No abstract

As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality datasets for testing and evaluating new techniques. In this paper we present a description and results from the first such Science Data Challenge (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8 h, 100 h and 1000 h), SDC1 asked participants to apply source detection, characterization and classification methods to simulated data. The challenge opened in November 2018, with nine teams submitting results by the deadline of April 2019. In this work we analyse the results for 8 of those teams, showcasing the variety of approaches that can be successfully used to find, characterise and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

Network slicing emerges as a key technology in next generation networks, boosted by the integration of software‐defined networking and network functions virtualization. However, while allowing resource sharing among multiple tenants, such networks must also ensure the security requirements needed for the scenarios they are employed. This letter presents the leading security challenges on the use of network slices at the packet core, the solutions that academy and industry are proposing to address them, pointing out some directions that should be considered.

Low Power Wide Area Networks (LPWAN) are becoming the powerful communication technologies of the IoT of tomorrow. LoRaWAN, SigFox, and NB-IoT are the three competing LPWAN technologies. On the other hand, Smart Water Grid (SWG) is an emerging paradigm that promises to overcome issues such as pipes leaks encountered by current water infrastructure by deploying smart devices into the water infrastructure for monitoring purposes. This paper firstly explores the physical and communication features of the above LPWAN technologies and provides a comprehensive comparison between them as well as their suitability for the Smart Water Grid (SWG) use case. The important aspect of SWG is to connect devices such as smart water meters and other tiny devices like sensors installed into the water pipelines for the system monitoring purpose. We consider Advanced Metering Infrastructure (AMI) also called Smart Water Metering when dealing with the water grid, which is the main application of SWG and we study the scalability of LoRaWAN, NB-IoT, and SigFox in such application. Under NS3, the simulation results show that NB-IoT provides the best scalability compared to LoRaWAN and SigFox and thus is able to support a huge number of devices with a low packet error rate.