João Marco C. Silva

A multiadaptive sampling technique for cost-effective network measurements

The deployment of efficient measurement solutions to assist network management tasks without interfering with normal network operation assumes a prominent role in todays high-speed networks attending to the huge amounts of traffic involved. From a myriad of proposals for traffic measurement, sampling techniques are particularly relevant contributing effectively for this purpose as only a subset of the overall traffic volume is handled for processing, preserving ideally the correct estimation of network statistical behavior.In this context, this paper proposes MuST - a multiadaptive sampling technique based on linear prediction, aiming at reducing significantly the measurement overhead and still assuring that traffic samples reflect the statistical characteristics of the global network traffic under analysis. Conversely to current sampling techniques, MuST is a multi and self-adaptive technique as both the sample size and interval between samples are self-adjustable parameters according to the ongoing network activity and the accuracy of prediction achieved.The tests carried out demonstrate that the proposed sampling technique is able to achieve accurate network estimations with reduced overhead, using throughput as reference parameter. The evaluation results, obtained resorting to real traffic traces representing wired and wireless aggregated traffic scenarios and actual network services, prove that the simplicity, flexibility and self-adaptability of the proposed technique can be successfully explored to improve network measurements efficiency over distinct traffic conditions. For optimization purposes, this paper also includes a study of the impact of varying the order of prediction, i.e., of considering different degrees of past memory in the self-adaptive estimation mechanism. The significance of the obtained results is demonstrated through statistical benchmarking.

Inside packet sampling techniques: exploring modularity to enhance network measurements

Summary n nTraffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general-purpose architecture is established to sustain the development of flexible sampling-based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area. Copyright

Analysing traffic flows through sampling: A comparative study

Understanding network workload through the characterization of network flows, being essential for assisting network management tasks, can benefit largely from traffic sampling as long as an accurate snapshot of network behavior is captured. This paper is devoted to evaluate the real applicability of using sampling to support flow analysis. Considering both classical and emerging sampling techniques, a comparative performance study is carried out to assess the accuracy of estimating flow parameters through sampling. After identifying the main building blocks of sampled-based measurements, a sampling framework has been implemented to provide a versatile and fair platform for carrying out the testing and comparison process. Through an encompassing coverage of representative sampling techniques, the present study aims to provide useful insights regarding the use of sampling in traffic flow analysis.

Robot orientation with histograms on MSL

One of the most important tasks on robot soccer is localization. The team robots should self-localize on the 18 × 12 meters soccer field. Since a few years ago the soccer field has increased and the corner posts were removed and that increased the localization task complexity. One important aspect to take care for a proper localization is to find out the robot orientation. This paper proposes a new technique to calculate the robot orientation. The proposed method consists of using a histogram of white-green transitions (to detect the lines on the field) to know the robot orientation. This technique does not take much computational time and proves to be very reliable.

Multiadaptive Sampling for Lightweight Network Measurements

Facing the huge traffic volumes involved in todays networks it is of utmost importance to deploy efficient network measurement solutions to assist network management and traffic engineering tasks correctly, without interfering with normal network operation. Sampling techniques contribute effectively for this purpose as the amount of traffic processed is reduced, ideally without endangering the accuracy of network statistical behavior estimation. Although recent proposals of sampling techniques tend to improve the correctness of the estimation process, their underlying overhead is yet considerably when handling high traffic volumes. This paper proposes a new traffic sampling technique for performing lightweight network measurements. This technique, based on linear prediction, is multiadaptive regarding the packet sampling process, allowing to reduce significantly the amount of traffic under analysis while maintaining the representativeness of network samples for accurate network parameters estimation. The performance evaluation of the sampling technique demonstrates the effectiveness and versatility of the proposal when considering real traces representing distinct traffic load scenarios. The statistical analysis provided evinces that the present solution outperforms classic sampling techniques, both in accuracy and amount of data involved in the measurement process.

A modular sampling framework for flexible traffic analysis

The paradigm of having everyone and everything connected in an ubiquitous way poses huge challenges to todays networks due to the massive traffic volumes involved. To turn treatable all network tasks requiring traffic analysis, sampling the traffic has become mandatory triggering substantial research in the area. Aiming at fostering the deployment and tuning of new sampling techniques, this paper presents a flexible sampling framework developed following a multilayer design in order to easily set up the characteristics of a sampling technique according to the measurement task to be assisted. The framework implementation relies on a comprehensive sampling taxonomy which identifies the granularity, selection scheme and selection trigger as the inner characteristics distinguishing current sampling proposals. As proof of concept of the versatility of this framework in testing the suitability of distinct sampling schemes, this work provides a comparative performance evaluation of classical and recent sampling techniques regarding the estimation accuracy, the volume of data involved in the sampling process and the computational weight in terms of CPU and memory usage.

Computational weight of network traffic sampling techniques

Within network measurement context, traffic sampling has been targeted as a promising solution to cope with the huge amount of traffic traversing network devices as only a subset of packets is elected for analysis. Although this brings an evident advantage to measurement overhead, the computational burden of performing sampling tasks in network equipment may overshadow the potential benefits of sampling. Attending that sampling techniques evince distinct temporal and spatial characteristics in handling traffic, this paper is focused on studying the computational weight of current and emerging techniques in terms of memory consumption, CPU load and data volume. Furthermore, the accuracy of these techniques in estimating network parameters such as throughput is evaluated. A sampling framework has also been implemented in order to provide a versatile and fair platform for carrying out the testing and comparison process.

Qualification offer in EGOV competencies in PALOP-TL

Information and Communications Technologies (ICT) have been successfully used in order to promote and pursue the goals of UNs 2030 Agenda for Sustainable Development. Meeting these goals, however, require significant efforts on public policy development, adequate planning and implementation, as well as qualified human resources working at every level of government, public administration and institutions. This paper presents a first quantitative analysis originated from Electronic Government-related training sessions that took place on all five Portuguese Speaking African Countries, and in Timor-Leste along 2017. The results focus on (i) the availability of higher education institutions offering courses related to EGOV on each of those countries; (ii) the qualification of the professionals attending those sessions; and (iii) how availability of local higher education courses translates into qualifications of local professionals serving at public administration level. This paper also discusses some perceptions gathered from the field, both from participants and lecturer teams, framing additional challenges that EGOV-related courses must take into account in those particular settings. It concludes by pointing out some of the works already taking place, which provides a deeper understanding of the workforce competencies in EGOV for each of those countries.

Accessibility in Software Engineering: Pursuing the Mainstream from a Classroom.

Though equal access to all digital devices, content and applications should be ensured by default in the Digital Age, reality has yet to match this ideal, despite the numerous efforts to raise awareness of the problem.

LiteSense: An adaptive sensing scheme for WSNs

Adaptability and energy-efficient sensing are essential properties to sustain the easy deployment and lifetime of WSNs. These properties assume a stronger role in autonomous sensing environments where the application objectives or the parameters under measurement vary, and human intervention is not viable. In this context, this paper proposes LiteSense, a self-adaptive sampling scheme for WSNs, which aims at capturing accurately the behavior of the physical parameters of interest in each WSN context yet reducing the overhead in terms of sensing events and, consequently, the energy consumption. For this purpose, a set of low-complexity rules auto-regulates the sensing frequency depending on the observed parameter variation. Resorting to real environmental datasets, we provide statistical results showing the ability of LiteSense in reducing sensing activity and power consumption, while keeping the estimation accuracy of sensing events.