Performance Analysis of a DEKF for Available Bandwidth Measurement

The paper presents a characterisation analysis of a measurement algorithm based on a Discrete-time Extended Kalman Filter (DEKF), which has recently been proposed for the estimation and tracking of end-to-end available bandwidth. The analysis is carried out by means of simulations for different rates of variations of the available bandwidth and permits assessing the performance of the measurement algorithm for different values of the filter parameters, that is, the covariance matrixes of the measurement and process noise

Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area

Smart agriculture and wildlife monitoring are one of the recent trends of Internet of Things (IoT) applications, which are evolving in providing sustainable solutions from producers. This article details the design, development and assessment of a wildlife monitoring application for IoT animal repelling devices that is able to cover large areas, thanks to the low power wide area networks (LPWAN), which bridge the gap between cellular technologies and short range wireless technologies. LoRa, the global de-facto LPWAN, continues to attract attention given its open specification and ready availability of off-the-shelf hardware, with claims of several kilometers of range in harsh challenging environments. At first, this article presents a survey of the LPWAN for smart agriculture applications. We proceed to evaluate the performance of LoRa transmission technology operating in the 433 MHz and 868 MHz bands, aimed at wildlife monitoring in a forest vegetation area. To characterize the communication link, we mainly use the signal-to-noise ratio (SNR), received signal strength indicator (RSSI) and packet delivery ratio (PDR). Findings from this study show that achievable performance can greatly vary between the 433 MHz and 868 MHz bands, and prompt caution is required when taking numbers at face value, as this can have implications for IoT applications. In addition, our results show that the link reaches up to 860 m in the highly dense forest vegetation environment, while in the not so dense forest vegetation environment, it reaches up to 2050 m

Performance Analysis of a DEKF for Available Bandwidth Measurement

The paper presents a characterisation analysis of a measurement algorithm based on a Discrete-time Extended Kalman Filter (DEKF), which has recently been proposed for the estimation and tracking of end-to-end available bandwidth. The analysis is carried out by means of simulations for different rates of variations of the available bandwidth and permits assessing the performance of the measurement algorithm for different values of the filter parameters, that is, the covariance matrixes of the measurement and process noise

A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications

Mobile traffics are becoming more dominant due to growing usage of mobile devices and proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic classifications; namely the diversity complexity and behavioral dynamism complexity. Existing traffic classifications methods are designed for classifying standard protocols and user applications with more deterministic behaviors in small diversity. Currently, flow statistics, payload signature and heuristic traffic attributes are some of the most effective features used to discriminate traffic classes. In this paper, we investigate the correlations of these features to the less-deterministic user application traffic classes based on corresponding classification accuracy. Then, we evaluate the impact of large-scale classification on feature's robustness based on sign of diminishing accuracy. Our experimental results consolidate the needs for unsupervised feature learning to address the dynamism of mobile application behavioral traits for accurate classification on rapidly growing mobile traffics

Detection of encrypted traffic generated by peer-to-peer live streaming applications using deep packet inspection

The number of applications using the peer-to-peer (P2P) networking paradigm and their popularity has substantially grown over the last decade. They evolved from the le-sharing applications to media streaming ones. Nowadays these applications commonly encrypt the communication contents or employ protocol obfuscation techniques. In this dissertation, it was conducted an investigation to identify encrypted traf c ows generated by three of the most popular P2P live streaming applications: TVUPlayer, Livestation and GoalBit. For this work, a test-bed that could simulate a near real scenario was created, and traf c was captured from a great variety of applications. The method proposed resort to Deep Packet Inspection (DPI), so we needed to analyse the payload of the packets in order to nd repeated patterns, that later were used to create a set of SNORT rules that can be used to detect key network packets generated by these applications. The method was evaluated experimentally on the test-bed created for that purpose, being shown that its accuracy is of 97% for GoalBit.A popularidade e o número de aplicações que usam o paradigma de redes par-a-par (P2P) têm crescido substancialmente na última década. Estas aplicações deixaram de serem usadas simplesmente para partilha de ficheiros e são agora usadas também para distribuir conteúdo multimédia. Hoje em dia, estas aplicações têm meios de cifrar o conteúdo da comunicação ou empregar técnicas de ofuscação directamente no protocolo. Nesta dissertação, foi realizada uma investigação para identificar fluxos de tráfego encriptados, que foram gerados por três aplicações populares de distribuição de conteúdo multimédia em redes P2P: TVUPlayer, Livestation e GoalBit. Para este trabalho, foi criada uma plataforma de testes que pretendia simular um cenário quase real, e o tráfego que foi capturado, continha uma grande variedade de aplicações. O método proposto nesta dissertação recorre à técnica de Inspecção Profunda de Pacotes (DPI), e por isso, foi necessário 21nalisar o conteúdo dos pacotes a fim de encontrar padrões que se repetissem, e que iriam mais tarde ser usados para criar um conjunto de regras SNORT para detecção de pacotes chave· na rede, gerados por estas aplicações, afim de se poder correctamente classificar os fluxos de tráfego. Após descobrir que a aplicação Livestation deixou de funcionar com P2P, apenas as duas regras criadas até esse momento foram usadas. Quanto à aplicação TVUPlayer, foram criadas várias regras a partir do tráfego gerado por ela mesma e que tiveram uma boa taxa de precisão. Várias regras foram também criadas para a aplicação GoalBit em que foram usados quatro cenários: com e sem encriptação usando a opção de transmissão tracker, e com e sem encriptação usando a opção de transmissão sem necessidade de tracker (aqui foi usado o protocolo Kademlia). O método foi avaliado experimentalmente na plataforma de testes criada para o efeito, sendo demonstrado que a precisão do conjunto de regras para a aplicação GoallBit é de 97%.Fundação para a Ciência e a Tecnologia (FCT

