Target to sensor allocation: A hierarchical dynamic Distributed Constraint Optimization approach

Semnani, Samaneh Hosseini; Basir, Otman

doi:10.1016/j.comcom.2013.01.001

Cited by 10 publications

(16 citation statements)

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“…Reference [1] presents a technique to overcome these limitations and to eliminate impractical simplifications in such methods.…”

Section: Target-to-sensor Allocation In Dcsp/dcop Modelingmentioning

confidence: 99%

“…In addition, modeling this problem as a DCOP/DCSP facilitates the use of various existing DCSP algorithms as bases for solving the instance at hand. This section introduces DCSP and DCOP briefly and highlights research work reported on the use of this modeling technique to address the target-to-sensor allocation problem, particularly focusing on hierarchical DCOP modeling [1].…”

Section: Dcop Modeling Approaches For Target-to-sensor Allocationmentioning

confidence: 99%

“…In this paper we applied the method proposed in [1] for modeling the target-to-sensor allocation problem. This method addresses the problem in two steps:…”

Section: Target-to-sensor Allocation In Dcsp/dcop Modelingmentioning

confidence: 99%

“…However, it also tends to be computationally expensive, and often intractable in large-scale problems. The hierarchical DCOP modeling approach proposed in [1] addresses this challenge by decomposing the sensorto-target allocation problem into smaller sub DCOPs with shared constraints, eliminating significant computation and communication costs.…”

Section: Introductionmentioning

confidence: 99%

“…The formulation captures the issues of sensor-to-target allocation, sensor mobility control, and sensor coordination as intertwined problems. For the sensor-to-target allocation problem, a distributed constraint optimization (DCOP) modeling based approach [1] is employed. Subsequently, the Semiflocking algorithm [2], a flocking-based approach, is employed for control and coordination.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Engagement in Complex Mobile Surveillance Sensor Networks

Semnani

Basir

2017

Un. Sys.

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Efficient use of the network's resources to collect information about objects (events) in a given volume of interest (VOI) is a key challenge in large-scale sensor networks. Multi-sensor multi-target tracking in surveillance applications is an example where the network's success in tracking targets, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance which minimizes the number of uncovered targets. This task can be even more complicated when both the sensors and the targets are mobile. To ensure timely tracking of mobile targets, the surveillance sensor network needs to perform the following tasks in real-time: (i) target-to-sensor allocation; (ii) sensor mobility control and coordination. The computational complexity of these two tasks presents a challenge, particularly in large scale dynamic network applications. This paper proposes a formulation based on the Semi-flocking algorithm and the distributed constraint optimization problem (DCOP). The semiflocking algorithm performs multi-target motion control and coordination, a DCOP modeling algorithm performs the target engagement task. As will be demonstrated experimentally in the paper, this algorithmic combination provides an effective approach to the multisensor/multi-target engagement problem, delivering optimal target coverage as well as maximum sensors utilization.

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“…Reference [1] presents a technique to overcome these limitations and to eliminate impractical simplifications in such methods.…”

Section: Target-to-sensor Allocation In Dcsp/dcop Modelingmentioning

confidence: 99%

Section: Dcop Modeling Approaches For Target-to-sensor Allocationmentioning

confidence: 99%

“…In this paper we applied the method proposed in [1] for modeling the target-to-sensor allocation problem. This method addresses the problem in two steps:…”

Section: Target-to-sensor Allocation In Dcsp/dcop Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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