Ipek Caliskanelli

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Wireless Sensor Networks (WSNs) consist of multiple, distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging trade-offs. This paper proposes a bio-inspired load balancing approach, based on pheromone signalling mechanisms, to solve the trade-off between(More)
Load balancing techniques for distributed embedded systems tend to be very parameter-rich, and finding adequate parameters for a given scenario is not trivial. Furthermore, the parameter values that are adequate for one scenario are rarely applicable to another that has, for instance, a different application profile or network topology. In this paper, we(More)
Wireless Sensor and Robot Networks (WSRNs) are heterogeneous collections of sensor nodes and robotic vehicles that communicate wirelessly. In the last decade many research studies have attempted to address the challenging trade-offs of Wireless Sensor Networks (WSNs) that arise due to their resource limitations, however, much work is still to be done. A(More)
In this paper we propose BeePCo, a multi-robot coverage approach based on honeybee colony behaviour. Specifically, we propose a honeybee inspired pheromone signalling method that allows a team of robots to maximise the total area covered in an environment in a distributed manner. The effectiveness of the proposed algorithm is experimentally evaluated on two(More)
Coordination is one of the most interesting and complicated research issues in distributed multi-robot systems (MRS), aiming to improve performance, energy consumption, robustness and reliability of a robotic system in accomplishing complex tasks. Social insect-inspired coordination techniques achieve these goals by applying simple but effective heuristics(More)
Many real-world scenarios can be modelled as multi-agent systems, where multiple autonomous decision makers interact in a single environment. The complex and dynamic nature of such interactions prevents hand-crafting solutions for all possible scenarios, hence learning is crucial. Studying the dynamics of multi-agent learning is imperative in selecting and(More)
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