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Sensors 2018, 18(3), 759;

A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation

Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
Institut de Robòtica i Informàtica Industrial, CSIC-UPC, 08028 Barcelona, Spain
PRISM Center and School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
This paper is an extended version of our paper published in Tkach, I.; Edan, Y.; Jevti´c, A.; Nof, S.Y. Automatic Multi-Sensor Task Allocation using Modified Distributed Bees Algorithm. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), Manchester, UK, 13–16 October 2013; pp. 1401–1406.
Author to whom correspondence should be addressed.
Received: 15 December 2017 / Revised: 24 February 2018 / Accepted: 27 February 2018 / Published: 2 March 2018
(This article belongs to the Special Issue Smart Decision-Making)
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Multi-sensor systems can play an important role in monitoring tasks and detecting targets. However, real-time allocation of heterogeneous sensors to dynamic targets/tasks that are unknown a priori in their locations and priorities is a challenge. This paper presents a Modified Distributed Bees Algorithm (MDBA) that is developed to allocate stationary heterogeneous sensors to upcoming unknown tasks using a decentralized, swarm intelligence approach to minimize the task detection times. Sensors are allocated to tasks based on sensors’ performance, tasks’ priorities, and the distances of the sensors from the locations where the tasks are being executed. The algorithm was compared to a Distributed Bees Algorithm (DBA), a Bees System, and two common multi-sensor algorithms, market-based and greedy-based algorithms, which were fitted for the specific task. Simulation analyses revealed that MDBA achieved statistically significant improved performance by 7% with respect to DBA as the second-best algorithm, and by 19% with respect to Greedy algorithm, which was the worst, thus indicating its fitness to provide solutions for heterogeneous multi-sensor systems. View Full-Text
Keywords: multi-agent systems; distributed task allocation; swarm intelligence; sensor deployment multi-agent systems; distributed task allocation; swarm intelligence; sensor deployment

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Tkach, I.; Jevtić, A.; Nof, S.Y.; Edan, Y. A Modified Distributed Bees Algorithm for Multi-Sensor Task Allocation. Sensors 2018, 18, 759.

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