Auflistung nach Autor:in "Mostaghim, Sanaz"
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- TextdokumentEnhancing Resilience in IoT Networks using Organic Computing(INFORMATIK 2020, 2021) Weikert, Dominik; Steup, Christoph; Mostaghim, SanazIn this paper we present and analyse the requirements and challenges of the Task Allocation Problem for Internet-of-Things (IoT) Networks, especially Wireless Sensor Networks (WSNs). As IoT is comprised of a variety of heterogeneous devices and network configuration may change regularly due to low-power nodes failing or communication disruptions, a static allocation of tasks to individual nodes cannot be assumed. Therefore, task allocation has to be carried out and managed for every dynamic change in the network along the lifetime of the network. In dynamic task allocation, a NP-hard problem, the calculation of a new optimal allocation could quickly become a bottleneck for network performance, giving rise to the need of organic computing solutions to provide self-organised task allocation solutions for such networks.
- ZeitschriftenartikelSelf-organized Invasive Parallel Optimization with Self-repairing Mechanism(PARS: Parallel-Algorithmen, -Rechnerstrukturen und -Systemsoftware: Vol. 28, No. 1, 2011) Mostaghim, Sanaz; Pfeiffer, Friederike; Schmeck, HartmutThe parallelization of optimization algorithms is very beneficial when the function evaluations of optimization problems are time consuming. However, parallelization gets very complicated when we deal with a large number of parallel resources. In this paper, we present a framework called Self-organized Invasive Parallel Optimization (SIPO) in which the resources are self-organized. The optimization starts with a small number of resources which decide the number of further required resources on-demand. This means that more resources are stepwise added or eventually released from the platform. In this paper, we study an undesired effect in such a self-organized system and propose a self-repairing mechanism called Recovering-SIPO. These frameworks are tested on a series of multi-objective optimization problems.