (Deaconu and Voinescu, 2014) proposed model to reduce the drone noise to improve the stability and anonymity of the recorder. The authors use convolutional auto encoder technique to improve the acoustic sensor network automation to reduce the noise in the drones.…”

Section: Related Workmentioning

confidence: 99%

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

K.A.

Priyadarshini

P.C.

et al. 2022

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Purpose Data traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordination Design/methodology/approach Fair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE. Findings A multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2. Research limitations/implications However, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture. Practical implications The individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level. Originality/value This study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

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Section: Related Workmentioning

confidence: 99%

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

K.A.

Priyadarshini

P.C.

et al. 2022

View full text Add to dashboard Cite

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“…The mobility and degree of freedom (DoF) of drones enables an instant deployment of networks into regions that are difficult to reach by humans or ground-based vehicles. Already, some research works [ 13 , 14 , 15 , 16 , 17 , 18 ] have tried to adopt the unmanned aerial vehicle (UAV) as the mobile sink node, but these research works have focused on only implementing the drones in the sensor network and have not considered the advanced operation of the drones, such as dynamic path-planning and constructing a network among the drones.…”

Section: Introductionmentioning

confidence: 99%

Devising Mobile Sensing and Actuation Infrastructure with Drones

Bae

Yoo

Jung

et al. 2018

Sensors

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Vast applications and services have been enabled as the number of mobile or sensing devices with communication capabilities has grown. However, managing the devices, integrating networks or combining services across different networks has become a new problem since each network is not directly connected via back-end core networks or servers. The issue is and has been discussed especially in wireless sensor and actuator networks (WSAN). In such systems, sensors and actuators are tightly coupled, so when an independent WSAN needs to collaborate with other networks, it is difficult to adequately combine them into an integrated infrastructure. In this paper, we propose drone-as-a-gateway (DaaG), which uses drones as mobile gateways to interconnect isolated networks or combine independent services. Our system contains features that focus on the service being provided in the order of importance, different from an adaptive simple mobile sink system or delay-tolerant system. Our simulation results have shown that the proposed system is able to activate actuators in the order of importance of the service, which uses separate sensors’ data, and it consumes almost the same time in comparison with other path-planning algorithms. Moreover, we have implemented DaaG and presented results in a field test to show that it can enable large-scale on-demand deployment of sensing and actuation infrastructure or the Internet of Things (IoT).

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