Abstract
Network data volumes have seen a substantial increase in recent years, in part due to the massive use of mobile devices, the dissemination of streaming services and the rise of concepts such as IoT. This growing trend highlights the need to improve network monitoring systems to cope with challenges related with performance, flexibility and security. Software-Defined Networking (SDN) and traffic sampling techniques can be combined to provide a toolset that can be used for enhancing network management activities and performance evaluation. In this context, this paper presents a proposal for supporting time-based sampling techniques in SDN, providing network statistics at the controller level and allowing the self-configuration of traffic sampling in network devices. The proposed solution, designed to improve the efficiency and flexibility of network measurement systems, takes into account the underlying need to establish a balance between the reliability of the collected data and the computational effort involved in the sampling process. The proof-of-concept results emphasize the potential of applying and configuring different time-based sampling techniques through a SDN framework and a small set of standard OpenFlow messages. Comparative results on the accuracy and overhead of each technique when sampling real traffic traces are also provided.

Abstract
The multitude of Wireless Sensor Networks (WSNs) environments, being typically resource-constrained, clearly benefit from properties such as adaptiveness and energy-awareness, in particular, in presence of demanding data gathering applications. This paper proposes a self-adaptive, energy-aware sensing scheme for WSNs (e-LiteSense), which aims at self-adjusting the data gathering process to each specific WSN context, capturing accurately the behaviour of physical parameters of interest yet reducing the sensing overhead. The adaptive scheme relies on a set of low-complexity rules capable of auto-regulate the sensing frequency according to the parameters variability and energy levels. The proof-of-concept resorts to real-world datasets to provide evidence of e-LiteSense ability to optimise the data gathering process according to energy levels, improving the trade-off between accuracy and WSN lifetime.

Abstract
Current network management systems urge for a context-aware perspective of the provided network services and the underlying infrastructure usage. This need results from the heterogeneity of services and technologies in place, and from the massive traffic volumes traversing today’s networks. To reduce complexity and improve interoperability, monitoring systems need to be flexible, context-aware, and able to self-configure measurement points (MPs) according to network monitoring tasks requirements. In addition, the use of sampling techniques in MPs to reduce the amount of traffic collected, analysed and stored has become mandatory and, currently, distinct sampling schemes are available for use in operational environments. In this context, the main objective of this paper is the ontological definition of measurement requirements and components in sampling-based monitoring environments, with the aim of supporting an expert recommendation system able to understand context and identify the appropriate configuration rules to apply to a selection of MPs. In this way, the ontology, defining management needs, network measurement topology and sampling techniques, is described and explored considering several network management activities. A use case focusing on traffic accounting as monitoring task is also provided, demonstrating the expressiveness of the ontology and the role of the recommendation system in assisting context-aware network monitoring based on traffic sampling. © Springer Nature Switzerland AG 2019.

Abstract
Providing public services through the internet is an effective approach towards an encompassing number of citizens being covered by them and for cost reduction. However, the fast development of this area has fostered discussion and legislation regarding information security and trustworthiness. In addition to security mechanisms for data processed and stored internally, service providers must ensure that data exchanged between their servers and citizens are not intercepted or modified when traversing heterogeneous and uncontrolled networks. Moreover, such institutions should provide means enabling the citizen to verify the authenticity of the services offered. In this way, the present work provides a comprehensive overview regarding the security posture of Portuguese public institutions in their online services. It consists of non-invasive robustness evaluation of the deployed solutions for end-to-end data encryption and the correct use of digital certificates. As a result, we provide some recommendations aiming to enhance the current panorama in the majority of the 111 online services considered in this study.

Abstract
In this work we investigate which aspects of data protection regulation must be carefully observed when implementing Blockchain-based projects in smart cities. This technology provides interesting properties and allows governments to develop flexible and innovative data management systems. Nevertheless, realizing the benefits of using Blockchains requires understanding the government processes along with the legal framework and political setting imposed on government. Though it is a buzzword, Blockchain may not always be the best solution for data processing, and carrying out a Data Protection Impact Assessment could allow an analysis of the necessity and proportionality of the mechanism. Furthermore, principles relating to security of data remain applicable to Blockchains. We discuss points of interaction between Blockchain technology and the European Union data protection framework, and provide recommendations on how to better develop Blockchain-based projects in smart cities. The findings of the study should provide public sector actors with a guideline to assess the real necessity and better format of a Blockchain-based application.

Abstract
Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource-constrained infrastructure. Consequently, characteristics such as WSN adaptability, low-overhead, and low-energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e-LiteSense as an adaptive, energy-aware sensing solution for WSNs, capable of auto-regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low-complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e-LiteSense effectiveness in self-regulating data sensing accurately through a low-overhead process where the WSN energy levels are preserved. This constitutes a step-forward for implementing self-adaptive energy-aware data sensing in dynamic WSN environments.