In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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When you are destined for an important appoint-ment, you would obviously opt for the most reliable route instead of the shortest in order to be well prepared". Modern networking is presently undergoing through a quantum leap. To cope up with ambitious demands and user expectations, it is becoming more complex both structurally and functionally. Software Defined Networking (SDN) happens to be an instance of such advancements. It has significantly leveraged the network programmability, abstraction, and automation. Eventually, with acceptance form all major network infrastructure such as 5G and Cloud, SDN is becoming the standard of future networking. Likewise, Machine Learning (ML) has become the trendiest skill-in-demand recently. With its superiority of analyzing data, makes it applicable for almost every possible domain. The attempt to applying the power of ML in networking has not been too long, it allows the network to be more intelligent and capable enough to take optimal decisions to address some of its native problems. This gives rise to Self-Organized Networking (SON). In this article, Routing using Deep Neural Network (DNN) on top of SDN is addressed. We proposed a Self-organized Knowledge Defined Network (SO-KDN) architecture and an intelligent routing algorithm, that reactively finds the most reliable route, i.e., a route having least probability of fluctuation. This reduces network overhead due to re-routing and optimizes traffic congestion. Experimental data show a mean 90% accurate forecast in reliability prediction.

Big Data and Artificial Intelligence are new technologies to improve indoor localization. It focuses on the use of machine learning probabilistic algorithms to extract, model and analyse live and historical signal data obtained from several sources. In this respect, the data generated by 5G network and the Internet of Things is quintessential for precise indoor positioning in complex building environments. In this paper, we present a new architecture for assets and personnel location management in 5G network with an emphasis on vertical sectors in smart cities. Moreover, we explain how Big Data and Machine learning can be used to offer positioning as service. Additionally, we implement a new deep learning model for 3D positioning using the proposed architecture. The performance of the proposed model is compared against other Machine Learning algorithms.

The usage of cloud systems is at an all-time high, and with more organizations reaching for Big Data the forensic implications must be analyzed. The Hadoop Distributed File System is widely used both as a cloud service and with organizations implementing it themselves. This paper analyzes the forensic viability of a RAM analysis method for Hadoop based investigations and compared it against targeted process data dumping through the Java heap information. The RAM analysis has been done through string searching and the use of the RAM analysis tool Volatility. This work found that RAM analysis can be a valuable tool for discovering artefacts of deleted resources from a Hadoop cluster but was unable to discover further information such as the block to node mapping. The targeted process analysis managed to provide some partial information about deleted resources and also produce important metadata on the current state of the file system.