This paper provides a contribution for NS-3 consisting of a new tool for generating Internet traffic. This tool is based on the Poisson Pareto Burst Process (PPBP), a Long-Range Dependent (LRD) model for network traffic. The PPBP model provides a simple and accurate network traffic generator that matches statistical properties of real-life IP networks. We have implemented this model in NS-3. We evaluate the computing performance of this PPBP implementation. Our results show a moderate overhead introduction in terms of memory needs and a roughly identical cost in CPU time as compared to a simple Poisson traffic generator.
Since the end of the 20 th century, bees are suffering from increasing stress factors, leading domesticated colonies to die or at least be less productive. Precision beekeeping (PB) is an emerging field of agriculture that aims at protecting bees, supporting beekeepers, and optimizing apiary production thanks to digital infrastructures. The digitalization of apiculture first involves systems from the field of the Internet of Things (IoT), with the development of sensors to collect and transfer bee-related data. Then, data analysis comes into play, providing models that connect the data with the biological states of beehives, sometimes thanks to artificial intelligence (AI).In this paper, we describe the recent advances in precision beekeeping as systems and as services. Different types of sensors, networks, and power sources in PB are covered. The collection and use of data are described, and the performances of PB services are assessed. We also estimate the sustainability of the proposed solutions, taking into account their scalability, efficiency, and economic cost, because beekeepers need deployable research results.
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