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Standard structural assessments of ship hulls include the evaluation of the elastic structural response. Elastic analysis neglects extreme and unpredicted loadings, which can produce catastrophic outcomes, such as the loss of the ship’s ultimate strength. Moreover, hull elements are considered unaffected by age-related degradation. Therefore, this study models and quantifies the effect of corrosion-induced structural degradation on the ultimate strength of a high-tensile-steel (HTS) cargo ship using progressive collapse and nonlinear finite element methods. Uniform and pitting corrosion are modeled through selected scenarios, which hull elements might encounter during exploitation, producing a total of 148 calculation models. The findings show that corrosion-induced degradation can significantly decrease the ultimate strength of the hull (up to 30% for the most severe scenarios assessed). Furthermore, ultimate strength decreases almost proportionally to the amount of wastage considered. It was found that stiffener corrosion has a significant effect on the total ultimate strength. This study’s aim is to emphasize the vast importance of including ultimate strength along with ageing effects in industry-standard structural assessments of large HTS ship structures, designed to last for several decades whilst exposed to excessive and unpredicted bending moments.

This paper discusses research results with regard to energy-efficient transmission of serialised data between servers and mobile devices. A test environment was created in which the research authors primarily measured electricity consumption during communication between a mobile device and server. Numerical results were used to determine how well data serialisation was performed on a dedicated server and its effects on the power consumption of a mobile device. The time spent in data serialisation and the size of the serialised file were found to significantly influence energy consumption. Based on that fact, results have been used to create a mathematical model which was later introduced with functional forms. The main variables in those functional forms were time of serialisation and size of a serialised file. The data collected through this research has been used for an experimental API-CB Saver, which based on mathematical models chooses the most favourable manner of serialisation and compression in real time. The results collected during the tests show that the CBSaver-Api approach performs with greater energy efficiency than current techniques. Furthermore, with optimal selection of data serialisation type and compression level in real time the considered system shows better performance in power saving. According to the results, the API-CBSaver tests indicate the direction which one should take for the purposes of improving energy efficiency.

Abstract In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera networks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly occulted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed adaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF), incorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or target existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast convergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest probability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise influence allows getting an insight into the relationship between the local trackers and the global consensus scheme. A comparison with analogous existing methods done by extensive simulations shows that the proposed method achieves the best performance, in spite of lower communication and computation requirements.

In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.