Optimal Configuration of Alarm Sensors for Monitoring Mobile Ergodic Markov Phenomena on Arbitrary Graphs

Fargeas, Jonathan Las; Kabamba, Pierre T.; Girard, Anouck

doi:10.1109/jsen.2015.2395416

Cited by 6 publications

(3 citation statements)

References 34 publications

(38 reference statements)

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“…[11] proposes a cooperative UAVs model where many targets are visited by a team of UAVs for persistent surveillance and pursuit. In this work, the UAVs do not communicate with each other but rather rely on the information from the static underground sensors, which are optimally placed as proposed in [12]. However, all these models do not consider the persistent data delivery and heterogeneity of UAVs, which might have different fabrics and characteristics.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT

Tuyishimire

Bagula

Rekhis³

et al. 2022

IoT

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The use of Unmanned Aerial Vehicles (UAVs) in data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs’ constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data, which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem, which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located in an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings.

show abstract

Section: Introductionmentioning

confidence: 99%

Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT

Tuyishimire

Bagula

Rekhis³

et al. 2022

IoT

View full text Add to dashboard Cite

show abstract

“…[9] proposes a cooperative UAVs model where many targets are visited by a team of UAVs for persistent surveillance and pursuit. In this work, the UAVs do not communicate with each other but rather rely on the information from the static underground sensors, which are optimally placed as proposed in [10]. However, all these models do not consider the persistent data delivery and heterogeneity of UAVs which might have different fabrics and characteristics.…”

Section: Introductionmentioning

confidence: 99%

Real-time data muling using a team of heterogeneous unmanned aerial vehicles

Tuyishimire¹,

Bagula²,

Rekhis³

et al. 2019

Preprint

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The use of Unmanned Aerial Vehicles (UAVs) in Data transport has attracted a lot of attention and applications, as a modern traffic engineering technique used in data sensing, transport, and delivery to where infrastructure is available for its interpretation. Due to UAVs constraints such as limited power lifetime, it has been necessary to assist them with ground sensors to gather local data which has to be transferred to UAVs upon visiting the sensors. The management of such ground sensor communication together with a team of flying UAVs constitutes an interesting data muling problem which still deserves to be addressed and investigated. This paper revisits the issue of traffic engineering in Internet-of-Things (IoT) settings, to assess the relevance of using UAVs for the persistent collection of sensor readings from the sensor nodes located into an environment and their delivery to base stations where further processing is performed. We propose a persistent path planning and UAV allocation model, where a team of heterogeneous UAVs coming from various base stations are used to collect data from ground sensors and deliver the collected information to their closest base stations. This problem is mathematically formalised as a real-time constrained optimisation model, and proven to be NP-hard. The paper proposes a heuristic solution to the problem and evaluates its relative efficiency through performing experiments on both artificial and real sensors networks, using various scenarios of UAVs settings.

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“…A mission designer is assumed to have set the UGSs locations and patrol parameters before the mission begins; optimal UGSs placement and patrol parameter selection for this base defense scenario is treated in [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Surveillance and Pursuit Using Unmanned Aerial Vehicles and Unattended Ground Sensors

Fargeas

Kabamba

Girard

2015

Sensors

Self Cite

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This paper considers the problem of path planning for a team of unmanned aerial vehicles performing surveillance near a friendly base. The unmanned aerial vehicles do not possess sensors with automated target recognition capability and, thus, rely on communicating with unattended ground sensors placed on roads to detect and image potential intruders. The problem is motivated by persistent intelligence, surveillance, reconnaissance and base defense missions. The problem is formulated and shown to be intractable. A heuristic algorithm to coordinate the unmanned aerial vehicles during surveillance and pursuit is presented. Revisit deadlines are used to schedule the vehicles' paths nominally. The algorithm uses detections from the sensors to predict intruders' locations and selects the vehicles' paths by minimizing a linear combination of missed deadlines and the probability of not intercepting intruders. An analysis of the algorithm's completeness and complexity is then provided. The effectiveness of the heuristic is illustrated through simulations in a variety of scenarios.

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Optimal Configuration of Alarm Sensors for Monitoring Mobile Ergodic Markov Phenomena on Arbitrary Graphs

Cited by 6 publications

References 34 publications

Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT

Trajectory Planing for Cooperating Unmanned Aerial Vehicles in the IoT

Real-time data muling using a team of heterogeneous unmanned aerial vehicles

Cooperative Surveillance and Pursuit Using Unmanned Aerial Vehicles and Unattended Ground Sensors

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