Wireless Sensor and Networking Technologies for Swarms of Aquatic Surface Drones

Velez, Fernando J.; Nadziejko, Aleksandra; Christensen, Anders Lyhne; Oliveira, Sancho; Rodrigues, Tiago; Costa, Vasco; Duarte, Miguel; Silva, Fernando; Gomes, Jorge

doi:10.1109/vtcfall.2015.7391193

Cited by 21 publications

(5 citation statements)

References 12 publications

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“…Indeed, drones have several application potentials for flow measurements in difficult-to-access water environments during adverse hydro-meteorological events. Instead, aquatic surface drones are used in marine environment monitoring for several purposes such as the monitoring of water quality, coral reefs, fish farms, and so on (Velez, 2015;Christensen et al, 2015). These drones operate on the surface of the water, but there exists another class of waterproof UAVs that operate underwater, the so-called underwater drones.…”

Section: Application Areamentioning

confidence: 99%

Drone-aided routing: A literature review

Macrina

Pugliese

Guerriero

et al. 2020

Transportation Research Part C: Emerging Technologies

255

109

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Section: Application Areamentioning

confidence: 99%

Drone-aided routing: A literature review

Macrina

Pugliese

Guerriero

et al. 2020

Transportation Research Part C: Emerging Technologies

255

109

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“…Authors use the Wi-Fi link budget model to measure the communication cost between the drones and IoT sensors to build the computational system. Landolsi et al proposed (Velez et al , 2015) the pollution monitoring system by using the drones. The big disadvantage of pollution-based monitoring cost, energy consumption and clarity of the captured resource.…”

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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“…The management of tradeoff between the security and performance is still an open issue and it becomes even bigger when more than one type of networks are involved in service layoffs. Technologies like Low-Power Wide-Area Network (LPWAN) [35], [36], IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) [37] and Zigbee [38], [39] can be used for obtaining low-power and energy-efficient solutions especially in the deployment of service gateways in ATCNs.…”

Section: Security-performance Tradeoffsmentioning

confidence: 99%

Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)

Sharma¹,

You²,

Seo³

et al. 2019

IEEE Access

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Aerial-terrestrial cloud networks (ATCNs), global integration of air and ground communication systems, pave a way for a large set of applications such as surveillance, on-demand transmissions, dataacquisition, and navigation. However, such networks suffer from crucial challenges of secure and reliable resource allocation and content-caching as the involved entities are highly dynamic and there is no finetuned strategy to accommodate their connectivity. To resolve this quandary, cog-chain, a novel paradigm for secure and reliable resource allocation and content-caching in ATCNs, is presented. Various requirements, key concepts, and issues with ATCNs are also presented along with basic concepts to establish a cogchain in ATCNs. Feed and fetch modes are utilized depending on the involved entities and caching servers. In addition, a cog-chain communication protocol is presented which avails to evaluate the formation of a virtual cog-chain between the nodes and the content-caching servers. The efficacy of the proposed solution is demonstrated through consequential gains observed for signaling overheads, computational time, reliability, and resource allocation growth. The proposed approach operates with the signaling overheads ranging between 30.36 and 303.6 bytes*hops/sec and the formation time between 186 and 195 ms. Furthermore, the overall time consumption is 83.33% lower than the sequential-verification model and the resource allocation growth is 27.17% better than the sequential-verification model. INDEX TERMS Security, reliability, content caching, ATCNs, STINs, cog-chain.

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Wireless Sensor and Networking Technologies for Swarms of Aquatic Surface Drones

Cited by 21 publications

References 12 publications

Drone-aided routing: A literature review

Drone-aided routing: A literature review

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

Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)

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