Keywords: 3G mobile communication;mobile radio;quality of service;software defined networking;telecommunication congestion control;telecommunication traffic;virtualisation;SDN-based end-to-end flow control;mobile slice environments;unpredictable traffic usage;OVSDB bandwidth configuration;UE-OpenFlow support;handle slice flow-based mobility;telecommunication providers;slice mobility capability;flow-based uplink airtime resources;network function virtualization;software defined networking;Internet service providers;multiinterfaced user equipment;NFV;SDN;virtualised representation of the UE;vUE;mobile network operators;MNO;ISP;over-the-top provider;OTT provider;quality of service;QoS;non3GPP links;3GPP links;Cloud computing;Quality of service;3GPP;Network slicing;Wireless communication;Network function virtualization;Network Slicing;SDN;NFV;Mobility

The fifth generation (5G) telecommunications network aims not only to enhance traffic performance and allow efficient management, but also to enable it to dynamically and flexibly adapt to the traffic demands of different vertical scenarios. In order to support that enablement, the underlying network procedures (i.e., network functions) are being virtualized and deployed in cloud-based environments, allowing for a more optimized usage of the infra-structure resources. In addition, such resources can be sliced, allowing isolated provisioning to specific network functions allocated to disparate vertical deployments. As network slices are envisaged by network operators to fulfill a small number of slices, able to cater towards essential 5G scenario demands (i.e., enhanced mobile broadband, massive machine-type communications and ultra reliable low-latency communications), the total amount of slices existing in a system is currently dictated by the underlying operational overhead placed over the cloud infra-structure. This paper explores the challenges associated to a vision where the network slicing concept is applied with a much greater level of granularity, ultimately allowing it to become a core mechanism of the network's operation, with large numbers of co-existing slices. In that respect, this paper proposes an architecture framework for instantiation of network slices among network providers, which in turn are able to instantiate sub-slices tailored to use cases and vertical tenants. The evaluation of this concept is done following a two-pronged approach: firstly, different slice dimensions (i.e., from micro to macro) are proposed and discussed, pointing out the benefits and challenges of each proposed slice; secondly, we deployed a mobile network provider (MNO), using OpenAirInterface and FlexRAN frameworks, and experimentally evaluated the its slicing mechanisms. The objective is to provide insight on the challenges and impact associated with the deployment of an increasing amount of slices, using the same available infra-structural resources.

Abstract Online access to information while on the move has conferred businesses with the capability to be constantly accessible and in operation, independently of geographical area or time zone. There are situations, however, that demand technical solutions for specific scenarios, such as controlled access to corporate-based content. Virtual Private Networks (VPNs) allow controlled remote access to content, supporting scenarios such as teleworking. Nonetheless, such mechanisms are not commonly associated with the highly mobile users of today, which can traverse different types of access networks, while still keeping access to content restricted to corporate network usage. In addition, as VPN mechanisms are disassociated from mobility procedures, service disruption can happen or specific mechanisms and clients can be required in end-user's equipment. This paper proposes a framework that leverages Network Slicing, enabled by Software Defined Networking and Network Function Virtualisation, to provide seamless and isolated access to corporate-based content while moving through heterogeneous networks. This solution allows Mobile Network Operators to dynamically instantiate isolated network slices for corporate users, and handover them between 3GPP and non-3GPP networks while users move away from the corporate network. In this way, they are able to keep access to corporate-based content in a transparent way, while maintaining access requirements for the service being used. The framework was implemented and validated over an experimental testbed composed by mobile and Wi-Fi accesses, with results presenting improvements in terms of overhead signaling and data redirection without downtime nor stream reconnection.

This paper proposes a framework that optimizes network slicing provisioning in over-the-top (OTT) scenarios, by reducing occupied resources of slices from where the User Equipment (UE) handovers from. To achieve this, the framework leverages an existing Software Defined Networking (SDN)-based UE virtualization scheme, which allows to instantiate in the cloud a representation of the physical device and its current connectivity context. This scheme was extended with the ability to anchor the UE's datapath and mask the underlying slices in a single end-to-end slice, allowing handover mechanisms between slices to become transparent to involved endpoints. This framework was implemented and evaluated in an experimental testbed where the UE moves between Wi-Fi and long-term evolution (LTE) slices, with results showing that upstream and downstream throughput is dynamically adapted to the UE requirements prior to the handover.

Keywords: handover, heterogeneous, mobile offloading, network slicing, NFV, OpenFlow, SDN

Heating appliances consume approximately 48 percent of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

Automatic animal monitoring can bring several advantages to the livestock sector. The emergence of low-cost and low-power miniaturized sensors, together with the ability of handling huge amounts of data, has led to a boost of new intelligent farming solutions. One example is the SheepIT solution that is being commercialized by iFarmtec. The main objectives of the solution are monitoring the sheep’s posture while grazing in vineyards, and conditioning their behaviour using appropriate stimuli, such that they only feed from the ground or from the lower branches of the vines. The quality of the monitoring procedure has a linear correlation with the animal condition capability of the solution, i.e., on the effectiveness of the applied stimuli. Thus, a Real-Time mechanism capable of identifying animal behaviour such as infraction, eating, walking or running movements and standing position is required. On a previous work we proposed a solution based on low-power microcontrollers enclosed in collars wearable by sheep. Machine Learning techniques have been rising as a useful tool for dealing with big amounts of data. From the wide range of techniques available, the use of Decision Trees is particularly relevant since it allows the retrieval of a set of conditions easily transformed in lightweight machine code. The goal of this paper is to evaluate an enhanced animal monitoring mechanism and compare it to existing ones. In order to achieve this goal, a real deployment scenario was availed to gather relevant data from sheep’s collar. After this step, we evaluated the impact of several feature transformations and pre-processing techniques on the model learned from the system. Due to the natural behaviour of sheep, which spend most of the time grazing, several pre-processing techniques were tested to deal with the unbalanced dataset, particularly resorting on features related with stateful history. Albeit presenting promising results, with accuracy over 96%, these features resulted in unfeasible implementations. Hence, the best feasible model was achieved with 10 features obtained from the sensors’ measurements plus an additional temporal feature. The global accuracy attained was above 91%. Howbeit, further research shall assess a way of dealing with this kind of unbalanced datasets and take advantage of the insights given by the results achieved when using the state’s history.

Keywords: Sheep, Animal behaviour, Machine learning, Decision trees, Microcontrollers

Key Performance Indicators (KPIs) are a higher-level characterization of the performance of a network slice, meant to be assessable at any time. Bodies such as the GSM Alliance have proposed the use of KPIs, including, but not limited to, latency, throughput, power consumption, and security. However, while latency, throughput, and power consumption are mensurable universally, security is much harder to measure. In this article, we propose using a Moving Target Defense (MTD) approach and measurable network properties to establish a new straightforward network security metric for underlying resilience against network-centric attacks. We called it DynSec, a comprehensive model for basic network security within the network slice. Monte Carlo experimentation showed that DynSec is accurate and suitable as a KPI.

Keywords: moving target defense, MTD, network slice, network slicing, security KPI