Key management for beyond 5G mobile small cells: a survey

The highly anticipated 5G network is projected to be introduced in 2020. 5G stakeholders are unanimous that densification of mobile networks is the way forward. The densification will be realized by means of small cell technology, and it is capable of providing coverage with a high data capacity. The EU-funded H2020-MSCA project “SECRET” introduced covering the urban landscape with mobile small cells, since these take advantages of the dynamic network topology and optimizes network services in a cost-effective fashion. By taking advantage of the device-to-device communications technology, large amounts of data can be transmitted over multiple hops and, therefore, offload the general network. However, this introduction of mobile small cells presents various security and privacy challenges. Cryptographic security solutions are capable of solving these as long as they are supported by a key management scheme. It is assumed that the network infrastructure and mobile devices from network users are unable to act as a centralized trust anchor since these are vulnerable targets to malicious attacks. Security must, therefore, be guaranteed by means of a key management scheme that decentralizes trust. Therefore, this paper surveys the state-of-the-art key management schemes proposed for similar network architectures (e.g., mobile ad hoc networks and ad hoc device-to-device networks) that decentralize trust. Furthermore, these key management schemes are evaluated for adaptability in a network of mobile small cells

Data Collection and Analysis in Urban Scenarios

The United Nations estimates that the world population will continue to grow, with a projection indicating a world population of up to approximately 8.5 billion people in 2030, 9.7 billion in 2050 and 10.9 billion in 2100. In addition to the phenomenon of population growth, the United Nations also estimates that in 2050 about 70% of the total world population will live in cities. These conditions increase the complexity of the services that public administrations and private companies must provide to citizens with the aim of optimising resources and increasing the level of quality of life. For an adequate design, implementation and management of these services, an extensive effort is required towards the design of effective solutions for data collection and analysis, applying Data Science and Artificial Intelligence techniques. Several approaches were addressed during the development of this research thesis. Furthermore, different real-world use cases are introduced where the presented work was tested and validated. The first thesis part focuses on data analysis on data collected using crowdsourcing. A real case study used for the analyses was a study conducted in Sheffield in which the goal was to understand people’s interaction with green areas and their wellbeing. In this study, an app with a chatbot was used to ask questions targeted to the study and collected not only the subjective answers but also objective data like users’ location. Through the analysis of this data, it was possible to extract insights that otherwise would not be easily reachable in other ways. Some limitations have arisen for less frequented areas, in fact, not enough information has been collected to have a statistical significance of the insights found. Conversely, more information than necessary was collected in the most frequented areas. For this reason, a framework that analyses the amount of information and its statistical significance in real-time has been developed. It increases the efficiency of the study and reduces intrusiveness towards the study participants. The limit that this approach presents is certainly the low sample of data that can be acquired. In the second part of this thesis, a move on to passive data collection is done, where the user does not have to interact in any way. Any data acquired is pseudonymised upon capture so that the dictates of the privacy legislation are respected. A system is then presented that collects probe requests generated by Wi-Fi devices while scanning radio channels to detect Access Points. The system processes the collected data to extract key information on people’s mobility, such as crowd density by area of interest, people flow, permanence time, return time, heat maps, origin-destination matrix and estimate of the locations of the people. The main novelty with respect to the state of the art is related to new powerful indicators necessary for some key services of the city, such as safety management and passenger transport services, and to experimental activities carried out in real scenarios. Furthermore, a de-randomisation algorithm to solve the problem of MAC address randomisation is presented.N/