Sensor scheduling using ant colony optimization

Schrage, Dan; Gonsalves, Paul

doi:10.1109/icif.2003.177471

Cited by 9 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Schrage et al (Schrage, Gonsalves, 2003), the goal of sensor allocation is to minimize the resource usage costs and to maximize the likelihood that all mission objectives will be completed.…”

Section: Sensor Schedulingmentioning

confidence: 99%

A time-triggered object tracking subsystem for advanced driver assistance systems

Elmenreich

Koplin

2011

Elektrotech. Inftech.

View full text Add to dashboard Cite

Multi-sensor object tracking is an important feature for advanced driver assistance systems in future automobiles. Most state-of-the-art systems cannot guarantee deterministic processing of the sensor values due to unsynchronized sensing and processing units. To overcome this shortcoming we propose a paradigm shift towards a time-triggered system architecture providing a deterministic bus system, synchronized nodes, and a global time-base. The paradigm shift is supported by results of a simulation of different synchronization and scheduling approaches which suggest that although non-time-triggered approaches perform well in scenarios with low process noise, the time-triggered model becomes advantageous in potentially dangerous scenarios with high dynamics. In order to validate the results of the simulation for real life scenarios, we analyzed test drives derived from a testbed featuring a Volkswagen Touran being equipped with a laser scanner, a stereo camera system, a FlexRay communication system, an object tracking subsystem and a differential GPS system as reference. Zeitgesteuerte Objekterkennung in Fahrerassistenzsystemen.Im Automobil der Zukunft spielen Fahrerassistenzsysteme eine wichtige Rolle. Ein wichtiges Untersystem sind dabei Objektverfolgungssysteme, welche andere Fahrzeuge mit mehreren Sensoren erfassen und deren Position berechnen. Die Architektur der derzeitigen Systeme kann jedoch oft weder Echtzeiteigenschaften noch Determinismus oder synchronisierte Verarbeitung garantieren. Um dieses Problem zu l€ osen, schlagen die Autoren einen Pradigmenwechsel zu einer zeitgesteuerten Architektur vor. Ein simulationsgest€ utzter Vergleich verschiedener Ans€ atze legte die Vermutung nahe, dass die eventgesteuerten Modelle in Szenarien mit niedriger Dynamik bessere Ergebnisse liefern, in potentiell gef€ ahrlichen Szenarien mit hoher Dynamik aber das zeitgesteuerte Modell von Vorteil ist. Um die Realit€ atsn€ ahe der Simulationsergebnisse zu € uberpr€ ufen, wurden beide Ans€ atze in einer Testumgebung mit einem Volkswagen Touran evaluiert. Das Testfahrzeug war hierf€ ur mit einem Laser-Scanner, einem Stereo-Kamera-System, einem FlexRay-Kommunikationssystem, einem Objektverfolgungssystem und einem Differential-GPS-System als Referenz ausgestattet.

show abstract

Section: Sensor Schedulingmentioning

confidence: 99%

A time-triggered object tracking subsystem for advanced driver assistance systems

Elmenreich

Koplin

2011

Elektrotech. Inftech.

View full text Add to dashboard Cite

show abstract

“…Hohlt et al [ 5 ] proposed a scheduling scheme for considering energy savings in a data collection process. Schrage et al [ 6 ] applied an ant colony optimization method for scheduling the visiting order of targeted areas in the sensing field such that their energy consumptions are minimized. Decker et al [ 7 ] developed a scheduler to manage the competition for resources among different sensing tasks at a single sensor node.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

Wen

Chen

2009

Sensors

View full text Add to dashboard Cite

This paper presents two scheduling management schemes for wireless sensor networks, which manage the sensors by utilizing the hierarchical network structure and allocate network resources efficiently. A local criterion is used to simultaneously establish the sensing coverage and connectivity such that dynamic cluster-based sleep scheduling can be achieved. The proposed schemes are simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithms provide efficient network power control and can achieve high scalability in wireless sensor networks.

show abstract

“…The Ant Colony Optimization (ACO) [1][2][3][4][5] algorithm is a new meta-heuristic that combines distributed computation, auto-catalysis (positive feedback) and constructive greedy heuristic in finding optimal solutions for combinational optimization problem [5]. Compared to previous meta-heuristics, such as genetic algorithms and simulated annealing algorithms, ACO algorithm is still one of best methods to solve TSP.…”

Section: Introductionmentioning

confidence: 99%

Architecture for high-speed Ant Colony Optimization

Yoshikawa

Terai

2007

2007 IEEE International Conference on Information Reuse and Integration

View full text Add to dashboard Cite

The traveling salesman problem (TSP) is one of the most important problems in combinational optimization. Many works have done for this problem using Ant Colony Optimization (ACO). The ACO is one of the most powerful optimization methods that combines distributed computation, auto-catalysis (positive feedback) and constructive greedy heuristic in finding optimal solutions for combinational optimization problems. Most of these previous works deal with software processing. However, ACO has the inherent problem of requiring substantial processing time. Therefore, the dedicated ACO hardware becomes important when applying ACO to combinational problems. In this paper, we propose a new hardware architecture for ACO. No previous studies have, to our knowledge, applied ACO hardware to TSP, as this study does using the proposed architecture. Experimental results to evaluate the proposed algorithm show improvement comparison with software processing.

show abstract

Sensor scheduling using ant colony optimization

Cited by 9 publications

References 6 publications

A time-triggered object tracking subsystem for advanced driver assistance systems

A time-triggered object tracking subsystem for advanced driver assistance systems

Dynamic Hierarchical Sleep Scheduling for Wireless Ad-Hoc Sensor Networks

Architecture for high-speed Ant Colony Optimization

Contact Info

Product

Resources

About