RESUMO
The Internet of Things (IoT) has been an area of growing research interest for decades. Task allocation is an important problem for the optimized operation of Internet-of-Things networks. This paper provides an overview of recent research in the field of Internet-of-Things task allocation optimization. First, the task allocation problem for the IoT itself is analyzed and divided into distinct sub-problem categories, such as deployment optimization, static or dynamic optimization as well as single- or multi-objective optimization. Following that, the commonly used optimization objectives are explained. Various recent works in the field of task allocation optimization are then summarized and catalogued according to the problem categories. Finally, the paper concludes with a qualitative analysis of the categorized approaches and a description of open problems and highlights promising directions for future research.
RESUMO
In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)-a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.
RESUMO
Evolving in groups can either enhance or reduce an individual's task performance. Still, we know little about the factors underlying group performance, which may be reduced to three major dimensions: (a) the individual's ability to perform a task, (b) the dependency on environmental conditions, and (c) the perception of, and the reaction to, other group members. In our research, we investigated how these dimensions interrelate in simulated evolution experiments using adaptive agents equipped with Markov brains ("animats"). We evolved the animats to perform a spatial-navigation task under various evolutionary setups. The last generation of each evolution simulation was tested across modified conditions to evaluate and compare the animats' reliability when faced with change. Moreover, the complexity of the evolved Markov brains was assessed based on measures of information integration. We found that, under the right conditions, specialized animats could be as reliable as animats already evolved for the modified tasks, and that reliability across varying group sizes correlated with evolved fitness in most tested evolutionary setups. Our results moreover suggest that balancing the number of individuals in a group may lead to higher reliability but also lower individual performance. Besides, high brain complexity was associated with balanced group sizes and, thus, high reliability under limited sensory capacity. However, additional sensors allowed for even higher reliability across modified environments without a need for complex, integrated Markov brains. Despite complex dependencies between the individual, the group, and the environment, our computational approach provides a way to study reliability in group behavior under controlled conditions. In all, our study revealed that balancing the group size and individual cognitive abilities prevents over-specialization and can help to evolve better reliability under unknown environmental situations.
